From 0e9e8034c31de555121f0b6b651aa019d94469e2 Mon Sep 17 00:00:00 2001 From: Mike Angstadt Date: Mon, 21 May 2012 16:01:59 -0400 Subject: [PATCH 01/27] initial alpha version of Connectonomic PPI scripts --- cPPI/cppi_ConnectonomicPPI.m | 115 +++++++++++++++ cPPI/cppi_CreateModel.m | 62 ++++++++ cPPI/cppi_CreateRegressors.m | 112 +++++++++++++++ cPPI/cppi_Extract.m | 34 +++++ cPPI/cppi_batch_mc_central.m | 217 ++++++++++++++++++++++++++++ cPPI/cppi_batch_mc_template.m | 261 ++++++++++++++++++++++++++++++++++ 6 files changed, 801 insertions(+) create mode 100644 cPPI/cppi_ConnectonomicPPI.m create mode 100644 cPPI/cppi_CreateModel.m create mode 100644 cPPI/cppi_CreateRegressors.m create mode 100644 cPPI/cppi_Extract.m create mode 100644 cPPI/cppi_batch_mc_central.m create mode 100644 cPPI/cppi_batch_mc_template.m diff --git a/cPPI/cppi_ConnectonomicPPI.m b/cPPI/cppi_ConnectonomicPPI.m new file mode 100644 index 00000000..22c9d9e1 --- /dev/null +++ b/cPPI/cppi_ConnectonomicPPI.m @@ -0,0 +1,115 @@ +function cppi = cppi_ConnectonomicPPI(D0,parameters) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% +%%% +%%% +%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% Initial setup and check code borrow from Robert's +% SOM_CalculateCorrelations.m code + +global SOM; + +cppi = -1; + +parameters.startCPU.cppi = cputime; + +% +% Check the roi parameters +% + +parameters.rois = SOM_CheckROIParameters(parameters); + +if parameters.rois.OK == -1 + SOM_LOG('FATAL ERROR : parameters.rois failed to meet criteria'); + return +end + +% +% Check the output information +% + +parameters.Output = SOM_CheckOutput(parameters); + +if parameters.Output.OK == -1 + SOM_LOG('FATAL ERROR : parameters.Output failed to meet criteria'); + return +end + +% +% Sanity check, if Output.type = 0, which is a correlation map, then you +% need at least 2 ROIs that survived any cleaning up. +% + +if parameters.Output.type == 0 & parameters.rois.nrois < 2 + SOM_LOG('FATAL ERROR : You specified output to be a correlation matrix, but insufficient number of ROIS'); + return +end + +% +% Okay - we can do the work now. +% + +% Take the ROI definitions and turn them into linear indices for +% calculations. + +parameters.rois = SOM_BuildROILinearIDX(parameters); + +% Now do the correlation work, either making maps or images. + +switch parameters.Output.type + + case 0 + % + % Correlation maps. + % + SOM_LOG('STATUS : Calculating Connectonomic PPI'); + % Array of ROI time courses. + + roiTC = zeros(size(D0,2),parameters.rois.nroisRequested); + + % Create an array of our data that is Time x ROI# + + for iROI = 1 : parameters.rois.nroisRequested + roiTC(:,iROI) = mean(D0(parameters.rois.IDX{iROI},:),1); + end + + % Now loop on the ROIs and calculate PPI models for each one + % separately to build up the cPPI grid. + cppi_grid = []; + for iROI = 1:parameters.rois.nroisRequested + cppiregressors = cppi_CreateRegressors(roiTC(:,iROI),parameters); + model = cppi_CreateModel(cppiregressors,roiTC,parameters); + [cppi_grid result] = cppi_Extract(cppiregressors,model,parameters,cppi_grid,iROI); + if (result) + [status result] = system(sprintf('rm -rf %s',model)); + if (status ~= 0) + mc_Error('There was an error deleting temporary files: %s',result); + end + else + mc_Error('There was an error during cPPI calculation. Only 0s were returned.'); + end + + end + %now save cppi grid results + GridFilename = [parameters.Output.name '_cppi_grid']; + GridPath = mc_GenPath(fullfile(parameters.Output.directory,GridFilename)); + save(GridPath,'cppi_grid'); + otherwise + % + % Error case + % + SOM_LOG('FATAL ERROR : parameters.Output.type was not 0'); + return +end + +parameters.stopCPU.cppi = cputime; + +% Now write out the parameters to a file. + +paraName = fullfile(parameters.Output.directory,[parameters.Output.name '_parameters']); + +save(paraName,'parameters','SOM'); + +return; diff --git a/cPPI/cppi_CreateModel.m b/cPPI/cppi_CreateModel.m new file mode 100644 index 00000000..69e7c5a3 --- /dev/null +++ b/cPPI/cppi_CreateModel.m @@ -0,0 +1,62 @@ +function model = cppi_CreateModel(cppiregressors,roiTC,parameters) +model = -1; +curpath = pwd; + +if (~isfield(parameters.cppi,'sandbox')) + [status hostname] = system('hostname -s'); + parameters.cppi.sandbox = fullfile(hostname,'cppi',parameters.Output.directory); +end +parameters.cppi.sandbox = '/dysthymia/sandbox/cppi/FirstLevel/5001/Tx1/Grid_test_temp/'; +mkdir(parameters.cppi.sandbox); +cd(parameters.cppi.sandbox); + +Vtemplate.fname = fullfile(parameters.cppi.sandbox,'roiTC'); +Vtemplate.mat = spm_matrix([0 0 0 0 0 0 1 1 1 0 0 0]); +Vtemplate.dim = [parameters.rois.nroisRequested 1 1]; +Vtemplate.dt = [4 0]; +Vtemplate.descrip = ''; +%create dummy images consisting of roiTC + +FDTC = zeros(size(roiTC,2),1,1,size(roiTC,1)); +FDTC(:,1,1,:) = roiTC'; + +V(1:size(roiTC,1)) = Vtemplate; +for iV = 1:size(V,2) + V(iV).fname = sprintf('%s_%04d.img',V(iV).fname,iV); + spm_write_vol(V(iV),FDTC(:,:,:,iV)); +end + +Vmask = Vtemplate; +Vmask.fname = sprintf('%s_mask.img',Vtemplate.fname); +spm_write_vol(Vmask,ones(size(roiTC,2),1,1)); + +%enter these dummy images into an SPM model with the cppi regressors +iRun = 1; +offset = 0; +SPM.Sess(iRun).U = []; +SPM.Sess(iRun).C.C = [cppiregressors(offset+1:offset+parameters.data.run(iRun).nTimeAnalyzed,:) parameters.data.run(iRun).MotionParameters]; +SPM.Sess(iRun).C.name = repmat({'reg'},1,size(cppiregressors,2) + size(parameters.data.run(iRun).MotionParameters,2)); +SPM.xY.P = spm_select('ExtFPList',parameters.cppi.sandbox,'roiTC_0.*',[1:size(roiTC,1)]); +SPM.nscan = size(roiTC,1); +SPM.xBF.name = 'hrf'; +SPM.xBF.length = 32; +SPM.xBF.order = 1; +SPM.xBF.T = 16; +SPM.xBF.T0 = 1; +SPM.xBF.UNITS = 'secs'; +SPM.xBF.Volterra = 1; +SPM.xGX.iGXcalc = 'none'; +SPM.xX.K.HParam = 128; +SPM.xVi.form = 'none'; +SPM.xY.RT = parameters.TIME.run(1).TR; +SPM.xM.VM = Vmask; +global defaults +defaults.mask.thresh = -Inf; +SPM = spm_fmri_spm_ui(SPM); +SPM = spm_spm(SPM); + +%return path to model when finished +model = pwd; +cd(curpath); + +return; diff --git a/cPPI/cppi_CreateRegressors.m b/cPPI/cppi_CreateRegressors.m new file mode 100644 index 00000000..7b9d9f92 --- /dev/null +++ b/cPPI/cppi_CreateRegressors.m @@ -0,0 +1,112 @@ +function regressors = cppi_CreateRegressors(roiTC,parameters) + +load(parameters.cppi.SPM); +RT = SPM.xY.RT; +dt = SPM.xBF.dt; +NT = RT/dt; +regressors = []; + +for iRun = 1:size(SPM.Sess,1) + + %calculate confounds + xY.X0 = SPM.xX.xKXs.X(:,[SPM.xX.iB SPM.xX.iG]); + xY.X0 = xY.X0(SPM.Sess(iRun).row,:); + xY.X0 = [xY.X0 SPM.xX.K(iRun).X0]; + xY.X0 = xY.X0(:,any(xY.X0)); + + Sess = SPM.Sess(iRun); + + %condition names, and includes, and weights (not needed since we'll include all at weight 1) + U.name = {}; + U.u = []; + U.w = []; + + u = length(Sess.U); + for i = 1:u + for j = 1:length(Sess.U(i).name) + U.w = [U.w 1]; + U.u = [U.u Sess.U(i).u(33:end,j)]; + U.name{end+1} = Sess.U(i).name{j}; + end + end + + N = length(roiTC); + k = 1:NT:N*NT; % microtime to scan time indices + + hrf = spm_hrf(dt); + + %create convolved explanatory {Hxb} variables in scan time + xb = spm_dctmtx(N*NT + 128,N); + Hxb = zeros(N,N); + for i = 1:N + Hx = conv(xb(:,i),hrf); + Hxb(:,i) = Hx(k+128); + end + xb = xb(129:end,:); + + %get confounds (in scan time) and constant term + X0 = xY.X0; + M = size(X0,2); + + Y = roiTC; + + %remove confounds and save Y in output structure + Yc = Y-X0*inv(X0'*X0)*X0'*Y; + PMP.Y = Yc(:,1); + + %specify covariance components; assume neuronal response is white + %treating confounds as fixed effects + Q = speye(N,N)*N/trace(Hxb'*Hxb); + Q = blkdiag(Q,speye(M,M)*1e6); + + %get whitening matrix (NB: confounds have already been whitened) + W = SPM.xX.W(Sess.row,Sess.row); + + %create structure for spm_PEB + P{1}.X = [W*Hxb X0]; + P{1}.C = speye(N,N)/4; + P{2}.X = sparse(N+M,1); + P{2}.C = Q; + + % COMPUTE PSYCHOPHYSIOLOGIC INTERACTIONS + % use basis set in microtime + %--------------------------------------------------------------------- + % get parameter estimates and neural signal; beta (C) is in scan time + % This clever trick allows us to compute the betas in scan time which is + % much quicker than with the large microtime vectors. Then the betas + % are applied to a microtime basis set generating the correct neural + % activity to convolve with the psychological variable in mircrotime + %--------------------------------------------------------------------- + C = spm_PEB(Y,P); + xn = xb*C{2}.E(1:N); + xn = spm_detrend(xn); + + % setup psychological variable from inputs and contast weights (DON'T need weights, all 1) + %multiply psychological variables by neural signal + %convolve and resample at each scan for bold signal + %--------------------------------------------------------------------- + %PSY = zeros(N*NT,1); + j = size(U.u,2) + 1; + for i = 1:size(U.u,2) + PSY{i} = full(U.u(:,i)); + PSYxn{i} = PSY{i}.*xn; + PSYHRF{i} = conv(PSY{i},hrf); + PSYHRF{i} = PSYHRF{i}(k); + pmp{i} = conv(PSYxn{i},hrf); + pmp{i} = pmp{i}(k); + pmp{i} = spm_detrend(pmp{i}); + R(:,i) = PSYHRF{i}; + R(:,j) = pmp{i}; + j = j + 1; + end + + R(:,j) = PMP.Y; + %if (parameters.RegressFLAGS.Motion) + % R = [R parameters.data.run(iRun).MotionParameters]; + %end + + regressors = [regressors; R]; +end + +return; + diff --git a/cPPI/cppi_Extract.m b/cPPI/cppi_Extract.m new file mode 100644 index 00000000..4544c76e --- /dev/null +++ b/cPPI/cppi_Extract.m @@ -0,0 +1,34 @@ +function [cppi_grid result] = cppi_Extract(cppiregressors,model,parameters,cppi_grid,iROI) +%cppi_grid - {B}[N x N] +% B = # of beta images +% N = # of ROIs + +%read beta images that were output by model +%save beta values into cell array grid +%need to skip/exclude betas from motion and run effects +result = 0; + +P = spm_select('FPList',model,'beta.*img'); +numimages = size(P,1); +nummotion = size(parameters.data.run(1).MotionParameters,2); +domotion = parameters.RegressFLAGS.motion; +numrun = size(parameters.data.run,1); +numregressors = size(cppiregressors,2); +domotion = 1; +if (numimages ~= (numrun + numregressors + (domotion * nummotion))) + mc_Error('There are an inconsistent number of beta images and regressors.'); +end + +goodbeta = repmat([ones(1,numregressors) zeros(1,domotion*nummotion)],1,numrun); +index = 1; +for iB = 1:size(goodbeta,2) + %extract beta iB and place in grid + if (~goodbeta(iB)) + continue; + end + V = spm_vol(P(iB,:)); + data = spm_read_vols(V); + cppi_grid{index}(iROI,:) = data'; + index = index + 1; +end +result = 1; diff --git a/cPPI/cppi_batch_mc_central.m b/cPPI/cppi_batch_mc_central.m new file mode 100644 index 00000000..ea3395da --- /dev/null +++ b/cPPI/cppi_batch_mc_central.m @@ -0,0 +1,217 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% General calculations that apply to both Preprocessing and First Level +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +if (alreadydone(1)) + basefile = [stp basefile]; +end +if (alreadydone(2)) + basefile = [rep basefile]; +end +if (alreadydone(3)) + basefile = [nop basefile]; +end +if (alreadydone(4)) + basefile = [smp basefile]; +end + + +RunMode = [0 0]; +if (strcmpi(Mode,'full')) + RunMode = [1 1]; +else + RunMode(1) = strcmpi(Mode,'parameters'); + RunMode(2) = strcmpi(Mode,'cppi'); +end + +spm('defaults','fmri'); +global defaults +warning off all + +spmver = spm('Ver'); +if (strcmp(spmver,'SPM8')==1) + spm_jobman('initcfg'); + spm_get_defaults('cmdline',true); +end + +if (RunMode(1) | sum(RunMode) == 0) + RunNamesTotal = RunDir; + NumScanTotal = NumScan; + + MaskBrain = 1; + + RegressGlobal = any(strfind(upper(RegressOrder),'G')); + RegressWhite = any(strfind(upper(RegressOrder),'W')); + RegressCSF = any(strfind(upper(RegressOrder),'C')); + DoBandpassFilter = any(strfind(upper(RegressOrder),'B')); + RegressMotion = any(strfind(upper(RegressOrder),'M')); + DoLinearDetrend = any(strfind(upper(RegressOrder),'D')); + + for iSubject = 1:size(SubjDir,1) + clear parameters; + clear global SOM; + + parameters.RegressFLAGS.prinComp = PrincipalComponents; + parameters.RegressFLAGS.global = RegressGlobal; + parameters.RegressFLAGS.csf = RegressCSF; + parameters.RegressFLAGS.white = RegressWhite; + parameters.RegressFLAGS.motion = RegressMotion; + parameters.RegressFLAGS.order = RegressOrder; + + Subject=SubjDir{iSubject,1}; + RunList=SubjDir{iSubject,3}; + + NumRun = size(RunList,2); + + TotalNumRun = size(NumScanTotal,2); %%% number of image runs if every run were present + + %%%%% This code cuts RunDir and NumScan based which Image Runs are present + NumScan=[]; + clear RunDir; + for iRun=1:NumRun + RunDir{iRun,1}=RunNamesTotal{RunList(1,iRun)}; + NumScan=horzcat(NumScan,NumScanTotal(1,iRun)); + end + + NumRun= size(NumScan,2); % number of runs + ImageNumRun=size(RunDir,1); %number of image folders + + GreyPath = mc_GenPath(GreyMatterTemplate); + WhitePath = mc_GenPath(WhiteMatterTemplate); + CSFPath = mc_GenPath(CSFTemplate); + BrainPath = mc_GenPath(BrainMaskTemplate); + + parameters.grey.File = GreyPath; + parameters.grey.ImgThreshold = GreyThreshold; + parameters.masks.white.File = WhitePath; + parameters.masks.csf.File = CSFPath; + parameters.masks.epi.File = BrainPath; + parameters.rois.mask.MaskFLAG = MaskBrain; + + for iRun = 1:ImageNumRun + Run = RunDir{iRun}; + + ImagePath = mc_GenPath(ImageTemplate); + ImageFiles = spm_select('FPList',ImagePath, ['^' basefile '.*.' imagetype]); + RealignmentParametersFile = mc_GenPath(RealignmentParametersTemplate); + + parameters.data.run(iRun).P = ImageFiles; + + RealignmentParameters = load(RealignmentParametersFile); + RealignmentParametersDeriv = diff(RealignmentParameters); + %RealignmentParametersDerivR = resample(RealignmentParametersDeriv,size(RealignmentParameters,1),size(RealignmentParametersDeriv,1)); + + %parameters.data.run(iRun).MotionParameters = [RealignmentParameters RealignmentParametersDerivR]; + parameters.data.run(iRun).MotionParameters = RealignmentParameters; + parameters.data.run(iRun).nTIME = NumScan(iRun); + parameters.data.MaskFLAG = MaskBrain; + + parameters.TIME.run(iRun).TR = TR; + parameters.TIME.run(iRun).BandFLAG = DoBandpassFilter; + parameters.TIME.run(iRun).TrendFLAG = DoLinearDetrend; + parameters.TIME.run(iRun).LowF = LowFrequency; + parameters.TIME.run(iRun).HiF = HighFrequency; + parameters.TIME.run(iRun).gentle = Gentle; + parameters.TIME.run(iRun).padding = Padding; + parameters.TIME.run(iRun).whichFilter = BandpassFilter; + parameters.TIME.run(iRun).fraction = Fraction; + end + + Run = RunDir{1}; + ImagePath = mc_GenPath(ImageTemplate); + SOM_Mask = mc_GenPath(fullfile(ImagePath,'som_mask.img')); + + if (isempty(BrainMaskTemplate)) + parameters.rois.mask.File = SOM_Mask; + else + parameters.rois.mask.File = BrainPath; + end + output.Template = OutputTemplate; + output.type = 1; + output.mode = 'makedir'; + if (RunMode(1)) + OutputPath = mc_GenPath(output); + else + OutputPath = mc_GenPath(OutputTemplate); + end + + switch (ROIInput) + case 'files' + ROIFolder = mc_GenPath(ROITemplate); + for iROIs = 1:size(ROIImages,1) + ROI{iROIs} = fullfile(ROIFolder,ROIImages{iROIs}); + end + parameters.rois.files = char(ROI); + + case 'coordinates' + parameters.rois.mni.coordinates = ROICenters; + if (iscell(ROISize)) + parameters.rois.mni.size = ROISize{1}; + else + XYZ = SOM_MakeSphereROI(ROISize); + parameters.rois.mni.size.XROI = XYZ(1,:); + parameters.rois.mni.size.YROI = XYZ(2,:); + parameters.rois.mni.size.ZROI = XYZ(3,:); + end + case 'grid' + ROIGridMask = mc_Genpath(ROIGridMaskTemplate); + ROIGridMaskHdr = spm_vol(ROIGridMask); + ROIGridBB = mc_GetBoundingBox(ROIGridMaskHdr); + grid_coord_cand = SOM_MakeGrid(ROIGridSpacing,ROIGridBB); + inOutIDX = SOM_roiPointsInMask(ROIGridMask,grid_coord_cand); + grid_coord = grid_coord_cand(inOutIDX,:); + parameters.rois.mni.coordinates = grid_coord; + if (iscell(ROIGridSize)) + parameters.rois.mni.size = ROIGridSize{1}; + else + XYZ = SOM_MakeSphereROI(ROIGridSize); + parameters.rois.mni.size.XROI = XYZ(1,:); + parameters.rois.mni.size.YROI = XYZ(2,:); + parameters.rois.mni.size.ZROI = XYZ(2,:); + end + end + + parameters.Output.correlation = ROIOutput; + %parameters.Output.description = 'description of output'; + parameters.Output.directory = OutputPath; + parameters.Output.name = OutputName; + ParameterFilename = [OutputName '_parameters']; + ParameterPath = mc_GenPath(fullfile(OutputPath,ParameterFilename)); + save(ParameterPath,'parameters'); + + end +end + +if (RunMode(2)) + for iSubject = 1:size(SubjDir,1) + clear D0 parameters results; + Subject=SubjDir{iSubject,1}; + %load existing parameter file + OutputPath = mc_GenPath(OutputTemplate); + load(fullfile(OutputPath,ParameterFilename)); + clear global SOM; + global SOM; + SOM.silent = 1; + SOM_LOG('STATUS : 01'); + + [D0 parameters] = SOM_PreProcessData(parameters); + if D0 == -1 + SOM_LOG('FATAL ERROR : No data returned'); + mc_Error('There is something wrong with your template or your data. No data was returned from SOM_PreProcessData'); + else + %results = SOM_CalculateCorrelations(D0,parameters); + + parameters.cppi.SPM = '/dysthymia/sandbox/cppi/FirstLevel/5001/Tx1/OrigModel/SPM.mat'; + + results = cppi_ConnectonomicPPI(D0,parameters); + results = 'a'; + if isnumeric(results) + SOM_LOG('FATAL ERROR : '); + mc_Error('There is something wrong with your template or your data. No results were returned from SOM_CalculateCorrelations'); + end + end + end +end + + + + diff --git a/cPPI/cppi_batch_mc_template.m b/cPPI/cppi_batch_mc_template.m new file mode 100644 index 00000000..b47869f9 --- /dev/null +++ b/cPPI/cppi_batch_mc_template.m @@ -0,0 +1,261 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% GENERAL OPTIONS +%%% These options are shared among many of our scripts +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The folder that contains your subject folders +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +Exp = '/dysthymia/sandbox/cppi/'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Path where your images are located +%%% +%%% Variables you can use in your template are: +%%% Exp = path to your experiment directory +%%% iSubject = index for subject +%%% Subject = name of subject from SubjDir (using iSubject as index of row) +%%% iRun = index of run (listed in Column 3 of SubjDir) +%%% Run = name of run from RunDir (using iRun as index of row) +%%% * = wildcard (can only be placed in final part of template) +%%% Examples: +%%% ImageTemplate = '[Exp]/Subjects/[Subject]/func/run_0[iRun]/'; +%%% ImageTemplate = '[Exp]/Subjects/[Subject]/TASK/func/[Run]/' +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ImageTemplate = '[Exp]/Subjects/[Subject]/TASK/func/[Run]/'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% A list of run folders where the script can find functional images +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +RunDir = { + 'run_01/'; +}; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The list of subjects to process +%%% The format is 'subjectfolder',subject number in masterfile,[runs to include] +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +SubjDir = { +'5001/Tx1',50011,[1]; + }; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The TR your data was collected at +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +TR = 2; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Prefixes for slicetiming, realignment, normalization, and smoothing +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +stp = 'a'; +rep = 'r'; +nop = 'w'; +smp = 's'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Preprocessing that has already been completed on images +%%% [slicetime realign normalize smooth] +%%% If you are only running First Level (i.e. Preprocessing is already done) +%%% setting these will add the appropriate prefix to the basefile +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +alreadydone = [1 1 1 1]; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The prefix of each functional file +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +basefile = 'restrun'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Image Type should be either 'nii' or 'img' +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +imagetype = 'nii'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Number of Functional scans per run +%%% (if you have more than 1 run, there should be more than 1 value here) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +NumScan = [180]; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% CONNECTONOMIC PPI OPTIONS +%%% These options are only used for cPPI +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Original First Level Model location +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +SPMTemplate = '[Exp]/FirstLevel/[Subject]/OrigModel'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Mode to run som_batch_mc_central in +%%% 'test' = test script but do not save parameters or run any +%%% SOM code +%%% 'parameters' = run script and save parameters for each subject +%%% but do not run any SOM code +%%% 'cppi' = run SOM code on previously saved parameters +%%% 'full' = generate parameters and immediately run SOM code +%%% +%%% NOTE: If you choose mode 'cppi' then most variables except +%%% SubjDir and OutputTemplate/OutputName will be ignored as they +%%% will be loaded from the already existing parameter file for each +%%% subject. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +Mode = 'full'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Paths to your anatomical images +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +GreyMatterTemplate = '[Exp]/Subjects/[Subject]/anatomy/rgrey.img'; +WhiteMatterTemplate = '[Exp]/Subjects/[Subject]/anatomy/wm_mask.nii'; +CSFTemplate = '[Exp]/Subjects/[Subject]/anatomy/csf_mask.nii'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Where to output the data +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +OutputTemplate = '[Exp]/FirstLevel/[Subject]/[OutputName]/'; +OutputName = 'Grid_test'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Path and name of explicit mask to use at first level. +%%% Leaving this blank ('') will use a subject-specific mask +%%% NOTE: Subject-specific masks are not recommended for grid usage below. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +BrainMaskTemplate = '[Exp]/ROIS/rEPI_MASK_NOEYES.img'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Path Template for realignment parameters file +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +RealignmentParametersTemplate = '[Exp]/Subjects/[Subject]/TASK/func/[Run]/rp_arestrun_*'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Constrain results to only regions in GreyMatterTemplate (1=yes, 0=no) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +MaskGrey = 0; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Value threshold to use for each mask. If left as [] use default 0.75 +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +GreyThreshold = []; +WhiteThreshold = []; +CSFThreshold = []; +EPIThreshold = []; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% the order to perform the regressions etc +%%% D = detrend +%%% G = global +%%% W = white matter +%%% C = csf +%%% M = motion +%%% B = bandpass +%%% +%%% Suggested order is "DCWM" +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +RegressOrder = 'D'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Use this many principle components for regression +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +PrincipalComponents = 5; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Bandpass Filter Settings +%%% LowFrequency - low frequency cutoff +%%% HighFrequency - high frequency cutoff +%%% Gentle - 0 = no rolling, 1 = rolling +%%% Padding - number of timepoints to pad on beginning/end +%%% BandpassFilter - 0 = Matlab filter, 1 = SOM_Filter_FFT +%%% Fraction - fraction of variance for principle components +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +LowFrequency = 0.01; +HighFrequency = 0.1; +Gentle = 1; +Padding = 10; +BandpassFilter = 1; +Fraction = 1; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Type of input +%%% coordinates - provide the center of each seed and a radius +%%% files - provide a list of ROI files +%%% grid - make a grid based on provided spacing and masked +%%% by provided mask +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROIInput = 'grid'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% If specifying ROI coordinates you need to provide a list of centers in +%%% MNI coordinates (mm) and a radius in voxels. +%%% NOTE: ROISize will be used as the radius of a sphere at each point. If +%%% you'd prefer to use the predefined 1,7,19, or 27 voxel sizes you will +%%% need to specify the size as a cell (i.e. {19}) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROICenters = [ + 9 9 -8; %VSi right + -9 9 -8; %VSi left + ]; +ROISize = {19}; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% If specifying ROI images you need to provide an ROI folder as well as a +%%% cell array list of ROI images. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROITemplate = '[Exp]/ROIS'; +ROIImages = { + 'image1.nii'; + 'image2.nii'; + }; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% If specifying ROI grid you need to provide a spacing and ROI size as +%%% well as an optional mask for grid point inclusion (a mask is strongly +%%% encouraged as not using one will return coordinates from across the entire +%%% bounding box). +%%% NOTE: ROIGridSize will be used as the radius of a sphere at each grid +%%% point. If you'd prefer to use the predefined 1,7,19, or 27 voxel sizes +%%% you will need to specify the size as a cell (i.e. {19}) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROIGridSpacing = 36; +ROIGridSize = {19}; +ROIGridMaskTemplate = '[Exp]/ROIS/rEPI_MASK_NOEYES.img'; + + + + + + + + + + + + + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Type of output +%%% images - output R and Z images of correlation with each seed +%%% maps - output R and P matrix of correlations between seeds +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROIOutput = 'maps'; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Do not edit below this line +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%DEVSTART +mcRoot = fullfile(fileparts(mfilename('fullpath')),'../../MethodsCore'); +%DEVSTOP + +%[DEVmcRootAssign] + +addpath(fullfile(mcRoot,'matlabScripts')); +addpath(fullfile(mcRoot,'cPPI')); +addpath(fullfile(mcRoot,'som')); +addpath(fullfile(mcRoot,'spm8')); + +cppi_batch_mc_central \ No newline at end of file From 615e204cc7b5dd2c8ef1c82eb79d274edfc30126 Mon Sep 17 00:00:00 2001 From: Mike Angstadt Date: Mon, 11 Jun 2012 12:54:15 -0400 Subject: [PATCH 02/27] Mostly stable version 1 of connectomic PPI. --- ...nnectonomicPPI.m => cppi_ConnectomicPPI.m} | 22 +++++- cPPI/cppi_CreateModel.m | 19 ++--- cPPI/cppi_CreateRegressors.m | 34 +++++++- cPPI/cppi_CreateRegressors_spm.m | 38 +++++++++ cPPI/cppi_Extract.m | 3 +- cPPI/cppi_batch_chunk.m | 30 ++++++++ cPPI/cppi_batch_mc_central.m | 77 +++++++++++++------ cPPI/cppi_batch_mc_template.m | 47 +++++++++-- 8 files changed, 220 insertions(+), 50 deletions(-) rename cPPI/{cppi_ConnectonomicPPI.m => cppi_ConnectomicPPI.m} (80%) create mode 100644 cPPI/cppi_CreateRegressors_spm.m create mode 100644 cPPI/cppi_batch_chunk.m diff --git a/cPPI/cppi_ConnectonomicPPI.m b/cPPI/cppi_ConnectomicPPI.m similarity index 80% rename from cPPI/cppi_ConnectonomicPPI.m rename to cPPI/cppi_ConnectomicPPI.m index 22c9d9e1..56d488b2 100644 --- a/cPPI/cppi_ConnectonomicPPI.m +++ b/cPPI/cppi_ConnectomicPPI.m @@ -1,4 +1,4 @@ -function cppi = cppi_ConnectonomicPPI(D0,parameters) +function results = cppi_ConnectomicPPI(D0,parameters) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% %%% @@ -11,7 +11,7 @@ global SOM; -cppi = -1; +results = -1; parameters.startCPU.cppi = cputime; @@ -64,7 +64,7 @@ % % Correlation maps. % - SOM_LOG('STATUS : Calculating Connectonomic PPI'); + SOM_LOG('STATUS : Calculating Connectomic PPI'); % Array of ROI time courses. roiTC = zeros(size(D0,2),parameters.rois.nroisRequested); @@ -78,10 +78,23 @@ % Now loop on the ROIs and calculate PPI models for each one % separately to build up the cPPI grid. cppi_grid = []; + load(parameters.cppi.SPM); for iROI = 1:parameters.rois.nroisRequested - cppiregressors = cppi_CreateRegressors(roiTC(:,iROI),parameters); + roiTCscaled(:,iROI) = roiTC(:,iROI).*SPM.xGX.gSF; + end + for iROI = 1:parameters.rois.nroisRequested + %[cppiregressors betanames] = + %cppi_CreateRegressors_spm(parameters.rois.mni.coordinates(iROI,:),parameters,roiTC(:,iROI)); + %roiTCtemp = roiTC(:,iROI).*SPM.xGX.gSF; + [cppiregressors betanames] = cppi_CreateRegressors(roiTCscaled(:,iROI),parameters); + for iB = 1:size(betanames,2) + cppi_grid{1,iB} = betanames{iB}; + end + model = cppi_CreateModel(cppiregressors,roiTC,parameters); + [cppi_grid result] = cppi_Extract(cppiregressors,model,parameters,cppi_grid,iROI); + if (result) [status result] = system(sprintf('rm -rf %s',model)); if (status ~= 0) @@ -112,4 +125,5 @@ save(paraName,'parameters','SOM'); +results = 'ok'; return; diff --git a/cPPI/cppi_CreateModel.m b/cPPI/cppi_CreateModel.m index 69e7c5a3..aba80c01 100644 --- a/cPPI/cppi_CreateModel.m +++ b/cPPI/cppi_CreateModel.m @@ -2,11 +2,6 @@ model = -1; curpath = pwd; -if (~isfield(parameters.cppi,'sandbox')) - [status hostname] = system('hostname -s'); - parameters.cppi.sandbox = fullfile(hostname,'cppi',parameters.Output.directory); -end -parameters.cppi.sandbox = '/dysthymia/sandbox/cppi/FirstLevel/5001/Tx1/Grid_test_temp/'; mkdir(parameters.cppi.sandbox); cd(parameters.cppi.sandbox); @@ -31,11 +26,13 @@ spm_write_vol(Vmask,ones(size(roiTC,2),1,1)); %enter these dummy images into an SPM model with the cppi regressors -iRun = 1; offset = 0; -SPM.Sess(iRun).U = []; -SPM.Sess(iRun).C.C = [cppiregressors(offset+1:offset+parameters.data.run(iRun).nTimeAnalyzed,:) parameters.data.run(iRun).MotionParameters]; -SPM.Sess(iRun).C.name = repmat({'reg'},1,size(cppiregressors,2) + size(parameters.data.run(iRun).MotionParameters,2)); +for iRun = 1:size(parameters.data.run,2) + SPM.Sess(iRun).U = []; + SPM.Sess(iRun).C.C = [cppiregressors(offset+1:offset+parameters.data.run(iRun).nTimeAnalyzed,:) parameters.data.run(iRun).MotionParameters]; + SPM.Sess(iRun).C.name = repmat({'reg'},1,size(cppiregressors,2) + size(parameters.data.run(iRun).MotionParameters,2)); + offset = offset + parameters.data.run(iRun).nTimeAnalyzed; +end SPM.xY.P = spm_select('ExtFPList',parameters.cppi.sandbox,'roiTC_0.*',[1:size(roiTC,1)]); SPM.nscan = size(roiTC,1); SPM.xBF.name = 'hrf'; @@ -45,9 +42,9 @@ SPM.xBF.T0 = 1; SPM.xBF.UNITS = 'secs'; SPM.xBF.Volterra = 1; -SPM.xGX.iGXcalc = 'none'; +SPM.xGX.iGXcalc = 'None'; SPM.xX.K.HParam = 128; -SPM.xVi.form = 'none'; +SPM.xVi.form = 'AR(0.2)'; SPM.xY.RT = parameters.TIME.run(1).TR; SPM.xM.VM = Vmask; global defaults diff --git a/cPPI/cppi_CreateRegressors.m b/cPPI/cppi_CreateRegressors.m index 7b9d9f92..015cbcf8 100644 --- a/cPPI/cppi_CreateRegressors.m +++ b/cPPI/cppi_CreateRegressors.m @@ -1,9 +1,15 @@ -function regressors = cppi_CreateRegressors(roiTC,parameters) +function [regressors betanames] = cppi_CreateRegressors(roiTC,parameters) +betanames = []; load(parameters.cppi.SPM); +roiTC = SPM.xX.W*roiTC; %applying whitening matrix from SPM model +roiTC = spm_filter(SPM.xX.K,roiTC); %filter here with SPM filter, or use Robert's filter in preprocessing? + RT = SPM.xY.RT; dt = SPM.xBF.dt; NT = RT/dt; +fMRI_T0 = SPM.xBF.T0; + regressors = []; for iRun = 1:size(SPM.Sess,1) @@ -14,6 +20,24 @@ xY.X0 = [xY.X0 SPM.xX.K(iRun).X0]; xY.X0 = xY.X0(:,any(xY.X0)); + [m n] = size(roiTC); + if m>n + [v s v] = svd(roiTC'*roiTC); + s = diag(s); + v = v(:,1); + u = roiTC*v/sqrt(s(1)); + else + [u s u] = svd(roiTC*roiTC'); + s = diag(s); + u = u(:,1); + v = roiTC'*u/sqrt(s(1)); + end + d = sign(sum(v)); + u = u*d; + v = v*d; + Y = u*sqrt(s(1)/n); + roiTC = Y; + Sess = SPM.Sess(iRun); %condition names, and includes, and weights (not needed since we'll include all at weight 1) @@ -63,6 +87,7 @@ W = SPM.xX.W(Sess.row,Sess.row); %create structure for spm_PEB + clear P P{1}.X = [W*Hxb X0]; P{1}.C = speye(N,N)/4; P{2}.X = sparse(N+M,1); @@ -91,16 +116,19 @@ PSY{i} = full(U.u(:,i)); PSYxn{i} = PSY{i}.*xn; PSYHRF{i} = conv(PSY{i},hrf); - PSYHRF{i} = PSYHRF{i}(k); + PSYHRF{i} = PSYHRF{i}((k-1)+fMRI_T0); pmp{i} = conv(PSYxn{i},hrf); - pmp{i} = pmp{i}(k); + pmp{i} = pmp{i}((k-1)+fMRI_T0); pmp{i} = spm_detrend(pmp{i}); R(:,i) = PSYHRF{i}; R(:,j) = pmp{i}; + betanames{i} = U.name{i}; + betanames{j} = [U.name{i} ' x Seed interaction']; j = j + 1; end R(:,j) = PMP.Y; + betanames{j} = 'Seed'; %if (parameters.RegressFLAGS.Motion) % R = [R parameters.data.run(iRun).MotionParameters]; %end diff --git a/cPPI/cppi_CreateRegressors_spm.m b/cPPI/cppi_CreateRegressors_spm.m new file mode 100644 index 00000000..119fa15f --- /dev/null +++ b/cPPI/cppi_CreateRegressors_spm.m @@ -0,0 +1,38 @@ +function [regressors betanames] = cppi_CreateRegressors_spm(coords,parameters,roiTC) + +% clear jobs +% jobs{1}.stats{1}.results.spmmat = cellstr(parameters.cppi.SPM); +% jobs{1}.stats{1}.results.conspec(1).titlestr = 'EOI'; +% jobs{1}.stats{1}.results.conspec(1).contrasts = 1; +% jobs{1}.stats{1}.results.conspec(1).threshdesc = 'none'; +% jobs{1}.stats{1}.results.conspec(1).thresh = 1; +% jobs{1}.stats{1}.results.conspec(1).extent = 0; +% jobs{1}.stats{1}.results.print = 0; +% spm_jobman('run',jobs); +[a b c d] = fileparts(parameters.cppi.SPM); + +xSPM.swd = a; +xSPM.title = 'EOI'; +xSPM.Ic = 1; +xSPM.u = 0.99; +xSPM.k = 0; +xSPM.thresDesc = 'none'; +xSPM.Im = []; +[hReg,xSPM,SPM] = spm_results_ui('Setup',xSPM); + +xY.xyz = spm_mip_ui('SetCoords',coords); +xY.name = 'test'; +xY.Ic = 0; +xY.Sess = 1; +xY.def = 'sphere'; +xY.spec = 0; +%xSPM = evalin('base',xSPM); +%SPM = evalin('base',SPM); +%hReg = evalin('base',hReg); +[Y,xY] = spm_regions(xSPM,SPM,hReg,xY); + +PPI1 = spm_peb_ppi(parameters.cppi.SPM,'ppi',xY,[1 1 1],'task1',0); +PPI2 = spm_peb_ppi(parameters.cppi.SPM,'ppi',xY,[2 1 1],'task2',0); + +betanames = {'task1','task2','ppi1','ppi2','y'}; +regressors = [PPI1.P PPI2.P PPI1.ppi PPI2.ppi PPI1.Y]; \ No newline at end of file diff --git a/cPPI/cppi_Extract.m b/cPPI/cppi_Extract.m index 4544c76e..d07f6ae7 100644 --- a/cPPI/cppi_Extract.m +++ b/cPPI/cppi_Extract.m @@ -23,12 +23,13 @@ index = 1; for iB = 1:size(goodbeta,2) %extract beta iB and place in grid + goodbeta(iB) = 1; if (~goodbeta(iB)) continue; end V = spm_vol(P(iB,:)); data = spm_read_vols(V); - cppi_grid{index}(iROI,:) = data'; + cppi_grid{2,index}(iROI,:) = data'; index = index + 1; end result = 1; diff --git a/cPPI/cppi_batch_chunk.m b/cPPI/cppi_batch_chunk.m new file mode 100644 index 00000000..fa4a99ec --- /dev/null +++ b/cPPI/cppi_batch_chunk.m @@ -0,0 +1,30 @@ +function results = cppi_batch_chunk(chunkFile) + spm_jobman('initcfg'); + spm_get_defaults('cmdline',true); + results = -1; + load(chunkFile); + SubjDir = tempSubjDir; + for iSubject = 1:size(SubjDir,1) + clear D0 parameters results; + Subject=SubjDir{iSubject,1}; + %load existing parameter file + OutputPath = mc_GenPath(OutputTemplate); + load(fullfile(OutputPath,ParameterFilename)); + clear global SOM; + global SOM; + SOM.silent = 1; + SOM_LOG('STATUS : 01'); + + [D0 parameters] = SOM_PreProcessData(parameters); + if D0 == -1 + SOM_LOG('FATAL ERROR : No data returned'); + mc_Error('There is something wrong with your template or your data. No data was returned from SOM_PreProcessData'); + else + results = cppi_ConnectomicPPI(D0,parameters); + if isnumeric(results) + SOM_LOG('FATAL ERROR : '); + mc_Error('There is something wrong with your template or your data. No results were returned from SOM_CalculateCorrelations'); + end + end + end +return; \ No newline at end of file diff --git a/cPPI/cppi_batch_mc_central.m b/cPPI/cppi_batch_mc_central.m index ea3395da..d57517f9 100644 --- a/cPPI/cppi_batch_mc_central.m +++ b/cPPI/cppi_batch_mc_central.m @@ -174,6 +174,17 @@ %parameters.Output.description = 'description of output'; parameters.Output.directory = OutputPath; parameters.Output.name = OutputName; + + parameters.cppi.SPM = mc_GenPath(fullfile(SPMTemplate,'SPM.mat')); + parameters.cppi.UseSandbox = UseSandbox; + + if (UseSandbox) + [status hostname] = system('hostname -s'); + parameters.cppi.sandbox = fullfile([filesep hostname(1:end-1)],'sandbox','cppi',parameters.Output.directory); + else + parameters.cppi.sandbox = fullfile(parameters.Output.directory,'temp'); + end + ParameterFilename = [OutputName '_parameters']; ParameterPath = mc_GenPath(fullfile(OutputPath,ParameterFilename)); save(ParameterPath,'parameters'); @@ -182,34 +193,50 @@ end if (RunMode(2)) - for iSubject = 1:size(SubjDir,1) - clear D0 parameters results; - Subject=SubjDir{iSubject,1}; - %load existing parameter file - OutputPath = mc_GenPath(OutputTemplate); - load(fullfile(OutputPath,ParameterFilename)); - clear global SOM; - global SOM; - SOM.silent = 1; - SOM_LOG('STATUS : 01'); - - [D0 parameters] = SOM_PreProcessData(parameters); - if D0 == -1 - SOM_LOG('FATAL ERROR : No data returned'); - mc_Error('There is something wrong with your template or your data. No data was returned from SOM_PreProcessData'); + %partition SubjDir into parameters.cppi.NumProcesses equal parts + NumSubj = size(SubjDir,1); + if (NumProcesses > 4) + NumProcesses = 4; + end + SubjPerChunk = floor(NumSubj / NumProcesses); + for iChunk = 1:NumProcesses + %save each piece of SubjDir into a chunkN.mat file in temp location + offset = (iChunk - 1) * SubjPerChunk; + tempSubjDir = []; + if (iChunk < NumProcesses) + for iSubject = 1+offset:SubjPerChunk+offset + tempSubjDir{end+1,1} = SubjDir{iSubject,1}; + tempSubjDir{end,2} = SubjDir{iSubject,2}; + tempSubjDir{end,3} = SubjDir{iSubject,3}; + end else - %results = SOM_CalculateCorrelations(D0,parameters); - - parameters.cppi.SPM = '/dysthymia/sandbox/cppi/FirstLevel/5001/Tx1/OrigModel/SPM.mat'; - - results = cppi_ConnectonomicPPI(D0,parameters); - results = 'a'; - if isnumeric(results) - SOM_LOG('FATAL ERROR : '); - mc_Error('There is something wrong with your template or your data. No results were returned from SOM_CalculateCorrelations'); + for iSubject = 1+offset:size(SubjDir,1) + tempSubjDir{end+1,1} = SubjDir{iSubject,1}; + tempSubjDir{end,2} = SubjDir{iSubject,2}; + tempSubjDir{end,3} = SubjDir{iSubject,3}; end - end + end + chunkFile = fullfile(mc_GenPath(Exp),['chunk_' num2str(iChunk) '.mat']); + save(chunkFile,'tempSubjDir','OutputTemplate','Exp','OutputName','ParameterFilename'); + end + %send a system call to start matlab, load a chunk file, and call + %cppi_batch_chunk with the loaded variables + + %spawn NumProcesses-1 other matlab processes + for iChunk = 1:NumProcesses-1 + chunkFile = fullfile(mc_GenPath(Exp),['chunk_' num2str(iChunk) '.mat']); + systemcall = sprintf('/net/misc/matlab2007b/bin/matlab -nosplash -nodesktop -r "addpath(fullfile(''%s'',''matlabScripts''));,addpath(fullfile(''%s'',''cPPI''));,addpath(fullfile(''%s'',''som''));,addpath(fullfile(''%s'',''spm8''));,cppi_batch_chunk(''%s'');,quit;" &',mcRoot,mcRoot,mcRoot,mcRoot,chunkFile); + [status result] = system(systemcall); + end + %now the last one in this matlab. This one will always be equal to + %or greater in number of subjects to the previous ones. + iChunk = NumProcesses; + chunkFile = fullfile(mc_GenPath(Exp),['chunk_' num2str(iChunk) '.mat']); + cppi_batch_chunk(chunkFile); + + + end diff --git a/cPPI/cppi_batch_mc_template.m b/cPPI/cppi_batch_mc_template.m index b47869f9..aa0abfe2 100644 --- a/cPPI/cppi_batch_mc_template.m +++ b/cPPI/cppi_batch_mc_template.m @@ -6,7 +6,7 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% The folder that contains your subject folders %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -Exp = '/dysthymia/sandbox/cppi/'; +Exp = '/dysthymia/sandbox/cppi'; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Path where your images are located @@ -37,6 +37,7 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SubjDir = { '5001/Tx1',50011,[1]; +%'5001TEST/Tx1',50011,[1]; }; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @@ -87,6 +88,24 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SPMTemplate = '[Exp]/FirstLevel/[Subject]/OrigModel'; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Analyze data on a local drive on the machine you run the script on +%%% If your data is located on /net/data4 or similar network drive, using +%%% this option will speed up the processing speed immensely. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +UseSandbox = 1; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The script will split the number of ROIs into this many seperate bins +%%% and run them simultaneously to speed up processing time. Be careful +%%% with setting this too high as it could cause the computer running the +%%% script to become unresponsive or crash. Please use the linux command +%%% top to investigate other processes running on the computer before +%%% starting your script. In general you should not set this higher than +%%% 4. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +NumProcesses = 2; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Mode to run som_batch_mc_central in %%% 'test' = test script but do not save parameters or run any @@ -152,7 +171,7 @@ %%% %%% Suggested order is "DCWM" %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -RegressOrder = 'D'; +RegressOrder = ''; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Use this many principle components for regression @@ -182,7 +201,7 @@ %%% grid - make a grid based on provided spacing and masked %%% by provided mask %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -ROIInput = 'grid'; +ROIInput = 'coordinates'; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% If specifying ROI coordinates you need to provide a list of centers in @@ -192,10 +211,26 @@ %%% need to specify the size as a cell (i.e. {19}) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% ROICenters = [ - 9 9 -8; %VSi right - -9 9 -8; %VSi left + 18 -1 1; %VSi right + -18 -1 1; %VSi left + 10 10 10; + 10 10 -10; + 10 -10 10; + 10 -10 -10; +% -10 10 10; +% -10 10 -10; +% -10 -10 10; +% -10 -10 -10; +% 5 5 5; +% 5 5 -5; +% 5 -5 5; +% 5 -5 -5; +% -5 5 5; +% -5 5 -5; +% -5 -5 5; +% -5 -5 -5; ]; -ROISize = {19}; +ROISize = {1}; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% If specifying ROI images you need to provide an ROI folder as well as a From d0cc92b2c8eb437ab1808f4c3e2f0172bf456712 Mon Sep 17 00:00:00 2001 From: Mike Angstadt Date: Mon, 2 Jul 2012 12:25:49 -0400 Subject: [PATCH 03/27] some fixes for multiple runs --- cPPI/cppi_CreateModel.m | 8 ++++++-- cPPI/cppi_CreateRegressors.m | 21 +++++++++++---------- cPPI/cppi_Extract.m | 6 +++--- cPPI/cppi_batch_mc_central.m | 7 ++++--- cPPI/cppi_batch_mc_template.m | 12 +++++++++--- 5 files changed, 33 insertions(+), 21 deletions(-) diff --git a/cPPI/cppi_CreateModel.m b/cPPI/cppi_CreateModel.m index aba80c01..58cfda82 100644 --- a/cPPI/cppi_CreateModel.m +++ b/cPPI/cppi_CreateModel.m @@ -29,8 +29,12 @@ offset = 0; for iRun = 1:size(parameters.data.run,2) SPM.Sess(iRun).U = []; - SPM.Sess(iRun).C.C = [cppiregressors(offset+1:offset+parameters.data.run(iRun).nTimeAnalyzed,:) parameters.data.run(iRun).MotionParameters]; - SPM.Sess(iRun).C.name = repmat({'reg'},1,size(cppiregressors,2) + size(parameters.data.run(iRun).MotionParameters,2)); + if (parameters.cppi.domotion) + SPM.Sess(iRun).C.C = [cppiregressors(offset+1:offset+parameters.data.run(iRun).nTimeAnalyzed,:) parameters.data.run(iRun).MotionParameters]; + else + SPM.Sess(iRun).C.C = [cppiregressors(offset+1:offset+parameters.data.run(iRun).nTimeAnalyzed,:)]; + end + SPM.Sess(iRun).C.name = repmat({'reg'},1,size(cppiregressors,2) + (parameters.cppi.domotion * size(parameters.data.run(iRun).MotionParameters,2))); offset = offset + parameters.data.run(iRun).nTimeAnalyzed; end SPM.xY.P = spm_select('ExtFPList',parameters.cppi.sandbox,'roiTC_0.*',[1:size(roiTC,1)]); diff --git a/cPPI/cppi_CreateRegressors.m b/cPPI/cppi_CreateRegressors.m index 015cbcf8..4f5bfd1f 100644 --- a/cPPI/cppi_CreateRegressors.m +++ b/cPPI/cppi_CreateRegressors.m @@ -11,32 +11,33 @@ fMRI_T0 = SPM.xBF.T0; regressors = []; - +offset = 0; for iRun = 1:size(SPM.Sess,1) - + roiTCtemp = roiTC(1+offset:parameters.data.run(iRun).nTimeAnalyzed+offset,:); + offset = offset + parameters.data.run(iRun).nTimeAnalyzed; %calculate confounds xY.X0 = SPM.xX.xKXs.X(:,[SPM.xX.iB SPM.xX.iG]); xY.X0 = xY.X0(SPM.Sess(iRun).row,:); xY.X0 = [xY.X0 SPM.xX.K(iRun).X0]; xY.X0 = xY.X0(:,any(xY.X0)); - [m n] = size(roiTC); + [m n] = size(roiTCtemp); if m>n - [v s v] = svd(roiTC'*roiTC); + [v s v] = svd(roiTCtemp'*roiTCtemp); s = diag(s); v = v(:,1); - u = roiTC*v/sqrt(s(1)); + u = roiTCtemp*v/sqrt(s(1)); else - [u s u] = svd(roiTC*roiTC'); + [u s u] = svd(roiTCtemp*roiTCtemp'); s = diag(s); u = u(:,1); - v = roiTC'*u/sqrt(s(1)); + v = roiTCtemp'*u/sqrt(s(1)); end d = sign(sum(v)); u = u*d; v = v*d; Y = u*sqrt(s(1)/n); - roiTC = Y; + roiTCtemp = Y; Sess = SPM.Sess(iRun); @@ -54,7 +55,7 @@ end end - N = length(roiTC); + N = length(roiTCtemp); k = 1:NT:N*NT; % microtime to scan time indices hrf = spm_hrf(dt); @@ -72,7 +73,7 @@ X0 = xY.X0; M = size(X0,2); - Y = roiTC; + Y = roiTCtemp; %remove confounds and save Y in output structure Yc = Y-X0*inv(X0'*X0)*X0'*Y; diff --git a/cPPI/cppi_Extract.m b/cPPI/cppi_Extract.m index d07f6ae7..d6112b1f 100644 --- a/cPPI/cppi_Extract.m +++ b/cPPI/cppi_Extract.m @@ -11,10 +11,10 @@ P = spm_select('FPList',model,'beta.*img'); numimages = size(P,1); nummotion = size(parameters.data.run(1).MotionParameters,2); -domotion = parameters.RegressFLAGS.motion; +domotion = parameters.cppi.domotion; numrun = size(parameters.data.run,1); numregressors = size(cppiregressors,2); -domotion = 1; +%domotion = 1; if (numimages ~= (numrun + numregressors + (domotion * nummotion))) mc_Error('There are an inconsistent number of beta images and regressors.'); end @@ -23,7 +23,7 @@ index = 1; for iB = 1:size(goodbeta,2) %extract beta iB and place in grid - goodbeta(iB) = 1; + %goodbeta(iB) = 1; if (~goodbeta(iB)) continue; end diff --git a/cPPI/cppi_batch_mc_central.m b/cPPI/cppi_batch_mc_central.m index d57517f9..8c8d400b 100644 --- a/cPPI/cppi_batch_mc_central.m +++ b/cPPI/cppi_batch_mc_central.m @@ -69,7 +69,7 @@ clear RunDir; for iRun=1:NumRun RunDir{iRun,1}=RunNamesTotal{RunList(1,iRun)}; - NumScan=horzcat(NumScan,NumScanTotal(1,iRun)); + NumScan=horzcat(NumScan,NumScanTotal(1,RunList(1,iRun))); end NumRun= size(NumScan,2); % number of runs @@ -87,7 +87,7 @@ parameters.masks.epi.File = BrainPath; parameters.rois.mask.MaskFLAG = MaskBrain; - for iRun = 1:ImageNumRun + for iRun = 1:NumRun Run = RunDir{iRun}; ImagePath = mc_GenPath(ImageTemplate); @@ -184,7 +184,8 @@ else parameters.cppi.sandbox = fullfile(parameters.Output.directory,'temp'); end - + parameters.cppi.domotion = IncludeMotion; + ParameterFilename = [OutputName '_parameters']; ParameterPath = mc_GenPath(fullfile(OutputPath,ParameterFilename)); save(ParameterPath,'parameters'); diff --git a/cPPI/cppi_batch_mc_template.m b/cPPI/cppi_batch_mc_template.m index aa0abfe2..06772770 100644 --- a/cPPI/cppi_batch_mc_template.m +++ b/cPPI/cppi_batch_mc_template.m @@ -29,6 +29,7 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% RunDir = { 'run_01/'; + 'run_02/'; }; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @@ -36,7 +37,7 @@ %%% The format is 'subjectfolder',subject number in masterfile,[runs to include] %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SubjDir = { -'5001/Tx1',50011,[1]; +'5001/Tx1',50011,[1 2]; %'5001TEST/Tx1',50011,[1]; }; @@ -75,7 +76,7 @@ %%% Number of Functional scans per run %%% (if you have more than 1 run, there should be more than 1 value here) %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -NumScan = [180]; +NumScan = [180 180]; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @@ -88,6 +89,11 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SPMTemplate = '[Exp]/FirstLevel/[Subject]/OrigModel'; +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Include Realignment Parameters in your PPI model +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +IncludeMotion = 1; + %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Analyze data on a local drive on the machine you run the script on %%% If your data is located on /net/data4 or similar network drive, using @@ -104,7 +110,7 @@ %%% starting your script. In general you should not set this higher than %%% 4. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -NumProcesses = 2; +NumProcesses = 1; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Mode to run som_batch_mc_central in From f51b5a41b69a6b7af2d20576f2290c29134aaa46 Mon Sep 17 00:00:00 2001 From: Mike Angstadt Date: Fri, 6 Jul 2012 11:52:58 -0400 Subject: [PATCH 04/27] alpha release --- cPPI/cppi_CreateModel.m | 2 +- cPPI/cppi_CreateRegressors.m | 10 +++++----- cPPI/cppi_Extract.m | 4 ++-- cPPI/cppi_batch_mc_central.m | 3 ++- 4 files changed, 10 insertions(+), 9 deletions(-) diff --git a/cPPI/cppi_CreateModel.m b/cPPI/cppi_CreateModel.m index 58cfda82..926ab623 100644 --- a/cPPI/cppi_CreateModel.m +++ b/cPPI/cppi_CreateModel.m @@ -38,7 +38,7 @@ offset = offset + parameters.data.run(iRun).nTimeAnalyzed; end SPM.xY.P = spm_select('ExtFPList',parameters.cppi.sandbox,'roiTC_0.*',[1:size(roiTC,1)]); -SPM.nscan = size(roiTC,1); +SPM.nscan = parameters.cppi.NumScan; SPM.xBF.name = 'hrf'; SPM.xBF.length = 32; SPM.xBF.order = 1; diff --git a/cPPI/cppi_CreateRegressors.m b/cPPI/cppi_CreateRegressors.m index 4f5bfd1f..d984f5ac 100644 --- a/cPPI/cppi_CreateRegressors.m +++ b/cPPI/cppi_CreateRegressors.m @@ -12,7 +12,7 @@ regressors = []; offset = 0; -for iRun = 1:size(SPM.Sess,1) +for iRun = 1:size(SPM.Sess,2) roiTCtemp = roiTC(1+offset:parameters.data.run(iRun).nTimeAnalyzed+offset,:); offset = offset + parameters.data.run(iRun).nTimeAnalyzed; %calculate confounds @@ -123,17 +123,17 @@ pmp{i} = spm_detrend(pmp{i}); R(:,i) = PSYHRF{i}; R(:,j) = pmp{i}; - betanames{i} = U.name{i}; - betanames{j} = [U.name{i} ' x Seed interaction']; + betanames2{i} = ['Run ' num2str(iRun) ' ' U.name{i}]; + betanames2{j} = ['Run ' num2str(iRun) ' ' U.name{i} ' x Seed interaction']; j = j + 1; end R(:,j) = PMP.Y; - betanames{j} = 'Seed'; + betanames2{j} = ['Run ' num2str(iRun) ' Seed']; %if (parameters.RegressFLAGS.Motion) % R = [R parameters.data.run(iRun).MotionParameters]; %end - + betanames = [betanames betanames2]; regressors = [regressors; R]; end diff --git a/cPPI/cppi_Extract.m b/cPPI/cppi_Extract.m index d6112b1f..46a3eef8 100644 --- a/cPPI/cppi_Extract.m +++ b/cPPI/cppi_Extract.m @@ -12,10 +12,10 @@ numimages = size(P,1); nummotion = size(parameters.data.run(1).MotionParameters,2); domotion = parameters.cppi.domotion; -numrun = size(parameters.data.run,1); +numrun = size(parameters.data.run,2); numregressors = size(cppiregressors,2); %domotion = 1; -if (numimages ~= (numrun + numregressors + (domotion * nummotion))) +if (numimages ~= (numrun + (numrun*numregressors) + (domotion*numrun*nummotion))) mc_Error('There are an inconsistent number of beta images and regressors.'); end diff --git a/cPPI/cppi_batch_mc_central.m b/cPPI/cppi_batch_mc_central.m index 8c8d400b..2cca98da 100644 --- a/cPPI/cppi_batch_mc_central.m +++ b/cPPI/cppi_batch_mc_central.m @@ -177,7 +177,8 @@ parameters.cppi.SPM = mc_GenPath(fullfile(SPMTemplate,'SPM.mat')); parameters.cppi.UseSandbox = UseSandbox; - + parameters.cppi.NumScan = NumScan; + if (UseSandbox) [status hostname] = system('hostname -s'); parameters.cppi.sandbox = fullfile([filesep hostname(1:end-1)],'sandbox','cppi',parameters.Output.directory); From c7abd27de6e89146a9dfd51f556ae38fe714c9e8 Mon Sep 17 00:00:00 2001 From: Mike Angstadt Date: Fri, 6 Jul 2012 12:00:59 -0400 Subject: [PATCH 05/27] added simpler template script for most users --- cPPI/cppi_batch_mc_central.m | 8 + cPPI/cppi_batch_mc_template_simple.m | 268 +++++++++++++++++++++++++++ 2 files changed, 276 insertions(+) create mode 100644 cPPI/cppi_batch_mc_template_simple.m diff --git a/cPPI/cppi_batch_mc_central.m b/cPPI/cppi_batch_mc_central.m index 2cca98da..5cf06c05 100644 --- a/cPPI/cppi_batch_mc_central.m +++ b/cPPI/cppi_batch_mc_central.m @@ -1,6 +1,14 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% General calculations that apply to both Preprocessing and First Level %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +if (~exist('ROIOutput','var')) + ROIOutput = 'maps'; +end +if (~exist('Mode','var')) + Mode = 'full'; +end + if (alreadydone(1)) basefile = [stp basefile]; end diff --git a/cPPI/cppi_batch_mc_template_simple.m b/cPPI/cppi_batch_mc_template_simple.m new file mode 100644 index 00000000..5e09a752 --- /dev/null +++ b/cPPI/cppi_batch_mc_template_simple.m @@ -0,0 +1,268 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% GENERAL OPTIONS +%%% These options are shared among many of our scripts +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The folder that contains your subject folders +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +Exp = '/dysthymia/sandbox/cppi'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Path where your images are located +%%% +%%% Variables you can use in your template are: +%%% Exp = path to your experiment directory +%%% iSubject = index for subject +%%% Subject = name of subject from SubjDir (using iSubject as index of row) +%%% iRun = index of run (listed in Column 3 of SubjDir) +%%% Run = name of run from RunDir (using iRun as index of row) +%%% * = wildcard (can only be placed in final part of template) +%%% Examples: +%%% ImageTemplate = '[Exp]/Subjects/[Subject]/func/run_0[iRun]/'; +%%% ImageTemplate = '[Exp]/Subjects/[Subject]/TASK/func/[Run]/' +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ImageTemplate = '[Exp]/Subjects/[Subject]/TASK/func/[Run]/'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% A list of run folders where the script can find functional images +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +RunDir = { + 'run_01/'; + 'run_02/'; +}; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The list of subjects to process +%%% The format is 'subjectfolder',subject number in masterfile,[runs to include] +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +SubjDir = { +'5001/Tx1',50011,[1 2]; + }; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The TR your data was collected at +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +TR = 2; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Prefixes for slicetiming, realignment, normalization, and smoothing +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +stp = 'a'; +rep = 'r'; +nop = 'w'; +smp = 's'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Preprocessing that has already been completed on images +%%% [slicetime realign normalize smooth] +%%% If you are only running First Level (i.e. Preprocessing is already done) +%%% setting these will add the appropriate prefix to the basefile +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +alreadydone = [1 1 1 1]; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The prefix of each functional file +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +basefile = 'restrun'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Image Type should be either 'nii' or 'img' +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +imagetype = 'nii'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Number of Functional scans per run +%%% (if you have more than 1 run, there should be more than 1 value here) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +NumScan = [180 180]; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% CONNECTONOMIC PPI OPTIONS +%%% These options are only used for cPPI +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Original First Level Model location +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +SPMTemplate = '[Exp]/FirstLevel/[Subject]/OrigModel'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Include Realignment Parameters in your PPI model +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +IncludeMotion = 1; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Analyze data on a local drive on the machine you run the script on +%%% If your data is located on /net/data4 or similar network drive, using +%%% this option will speed up the processing speed immensely. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +UseSandbox = 1; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The script will split the number of ROIs into this many seperate bins +%%% and run them simultaneously to speed up processing time. Be careful +%%% with setting this too high as it could cause the computer running the +%%% script to become unresponsive or crash. Please use the linux command +%%% top to investigate other processes running on the computer before +%%% starting your script. In general you should not set this higher than +%%% 4. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +NumProcesses = 1; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Where to output the data +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +OutputTemplate = '[Exp]/FirstLevel/[Subject]/[OutputName]/'; +OutputName = 'Grid_test'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Path and name of explicit mask to use at first level. +%%% Leaving this blank ('') will use a subject-specific mask +%%% NOTE: Subject-specific masks are not recommended for grid usage below. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +BrainMaskTemplate = '[Exp]/ROIS/rEPI_MASK_NOEYES.img'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Path Template for realignment parameters file +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +RealignmentParametersTemplate = '[Exp]/Subjects/[Subject]/TASK/func/[Run]/rp_arestrun_*'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Type of input +%%% coordinates - provide the center of each seed and a radius +%%% files - provide a list of ROI files +%%% grid - make a grid based on provided spacing and masked +%%% by provided mask +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROIInput = 'grid'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% If specifying ROI coordinates you need to provide a list of centers in +%%% MNI coordinates (mm) and a radius in voxels. +%%% NOTE: ROISize will be used as the radius of a sphere at each point. If +%%% you'd prefer to use the predefined 1,7,19, or 27 voxel sizes you will +%%% need to specify the size as a cell (i.e. {19}) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROICenters = [ + 18 -1 1; %VSi right + -18 -1 1; %VSi left + 10 10 10; + 10 10 -10; + 10 -10 10; + 10 -10 -10; + ]; +ROISize = {1}; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% If specifying ROI images you need to provide an ROI folder as well as a +%%% cell array list of ROI images. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROITemplate = '[Exp]/ROIS'; +ROIImages = { + 'image1.nii'; + 'image2.nii'; + }; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% If specifying ROI grid you need to provide a spacing and ROI size as +%%% well as an optional mask for grid point inclusion (a mask is strongly +%%% encouraged as not using one will return coordinates from across the entire +%%% bounding box). +%%% NOTE: ROIGridSize will be used as the radius of a sphere at each grid +%%% point. If you'd prefer to use the predefined 1,7,19, or 27 voxel sizes +%%% you will need to specify the size as a cell (i.e. {19}) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROIGridSpacing = 12; +ROIGridSize = {19}; +ROIGridMaskTemplate = '[Exp]/ROIS/rEPI_MASK_NOEYES.img'; + + + + + + + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Advanced Options +%%% These options are not required unless you are planning to do +%%% similar preprocessing steps to Connectivity data using Robert's +%%% SOM toolbox. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Paths to your anatomical images +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +GreyMatterTemplate = '[Exp]/Subjects/[Subject]/anatomy/rgrey.img'; +WhiteMatterTemplate = '[Exp]/Subjects/[Subject]/anatomy/wm_mask.nii'; +CSFTemplate = '[Exp]/Subjects/[Subject]/anatomy/csf_mask.nii'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Constrain results to only regions in GreyMatterTemplate (1=yes, 0=no) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +MaskGrey = 0; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Value threshold to use for each mask. If left as [] use default 0.75 +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +GreyThreshold = []; +WhiteThreshold = []; +CSFThreshold = []; +EPIThreshold = []; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% the order to perform the regressions etc +%%% D = detrend +%%% G = global +%%% W = white matter +%%% C = csf +%%% M = motion +%%% B = bandpass +%%% +%%% Suggested order is "DCWM" +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +RegressOrder = ''; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Use this many principle components for regression +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +PrincipalComponents = 5; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Bandpass Filter Settings +%%% LowFrequency - low frequency cutoff +%%% HighFrequency - high frequency cutoff +%%% Gentle - 0 = no rolling, 1 = rolling +%%% Padding - number of timepoints to pad on beginning/end +%%% BandpassFilter - 0 = Matlab filter, 1 = SOM_Filter_FFT +%%% Fraction - fraction of variance for principle components +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +LowFrequency = 0.01; +HighFrequency = 0.1; +Gentle = 1; +Padding = 10; +BandpassFilter = 1; +Fraction = 1; + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Do not edit below this line +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%DEVSTART +mcRoot = fullfile(fileparts(mfilename('fullpath')),'../../MethodsCore'); +%DEVSTOP + +%[DEVmcRootAssign] + +addpath(fullfile(mcRoot,'matlabScripts')); +addpath(fullfile(mcRoot,'cPPI')); +addpath(fullfile(mcRoot,'som')); +addpath(fullfile(mcRoot,'spm8')); + +cppi_batch_mc_central \ No newline at end of file From cc3d324c52ec785c17c77eff334dbe77024996be Mon Sep 17 00:00:00 2001 From: Mike Angstadt Date: Fri, 6 Jul 2012 13:12:49 -0400 Subject: [PATCH 06/27] finished preliminary testing --- cPPI/cppi_CreateModel.m | 2 +- cPPI/cppi_batch_mc_template_simple.m | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/cPPI/cppi_CreateModel.m b/cPPI/cppi_CreateModel.m index 926ab623..f77993c2 100644 --- a/cPPI/cppi_CreateModel.m +++ b/cPPI/cppi_CreateModel.m @@ -2,7 +2,7 @@ model = -1; curpath = pwd; -mkdir(parameters.cppi.sandbox); +mc_GenPath(struct('Template',parameters.cppi.sandbox,'mode','makedir')); cd(parameters.cppi.sandbox); Vtemplate.fname = fullfile(parameters.cppi.sandbox,'roiTC'); diff --git a/cPPI/cppi_batch_mc_template_simple.m b/cPPI/cppi_batch_mc_template_simple.m index 5e09a752..3beffab0 100644 --- a/cPPI/cppi_batch_mc_template_simple.m +++ b/cPPI/cppi_batch_mc_template_simple.m @@ -6,7 +6,7 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% The folder that contains your subject folders %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -Exp = '/dysthymia/sandbox/cppi'; +Exp = '/net/dysthymia/mangstad/cppi'; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Path where your images are located @@ -38,6 +38,7 @@ %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% SubjDir = { '5001/Tx1',50011,[1 2]; +'5001test/Tx1',50011,[1 2]; }; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @@ -109,7 +110,7 @@ %%% starting your script. In general you should not set this higher than %%% 4. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -NumProcesses = 1; +NumProcesses = 2; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Where to output the data @@ -136,7 +137,7 @@ %%% grid - make a grid based on provided spacing and masked %%% by provided mask %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% -ROIInput = 'grid'; +ROIInput = 'coordinates'; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% If specifying ROI coordinates you need to provide a list of centers in From 2da516fa86e05aae3883cb7769dca90083b5e248 Mon Sep 17 00:00:00 2001 From: Mike Angstadt Date: Fri, 12 Oct 2012 11:04:58 -0400 Subject: [PATCH 07/27] fixed matlab call --- cPPI/cppi_batch_mc_central.m | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cPPI/cppi_batch_mc_central.m b/cPPI/cppi_batch_mc_central.m index 5cf06c05..449c987a 100644 --- a/cPPI/cppi_batch_mc_central.m +++ b/cPPI/cppi_batch_mc_central.m @@ -236,7 +236,7 @@ %spawn NumProcesses-1 other matlab processes for iChunk = 1:NumProcesses-1 chunkFile = fullfile(mc_GenPath(Exp),['chunk_' num2str(iChunk) '.mat']); - systemcall = sprintf('/net/misc/matlab2007b/bin/matlab -nosplash -nodesktop -r "addpath(fullfile(''%s'',''matlabScripts''));,addpath(fullfile(''%s'',''cPPI''));,addpath(fullfile(''%s'',''som''));,addpath(fullfile(''%s'',''spm8''));,cppi_batch_chunk(''%s'');,quit;" &',mcRoot,mcRoot,mcRoot,mcRoot,chunkFile); + systemcall = sprintf('matlab -nosplash -nodesktop -r "addpath(fullfile(''%s'',''matlabScripts''));,addpath(fullfile(''%s'',''cPPI''));,addpath(fullfile(''%s'',''som''));,addpath(fullfile(''%s'',''SPM/SPM8/spm8_with_R4667''));,cppi_batch_chunk(''%s'');,quit;" &',mcRoot,mcRoot,mcRoot,mcRoot,chunkFile); [status result] = system(systemcall); end %now the last one in this matlab. This one will always be equal to From afea66b91f5cf17e9a0438ce2278787ab3d3bbee Mon Sep 17 00:00:00 2001 From: Mike Angstadt Date: Tue, 30 Oct 2012 14:18:56 -0400 Subject: [PATCH 08/27] fixed typo in a call to SOM_LOG --- cPPI/cppi_CreateModel.m | 2 +- cPPI/cppi_CreateRegressors.m | 1 + cPPI/cppi_batch_chunk.m | 10 +- cPPI/cppi_batch_mc_central.m | 37 ++- cPPI/cppi_batch_mc_template_MAS_MSIT.m | 366 +++++++++++++++++++++++++ som/SOM_CheckOutput.m | 2 +- 6 files changed, 407 insertions(+), 11 deletions(-) create mode 100644 cPPI/cppi_batch_mc_template_MAS_MSIT.m diff --git a/cPPI/cppi_CreateModel.m b/cPPI/cppi_CreateModel.m index f77993c2..dc7ec067 100644 --- a/cPPI/cppi_CreateModel.m +++ b/cPPI/cppi_CreateModel.m @@ -38,7 +38,7 @@ offset = offset + parameters.data.run(iRun).nTimeAnalyzed; end SPM.xY.P = spm_select('ExtFPList',parameters.cppi.sandbox,'roiTC_0.*',[1:size(roiTC,1)]); -SPM.nscan = parameters.cppi.NumScan; +SPM.nscan = [parameters.data.run(:).nTIME]; SPM.xBF.name = 'hrf'; SPM.xBF.length = 32; SPM.xBF.order = 1; diff --git a/cPPI/cppi_CreateRegressors.m b/cPPI/cppi_CreateRegressors.m index d984f5ac..67212153 100644 --- a/cPPI/cppi_CreateRegressors.m +++ b/cPPI/cppi_CreateRegressors.m @@ -113,6 +113,7 @@ %--------------------------------------------------------------------- %PSY = zeros(N*NT,1); j = size(U.u,2) + 1; + clear R; for i = 1:size(U.u,2) PSY{i} = full(U.u(:,i)); PSYxn{i} = PSY{i}.*xn; diff --git a/cPPI/cppi_batch_chunk.m b/cPPI/cppi_batch_chunk.m index fa4a99ec..4f584810 100644 --- a/cPPI/cppi_batch_chunk.m +++ b/cPPI/cppi_batch_chunk.m @@ -3,10 +3,15 @@ spm_get_defaults('cmdline',true); results = -1; load(chunkFile); + global mcLog; SubjDir = tempSubjDir; for iSubject = 1:size(SubjDir,1) clear D0 parameters results; Subject=SubjDir{iSubject,1}; + + logstring = sprintf('%s: Now running subject %s\n',datestr(now),Subject); + mc_Logger('log',logstring,3); + %load existing parameter file OutputPath = mc_GenPath(OutputTemplate); load(fullfile(OutputPath,ParameterFilename)); @@ -25,6 +30,9 @@ SOM_LOG('FATAL ERROR : '); mc_Error('There is something wrong with your template or your data. No results were returned from SOM_CalculateCorrelations'); end - end + end + + logstring = sprintf('%s: Finished subject %s\n',datestr(now),Subject); + mc_Logger('log',logstring,3); end return; \ No newline at end of file diff --git a/cPPI/cppi_batch_mc_central.m b/cPPI/cppi_batch_mc_central.m index 449c987a..650842e8 100644 --- a/cPPI/cppi_batch_mc_central.m +++ b/cPPI/cppi_batch_mc_central.m @@ -2,6 +2,19 @@ %%% General calculations that apply to both Preprocessing and First Level %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Code to create logfile name +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +LogDirectory = mc_GenPath(struct('Template',LogTemplate,'mode','makedir')); +result = mc_Logger('setup',LogDirectory); +if (~result) + %error with setting up logging + mc_Error('There was an error creating your logfiles.\nDo you have permission to write to %s?',LogDirectory); +end + +global mcLog; +tempLog = mcLog; + if (~exist('ROIOutput','var')) ROIOutput = 'maps'; end @@ -103,14 +116,18 @@ RealignmentParametersFile = mc_GenPath(RealignmentParametersTemplate); parameters.data.run(iRun).P = ImageFiles; - + if (SubjDir{iSubject,4}(1) ~= 0) + parameters.data.run(iRun).nTIME = SubjDir{iSubject,4}(iRun); + else + parameters.data.run(iRun).nTIME = NumScan(iRun); + end RealignmentParameters = load(RealignmentParametersFile); RealignmentParametersDeriv = diff(RealignmentParameters); %RealignmentParametersDerivR = resample(RealignmentParametersDeriv,size(RealignmentParameters,1),size(RealignmentParametersDeriv,1)); %parameters.data.run(iRun).MotionParameters = [RealignmentParameters RealignmentParametersDerivR]; - parameters.data.run(iRun).MotionParameters = RealignmentParameters; - parameters.data.run(iRun).nTIME = NumScan(iRun); + parameters.data.run(iRun).MotionParameters = RealignmentParameters(1:parameters.data.run(iRun).nTIME,:); + parameters.data.MaskFLAG = MaskBrain; parameters.TIME.run(iRun).TR = TR; @@ -161,7 +178,7 @@ parameters.rois.mni.size.ZROI = XYZ(3,:); end case 'grid' - ROIGridMask = mc_Genpath(ROIGridMaskTemplate); + ROIGridMask = mc_GenPath(ROIGridMaskTemplate); ROIGridMaskHdr = spm_vol(ROIGridMask); ROIGridBB = mc_GetBoundingBox(ROIGridMaskHdr); grid_coord_cand = SOM_MakeGrid(ROIGridSpacing,ROIGridBB); @@ -205,8 +222,8 @@ if (RunMode(2)) %partition SubjDir into parameters.cppi.NumProcesses equal parts NumSubj = size(SubjDir,1); - if (NumProcesses > 4) - NumProcesses = 4; + if (NumProcesses > 12) + NumProcesses = 12; end SubjPerChunk = floor(NumSubj / NumProcesses); for iChunk = 1:NumProcesses @@ -218,17 +235,21 @@ tempSubjDir{end+1,1} = SubjDir{iSubject,1}; tempSubjDir{end,2} = SubjDir{iSubject,2}; tempSubjDir{end,3} = SubjDir{iSubject,3}; + tempSubjDir{end,4} = SubjDir{iSubject,4}; end else for iSubject = 1+offset:size(SubjDir,1) tempSubjDir{end+1,1} = SubjDir{iSubject,1}; tempSubjDir{end,2} = SubjDir{iSubject,2}; tempSubjDir{end,3} = SubjDir{iSubject,3}; + tempSubjDir{end,4} = SubjDir{iSubject,4}; end end + [fd fn fe] = fileparts(mcLog); + mcLog = fullfile(fd,[fn '_chunk' num2str(iChunk) fe]); chunkFile = fullfile(mc_GenPath(Exp),['chunk_' num2str(iChunk) '.mat']); - save(chunkFile,'tempSubjDir','OutputTemplate','Exp','OutputName','ParameterFilename'); - + save(chunkFile,'tempSubjDir','OutputTemplate','Exp','OutputName','ParameterFilename','NumScan'); + mcLog = tempLog; end %send a system call to start matlab, load a chunk file, and call %cppi_batch_chunk with the loaded variables diff --git a/cPPI/cppi_batch_mc_template_MAS_MSIT.m b/cPPI/cppi_batch_mc_template_MAS_MSIT.m new file mode 100644 index 00000000..9a7b5eb2 --- /dev/null +++ b/cPPI/cppi_batch_mc_template_MAS_MSIT.m @@ -0,0 +1,366 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% GENERAL OPTIONS +%%% These options are shared among many of our scripts +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The folder that contains your subject folders +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +Exp = '/net/data4/MAS'; + +LogTemplate = '[Exp]/Logs/cPPI/'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Path where your images are located +%%% +%%% Variables you can use in your template are: +%%% Exp = path to your experiment directory +%%% iSubject = index for subject +%%% Subject = name of subject from SubjDir (using iSubject as index of row) +%%% iRun = index of run (listed in Column 3 of SubjDir) +%%% Run = name of run from RunDir (using iRun as index of row) +%%% * = wildcard (can only be placed in final part of template) +%%% Examples: +%%% ImageTemplate = '[Exp]/Subjects/[Subject]/func/run_0[iRun]/'; +%%% ImageTemplate = '[Exp]/Subjects/[Subject]/TASK/func/[Run]/' +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ImageTemplate = '[Exp]/Subjects/[Subject]/TASK/func/[Run]/'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% A list of run folders where the script can find functional images +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +RunDir = { + 'run_05_lss/'; + 'run_06_lss/'; +}; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The list of subjects to process +%%% The format is 'subjectfolder',subject number in masterfile,[runs to include] +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +SubjDir = { +% '5001/Tx1',50011,[1 2], [215 235], 0 +% '5001/Tx2',50012,[1 2], [215 235], 0 +% '5002/Tx1',50021,[1 2], [225 240], 0; +% '5002/Tx2',50022,[1 2], [225 240], 0; +% '5003/Tx1',50031,[1 2], [225 240], {'run_04/';'run_05/'}; +% '5003/Tx2',50032,[1 2], [220 235], 0; +% '5004/Tx1',50041,[1 2], 0, 0; +% '5004/Tx2',50042,[1 2], 0, 0; +% '5005/Tx1',50051,[1 2], 0, 0; +% '5005/Tx2',50052,[1 2], 0, 0; +% '5010/Tx1',50101,[1 2], 0, 0; +% '5010/Tx2',50102,[1 2], 0, 0; +% '5012/Tx1',50121,[1 2], 0, 0; +% '5012/Tx2',50122,[1 2], 0, 0; +% '5014/Tx1',50141,[1 2], 0, 0; +% '5014/Tx2',50142,[1 2], 0, 0; +% '5015/Tx1',50151,[1 2], 0, 0; +% '5015/Tx2',50152,[1 2], 0, 0; +% '5016/Tx1',50161,[1 2], 0, 0; +% '5016/Tx2',50162,[1 2], 0, 0; +% '5017/Tx1',50171,[1 2], 0, 0; +% '5017/Tx2',50172,[1 2], 0, 0; +% '5018/Tx1',50181,[1 2], 0, 0; +% '5018/Tx2',50182,[1 2], 0, 0; +% '5019/Tx1',50191,[1 2], 0, 0; +% '5019/Tx2',50192,[1 2], 0, 0; +% '5020/Tx1',50201,[1 2], 0, 0; +% '5020/Tx2',50202,[1 2], 0, 0; +% '5021/Tx1',50211,[1 2], 0, 0; +% '5021/Tx2',50212,[1 2], 0, 0; +% '5023/Tx1',50231,[1 2], 0, 0; +% '5023/Tx2',50232,[1 2], 0, 0; +% '5024/Tx1',50241,[1 2], 0, 0; +% '5024/Tx2',50242,[1 2], 0, 0; +% '5025/Tx1',50251,[1 2], 0, 0; +% '5025/Tx2',50252,[1 2], 0, 0; +% '5026/Tx1',50261,[1 2], 0, 0; +% '5026/Tx2',50262,[1 2], 0, 0; +% '5028/Tx1',50281,[1 2], 0, 0; +% '5028/Tx2',50282,[1 2], 0, 0; +% '5029/Tx1',50291,[1 2], 0, 0; +% '5029/Tx2',50292,[1 2], 0, 0; +% '5031/Tx1',50311,[1 2], 0, 0; +% '5031/Tx2',50312,[1 2], 0, 0; +% '5032/Tx1',50321,[1 2], 0, 0; +% '5032/Tx2',50322,[1 2], 0, 0; +% '5034/Tx1',50341,[1 2], 0, 0; +% '5034/Tx2',50342,[1 2], 0, 0; +% '5035/Tx1',50351,[1 2], 0, 0; +% '5035/Tx2',50352,[1 2], 0, 0; +% '5036/Tx1',50361,[1 2], 0, 0; +% '5036/Tx2',50362,[1 2], 0, 0; +% '5037/Tx1',50371,[1 2], 0, 0; +% '5037/Tx2',50372,[1 2], 0, 0; +% '5038/Tx1',50381,[1 2], 0, 0; +'5038/Tx2',50382,[1 2], 0, 0; +% '5039/Tx1',50391,[1 2] ,[220 235], 0; +% '5039/Tx2',50392,[1 2], [220 235], {'run_03/';'run_04/'}; +% '5040/Tx1',50401,[1 2], 0, 0; +% '5040/Tx2',50402,[1 2], 0, 0; +% '5041/Tx1',50411,[1 2], 0, 0; +% '5041/Tx2',50412,[1 2], 0, 0; +% '5042/Tx1',50421,[1 2], 0, 0; +% '5042/Tx2',50422,[1 2], 0, 0; + }; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The TR your data was collected at +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +TR = 2; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Prefixes for slicetiming, realignment, normalization, and smoothing +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +stp = 'a'; +rep = 'r'; +nop = 'w'; +smp = 's'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Preprocessing that has already been completed on images +%%% [slicetime realign normalize smooth] +%%% If you are only running First Level (i.e. Preprocessing is already done) +%%% setting these will add the appropriate prefix to the basefile +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +alreadydone = [1 1 1 1]; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The prefix of each functional file +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +basefile = '_MSITrun'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Image Type should be either 'nii' or 'img' +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +imagetype = 'nii'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Number of Functional scans per run +%%% (if you have more than 1 run, there should be more than 1 value here) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +NumScan = [220 235]; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% CONNECTONOMIC PPI OPTIONS +%%% These options are only used for cPPI +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Original First Level Model location +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +SPMTemplate = '[Exp]/FirstLevel/[Subject]/MSIT/HRF/FixDur/Congruency_NORT_new/'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Include Realignment Parameters in your PPI model +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +IncludeMotion = 1; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Analyze data on a local drive on the machine you run the script on +%%% If your data is located on /net/data4 or similar network drive, using +%%% this option will speed up the processing speed immensely. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +UseSandbox = 1; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% The script will split the number of ROIs into this many seperate bins +%%% and run them simultaneously to speed up processing time. Be careful +%%% with setting this too high as it could cause the computer running the +%%% script to become unresponsive or crash. Please use the linux command +%%% top to investigate other processes running on the computer before +%%% starting your script. In general you should not set this higher than +%%% 4. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +NumProcesses = 1; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Mode to run som_batch_mc_central in +%%% 'test' = test script but do not save parameters or run any +%%% SOM code +%%% 'parameters' = run script and save parameters for each subject +%%% but do not run any SOM code +%%% 'cppi' = run SOM code on previously saved parameters +%%% 'full' = generate parameters and immediately run SOM code +%%% +%%% NOTE: If you choose mode 'cppi' then most variables except +%%% SubjDir and OutputTemplate/OutputName will be ignored as they +%%% will be loaded from the already existing parameter file for each +%%% subject. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +Mode = 'full'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Paths to your anatomical images +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +GreyMatterTemplate = '[Exp]/ROIS/rEPI_MASK_NOEYES_betaspace.img'; +WhiteMatterTemplate = '[Exp]/ROIS/rEPI_MASK_NOEYES_betaspace.img'; +CSFTemplate = '[Exp]/ROIS/rEPI_MASK_NOEYES_betaspace.img'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Where to output the data +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +OutputTemplate = '[Exp]/FirstLevel/[Subject]/MSIT/HRF/FixDur/[OutputName]/'; +OutputName = 'Congruency_NORT_new_cppi'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Path and name of explicit mask to use at first level. +%%% Leaving this blank ('') will use a subject-specific mask +%%% NOTE: Subject-specific masks are not recommended for grid usage below. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +BrainMaskTemplate = '[Exp]/ROIS/rEPI_MASK_NOEYES_betaspace.img'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Path Template for realignment parameters file +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +RealignmentParametersTemplate = '[Exp]/Subjects/[Subject]/TASK/func/[Run]/mcflirt*.dat'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Constrain results to only regions in GreyMatterTemplate (1=yes, 0=no) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +MaskGrey = 0; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Value threshold to use for each mask. If left as [] use default 0.75 +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +GreyThreshold = []; +WhiteThreshold = []; +CSFThreshold = []; +EPIThreshold = []; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% the order to perform the regressions etc +%%% D = detrend +%%% G = global +%%% W = white matter +%%% C = csf +%%% M = motion +%%% B = bandpass +%%% +%%% Suggested order is "DCWM" +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +RegressOrder = ''; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Use this many principle components for regression +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +PrincipalComponents = 5; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Bandpass Filter Settings +%%% LowFrequency - low frequency cutoff +%%% HighFrequency - high frequency cutoff +%%% Gentle - 0 = no rolling, 1 = rolling +%%% Padding - number of timepoints to pad on beginning/end +%%% BandpassFilter - 0 = Matlab filter, 1 = SOM_Filter_FFT +%%% Fraction - fraction of variance for principle components +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +LowFrequency = 0.01; +HighFrequency = 0.1; +Gentle = 1; +Padding = 10; +BandpassFilter = 1; +Fraction = 1; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Type of input +%%% coordinates - provide the center of each seed and a radius +%%% files - provide a list of ROI files +%%% grid - make a grid based on provided spacing and masked +%%% by provided mask +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROIInput = 'grid'; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% If specifying ROI coordinates you need to provide a list of centers in +%%% MNI coordinates (mm) and a radius in voxels. +%%% NOTE: ROISize will be used as the radius of a sphere at each point. If +%%% you'd prefer to use the predefined 1,7,19, or 27 voxel sizes you will +%%% need to specify the size as a cell (i.e. {19}) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROICenters = [ + 18 -1 1; %VSi right + -18 -1 1; %VSi left + 10 10 10; + 10 10 -10; + 10 -10 10; + 10 -10 -10; +% -10 10 10; +% -10 10 -10; +% -10 -10 10; +% -10 -10 -10; +% 5 5 5; +% 5 5 -5; +% 5 -5 5; +% 5 -5 -5; +% -5 5 5; +% -5 5 -5; +% -5 -5 5; +% -5 -5 -5; + ]; +ROISize = {1}; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% If specifying ROI images you need to provide an ROI folder as well as a +%%% cell array list of ROI images. +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROITemplate = '[Exp]/ROIS'; +ROIImages = { + 'image1.nii'; + 'image2.nii'; + }; + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% If specifying ROI grid you need to provide a spacing and ROI size as +%%% well as an optional mask for grid point inclusion (a mask is strongly +%%% encouraged as not using one will return coordinates from across the entire +%%% bounding box). +%%% NOTE: ROIGridSize will be used as the radius of a sphere at each grid +%%% point. If you'd prefer to use the predefined 1,7,19, or 27 voxel sizes +%%% you will need to specify the size as a cell (i.e. {19}) +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROIGridSpacing = 12; +ROIGridSize = {19}; +ROIGridMaskTemplate = '[Exp]/ROIS/rEPI_MASK_NOEYES_betaspace.img'; + + + + + + + + + + + + + + + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Type of output +%%% images - output R and Z images of correlation with each seed +%%% maps - output R and P matrix of correlations between seeds +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +ROIOutput = 'maps'; + + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%% Do not edit below this line +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +%DEVSTART +mcRoot = fullfile(fileparts(mfilename('fullpath')),'../../MethodsCore'); +%DEVSTOP + +%[DEVmcRootAssign] + +addpath(fullfile(mcRoot,'matlabScripts')); +addpath(fullfile(mcRoot,'cPPI')); +addpath(fullfile(mcRoot,'som')); +addpath(fullfile(mcRoot,'SPM/SPM8/spm8_with_R4667')); + +cppi_batch_mc_central \ No newline at end of file diff --git a/som/SOM_CheckOutput.m b/som/SOM_CheckOutput.m index 9e65a504..1b6454ce 100755 --- a/som/SOM_CheckOutput.m +++ b/som/SOM_CheckOutput.m @@ -47,7 +47,7 @@ if ~isfield(Output,'saveroiTC') Output.saveroiTC = 0; - SOM_Log('STATUS : Using saveroiTC = 0'); + SOM_LOG('STATUS : Using saveroiTC = 0'); end From d53810c8d540783a27d89cdfe6fea5887e19abca Mon Sep 17 00:00:00 2001 From: Mike Angstadt Date: Tue, 30 Oct 2012 14:38:41 -0400 Subject: [PATCH 09/27] Revert "Hide som" This reverts commit 744913e5db7742b9921b8790e9ae3b7c5d46aeba. --- som/README.txt | 22 + som/SOM_Alpha.m | 26 ++ som/SOM_Bootstrap.m | 126 +++++ som/SOM_BuildMap.m | 37 ++ som/SOM_BuildROILinearIDX.m | 114 +++++ som/SOM_Ca_back.m | 457 ++++++++++++++++++ som/SOM_CalcInOutComp.m | 53 +++ som/SOM_CalcP.m | 262 +++++++++++ som/SOM_CalcRCutoff.m | 53 +++ som/SOM_CalculateCorrelationImages.m | 105 +++++ som/SOM_CalculateCorrelationMaps.m | 80 ++++ som/SOM_CalculateCorrelationMapsFaster.m | 84 ++++ som/SOM_CalculateCorrelationMapsSlow.m | 72 +++ som/SOM_CalculateCorrelations.m | 153 ++++++ som/SOM_CalculateMap.m | 490 +++++++++++++++++++ som/SOM_CalculateMapV2.m | 428 +++++++++++++++++ som/SOM_CheckDataStructure.m | 170 +++++++ som/SOM_CheckMasks.m | 92 ++++ som/SOM_CheckOutput.m | 127 +++++ som/SOM_CheckROIParameters.m | 326 +++++++++++++ som/SOM_CheckRegressFLAGS.m | 148 ++++++ som/SOM_CheckTimeParams.m | 139 ++++++ som/SOM_CostFunction.c | 572 +++++++++++++++++++++++ som/SOM_CostFunction.m | 46 ++ som/SOM_CostFunctionMI.c | 494 ++++++++++++++++++++ som/SOM_CostFunctionMI.m | 54 +++ som/SOM_CreateMask.m | 182 ++++++++ som/SOM_Detrend.m | 65 +++ som/SOM_Ex2SC.m | 66 +++ som/SOM_FTest.m | 62 +++ som/SOM_Filter.m | 233 +++++++++ som/SOM_FindClosest.m | 112 +++++ som/SOM_FindSCMembers.m | 41 ++ som/SOM_FitGaussian.m | 57 +++ som/SOM_Fix.m | 27 ++ som/SOM_Gaussian.m | 57 +++ som/SOM_GlobalCalc.m | 34 ++ som/SOM_Grid4SCing.m | 32 ++ som/SOM_GridSC.m | 42 ++ som/SOM_HowTo.key | Bin 0 -> 62338602 bytes som/SOM_HowTo.pdf | Bin 0 -> 1909000 bytes som/SOM_HowTo_MakingROIS.pdf | Bin 0 -> 1838329 bytes som/SOM_InOutComponent.m | 58 +++ som/SOM_LOG.m | 106 +++++ som/SOM_MakeGrid.m | 76 +++ som/SOM_MakeROI.m | 43 ++ som/SOM_MakeSphereROI.m | 49 ++ som/SOM_MaskData.m | 110 +++++ som/SOM_MaskRead.m | 25 + som/SOM_MatchBestComponent.m | 58 +++ som/SOM_MergeSOMS.m | 59 +++ som/SOM_ModBasis.m | 29 ++ som/SOM_NeighborDist.m | 33 ++ som/SOM_NeighborMap.m | 25 + som/SOM_ParseFileParam.m | 94 ++++ som/SOM_Pearson.m | 72 +++ som/SOM_PearsonEngin.c | 280 +++++++++++ som/SOM_PowerSpect.m | 61 +++ som/SOM_PreProcessData.m | 569 ++++++++++++++++++++++ som/SOM_PrepData.m | 250 ++++++++++ som/SOM_PrinComp.m | 78 ++++ som/SOM_ROIIDXnMASK.m | 54 +++ som/SOM_ReadMask.m | 40 ++ som/SOM_ReadNII.m | 44 ++ som/SOM_RemoveConfound.m | 69 +++ som/SOM_RemoveMotion.m | 90 ++++ som/SOM_Rho2Z.m | 19 + som/SOM_SetDefaults.m | 53 +++ som/SOM_SpaceVerify.m | 69 +++ som/SOM_Sphere_Make.m | 117 +++++ som/SOM_SuperClust7.m | 137 ++++++ som/SOM_SuperClusterEasy.m | 109 +++++ som/SOM_SuperClusterTC.m | 96 ++++ som/SOM_TTest.m | 96 ++++ som/SOM_TestMEX.c | 380 +++++++++++++++ som/SOM_Texturizer.m | 16 + som/SOM_UnitNormMatrix.m | 56 +++ som/SOM_VarianceMap.m | 60 +++ som/SOM_ViewMap.m | 42 ++ som/SOM_Weighted_SUM.m | 37 ++ som/SOM_WriteData.m | 84 ++++ som/SOM_WriteIMGS.m | 42 ++ som/SOM_WriteIMGSByAvg.m | 48 ++ som/SOM_WriteIMGSByMask.m | 76 +++ som/SOM_WriteNII.m | 100 ++++ som/SOM_corr4D.m | 27 ++ som/SOM_editTimeSeries.m | 41 ++ som/SOM_img_endian.m | 39 ++ som/SOM_read_nii_hdr.m | 119 +++++ som/SOM_read_nii_img.m | 91 ++++ som/SOM_roiPointsInMask.m | 126 +++++ som/SOM_tVar.m | 78 ++++ som/SOM_tVarMask.m | 123 +++++ som/SOM_texturizer/sphere_hist.m | 58 +++ som/SOM_texturizer/texturizer_v4.m | 96 ++++ som/autoCorrelatePlane.m | 85 ++++ som/autoCorrelateVol.m | 91 ++++ som/diffCode.sh | 24 + som/draw_rois.m | 38 ++ som/nVoxelsPerRIO.pdf | Bin 0 -> 3948 bytes som/nVoxelsPerRIO.png | Bin 0 -> 25822 bytes som/roi_n_001_0.00_radius.png | Bin 0 -> 9485 bytes som/roi_n_007_1.00_radius.png | Bin 0 -> 10291 bytes som/roi_n_019_1.45_radius.png | Bin 0 -> 13145 bytes som/roi_n_027_1.75_radius.png | Bin 0 -> 14551 bytes som/roi_n_033_2.00_radius.png | Bin 0 -> 11188 bytes som/roi_n_057_2.25_radius.png | Bin 0 -> 13115 bytes som/roi_n_081_2.45_radius.png | Bin 0 -> 14278 bytes som/roi_n_093_2.85_radius.png | Bin 0 -> 15207 bytes som/roi_n_123_3.00_radius.png | Bin 0 -> 15302 bytes som/roi_n_147_3.20_radius.png | Bin 0 -> 16245 bytes som/roi_n_171_3.35_radius.png | Bin 0 -> 16891 bytes som/roi_n_179_3.50_radius.png | Bin 0 -> 17226 bytes som/roi_n_203_3.65_radius.png | Bin 0 -> 18525 bytes som/roi_n_251_3.75_radius.png | Bin 0 -> 19551 bytes som/roi_n_257_4.00_radius.png | Bin 0 -> 17220 bytes som/roi_n_305_4.15_radius.png | Bin 0 -> 18393 bytes som/roi_n_341_4.25_radius.png | Bin 0 -> 19292 bytes som/roi_n_365_4.40_radius.png | Bin 0 -> 19580 bytes som/roi_n_389_4.50_radius.png | Bin 0 -> 20217 bytes som/roi_n_437_4.60_radius.png | Bin 0 -> 20878 bytes som/roi_n_461_4.70_radius.png | Bin 0 -> 21262 bytes som/roi_n_485_4.90_radius.png | Bin 0 -> 21700 bytes som/roi_n_515_5.00_radius.png | Bin 0 -> 17201 bytes som/show.m | 26 ++ som/som_batch_mc_central.m | 211 +++++++++ som/som_batch_mc_template.m | 316 +++++++++++++ som/som_xyz.m | 66 +++ som/sphere_iter.m | 109 +++++ som/sphere_make.m | 20 + som/tile.m | 29 ++ 131 files changed, 11562 insertions(+) create mode 100644 som/README.txt create mode 100755 som/SOM_Alpha.m create mode 100755 som/SOM_Bootstrap.m create mode 100755 som/SOM_BuildMap.m create mode 100755 som/SOM_BuildROILinearIDX.m create mode 100755 som/SOM_Ca_back.m create mode 100755 som/SOM_CalcInOutComp.m create mode 100755 som/SOM_CalcP.m create mode 100755 som/SOM_CalcRCutoff.m create mode 100755 som/SOM_CalculateCorrelationImages.m create mode 100755 som/SOM_CalculateCorrelationMaps.m create mode 100755 som/SOM_CalculateCorrelationMapsFaster.m create mode 100755 som/SOM_CalculateCorrelationMapsSlow.m create mode 100755 som/SOM_CalculateCorrelations.m create mode 100755 som/SOM_CalculateMap.m create mode 100755 som/SOM_CalculateMapV2.m create mode 100644 som/SOM_CheckDataStructure.m create mode 100644 som/SOM_CheckMasks.m create mode 100755 som/SOM_CheckOutput.m create mode 100644 som/SOM_CheckROIParameters.m create mode 100755 som/SOM_CheckRegressFLAGS.m create mode 100755 som/SOM_CheckTimeParams.m create mode 100755 som/SOM_CostFunction.c create mode 100755 som/SOM_CostFunction.m create mode 100755 som/SOM_CostFunctionMI.c create mode 100755 som/SOM_CostFunctionMI.m create mode 100755 som/SOM_CreateMask.m create mode 100644 som/SOM_Detrend.m create mode 100755 som/SOM_Ex2SC.m create mode 100755 som/SOM_FTest.m create mode 100755 som/SOM_Filter.m create mode 100755 som/SOM_FindClosest.m create mode 100755 som/SOM_FindSCMembers.m create mode 100755 som/SOM_FitGaussian.m create mode 100755 som/SOM_Fix.m create mode 100755 som/SOM_Gaussian.m create mode 100755 som/SOM_GlobalCalc.m create mode 100755 som/SOM_Grid4SCing.m create mode 100755 som/SOM_GridSC.m create mode 100644 som/SOM_HowTo.key create mode 100644 som/SOM_HowTo.pdf create mode 100644 som/SOM_HowTo_MakingROIS.pdf create mode 100755 som/SOM_InOutComponent.m create mode 100755 som/SOM_LOG.m create mode 100644 som/SOM_MakeGrid.m create mode 100755 som/SOM_MakeROI.m create mode 100755 som/SOM_MakeSphereROI.m create mode 100755 som/SOM_MaskData.m create mode 100644 som/SOM_MaskRead.m create mode 100755 som/SOM_MatchBestComponent.m create mode 100755 som/SOM_MergeSOMS.m create mode 100755 som/SOM_ModBasis.m create mode 100755 som/SOM_NeighborDist.m create mode 100755 som/SOM_NeighborMap.m create mode 100755 som/SOM_ParseFileParam.m create mode 100755 som/SOM_Pearson.m create mode 100755 som/SOM_PearsonEngin.c create mode 100755 som/SOM_PowerSpect.m create mode 100644 som/SOM_PreProcessData.m create mode 100755 som/SOM_PrepData.m create mode 100755 som/SOM_PrinComp.m create mode 100755 som/SOM_ROIIDXnMASK.m create mode 100755 som/SOM_ReadMask.m create mode 100644 som/SOM_ReadNII.m create mode 100755 som/SOM_RemoveConfound.m create mode 100755 som/SOM_RemoveMotion.m create mode 100755 som/SOM_Rho2Z.m create mode 100644 som/SOM_SetDefaults.m create mode 100644 som/SOM_SpaceVerify.m create mode 100755 som/SOM_Sphere_Make.m create mode 100755 som/SOM_SuperClust7.m create mode 100755 som/SOM_SuperClusterEasy.m create mode 100755 som/SOM_SuperClusterTC.m create mode 100755 som/SOM_TTest.m create mode 100755 som/SOM_TestMEX.c create mode 100755 som/SOM_Texturizer.m create mode 100755 som/SOM_UnitNormMatrix.m create mode 100755 som/SOM_VarianceMap.m create mode 100755 som/SOM_ViewMap.m create mode 100755 som/SOM_Weighted_SUM.m create mode 100755 som/SOM_WriteData.m create mode 100755 som/SOM_WriteIMGS.m create mode 100755 som/SOM_WriteIMGSByAvg.m create mode 100755 som/SOM_WriteIMGSByMask.m create mode 100644 som/SOM_WriteNII.m create mode 100644 som/SOM_corr4D.m create mode 100644 som/SOM_editTimeSeries.m create mode 100755 som/SOM_img_endian.m create mode 100755 som/SOM_read_nii_hdr.m create mode 100755 som/SOM_read_nii_img.m create mode 100644 som/SOM_roiPointsInMask.m create mode 100644 som/SOM_tVar.m create mode 100644 som/SOM_tVarMask.m create mode 100755 som/SOM_texturizer/sphere_hist.m create mode 100755 som/SOM_texturizer/texturizer_v4.m create mode 100644 som/autoCorrelatePlane.m create mode 100755 som/autoCorrelateVol.m create mode 100755 som/diffCode.sh create mode 100644 som/draw_rois.m create mode 100644 som/nVoxelsPerRIO.pdf create mode 100644 som/nVoxelsPerRIO.png create mode 100644 som/roi_n_001_0.00_radius.png create mode 100644 som/roi_n_007_1.00_radius.png create mode 100644 som/roi_n_019_1.45_radius.png create mode 100644 som/roi_n_027_1.75_radius.png create mode 100644 som/roi_n_033_2.00_radius.png create mode 100644 som/roi_n_057_2.25_radius.png create mode 100644 som/roi_n_081_2.45_radius.png create mode 100644 som/roi_n_093_2.85_radius.png create mode 100644 som/roi_n_123_3.00_radius.png create mode 100644 som/roi_n_147_3.20_radius.png create mode 100644 som/roi_n_171_3.35_radius.png create mode 100644 som/roi_n_179_3.50_radius.png create mode 100644 som/roi_n_203_3.65_radius.png create mode 100644 som/roi_n_251_3.75_radius.png create mode 100644 som/roi_n_257_4.00_radius.png create mode 100644 som/roi_n_305_4.15_radius.png create mode 100644 som/roi_n_341_4.25_radius.png create mode 100644 som/roi_n_365_4.40_radius.png create mode 100644 som/roi_n_389_4.50_radius.png create mode 100644 som/roi_n_437_4.60_radius.png create mode 100644 som/roi_n_461_4.70_radius.png create mode 100644 som/roi_n_485_4.90_radius.png create mode 100644 som/roi_n_515_5.00_radius.png create mode 100755 som/show.m create mode 100644 som/som_batch_mc_central.m create mode 100644 som/som_batch_mc_template.m create mode 100755 som/som_xyz.m create mode 100755 som/sphere_iter.m create mode 100755 som/sphere_make.m create mode 100755 som/tile.m diff --git a/som/README.txt b/som/README.txt new file mode 100644 index 00000000..69b6a3e6 --- /dev/null +++ b/som/README.txt @@ -0,0 +1,22 @@ +# +# +# +# SOM code for doing two functionalities: +# +# +# 1) Self-Organizing Maps +# +# SOM_PreProcessData (needs updating to work with SOM_CalculateMaps) +# SOM_CalculateMaps +# +# 2) Seed based correlation maps/images +# +# SOM_PreProcessData +# SOM_CalculateMaps +# +# +# Use is only allowed with explicit permission of Robert C. Welsh +# +# rcwelsh@med.umich.edu +# + diff --git a/som/SOM_Alpha.m b/som/SOM_Alpha.m new file mode 100755 index 00000000..d4f0b353 --- /dev/null +++ b/som/SOM_Alpha.m @@ -0,0 +1,26 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2005 +% Ann Arbor MI. +% +% function results = SOM_Alpha(iter,nIter) +% +% Routine to return current value of the learning rate. +% +% You can change this to some other function. We use exponetial +% decay. +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_Alpha(iter,nIter); + +global SOM + +results = SOM.alpha*exp(-iter/nIter/SOM.learningTimeConstant); + +return + +% +% All done. +% \ No newline at end of file diff --git a/som/SOM_Bootstrap.m b/som/SOM_Bootstrap.m new file mode 100755 index 00000000..f0adf890 --- /dev/null +++ b/som/SOM_Bootstrap.m @@ -0,0 +1,126 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2006 +% +% +% A routine to mix up the phase of the individual data time-series +% for calculation of the false rate. +% +% function SOM_Bootstrap +% +% What goes in is the data, what comes out is the data +% with fourier components having their phases randomly +% assigned to -pi to pi. +% +% global SOMMem +% +% Input +% +% SOMMem{slot}.theData(nVoxel,nTime) - the data. +% SOMMem{slot}.fftData(nVoxe,nTime) - fft of the above data. +% SOMMem{slot}.PData(nVoxel,nTime) - phase of above data. +% SOMMem{slot}.afftData(nVoxel,nTime) - magniture of the above data. +% +% SOMMem{slot}.Padding - Size of padding. +% +% Output +% +% SOMMem{slot}.theDataRP - data with the phase of the frequency components +% randomly assigned. +% SOMMem{slot}.fftDataN - new fft of the data (fftDataN = fft(theDataRP,[],2); +% +% SOMMem{slot}.Dphase - the random phase used. +% +% Change to used global memory to help with out of memory issues. +% +% +% According to MRM 51:418-422 (2004) Laird, Rogers and Meyerand +% the phase should be randomly shifted simultaneously by the +% same amount for each frequency component. This will keep the +% spatial correlations. +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function SOM_Bootstrap + +global SOMMem + +% Force to use slot #1 to grab the time course data +% However, we need to place the randomized data into slot 3. + +slot = 1; + +% Make sure that there is data present + +SOMMem{slot}.Error = 0; + +if isfield(SOMMem{slot},'theData') == 0 + fprintf('Error - no data present in the SOMMem global structure.\n'); + SOMMem{slot}.Error = 1; + return +end + +% Check that padding is there. + +if isfield(SOMMem{slot},'Padding') == 0 + SOMMem{slot}.Padding = 10; + fprintf('Forcing SOMMem{slot}.Padding = 10;\n'); +end + +SOMMem{slot}.Padding = floor(SOMMem{slot}.Padding); + +if SOMMem{slot}.Padding < 0 + SOMMem{slot}.Padding = 10; + fprintf('Forcing SOMMem{slot}.Padding = 10;\n'); +end + +% Get the fft of the data, save time by only doing once per looping call. +% You must CLEAR THIS IF YOU ARE DOING DIFFERENT DATA SET. + +if isfield(SOMMem{slot},'fftData') == 0 | ... + isfield(SOMMem{slot},'afftData') == 0 | ... + isfield(SOMMem{slot},'PData') == 0 | ... + any(size(SOMMem{slot}.fftData) - (size(SOMMem{slot}.theData)+[0 2*SOMMem{slot}.Padding])) + fprintf('Calculating Original Data Fourier Components.\n'); + % Calculate the padding. + Padding1 = mean(SOMMem{slot}.theData(:,1:SOMMem{slot}.Padding),[2]); + Padding2 = mean(SOMMem{slot}.theData(:,end-SOMMem{slot}.Padding+1:end),[2]); + SOMMem{slot}.fftData = fft([repmat(Padding1,[1 SOMMem{slot}.Padding]) SOMMem{slot}.theData repmat(Padding2,[1 SOMMem{slot}.Padding])],[],2); + % Get the magnitude of the data. + SOMMem{slot}.afftData = abs(SOMMem{slot}.fftData); + % Get the phase of the data. + SOMMem{slot}.PData = angle(SOMMem{slot}.fftData); +else + fprintf('Using Pre-existing FFT of ''theData''.\n'); +end + +% How big is our sample. + +N = size(SOMMem{slot}.fftData,2); + +Npnts2 = floor((N-1)/2); + +% Determine even/oddness of number of time points. +OddEven = 1-mod(N,2); + +% randomize the new phase change. +dphase = pi-2*pi*rand(1,Npnts2); + +SOMMem{slot}.Dphase = repmat([0 -fliplr(dphase) pi-2*pi*rand(1,OddEven) dphase],[size(SOMMem{slot}.theData,1) 1]); + +% Reassemble the data. +SOMMem{slot}.fftDataN = SOMMem{slot}.afftData.*exp((SOMMem{slot}.PData+SOMMem{slot}.Dphase)*i); + +% Calculate the new signal. Taking the real part is fine as the original +% signal is real. + +SOMMem{slot}.DataN = real(ifft(SOMMem{slot}.fftDataN,[],2)); + +% And unit norm it at the same time. + +SOMMem{3}.theData = SOM_UnitNormMatrix(SOMMem{slot}.DataN(:,1+SOMMem{slot}.Padding:end-SOMMem{slot}.Padding),2); + +% +% All done. +% diff --git a/som/SOM_BuildMap.m b/som/SOM_BuildMap.m new file mode 100755 index 00000000..a4512082 --- /dev/null +++ b/som/SOM_BuildMap.m @@ -0,0 +1,37 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2007 +% +% A routine to build a map out of a SOM element. +% +% You need to pass the cost function and which element +% +% global SOMMem +% +% SOMMem{slot}.theData(nVoxel,nTime); +% SelfOMap(nTime,nSOM); +% +% SOMMem{1}.maskInfo.hdr is necessary +% +% This is a kludge at the moment. +% +% function CFMap = SOM_BuildMap(costFunction,whichElement) +% +% Utilized SOMMem and uses memory slot #1 +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function CFMap = SOM_BuildMap(costFunction,whichElement) + +global SOMMem + +CFMap = zeros(SOMMem{1}.maskInfo.size); + +CFMap(SOMMem{1}.maskInfo.iMask) = costFunction(:,whichElement); + +return + +% +% All done +% diff --git a/som/SOM_BuildROILinearIDX.m b/som/SOM_BuildROILinearIDX.m new file mode 100755 index 00000000..bdd8ab47 --- /dev/null +++ b/som/SOM_BuildROILinearIDX.m @@ -0,0 +1,114 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% Build array of linear indices. +% +% This routine should only be called from SOM_CalculateCorrelations +% +% +% INPUT +% +% parameters -- see SOM_PreProcessData and SOM_CalculateCorrelations +% +% OUTPUT +% +% rois -- with full linear indices. +% +% function rois = SOM_BuildROILinearIDX(parameters) +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function rois = SOM_BuildROILinearIDX(parameters) + +% +% Read in the mask of the brain +% + +rois = parameters.rois; + +MVol = spm_read_vols(parameters.maskInfo.header); + +% Rebinerize it. + +MVol = MVol > 0; + +% +% Load the ROI voxel indices for the calculation. +% + +rois.IDX = {}; + +for iROI = 1:rois.nroisRequested + + if rois.ROIOK(iROI) + + switch rois.type + + % + % this case is mni coordinates + % + + case 0 + + + % Get the MNI coordinates of the middle of this ROI. + + ROICoordsMM = rois.mni.coordinates(iROI,:); + + % Convert to voxel indices. + ROICoordsVoxel = round((inv(parameters.maskHdr.mat)*([ROICoordsMM 1]'))); + + % Build the actual ROI into an array of indices + VOXELIDX = [ + rois.mni.size.XROI+ROICoordsVoxel(1) + rois.mni.size.YROI+ROICoordsVoxel(2) + rois.mni.size.ZROI+ROICoordsVoxel(3)] ; + + % Convert back to MNI + VOXELMM = parameters.maskHdr.mat*([VOXELIDX;ones(1,size(VOXELIDX,2))]); + + % Now check the ones that are inside the mask, we have to do + % this in the event that the ROI has expanded past the center + % to be outside of the image. + + THISROI_inIDX = round(SOM_roiPointsInMask(parameters.maskInfo.header.fname,(VOXELMM(1:3,:))')); + + % Convert to linear indices + rois.IDX{iROI} = sub2ind(parameters.maskHdr.dim(1:3),VOXELIDX(1,THISROI_inIDX),VOXELIDX(2,THISROI_inIDX),VOXELIDX(3,THISROI_inIDX)); + + % Recount how many voxels survived the masking in this ROI. + rois.nvoxels(iROI) = length(rois.IDX{iROI}); + case 1 + + % Read in the image and "AND" it with the ROI + ROIVOL = spm_read_vols(rois.hdr(iROI)); + ROIVOL = ROIVOL.*MVol; + + % Determine linear indices + rois.IDX{iROI} = find(ROIVOL); + + % Recount how many voxels survived the masking in this ROI. + rois.nvoxels(iROI) = length(rois.IDX{iROI}); + + end + + % + % Now find out where these exist in the data stream + % + + rois.IDX{iROI} = SOM_ROIIDXnMASK(parameters,rois.IDX{iROI}); + + % Redetermine if this ROI really survived masking etc. + rois.ROIOK(iROI) = (length(rois.IDX{iROI}) > 0); + end +end + +return + +% +% All done +% diff --git a/som/SOM_Ca_back.m b/som/SOM_Ca_back.m new file mode 100755 index 00000000..d95d37bf --- /dev/null +++ b/som/SOM_Ca_back.m @@ -0,0 +1,457 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2005 +% +% function results = SOM_CalculateMap(theData,nSOM,nIter,[SelfOMap],[slot]) +% +% theData = theData(nSpace,nTime); +% nSOM = number of basis functions in the SOM +% nIter = number of iterations. +% slot = memory slot to use. 1 = time series data (default) +% 2 = super cluster use. +% 3 = boostrap probabilities. +% +% NOTE : The "slot" is used to help with memory management. +% Self-Organzing Map calculations are very memory +% intensive, so we are using global memory. However, +% when calculating the supercluster map it would be useful +% to not wipe out the data, so we have two slots +% available for memory. +% +% Please use them wisely. +% +% You will break the calculation if you place +% your time-series data in slot #2! +% +% Hopefully you can avoid "Out of memory" error by +% specifying slot 1 for the big time series matrix, and +% slot 2 for super cluster calculation. +% +% Between subjects you may need to execute the "pack" +% command. +% +% Worst case, trim you session, save appropriate +% things to disk and restart matlab. +% +% +% results = results structure, it will at least contain +% the resulting SOM. +% +% results = results.SOM (the map) +% results.IDX (for each space element which SOM it is related to +% best) +% results.WT (how much of the variance is explained by that) +% +% this is a work in progress! +% +% Currently the cost functionm is Cos(Theat) = U dot V +% where U and V have unit length. +% +% You can set the SOM parameters with +% +% global SOM +% +% SOM.sigma = Initial neighborhood size. +% SOM.sigmaTimeConstant = Rate the neighborhood shrinks. +% SOM.learningTimeConstant = Rate that learning changes. +% SOM.alpha = Initial learning rate +% SOM.Cost = Cost function. (0=U.V, +% 1=|U-V|, 2=|U-V|^2, 3=M.I.). +% NOTE U.V is fastest metric. +% SOM.nPnts = number of points in histogram range : 0 - 1[2]. +% NOTE used by SOM_CalcP. (2 is upper range for |U-V| +% cost, a guess. See "SOM_CalcP.m") +% SOM.saveHistory = History level to save. (0/1/2); +% 0 = none; +% 1 = some; +% 2 = full - memory intensive. +% SOM.WeightOption 0 = no weighting of components. +% 1 = weight by variance +% 2 = weight by st-dev +% +% If you don't specify these parameters they will assume default +% values of: +% +% SOM.alpha = .1 (See "SOM_Alpha.m" on evolution) +% SOM.learningTimeConstant = 2; +% SOM.sigmaTimeConstant = .25; +% SOM.sigma = nSOM/2; guess? +% SOM.Cost = 0; (Opening angle cost function.) +% SON.nPnts = 1000; +% SOM.saveHistory = 1; - Save most of history, but +% not full weights. +% SOM.WeightOption = 0; +% +% Alpha (SOM_Alpha.m) changes according to: +% +% exp(-iter/(nIter * SOM.learningTimeConstant)); +% +% Neighborhood size changes according to: +% +% exp(-iter/(nIter * SOM.sigmaTimeConstant)); +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_CalculateMap(theData,nIter,v,SelfOMap,slot) + +global time; +global SOM +global SOMMem +%SOM.silent = true; +%--------------------------Ben added 6/28/07-------------------- +%[b,v] = sphere_iter(1,1); +nSOM = length(v); + +% Keep track of the version. + +MajorVersion = 1; +MinorVersion = 001; + +SOM.Version = sprintf('%d.%03d',MajorVersion,MinorVersion); + +% Keep track of the time. + +results.StartTime=cputime; + +tic; + +% Which method to determine neighborhood (same code, just cleaned +% up?). + +if ~isfield(SOM,'OldMethod') + SOM.OldMethod = 0; +end + +% Check to see which memory slot + +if exist('slot') == 0 + slot = 1; + if ~SOM.silent + fprintf('Defaulting to use memory slot 1.\n'); + end +end + +if slot ~= 1 & slot ~= 2 + if ~SOM.silent + fprintf('Forcing to use slot #1.\n'); + end + slot = 1; +end + +if ~SOM.silent + fprintf('Using memory slot #%d\n',slot); +end + +SOM.slot = slot; + +% Check to see if the necessary fields exist. + +% The neighborhood size +if ~isfield(SOM,'sigma') + SOM.sigma = sqrt(nSOM)/2; + if ~SOM.silent + fprintf('SOM.sigma -> %f\n',SOM.sigma); + end +end + +% How quickly to modify the neighborhood size. +if ~isfield(SOM,'sigmaTimeConstant') + SOM.sigmaTimeConstant = 1/4; + if ~SOM.silent + fprintf('SOM.sigmaTimeConstant -> %f\n',SOM.sigmaTimeConstant); + end +end + +% How quickly to modify the map learning rate. +if ~isfield(SOM,'learningTimeConstant') + SOM.learningTimeConstant = 2; + if ~SOM.silent + fprintf('SOM.learningTimeConstant -> %f\n',SOM.learningTimeConstant); + end +end + +% The initial map learning rate. +if ~isfield(SOM,'alpha') + SOM.alpha = .1; + if ~SOM.silent + fprintf('SOM.alpha -> %f\n',SOM.alpha); + end +end + +% The cost function +if ~isfield(SOM,'Cost') + SOM.Cost = 0; + if ~SOM.silent + fprintf('SOM.Cost -> %f\n',SOM.Cost); + end +end + +% The saving history. +if ~isfield(SOM,'saveHistory') + SOM.saveHistory = 1; + if ~SOM.silent + fprintf('SOM.saveHistory -> %f\n',SOM.saveHistory); + end +end + +% The saving history. +if ~isfield(SOM,'WeightOption') + SOM.WeightOption = 0; + if ~SOM.silent + fprintf('SOM.WeightOption -> %f\n',SOM.WeightOption); + end +end + +% Make sure that the Weight Option is Valid. +SOM.WeightOption = SOM.WeightOption(1); + +if sum(ismember([0 1 2],SOM.WeightOption)) == 0 + SOM.WeightOption = 0; + if ~SOM.silent + fprintf('SOM.WeightOption -> %f\n',SOM.WeightOption); + end +end + +% Determine the size of the space we are dealing with. + +%if sqrt(nSOM)~=floor(sqrt(nSOM)) +% fprintf('Can only do square SOM!\n'); +% fprintf('Forcing SOM to be square\n'); +% nSOM = floor(sqrt(nSOM)+.5)^2; +% fprintf('New size is : %d\n',nSOM); +%end + +% Record the size of the SOM. +SOM.nSOM = nSOM; +SOM.nIter = nIter; + +nGrid = sqrt(nSOM); +SOM.nGrid = nGrid; + +nSpace = size(theData,1); +nTime = size(theData,2); + +if ~SOM.silent +fprintf('Number of time points : %d\n',nTime); +fprintf('Number of space points : %d\n',nSpace); +end + +% Randomize our initial SOM and normalize the basis. +% +% Modified on 8/23/2005 to use normaly distributed +% elements of vector. This will make it uniformly +% distributed on a sphere in n-space. +% +% Found by doing google search and getting +% argument of Steve Rayhawk at Harvey Mudd. +% Seems to work by graphical validation - still need +% to find the paper for citation. See reference in +% Donald Knuth book: The Art of Computer Programming, vol2 +% Seminumerical Algorithms, Addison-Wesley, 1969. +% + +if exist('SelfOMap') == 1 & ~any([nTime nSOM]-size(SelfOMap)) + if ~SOM.silent + fprintf('Using passed initialized SelfOMap\n'); + end +else + if ~SOM.silent + fprintf('Randomizing SelfOMap\n'); + end + SelfOMap = randn(nTime,nSOM); + SelfOMap = SOM_UnitNormMatrix(SelfOMap,1); +end + +% Store a copy of the initial one. +iSelfOMap = SelfOMap; + +clear SOM_UnitNormMatrix; + +% Unit norm the data. We are not necessarily interested in effect +% size at the moment versus explanatory basis. We can +% get the "beta's" later! + +% This is not the best, as when SOM_SuperClusterEasy calls us, we globber +% what was in here. Not good! 2007-03-23. +% Come up with a mex solution? + +SOMMem{slot}.theData = SOM_UnitNormMatrix(theData,2); + +% Calculate the Variance for weights. +SOMMem{slot}.varData = var(SOMMem{slot}.theData,[],2); + +% Weights for biasing the SOM to voxels that have high variance but +% low population number. + +switch SOM.WeightOption + case 0 + SOMMem{slot}.Weights = ones(size(theData,1),1); + case 1 + SOMMem{slot}.Weights = var(SOMMem{slot}.theData,[],2); + case 2 + SOMMem{slot}.Weights = std(SOMMem{slot}.theData,[],2); + case default + SOMMem{slot}.Weights = ones(size(theData,1),1); +end +SOMMem{slot}.Weights = repmat(SOMMem{slot}.Weights,[1 size(SOMMem{slot}.theData,2)]); +clear theData + +clear SOM_UnitNormMatrix; + +% Ok, let's go iterate the map. At the moment +% we'll just stop the calculation after a sufficient number +% iterations. + +iter = 0; + +%neighborDist = SOM_NeighborDist(nGrid); + +%lneighborDist = reshape(neighborDist,[nGrid nGrid nGrid*nGrid]); +%--------------------------Ben added 6/12/07-------------------- + + +lneighborDist = zeros(nSOM,nSOM); +for ii=1:nSOM + for jj=1:nSOM + lneighborDist(ii,jj) = abs(acos(dot(v(ii,:),v(jj,:)))*2); + end +end + +history = {}; + +pV_init = (SOMMem{slot}.theData.*SOMMem{slot}.Weights)'; +if ~SOM.silent +fprintf('iter,alpha,NeighSigma,cpu\n'); +end + +%mov = avifile('animated4.avi','compression','Cinepak') + +while iter < nIter + tic; + % Increment the iteration + iter = iter + 1; + % Learning function = g(time); + alpha = SOM_Alpha(iter,nIter); + % Determine the neighbor map. + % For each element of the SOM, there will be an associated + % neighborhood. The call should return the indices of these + % SOM vectors as well as the distance for use in the weight + % calculation. + NeighSigma = SOM.sigma*exp(-iter/nIter/SOM.sigmaTimeConstant); + if SOM.OldMethod == 0 + SOMNeighborMap = exp(-lneighborDist.^2/NeighSigma^2); + %keyboard; + else + SOMNeighborMap = SOM_NeighborMap([nGrid nGrid],NeighSigma); + end + + % Determine the SOM vectors the data are closest to and save the + % history. + [idx wts] = SOM_FindClosest(SelfOMap,slot); + if SOM.saveHistory > 0 + history{iter}.idx = idx; + history{iter}.wts = wts; + history{iter}.SelfOMap = SelfOMap; + history{iter}.sigma = NeighSigma; + history{iter}.alpha = alpha; + %history{iter}.neighMap = SOMNeighborMap(:,:,(nGrid-1)*floor(nGrid/2)); + if SOM.saveHistory > 1 + history{iter}.dataBySOM = SOMMem{slot}.dataBySOM; + end + history{iter}.cpuTime = cputime; + end + % + clear SOM_FindClosest; + % Now calculate the perturbation to the SOM. + dSelfOMap = 0*SelfOMap; + + nullSOM = 0*SelfOMap; + for iSOM = 1:nSOM + % Deterimine which data affect this SOM vector. + dI = find(idx==iSOM); + if length(dI) > 0 + % pertube all the same regardless of number of perturbers. + % + % Modified to allow heavier weighting based on variance of + % the time-series vector for a voxel. This is under the + % assumption that we want variance explained by the model. + % + % This also makes the assumption that signal is high + % variance? + % + % 2007.03.20 Robert C. Welsh + % + pV = sum(pV_init(:,dI),2); + %pV = sum(pV_init(:,dI),2)/sum(SOMMem{slot}.Weights(dI,1)); + %pV = SOM_UnitNormMatrix(pV,1); + %-------------------Ben changed 6/13/07----------------------- + if SOM.OldMethod == 0 + vlx = pV*reshape(SOMNeighborMap(:,iSOM),[1 nSOM]); + dSelfOMap = dSelfOMap + vlx; + else + dSelfOMap = dSelfOMap + SOM_ModBasis(pV,nullSOM, ... + SOMNeighborMap{iSOM},iter,nIter); + end + clear SOM_ModBasis; + end + end + % Update the SOM. + %dSelfOMap = dSelfOMap/(nSpace/nSOM); + SelfOMap = SelfOMap + alpha*dSelfOMap; + SelfOMap = SOM_UnitNormMatrix(SelfOMap,1); + if(any(any(isnan(SelfOMap)))) +% keyboard + end + clear SOM_UnitNormMatrix; + xx=toc; + if SOM.saveHistory > 0 + history{iter}.toc = xx; + end + if ~SOM.silent + fprintf('%03d %f %f %f\n',iter,alpha,NeighSigma,xx); + end +end +SelfOMap = SOM_UnitNormMatrix(SelfOMap,1); +%mov = close(mov); + +% All done, and now pack up the results to be sent back to the calling +% function. + +results.SelfOMap = SelfOMap; + +[results.IDX results.WTS results.DIDX results.DWTS] = SOM_FindClosest(SelfOMap,slot); + +clear SOM_FindClosest; + +% Final cost function evaluation. + +results.dataBySOM = SOMMem{slot}.dataBySOM; + +% Return a copy of the initial one used. + +results.iSelfOMap = iSelfOMap; +results.history = history; + +results.weights = []; +for iter = 1:nIter + results.weights = [results.weights sum(results.history{iter}.wts)]; +end + +% Record the configuation. + +results.SOM = SOM; +results.H = hist(results.IDX,[0:results.SOM.nSOM]); + +clear history; +clear SelfOMap; + +% Store the finish time. + +results.StopTime=cputime; + +return + +% +% All done. +% diff --git a/som/SOM_CalcInOutComp.m b/som/SOM_CalcInOutComp.m new file mode 100755 index 00000000..f9c71b3e --- /dev/null +++ b/som/SOM_CalcInOutComp.m @@ -0,0 +1,53 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2010 +% +% Loop on the components passed and calculate the in/out ratio +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_CalcInOutComp(DataBySOM,pVal,TheTemplate) + +global SOMMem + +results = []; + +if isfield(SOMMem{1},'maskInfo') == 0 + return +end + +maskDim = size(TheTemplate); + +if sum(any(size(TheTemplate) - SOMMem{1}.maskInfo.size)) ~= 0 + fprintf('Masking template doesn''t match the size of the original data.\n'); + return +end + +BrainMask = zeros(SOMMem{1}.maskInfo.size); + +BrainMask(SOMMem{1}.maskInfo.iMask) = 1; + +NTheTemplate = TheTemplate .* BrainMask; + +ROIRemoved = sum(TheTemplate(:)-NTheTemplate(:)); + +NotTemplate = BrainMask.*(1 - NTheTemplate); + +rCutOff = SOM_CalcRCutoff(DataBySOM,pVal); + +results = []; + +BinConnMap = 0*BrainMask; + +for iEXP = 1:size(DataBySOM,2) + BinConnMap = SOM_BuildMap(DataBySOM,iEXP)>=rCutOff(iEXP,2); + results = [results ; SOM_InOutComponent(BinConnMap,TheTemplate)]; +end + +return + +% +% All done. +% + \ No newline at end of file diff --git a/som/SOM_CalcP.m b/som/SOM_CalcP.m new file mode 100755 index 00000000..59caf44f --- /dev/null +++ b/som/SOM_CalcP.m @@ -0,0 +1,262 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2006-2007 +% +% +% A routine to calculate the probability "p" for the +% voxel to be associated with a random examplar. +% +% This uses the metric : U.V +% +% function [results] = SOM_CalcP(SelfOMap,[N]) +% +% +% global SOMMem +% +% SOMMem.theData - time courses (space,time) +% +% Input: +% SelfOMap - self-organizing map (time,exemplar#) +% +% Option: +% |N| - number of times to run +% bootstrap calculation. +% +% Sign(N) - + = include all voxels in calculation. +% - - = exclude assosciated voxels from null. +% dist. +% global SOM +% +% SOM.nPnts - number of points in histogram range : 0 - 1. +% +% NOTE : Other code uses the SOM global as well. +% +% Output: +% results.dataBySOMNull - weights. +% results.SOMHist - histograms of null dist. +% results.SOMCDFH - tail cdf. 0 = very end of dist. +% results.pVals - if < 0 then anti-correlated. +% +% Probability calcuted is the probability of making observation >= +% U.V or some other cost function. +% +% +% Modified on 2007-03-18 +% +% Using SOMMem global memory to avoid out of memory errors? +% +% Also made some changes in the histogram so that we don't store +% huge cost metric array. +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function [results] = SOM_CalcP(SelfOMap,varargin) + +global SOM + +global SOMMem + +global SOMGaussian + +% Force to use slot #1 + +slot = 1; + +% However, bootstrap returns data into slot 3. + +% We need to lookup the SOM associated with a given voxel. + +[results.IDX results.WTS results.DIDX results.DWTS] = SOM_FindClosest(SelfOMap,1); + +% Have 1001 bins by default. Should this +% be increased for resolution of the p-value? + +nPnts = 1000; + +% Hard code limits on the histograms to build for PDF'.s This is +% not good, but we need to have something? Fix later. + +histUpperLimits = [1 2 4 2]; + +% Did the user set the number of points to use in histograms? + +if ~isfield(SOM,'nPnts') + SOM.nPnts = nPnts; +end + +% Grab the number of points used in the histogram. + +nPnts = SOM.nPnts; + +% Size of the Self Organizing Map. + +nSOM = size(SelfOMap,2); + +% Check for the N option, that is the number of times to run the +% null distribution. + +N = 1; + +if nargin >= 2 + if isnumeric(varargin{1}) + N = varargin{1}(1); + end +end + +N = floor(N); + +if N < 0 + fprintf('Excluding associated voxels from null calculation.\n'); +end + +results.N = N; + +fprintf('Running %d iteration(s) of the null distributions.\n',N); + +% Do this just in case. + +SelfOMapN = SOM_UnitNormMatrix(SelfOMap,1); + +clear SOM_UnitNormMatrix; + +% Now calculate the null hypothesis distribution. +% and then calculate the new dataBySOM for nulls. Question is how many times to +% do this? Assuming that our data is on order of 30,000 voxels, then +% once should be enough? + +%results.dataBySOMNull = zeros(size(SOMMem{3}.theData,1),size(SelfOMap,2)); + +% Get the upper limit of the histograms to accumulate. + +histUPPER = histUpperLimits(SOM.Cost+1); + +% Store original data. + +results.SOMHist = zeros(nSOM,nPnts+1); +results.SOMCDFH = zeros(nSOM,nPnts+1); + +results.NAssoc = zeros(nSOM,1); + +for iNull = 1:abs(N) % Run 'N' null distributions? + SOM_Bootstrap; % This creates slot #3.s + SOM_CostFunction(3,SelfOMapN,SOM.Cost); + % SOMMem{slot}.theData = SOMMem{slot}.theDataO; + for iSOM = 1:nSOM; + % Only construct null distribution from voxels NOT associated with + % that exemplar. + % idxVoxel = ones(size(results.dataBySOMNull,1),1); + idxVoxel = ones(size(SOMMem{3}.dataBySOM,1),1); + % Find those voxels associated with this exemplar. + idxTmp = find(results.IDX == iSOM); + results.NAssoc(iSOM) = length(idxTmp); + if N > 0 + idxTmp = []; + end + idxVoxel(idxTmp) = 0; + idxTmp = find(idxVoxel==1); + if length(idxTmp) == 0 + fprintf('Fatal Error in Determining null distribution for %d exemplar, all voxels are associated!\n',iSOM); + else + tHist = hist(abs(SOMMem{3}.dataBySOM(idxTmp,iSOM)),(0:histUPPER/nPnts:histUPPER)); % Do just one tailed our cost function is symmetric. +% tHist = hist(abs(results.dataBySOMNull(idxTmp,iSOM)),(0:histUPPER/nPnts:histUPPER)); % Do just one tailed our cost function is symmetric. + if SOM.Cost ~= 0 & SOM.Cost ~= 3 + tHist = fliplr(tHist) + end + results.SOMHist(iSOM,:) = results.SOMHist(iSOM,:) + tHist; + end + end +end + +fprintf('Done with histogram building.\n'); + +% Now calculate the histograms for each SOM exemplar. +% Min and Max of the histograms are -1 and 1. + +results.xBins = [0:histUPPER/nPnts:histUPPER]; + +results.nEntries = N*size(SOMMem{3}.theData,1); + +results.SOMHistE = sqrt(results.SOMHist); + +% Place to put the fit of the gaussian and the theoritical curve. +results.SOMpdfParms = zeros(nSOM,2); +results.SOMpdfChi2 = zeros(nSOM,1); +results.SOMpdfNDFS = zeros(nSOM,1); +results.SOMpdfFit = zeros(nSOM,length(results.xBins)); + +% Make the theoritical pdfs have 10x the resolution of the histograms. +results.xBins10 = [0:histUPPER/(nPnts*10):histUPPER]; +results.SOMpdf = zeros(iSOM,length(results.xBins10)); +results.SOMcdf = zeros(iSOM,length(results.xBins10)); + +fprintf('Calculating pdf''s and cdf''s. Fitting curves\n'); + +for iSOM = 1:nSOM; + % Unit norm the histogram. + NormFactor = sum(results.SOMHist(iSOM,:)); + results.SOMHist(iSOM,:) = results.SOMHist(iSOM,:)/NormFactor; + results.SOMHistE(iSOM,:) = results.SOMHistE(iSOM,:)/NormFactor; + results.SOMCDFH(iSOM,:) = 1-cumsum(results.SOMHist(iSOM,:)); + results.SOMCDFE(iSOM,:) = sqrt(cumsum(results.SOMHistE(iSOM,:).^2)); + SOMGaussian.Y = results.SOMHist(iSOM,:); + SOMGaussian.Ye = results.SOMHistE(iSOM,:); + SOMGaussian.X = results.xBins; + [results.SOMpdfParms(iSOM,:) results.SOMpdfChi2(iSOM)] = ... + fminsearch(@SOM_FitGaussian,[max(SOMGaussian.Y) .08]); + results.SOMpdfFit(iSOM,:) = SOMGaussian.Yth; + results.SOMpdfNDFS(iSOM) = SOMGaussian.nDF-2; + results.SOMpdf(iSOM,:) = exp(-.5*((results.xBins10/ ... + results.SOMpdfParms(iSOM,2)).^2))*results.SOMpdfParms(iSOM,1); + % Unit norm the pdf + results.SOMpdf(iSOM,:) = results.SOMpdf(iSOM,:)/sum(results.SOMpdf(iSOM,:)); + results.SOMcdf(iSOM,:) = 1-cumsum(results.SOMpdf(iSOM,:)); +end + +% Find the index of a given weight for the voxels based on 1001 bins. +% Bounded by 1 and 1001. + +fprintf('Find P values for associated Exemplars.\n'); + +IWTS = max( [ ones(length(results.WTS),1) ... + min([(nPnts*10+1)*ones(length(results.WTS),1) floor((abs(results.WTS*nPnts*10)+.5)/histUPPER)],[],2)],[],2); +% Now the index for the whole array. + +fprintf('Calculating p-value indices\n'); + +% We use the cost-function that is stored in slot #1. + +IArray = reshape(max( [ ones(prod(size(SOMMem{slot}.dataBySOM)),1) ... + min([(nPnts*10+1)*ones(prod(size(SOMMem{slot}.dataBySOM)),1) ... + floor((abs(reshape(SOMMem{slot}.dataBySOM, ... + [prod(size(SOMMem{slot}.dataBySOM)) ... + 1])*nPnts*10)+.5)/histUPPER)],[],2)],[],2),[size(SOMMem{slot}.dataBySOM)]); + +IISOM = [0:nSOM-1]*(nPnts*10+1); + +% +% Now lookup the probability. +% + +% The table is P(iSOM,metric); + +fprintf('Looking up p-values.\n'); + +results.pVals = results.SOMcdf( ( results.IDX - 1 ) * size(results.SOMCDFH,2) + IWTS).*sign(results.WTS); + +SOMMem{slot}.pVals = 0*SOMMem{slot}.dataBySOM; + +crap = results.SOMcdf'; + +for iV = 1:size(SOMMem{slot}.dataBySOM,1) + SOMMem{slot}.pVals(iV,:) = crap(IArray(iV,:)+IISOM); +end + +clear crap + +% +% All done. +% + + + diff --git a/som/SOM_CalcRCutoff.m b/som/SOM_CalcRCutoff.m new file mode 100755 index 00000000..eab7010d --- /dev/null +++ b/som/SOM_CalcRCutoff.m @@ -0,0 +1,53 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2010 +% +% Calculate the cutoff value for r (lower and upper) based +% on the CDF of the r-values observed +% +% function results = SOM_CalcRCutoff(dataBySOM) +% +% results = [lower, upper] +% +% dataBySOM = can be any distribution of r-values. +% if 2-d, then dataBySOM = Space x R +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_CalcRCutoff(dataBySOM,pVal) + +if exist('pVal') == 0 + pVal = .05; +end + +if pVal > .5 | pVal < 0 + pVal = .05; +end + +dataBySOM = squeeze(dataBySOM); + +if size(dataBySOM,1) == 1 + dataBySOM = dataBySOM'; +end + +nEXP = size(dataBySOM,2); + +results = []; + +xBase = [-1:.01:1]; + +for iEXP = 1:nEXP + hR = hist(dataBySOM(:,iEXP),xBase); + cHR = cumsum(hR); + cHR = cHR/max(cHR); + i1 = find(cHR<=pVal); + i1 = max([1 max(i1)]); + i2 = find(cHR>=(1-pVal)); + i2 = min([max([min(i2) 1]) length(cHR)]); + results = [results ; xBase(i1) xBase(i2)]; +end + + + + \ No newline at end of file diff --git a/som/SOM_CalculateCorrelationImages.m b/som/SOM_CalculateCorrelationImages.m new file mode 100755 index 00000000..652ff95c --- /dev/null +++ b/som/SOM_CalculateCorrelationImages.m @@ -0,0 +1,105 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% Calculate correlation images. +% +% INPUT +% +% D0 -- see SOM_PreProcessData +% parameters -- see SOM_PreProcessData and SOM_CalculateCorrelations +% +% OUTPUT +% +% results = -1 error +% array of output written. +% +% +% function results = SOM_CalculateCorrelationImages(D0,parameters) +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_CalculateCorrelationImages(D0,parameters) + +global SOM + +% +% Initialize the output matrix. +% + +results = []; + +rMap = zeros(size(D0,1),1); +rMapVol = zeros(parameters.maskHdr.dim(1:3)); %this fails if user specified no masking %% This should now be fixed with change in SOM_PreProcessData - 2012-03-29 - RCWelsh + +for iROI = 1 : parameters.rois.nroisRequested + + if parameters.rois.ROIOK(iROI) + roiTC = mean(D0(parameters.rois.IDX{iROI},:),1); + + SOM_LOG('STATUS : Entering CorrCoeff Image Calculation'); + + rMap = 0*rMap; + pMap = 0*rMap; + + %for iV = 1:size(D0,1); + % [rMap(iV) pMap(iV)] = corr(D0(iV,:)',roiTC'); + %end + + % Much faster! + + [rMap pMap] = corr(D0',roiTC'); + + % Now turn into maps (r and p) and write out. + + rMapVol = 0*rMapVol; + pMapVol = ones(size(rMapVol)); + + rMapVol(parameters.maskInfo.iMask) = rMap; + pMapVol(parameters.maskInfo.iMask) = pMap; + + clear rMapHdr; + + rMapHdr.fname = fullfile(parameters.Output.directory,sprintf('rmap_%s_%04d.nii',parameters.Output.name,iROI)); + rMapHdr.mat = parameters.maskHdr.mat; + + % Make sure we write out float32..... + + rMapHdr.dim = parameters.maskHdr.dim(1:3); + rMapHdr.dt = [16 0]; + rMapHdr.descrip = sprintf('%s : %d',parameters.Output.description,iROI); + + % Now the probability map. + + pMapHdr = rMapHdr; + pMapHdr.fname = fullfile(parameters.Output.directory,sprintf('pmap_%s_%04d.nii',parameters.Output.name,iROI)); + + spm_write_vol(rMapHdr,rMapVol); + spm_write_vol(pMapHdr,pMapVol); + + % now transform the results to a z score. + + if exist(rMapHdr.fname) == 2 + clear Vo + Vi = spm_vol(rMapHdr.fname); + Vo.fname = fullfile(parameters.Output.directory,sprintf('zmap_%s_%04d.nii',parameters.Output.name,iROI)); + Vo.mat = Vi.mat; + Vo.dim = Vi.dim; + Vo.dt = [16 0]; + Vo.descrip = ['z-score for ' rMapHdr.descrip]; + spm_imcalc(Vi,Vo,'1/2*log((1+i1)./(1-i1))'); + else + SOM_LOG(sprintf('WARNING : I guess the corr didn''t work for ROI %d',iROI)); + end + end + results = strvcat(results,rMapHdr.fname); +end + +return + +% +% All done. +% \ No newline at end of file diff --git a/som/SOM_CalculateCorrelationMaps.m b/som/SOM_CalculateCorrelationMaps.m new file mode 100755 index 00000000..237e69f1 --- /dev/null +++ b/som/SOM_CalculateCorrelationMaps.m @@ -0,0 +1,80 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% Calculate correlation matrix. +% +% INPUT +% +% D0 -- see SOM_PreProcessData +% parameters -- see SOM_PreProcessData and SOM_CalculateCorrelations +% +% OUTPUT +% +% results = -1 error +% array of output written. +% +% +% function results = SOM_CalculateCorrelationMaps(D0,parameters) +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_CalculateCorrelationMaps(D0,parameters) + +global SOM + +% +% Initialize the output matrix. +% + +SOM_LOG('STATUS : Entering CorrCoeff Map Calculation'); + +rMatrix = zeros(parameters.rois.nroisRequested,parameters.rois.nroisRequested); +pMatrix = ones(parameters.rois.nroisRequested,parameters.rois.nroisRequested); + +% Array of ROI time courses. + +roiTC = zeros(size(D0,2),parameters.rois.nroisRequested); + +% Create an array of our data that is Time x ROI# + +for iROI = 1 : parameters.rois.nroisRequested + roiTC(:,iROI) = mean(D0(parameters.rois.IDX{iROI},:),1); +end + +% Corr wants data as NxP1 and NxP2 and returns the correlation +% matrix of P1 x P2. + +[rMatrix pMatrix] = corr(roiTC,roiTC); + +% It is quite possible that some of the ROI's are messed up? That +% is they don't really exist, even though specified. We can easily +% known those out. + +for iROI = 1 : parameters.rois.nroisRequested + if parameters.rois.ROIOK(iROI) == 0 + rMatrix(iROI,:) = 0; + pMatrix(iROI,:) = 1; + rMatrix(:,iROI) = 0; + pMatrix(:,iROI) = 1; + end +end + +% +% Now save the matrix and parameters. +% + +corrName = fullfile(parameters.Output.directory,[parameters.Output.name '_corr']); + +save(corrName,'rMatrix','pMatrix'); + +results = corrName; + +return + +% +% All done. +% \ No newline at end of file diff --git a/som/SOM_CalculateCorrelationMapsFaster.m b/som/SOM_CalculateCorrelationMapsFaster.m new file mode 100755 index 00000000..ffb21ef6 --- /dev/null +++ b/som/SOM_CalculateCorrelationMapsFaster.m @@ -0,0 +1,84 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% Calculate correlation matrix. +% +% INPUT +% +% D0 -- see SOM_PreProcessData +% parameters -- see SOM_PreProcessData and SOM_CalculateCorrelations +% +% OUTPUT +% +% results = -1 error +% array of output written. +% +% +% function results = SOM_CalculateCorrelationMaps(D0,parameters) +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_CalculateCorrelationMapsFaster(D0,parameters) + +global SOM + +% +% Initialize the output matrix. +% + +SOM_LOG('STATUS : Entering CorrCoeff Map Calculation'); + +rMatrix = zeros(parameters.rois.nroisRequested,parameters.rois.nroisRequested); +pMatrix = ones(parameters.rois.nroisRequested,parameters.rois.nroisRequested); + +% Array of ROI time courses. + +roiTC = zeros(size(D0,2),parameters.rois.nroisRequested); + +% Create an array of our data that is Time x ROI# + +for iROI = 1 : parameters.rois.nroisRequested + roiTC(:,iROI) = mean(D0(parameters.rois.IDX{iROI},:),1); +end + +% Corr wants data as NxP1 and NxP2 and returns the correlation +% matrix of P1 x P2. + +[rMatrix pMatrix] = corr(roiTC,roiTC); + +% It is quite possible that some of the ROI's are messed up? That +% is they don't really exist, even though specified. We can easily +% known those out. + +for iROI = 1 : parameters.rois.nroisRequested + if parameters.rois.ROIOK(iROI) == 0 + rMatrix(iROI,:) = 0; + pMatrix(iROI,:) = 1; + rMatrix(:,iROI) = 0; + pMatrix(:,iROI) = 1; + end +end + +% +% Now save the matrix and parameters. +% + +corrName = fullfile(parameters.Output.directory,[parameters.Output.name '_corr']); +paraName = fullfile(parameters.Output.directory,[parameters.Output.name '_parameters']); + +parameters.stopCPU = cputime; + +save(corrName,'rMatrix','pMatrix'); +save(paraName,'parameters','SOM'); + +results = strvcat(corrName,paraName); + +return + +% +% All done. +% \ No newline at end of file diff --git a/som/SOM_CalculateCorrelationMapsSlow.m b/som/SOM_CalculateCorrelationMapsSlow.m new file mode 100755 index 00000000..46c88a14 --- /dev/null +++ b/som/SOM_CalculateCorrelationMapsSlow.m @@ -0,0 +1,72 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% Calculate correlation matrix. +% +% INPUT +% +% D0 -- see SOM_PreProcessData +% parameters -- see SOM_PreProcessData and SOM_CalculateCorrelations +% +% OUTPUT +% +% results = -1 error +% array of output written. +% +% +% function results = SOM_CalculateCorrelationMaps(D0,parameters) +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_CalculateCorrelationMapsSlow(D0,parameters) + +global SOM + +% +% Initialize the output matrix. +% + +SOM_LOG('STATUS : Entering CorrCoeff Map Calculation'); + +rMatrix = zeros(parameters.rois.nroisRequested,parameters.rois.nroisRequested); +pMatrix = ones(parameters.rois.nroisRequested,parameters.rois.nroisRequested); + +for iROI = 1 : parameters.rois.nroisRequested - 1 + if parameters.rois.ROIOK(iROI) + for jROI = iROI+1 : parameters.rois.nroisRequested + if parameters.rois.ROIOK(jROI) + tc1 = mean(D0(parameters.rois.IDX{iROI},:),1)'; + tc2 = mean(D0(parameters.rois.IDX{jROI},:),1)'; + [rMatrix(iROI,jROI) pMatrix(iROI,jROI)] = corr(tc1,tc2); + else + rMatrix(iROI,jROI) = -3; % Error + end + end + else + rMatrix(iROI,:) = -2; % Error + end +end + +% +% Now save the matrix and parameters. +% + +corrName = fullfile(parameters.Output.directory,[parameters.Output.name '_corr']); +paraName = fullfile(parameters.Output.directory,[parameters.Output.name '_parameters']); + +parameters.stopCPU = cputime; + +save(corrName,'rMatrix','pMatrix'); +save(paraName,'parameters','SOM'); + +results = strvcat(corrName,paraName); + +return + +% +% All done. +% \ No newline at end of file diff --git a/som/SOM_CalculateCorrelations.m b/som/SOM_CalculateCorrelations.m new file mode 100755 index 00000000..33214a2c --- /dev/null +++ b/som/SOM_CalculateCorrelations.m @@ -0,0 +1,153 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% +% A process to calculate correlations in time-series data. +% +% It is assumed that the data being passed in has be pre-processed using +% SOM_PreProcessData. +% +% You have multiple options for specifying what to do with the correlations +% +% 1) If you pass a single ROI (either as a MNI coordinate or a ROI +% file) then it will produce a correlation map image and the +% Fisher-transform r-z Z image. +% +% 2) If you pass multiple ROI (either as MNI coordinates or ROI files) then +% it will produce either multiple output images in the results directory or +% a correlation map between the ROIs. +% +% INPUT +% +% D0 -- data table from SOM_PreProcessData +% +% parameters -- see SOM_PreProcesData +% +% .rois +% [ specify one or the other: "mni" or "files"] +% .mni +% .coordinates - table of n coordinates (x,y,z) +% .size - which size, 1, 7, 19, 27 voxels +% .XROI - optional array of user specficied size. +% .YROI see below on how to build it. +% .ZROI +% .files - table of ROI files +% +% .mask +% .File - full directory path and name to file. +% .MaskFLAG - 0 no mask, 1 mask +% +% +% .Output +% .correlation - 'maps' - save a single correlation matrix +% 'images' - save a single correlation image +% per ROI +% +% .directory - full directory path to output +% .name - name of output file (generic) +% +% +% OUTPUT +% +% results = -1 error +% array of output written. +% +% +% function results = SOM_CalculateCorrelations(D0,parameters) +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + +function results = SOM_CalculateCorrelations(D0,parameters) + +global SOM + +results = -1; + +parameters.startCPU.corr = cputime; + +% +% Check the roi parameters +% + +parameters.rois = SOM_CheckROIParameters(parameters); + +if parameters.rois.OK == -1 + SOM_LOG('FATAL ERROR : parameters.rois failed to meet criteria'); + return +end + +% +% Check the output information +% + +parameters.Output = SOM_CheckOutput(parameters); + +if parameters.Output.OK == -1 + SOM_LOG('FATAL ERROR : parameters.Output failed to meet criteria'); + return +end + +% +% Sanity check, if Output.type = 0, which is a correlation map, then you +% need at least 2 ROIs that survived any cleaning up. +% + +if parameters.Output.type == 0 & parameters.rois.nrois < 2 + SOM_LOG('FATAL ERROR : You specified output to be a correlation matrix, but insufficient number of ROIS'); + return +end + +% +% Okay - we can do the work now. +% + +% Take the ROI definitions and turn them into linear indices for +% calculations. + +parameters.rois = SOM_BuildROILinearIDX(parameters); + +% Now do the correlation work, either making maps or images. + +switch parameters.Output.type + + case 0 + % + % Correlation maps. + % + SOM_LOG('STATUS : Calling SOM_CalculateCorrelationMaps'); + results = SOM_CalculateCorrelationMaps(D0,parameters); + + case 1 + % + % Correlation images and Z-images + % + SOM_LOG('STATUS : Calling SOM_CalculateCorrelationImages'); + results = SOM_CalculateCorrelationImages(D0,parameters); + + otherwise + % + % Error case + % + SOM_LOG('FATAL ERROR : Somehow we are not doing correlation map or image output'); + return +end + +parameters.stopCPU.corr = cputime; + +% Now write out the parameters to a file. + +paraName = fullfile(parameters.Output.directory,[parameters.Output.name '_parameters']); +results = strvcat(results,paraName); + +save(paraName,'parameters','SOM'); + +return + +% +% All done. +% diff --git a/som/SOM_CalculateMap.m b/som/SOM_CalculateMap.m new file mode 100755 index 00000000..f875393f --- /dev/null +++ b/som/SOM_CalculateMap.m @@ -0,0 +1,490 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2005 +% +% function results = SOM_CalculateMap(theData,nIter,mapSwitch,nSOM,[SelfOMap],[slot]) +% +% theData = theData(nSpace,nTime); +% nSOM = number of basis functions in the SOM +% nIter = number of iterations. +% mapSwitch = vertex layout 'grid' = square layout +% 'sphere' = spherical layout +% slot = memory slot to use. 1 = time series data (default) +% 2 = super cluster use. +% 3 = boostrap probabilities. +% +% NOTE : The "slot" is used to help with memory management. +% Self-Organzing Map calculations are very memory +% intensive, so we are using global memory. However, +% when calculating the supercluster map it would be useful +% to not wipe out the data, so we have two slots +% available for memory. +% +% Please use them wisely. +% +% You will break the calculation if you place +% your time-series data in slot #2! +% +% Hopefully you can avoid "Out of memory" error by +% specifying slot 1 for the big time series matrix, and +% slot 2 for super cluster calculation. +% +% Between subjects you may need to execute the "pack" +% command. +% +% Worst case, trim you session, save appropriate +% things to disk and restart matlab. +% +% +% results = results structure, it will at least contain +% the resulting SOM. +% +% results = results.SOM (the map) +% results.IDX (for each space element which SOM it is related to +% best) +% results.WT (how much of the variance is explained by that) +% +% this is a work in progress! +% +% Currently the cost functionm is Cos(Theat) = U dot V +% where U and V have unit length. +% +% You can set the SOM parameters with +% +% global SOM +% +% SOM.sigma = Initial neighborhood size. +% SOM.sigmaTimeConstant = Rate the neighborhood shrinks. +% SOM.learningTimeConstant = Rate that learning changes. +% SOM.alpha = Initial learning rate +% SOM.Cost = Cost function. (0=U.V, +% 1=|U-V|, 2=|U-V|^2, 3=M.I.). +% NOTE U.V is fastest metric. +% SOM.nPnts = number of points in histogram range : 0 - 1[2]. +% NOTE used by SOM_CalcP. (2 is upper range for |U-V| +% cost, a guess. See "SOM_CalcP.m") +% SOM.saveHistory = History level to save. (0/1/2); +% 0 = none; +% 1 = some; +% 2 = full - memory intensive. +% SOM.WeightOption 0 = no weighting of components. +% 1 = weight by variance +% 2 = weight by st-dev +% SOM.silent 0 = verbose +% 1 = non-verbose +% +% +% SOM.maskThresh = 12.5% - by default. +% +% If you don't specify these parameters they will assume default +% values of: +% +% SOM.alpha = .1 (See "SOM_Alpha.m" on evolution) +% SOM.learningTimeConstant = 2; +% SOM.sigmaTimeConstant = .25; +% SOM.sigma = nSOM/2; guess? +% SOM.Cost = 0; (Opening angle cost function.) +% SON.nPnts = 1000; +% SOM.saveHistory = 1; - Save most of history, but +% not full weights. +% SOM.WeightOption = 0; +% +% Alpha (SOM_Alpha.m) changes according to: +% +% exp(-iter/(nIter * SOM.learningTimeConstant)); +% +% Neighborhood size changes according to: +% +% exp(-iter/(nIter * SOM.sigmaTimeConstant)); +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_CalculateMap(theData,nIter,map_switch,nSOM,SelfOMap,slot) + +global time; +global SOM +global SOMMem + +if ~isfield(SOM,'silent') + SOM.silent = false; +end + +% Determine whether a sphere or grid for the SOM examplar organization. + +if length(map_switch) == 6 & lower(map_switch) == 'sphere' + if ~isfield(SOM,'sphereIter') + SOM.sphereIter = 1000; + end + v = SOM_Sphere_Make(nSOM, SOM.sphereIter); + results.v2 = v(:,1:3); + results.vn = v; + v = v(:,1:3); + SOM.sphere_option = 1; +elseif length(map_switch) == 4 & lower(map_switch) == 'grid' + if sqrt(nSOM)~=floor(sqrt(nSOM)) + fprintf('Can only do square SOM!\n'); + fprintf('Forcing SOM to be square\n'); + nSOM = floor(sqrt(nSOM)+.5)^2; + fprintf('New size is : %d\n',nSOM); + end + v = zeros(nSOM,3); + [x,y] = meshgrid(1:sqrt(nSOM),1:sqrt(nSOM)); + v(:,1) = reshape(x,[nSOM,1]); + v(:,2) = reshape(y,[nSOM,1]); + SOM.sphere_option = 0; +else + fprintf('Not a valid map switch option, valid options are:\n'); + fprintf(' ''Sphere'' & ''Grid''\n'); +end + +% Keep track of the version. + +MajorVersion = 1; +MinorVersion = 101; + +SOM.Version = sprintf('%d.%03d',MajorVersion,MinorVersion); + +% Keep track of the time. + +results.StartTime=cputime; + +tic; + +% Which method to determine neighborhood (same code, just cleaned +% up?). + +if ~isfield(SOM,'OldMethod') + SOM.OldMethod = 0; +end + +% Check to see which memory slot + +if exist('slot') == 0 + slot = 1; + if ~SOM.silent + fprintf('Defaulting to use memory slot 1.\n'); + end +end + +if slot ~= 1 & slot ~= 2 + if ~SOM.silent + fprintf('Forcing to use slot #1.\n'); + end + slot = 1; +end + +if ~SOM.silent + fprintf('Using memory slot #%d\n',slot); +end + +SOM.slot = slot; + +% Check to see if the necessary fields exist. + +% The neighborhood size +if ~isfield(SOM,'sigma') + SOM.sigma = sqrt(nSOM)/2; + if ~SOM.silent + fprintf('SOM.sigma -> %f\n',SOM.sigma); + end +end + +% How quickly to modify the neighborhood size. +if ~isfield(SOM,'sigmaTimeConstant') + SOM.sigmaTimeConstant = 1/4; + if ~SOM.silent + fprintf('SOM.sigmaTimeConstant -> %f\n',SOM.sigmaTimeConstant); + end +end + +% How quickly to modify the map learning rate. +if ~isfield(SOM,'learningTimeConstant') + SOM.learningTimeConstant = 2; + if ~SOM.silent + fprintf('SOM.learningTimeConstant -> %f\n',SOM.learningTimeConstant); + end +end + +% The initial map learning rate. +if ~isfield(SOM,'alpha') + SOM.alpha = .1; + if ~SOM.silent + fprintf('SOM.alpha -> %f\n',SOM.alpha); + end +end + +% The cost function +if ~isfield(SOM,'Cost') + SOM.Cost = 0; + if ~SOM.silent + fprintf('SOM.Cost -> %f\n',SOM.Cost); + end +end + +% The saving history. +if ~isfield(SOM,'saveHistory') + SOM.saveHistory = 1; + if ~SOM.silent + fprintf('SOM.saveHistory -> %f\n',SOM.saveHistory); + end +end + +% The saving history. +if ~isfield(SOM,'WeightOption') + SOM.WeightOption = 0; + if ~SOM.silent + fprintf('SOM.WeightOption -> %f\n',SOM.WeightOption); + end +end + +% Make sure that the Weight Option is Valid. +SOM.WeightOption = SOM.WeightOption(1); + +if sum(ismember([0 1 2],SOM.WeightOption)) == 0 + SOM.WeightOption = 0; + if ~SOM.silent + fprintf('SOM.WeightOption -> %f\n',SOM.WeightOption); + end +end + +% Determine the size of the space we are dealing with. + +% Record the size of the SOM. + +SOM.nSOM = nSOM; +SOM.nIter = nIter; + +% This is if we have square grid, else the info is meaningless +% if we have spherical space. + +nGrid = sqrt(nSOM); +SOM.nGrid = nGrid; + +nSpace = size(theData,1); +nTime = size(theData,2); + +if ~SOM.silent + fprintf('Number of time points : %d\n',nTime); + fprintf('Number of space points : %d\n',nSpace); +end + +% Randomize our initial SOM and normalize the basis. +% +% Modified on 8/23/2005 to use normaly distributed +% elements of vector. This will make it uniformly +% distributed on a sphere in n-space. +% +% Found by doing google search and getting +% argument of Steve Rayhawk at Harvey Mudd. +% Seems to work by graphical validation - still need +% to find the paper for citation. See reference in +% Donald Knuth book: The Art of Computer Programming, vol2 +% Seminumerical Algorithms, Addison-Wesley, 1969. +% + +if exist('SelfOMap') == 1 & ~any([nTime nSOM]-size(SelfOMap)) + if ~SOM.silent + fprintf('Using passed initialized SelfOMap\n'); + end +else + if ~SOM.silent + fprintf('Randomizing SelfOMap\n'); + end + SelfOMap = randn(nTime,nSOM); + SelfOMap = SOM_UnitNormMatrix(SelfOMap,1); +end + +% Store a copy of the initial one. +iSelfOMap = SelfOMap; + +clear SOM_UnitNormMatrix; + +% Unit norm the data. We are not necessarily interested in effect +% size at the moment versus explanatory basis. We can +% get the "beta's" later! + +% This is not the best, as when SOM_SuperClusterEasy calls us, we globber +% what was in here. Not good! 2007-03-23. +% Come up with a mex solution? + +SOMMem{slot}.theData = SOM_UnitNormMatrix(theData,2); + +% Calculate the Variance for weights. +SOMMem{slot}.varData = var(SOMMem{slot}.theData,[],2); + +% Weights for biasing the SOM to voxels that have high variance but +% low population number. + +switch SOM.WeightOption + case 0 + SOMMem{slot}.Weights = ones(size(theData,1),1); + case 1 + SOMMem{slot}.Weights = var(SOMMem{slot}.theData,[],2); + case 2 + SOMMem{slot}.Weights = std(SOMMem{slot}.theData,[],2); + case default + SOMMem{slot}.Weights = ones(size(theData,1),1); +end +SOMMem{slot}.Weights = repmat(SOMMem{slot}.Weights,[1 size(SOMMem{slot}.theData,2)]); +clear theData + +clear SOM_UnitNormMatrix; + +% Ok, let's go iterate the map. At the moment +% we'll just stop the calculation after a sufficient number +% iterations. + +iter = 0; + +%--------------------------Ben added 6/12/07-------------------- + +if SOM.sphere_option %sphere distance map + lneighborDist = zeros(nSOM,nSOM); + for ii=1:nSOM + for jj=1:nSOM + lneighborDist(ii,jj) = abs(acos(dot(v(ii,:),v(jj,:)))*2); + end + end +else %grid distance mapping + neighborDist = SOM_NeighborDist(nGrid); + lneighborDist = reshape(neighborDist,[nGrid*nGrid nGrid*nGrid]); +end + +history = {}; + +pV_init = (SOMMem{slot}.theData.*SOMMem{slot}.Weights)'; +if ~SOM.silent + fprintf('iter,alpha,NeighSigma,cpu\n'); +end + +%mov = avifile('animated4.avi','compression','Cinepak') + +while iter < nIter + tic; + % Increment the iteration + iter = iter + 1; + % Learning function = g(time); + alpha = SOM_Alpha(iter,nIter); + % Determine the neighbor map. + % For each element of the SOM, there will be an associated + % neighborhood. The call should return the indices of these + % SOM vectors as well as the distance for use in the weight + % calculation. + NeighSigma = SOM.sigma*exp(-iter/nIter/SOM.sigmaTimeConstant); + if SOM.OldMethod == 0 + SOMNeighborMap = exp(-lneighborDist.^2/NeighSigma^2); + %keyboard; + else + SOMNeighborMap = SOM_NeighborMap([nGrid nGrid],NeighSigma); + end + + % Determine the SOM vectors the data are closest to and save the + % history. + [idx wts] = SOM_FindClosest(SelfOMap,slot); + if SOM.saveHistory > 0 + history{iter}.idx = idx; + history{iter}.wts = wts; + history{iter}.SelfOMap = SelfOMap; + history{iter}.sigma = NeighSigma; + history{iter}.alpha = alpha; + %history{iter}.neighMap = SOMNeighborMap(:,:,(nGrid-1)*floor(nGrid/2)); + if SOM.saveHistory > 1 + history{iter}.dataBySOM = SOMMem{slot}.dataBySOM; + end + history{iter}.cpuTime = cputime; + end + % + clear SOM_FindClosest; + % Now calculate the perturbation to the SOM. + dSelfOMap = 0*SelfOMap; + + nullSOM = 0*SelfOMap; + for iSOM = 1:nSOM + % Deterimine which data affect this SOM vector. + dI = find(idx==iSOM); + if length(dI) > 0 + % pertube all the same regardless of number of perturbers. + % + % Modified to allow heavier weighting based on variance of + % the time-series vector for a voxel. This is under the + % assumption that we want variance explained by the model. + % + % This also makes the assumption that signal is high + % variance? + % + % 2007.03.20 Robert C. Welsh + % + pV = sum(pV_init(:,dI),2); + %pV = sum(pV_init(:,dI),2)/sum(SOMMem{slot}.Weights(dI,1)); + %pV = SOM_UnitNormMatrix(pV,1); + %-------------------Ben changed 6/13/07----------------------- + if SOM.OldMethod == 0 + vlx = pV*reshape(SOMNeighborMap(:,iSOM),[1 nSOM]); + dSelfOMap = dSelfOMap + vlx; + else + dSelfOMap = dSelfOMap + SOM_ModBasis(pV,nullSOM, ... + SOMNeighborMap{iSOM},iter,nIter); + end + clear SOM_ModBasis; + end + end + % Update the SOM. + %dSelfOMap = dSelfOMap/(nSpace/nSOM); + SelfOMap = SelfOMap + alpha*dSelfOMap; + SelfOMap = SOM_UnitNormMatrix(SelfOMap,1); + if(any(any(isnan(SelfOMap)))) + % keyboard + end + clear SOM_UnitNormMatrix; + xx=toc; + if SOM.saveHistory > 0 + history{iter}.toc = xx; + end + if ~SOM.silent + fprintf('%03d %f %f %f\n',iter,alpha,NeighSigma,xx); + end +end +SelfOMap = SOM_UnitNormMatrix(SelfOMap,1); +%mov = close(mov); + +% All done, and now pack up the results to be sent back to the calling +% function. + +results.SelfOMap = SelfOMap; + +[results.IDX results.WTS results.DIDX results.DWTS] = SOM_FindClosest(SelfOMap,slot); + +clear SOM_FindClosest; + +% Final cost function evaluation. + +results.dataBySOM = SOMMem{slot}.dataBySOM; + +% Return a copy of the initial one used. + +results.iSelfOMap = iSelfOMap; +results.history = history; + +results.weights = []; +for iter = 1:nIter + results.weights = [results.weights sum(results.history{iter}.wts)]; +end + +% Record the configuation. + +results.SOM = SOM; +results.H = hist(results.IDX,[0:results.SOM.nSOM]); + +clear history; +clear SelfOMap; + +% Store the finish time. + +results.StopTime=cputime; + +return + +% +% All done. +% diff --git a/som/SOM_CalculateMapV2.m b/som/SOM_CalculateMapV2.m new file mode 100755 index 00000000..65288842 --- /dev/null +++ b/som/SOM_CalculateMapV2.m @@ -0,0 +1,428 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2005 +% +% function results = SOM_CalculateMap(theData,nSOM,nIter,[SelfOMap],[slot]) +% +% theData = theData(nSpace,nTime); +% nSOM = number of basis functions in the SOM +% nIter = number of iterations. +% slot = memory slot to use. 1 = time series data (default) +% 2 = super cluster use. +% 3 = boostrap probabilities. +% +% NOTE : The "slot" is used to help with memory management. +% Self-Organzing Map calculations are very memory +% intensive, so we are using global memory. However, +% when calculating the supercluster map it would be useful +% to not wipe out the data, so we have two slots +% available for memory. +% +% Please use them wisely. +% +% You will break the calculation if you place +% your time-series data in slot #2! +% +% Hopefully you can avoid "Out of memory" error by +% specifying slot 1 for the big time series matrix, and +% slot 2 for super cluster calculation. +% +% Between subjects you may need to execute the "pack" +% command. +% +% Worst case, trim you session, save appropriate +% things to disk and restart matlab. +% +% +% results = results structure, it will at least contain +% the resulting SOM. +% +% results = results.SOM (the map) +% results.IDX (for each space element which SOM it is related to +% best) +% results.WT (how much of the variance is explained by that) +% +% this is a work in progress! +% +% Currently the cost functionm is Cos(Theat) = U dot V +% where U and V have unit length. +% +% You can set the SOM parameters with +% +% global SOM +% +% SOM.sigma = Initial neighborhood size. +% SOM.sigmaTimeConstant = Rate the neighborhood shrinks. +% SOM.learningTimeConstant = Rate that learning changes. +% SOM.alpha = Initial learning rate +% SOM.Cost = Cost function. (0=U.V, +% 1=|U-V|, 2=|U-V|^2, 3=M.I.). +% NOTE U.V is fastest metric. +% SOM.nPnts = number of points in histogram range : 0 - 1[2]. +% NOTE used by SOM_CalcP. (2 is upper range for |U-V| +% cost, a guess. See "SOM_CalcP.m") +% SOM.saveHistory = History level to save. (0/1/2); +% 0 = none; +% 1 = some; +% 2 = full - memory intensive. +% SOM.WeightOption 0 = no weighting of components. +% 1 = weight by variance +% 2 = weight by st-dev +% +% If you don't specify these parameters they will assume default +% values of: +% +% SOM.alpha = .1 (See "SOM_Alpha.m" on evolution) +% SOM.learningTimeConstant = 2; +% SOM.sigmaTimeConstant = .25; +% SOM.sigma = nSOM/2; guess? +% SOM.Cost = 0; (Opening angle cost function.) +% SON.nPnts = 1000; +% SOM.saveHistory = 1; - Save most of history, but +% not full weights. +% SOM.WeightOption = 0; +% +% Alpha (SOM_Alpha.m) changes according to: +% +% exp(-iter/(nIter * SOM.learningTimeConstant)); +% +% Neighborhood size changes according to: +% +% exp(-iter/(nIter * SOM.sigmaTimeConstant)); +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_CalculateMap(theData,nIter,SelfOMap,slot) + +global time; +global SOM +global SOMMem +%--------------------------Ben added 6/28/07-------------------- +[b,v] = sphere_iter(2,1); +nSOM = length(v); + +% Keep track of the version. + +MajorVersion = 1; +MinorVersion = 001; + +SOM.Version = sprintf('%d.%03d',MajorVersion,MinorVersion); + +% Keep track of the time. + +results.StartTime=cputime; + +tic; + +% Which method to determine neighborhood (same code, just cleaned +% up?). + +if ~isfield(SOM,'OldMethod') + SOM.OldMethod = 0; +end + +% Check to see which memory slot + +if exist('slot') == 0 + slot = 1; + fprintf('Defaulting to use memory slot 1.\n'); +end + +if slot ~= 1 & slot ~= 2 + fprintf('Forcing to use slot #1.\n'); + slot = 1; +end + +fprintf('Using memory slot #%d\n',slot); + +SOM.slot = slot; + +% Check to see if the necessary fields exist. + +% The neighborhood size +if ~isfield(SOM,'sigma') + SOM.sigma = sqrt(nSOM)/2; + fprintf('SOM.sigma -> %f\n',SOM.sigma); +end + +% How quickly to modify the neighborhood size. +if ~isfield(SOM,'sigmaTimeConstant') + SOM.sigmaTimeConstant = 1/4; + fprintf('SOM.sigmaTimeConstant -> %f\n',SOM.sigmaTimeConstant); +end + +% How quickly to modify the map learning rate. +if ~isfield(SOM,'learningTimeConstant') + SOM.learningTimeConstant = 2; + fprintf('SOM.learningTimeConstant -> %f\n',SOM.learningTimeConstant); +end + +% The initial map learning rate. +if ~isfield(SOM,'alpha') + SOM.alpha = .1; + fprintf('SOM.alpha -> %f\n',SOM.alpha); +end + +% The cost function +if ~isfield(SOM,'Cost') + SOM.Cost = 0; + fprintf('SOM.Cost -> %f\n',SOM.Cost); +end + +% The saving history. +if ~isfield(SOM,'saveHistory') + SOM.saveHistory = 1; + fprintf('SOM.saveHistory -> %f\n',SOM.saveHistory); +end + +% The saving history. +if ~isfield(SOM,'WeightOption') + SOM.WeightOption = 0; + fprintf('SOM.WeightOption -> %f\n',SOM.WeightOption); +end + +% Make sure that the Weight Option is Valid. +SOM.WeightOption = SOM.WeightOption(1); + +if sum(ismember([0 1 2],SOM.WeightOption)) == 0 + SOM.WeightOption = 0; + fprintf('SOM.WeightOption -> %f\n',SOM.WeightOption); +end + +% Determine the size of the space we are dealing with. + +%if sqrt(nSOM)~=floor(sqrt(nSOM)) +% fprintf('Can only do square SOM!\n'); +% fprintf('Forcing SOM to be square\n'); +% nSOM = floor(sqrt(nSOM)+.5)^2; +% fprintf('New size is : %d\n',nSOM); +%end + +% Record the size of the SOM. +SOM.nSOM = nSOM; +SOM.nIter = nIter; + +nGrid = sqrt(nSOM); +SOM.nGrid = nGrid; + +nSpace = size(theData,1); +nTime = size(theData,2); + +fprintf('Number of time points : %d\n',nTime); +fprintf('Number of space points : %d\n',nSpace); + +% Randomize our initial SOM and normalize the basis. +% +% Modified on 8/23/2005 to use normaly distributed +% elements of vector. This will make it uniformly +% distributed on a sphere in n-space. +% +% Found by doing google search and getting +% argument of Steve Rayhawk at Harvey Mudd. +% Seems to work by graphical validation - still need +% to find the paper for citation. See reference in +% Donald Knuth book: The Art of Computer Programming, vol2 +% Seminumerical Algorithms, Addison-Wesley, 1969. +% + +if exist('SelfOMap') == 1 & ~any([nTime nSOM]-size(SelfOMap)) + fprintf('Using passed initialized SelfOMap\n'); +else + fprintf('Randomizing SelfOMap\n'); + SelfOMap = randn(nTime,nSOM); + SelfOMap = SOM_UnitNormMatrix(SelfOMap,1); +end + +% Store a copy of the initial one. +iSelfOMap = SelfOMap; + +clear SOM_UnitNormMatrix; + +% Unit norm the data. We are not necessarily interested in effect +% size at the moment versus explanatory basis. We can +% get the "beta's" later! + +% This is not the best, as when SOM_SuperClusterEasy calls us, we globber +% what was in here. Not good! 2007-03-23. +% Come up with a mex solution? + +SOMMem{slot}.theData = SOM_UnitNormMatrix(theData,2); + +% Calculate the Variance for weights. +SOMMem{slot}.varData = var(SOMMem{slot}.theData,[],2); + +% Weights for biasing the SOM to voxels that have high variance but +% low population number. + +switch SOM.WeightOption + case 0 + SOMMem{slot}.Weights = ones(size(theData,1),1); + case 1 + SOMMem{slot}.Weights = var(SOMMem{slot}.theData,[],2); + case 2 + SOMMem{slot}.Weights = std(SOMMem{slot}.theData,[],2); + case default + SOMMem{slot}.Weights = ones(size(theData,1),1); +end +SOMMem{slot}.Weights = repmat(SOMMem{slot}.Weights,[1 size(SOMMem{slot}.theData,2)]); +clear theData + +clear SOM_UnitNormMatrix; + +% Ok, let's go iterate the map. At the moment +% we'll just stop the calculation after a sufficient number +% iterations. + +iter = 0; + +%neighborDist = SOM_NeighborDist(nGrid); + +%lneighborDist = reshape(neighborDist,[nGrid nGrid nGrid*nGrid]); +%--------------------------Ben added 6/12/07-------------------- + + +lneighborDist = zeros(nSOM,nSOM); +for ii=1:nSOM + for jj=1:nSOM + lneighborDist(ii,jj) = abs(acos(dot(v(ii,:),v(jj,:)))*2); + end +end + +history = {}; + +fprintf('iter,alpha,NeighSigma,cpu\n'); + +%mov = avifile('animated4.avi','compression','Cinepak') + +while iter < nIter + tic; + % Increment the iteration + iter = iter + 1; + % Learning function = g(time); + alpha = SOM_Alpha(iter,nIter); + % Determine the neighbor map. + % For each element of the SOM, there will be an associated + % neighborhood. The call should return the indices of these + % SOM vectors as well as the distance for use in the weight + % calculation. + NeighSigma = SOM.sigma*exp(-iter/nIter/SOM.sigmaTimeConstant); + if SOM.OldMethod == 0 + SOMNeighborMap = exp(-lneighborDist.^2/NeighSigma^2); + %keyboard; + else + SOMNeighborMap = SOM_NeighborMap([nGrid nGrid],NeighSigma); + end + + % Determine the SOM vectors the data are closest to and save the + % history. + [idx wts] = SOM_FindClosest(SelfOMap,slot); + if SOM.saveHistory > 0 + history{iter}.idx = idx; + history{iter}.wts = wts; + history{iter}.SelfOMap = SelfOMap; + history{iter}.sigma = NeighSigma; + history{iter}.alpha = alpha; + %history{iter}.neighMap = SOMNeighborMap(:,:,(nGrid-1)*floor(nGrid/2)); + if SOM.saveHistory > 1 + history{iter}.dataBySOM = SOMMem{slot}.dataBySOM; + end + history{iter}.cpuTime = cputime; + end + % + clear SOM_FindClosest; + % Now calculate the perturbation to the SOM. + dSelfOMap = 0*SelfOMap; + nullSOM = 0*SelfOMap; + pV_init = SOMMem{slot}.theData.*SOMMem{slot}.Weights; + for iSOM = 1:nSOM + % Deterimine which data affect this SOM vector. + dI = find(idx==iSOM); + if length(dI) > 0 + % pertube all the same regardless of number of perturbers. + % + % Modified to allow heavier weighting based on variance of + % the time-series vector for a voxel. This is under the + % assumption that we want variance explained by the model. + % + % This also makes the assumption that signal is high + % variance? + % + % 2007.03.20 Robert C. Welsh + % + pV = sum(pV_init(dI,:),1)'/... + sum(SOMMem{slot}.Weights(dI,1)); +%tic +%sum(SOMMem{slot}.theData(dI,:)); +%toc +%tic +%SOMMem{slot}.Weights(dI); +%toc +%keyboard; + %-------------------Ben changed 6/13/07----------------------- + if SOM.OldMethod == 0 + vlx = pV*reshape(SOMNeighborMap(:,iSOM),[1 nSOM]); + %SOM_ModBasis(pV,nullSOM,SOMNeighborMap(:,iSOM)); + dSelfOMap = dSelfOMap + vlx; + else + dSelfOMap = dSelfOMap + SOM_ModBasis(pV,nullSOM, ... + SOMNeighborMap{iSOM},iter,nIter); + end + clear SOM_ModBasis; + end + end + % Update the SOM. + SelfOMap = SelfOMap + alpha*dSelfOMap; + SelfOMap = SOM_UnitNormMatrix(SelfOMap,1); + if(any(any(isnan(SelfOMap)))) + keyboard + end + clear SOM_UnitNormMatrix; + xx=toc; + if SOM.saveHistory > 0 + history{iter}.toc = xx; + end + fprintf('%03d %f %f %f\n',iter,alpha,NeighSigma,xx); +end +%mov = close(mov); + +% All done, and now pack up the results to be sent back to the calling +% function. + +results.SelfOMap = SelfOMap; + +[results.IDX results.WTS results.DIDX results.DWTS] = SOM_FindClosest(SelfOMap,slot); + +clear SOM_FindClosest; + +% Final cost function evaluation. + +results.dataBySOM = SOMMem{slot}.dataBySOM; + +% Return a copy of the initial one used. + +results.iSelfOMap = iSelfOMap; +results.history = history; + +results.weights = []; +for iter = 1:nIter + results.weights = [results.weights sum(results.history{iter}.wts)]; +end + +% Record the configuation. + +results.SOM = SOM; +results.H = hist(results.IDX,[0:results.SOM.nSOM]); + +clear history; +clear SelfOMap; + +% Store the finish time. + +results.StopTime=cputime; + +return + +% +% All done. +% diff --git a/som/SOM_CheckDataStructure.m b/som/SOM_CheckDataStructure.m new file mode 100644 index 00000000..16b69e63 --- /dev/null +++ b/som/SOM_CheckDataStructure.m @@ -0,0 +1,170 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% Validate the data parameters +% pass for SOM_PreProcessData +% +% +% function data = SOM_CheckDataStructure(parameters) +% +% data. +% +% run{i}. +% +% P = full directory path to time-series data. +% +% MotionParameters = array of motion parameters +% +% MaskFLAG = 0 don't do any masking and grab all of the data +% = 1 mask using either what is in parameters.epi +% or by building a subject specific mask with +% SOM_CreateMask +% nTIME = number of time points to +% analyze. +% +% censorVector = vector of 0's and 1's on which +% specific TR's to include in the analysis, this is after +% the nTIME has trimmed the data. NOT REQUIRED. +% +% OK = -1 returned if bad +% 1 if things are okay. +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +% Modified Nov 8, 2011 to have nTIME be part of data.run +% structure, previously it was part of the TIME.run structure. + +% 2012.01.17 - put in check for censor vector - RCWelsh + +function data = SOM_CheckDataStructure(parameters) + +data.OK = -1; + +if isfield(parameters,'data') == 0 + SOM_LOG('FATAL ERROR : Missing data structure from parameters'); + return +end + +data = parameters.data; +data.OK = -1; + +if isfield(data,'run') == 0 + SOM_LOG('FATAL ERROR : Missing run variable from parameters.data'); + return +end + +% Now loop on each run. + +for iRUN = 1:length(data.run) + if isfield(data.run(iRUN),'P') == 0 + SOM_LOG('FATAL ERROR : Missing P variable in parameters.data'); + return + end + + if ischar(data.run(iRUN).P) == 0 + SOM_LOG('FATAL ERROR : You need to specify a character array of files'); + return + end + + % Now check to see if the input files actually exist. + + if exist(data.run(iRUN).P(1,:),'file') == 0 + SOM_LOG(sprintf('FATAL ERROR : File %s does not exist',data.run(iRUN).P(1,:))); + return + end + + % Read in a single header for this run from the first file. + + data.run(iRUN).hdr = spm_vol(data.run(iRUN).P(1,:)); + + % How many points in this run? If the hdr is multi-dimensional + % than most likely a nifti file, and if P has more than one line + % than it is img/hdr. + + nTIME = max( [ length(data.run(iRUN).hdr) size(data.run(iRUN).P,1) ] ); + + if isfield(data.run(iRUN),'nTIME') == 0 + data.run(iRUN).nTIME = nTIME; + SOM_LOG(sprintf('STATUS : Determined that for run %d, there are %d time-points.',iRUN,nTIME)); + end + + if isfield(data.run(iRUN),'censorVector') + if length(data.run(iRUN).censorVector) ~= data.run(iRUN).nTIME + SOM_LOG(sprintf('FATAL : Specified censorVector of length %d for run %d does not match %d'),iRUN,data.run(iRUN).nTIME) + return + end + end + + % Check to see if the motion parameters are there + + if isfield(data.run(iRUN),'MotionParameters') == 1 + if size(data.run(iRUN).MotionParameters,1) ~= data.run(iRUN).nTIME + SOM_LOG('WARNING : Motion parameters length does not match expected number of time points'); + if length(data.run(iRUN).MotionParameters) > 0 & size(data.run(iRUN).MotionParameters,1) < data.run(iRUN).nTIME + SOM_LOG(sprintf('FATAL ERROR : Motion parameter array is too short for run %d',iRUN)); + return + end + end + else + data.run(iRUN).MotionParameters = []; + SOM_LOG(sprintf('STATUS : No motion parameters for run %d',iRUN)); + end + + % And now make sure all files for this run are consistent. + + for iFILE = 2:size(data.run(iRUN).P,1) + if exist(data.run(iRUN).P(iFILE,:),'file') == 0 + SOM_LOG(sprintf('FATAL ERROR : File %s does not exist'),data.run(iRUN).P(iFILE,:)); + return + else + thisHDR = spm_vol(data.run(iRUN).P(iFILE,:)); % fixed on 5/2/2012 + if SOM_SpaceVerify(data.run(iRUN).hdr,thisHDR) ~= 1 + SOM_LOG('FATAL ERROR : Error with consistent in-run image space definition.'); + return + end + end + end + +end + +% Now check that the headers of each run specifies the same space: + +for iRUN = 2:length(data.run) + if SOM_SpaceVerify(data.run(1).hdr,data.run(iRUN).hdr) ~= 1 + SOM_LOG('FATAL ERROR : Error with consistent cross-run image space definition.'); + return + end +end + +% Check to see what type of masking +% The masking flag will apply to all of the runs, else +% we'd end up with a different number of space points per run +% which does NOT make sense. + +if isfield(data,'MaskFLAG') == 0 + SOM_LOG('WARNING : Defaulting to using a mask on the data'); + data.MaskFLAG = 1; +end + +if isnumeric(data.MaskFLAG) == 0 + SOM_LOG('WARNING : data.MaskFLAG is not numeric, forcing it to 1'); + data.MaskFLAG = 1; +end + +if data.MaskFLAG ~= 0 + data.MaskFLAG = 1; +end + +% Everything is ok. + +data.OK = 1; + +return + + + + diff --git a/som/SOM_CheckMasks.m b/som/SOM_CheckMasks.m new file mode 100644 index 00000000..73a40c3f --- /dev/null +++ b/som/SOM_CheckMasks.m @@ -0,0 +1,92 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% A routine to loop on the different masks that are potentially +% used in the SOM code and to check them for correctness etc. +% +% function masks = SOM_CheckMasks(parameters) +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function masks = SOM_CheckMasks(parameters) + +global SOM + +% Does 'masks' field exist? + +if isfield(parameters,'masks') == 0 + SOM_LOG('WARNING : You have not specified a ".masks" structure. Using defaults'); + masks.epi = []; + masks.grey = []; + masks.csf = []; + masks.white = []; +else + masks = parameters.masks; +end + +% Default is error state + +masks.OK = -1; + +% Files needed for masking? + +if isfield(masks,'grey') == 0 + masks.grey = []; +end + +masks.grey = SOM_ParseFileParam(masks.grey); + +if masks.grey.OK == -1 + SOM_LOG('FATAL ERROR : You specified an grey mask that doesn''t exist'); + return +end + +% White Matter ROI for regression? + +if isfield(masks,'white') == 0 + masks.white = []; +end + +masks.white = SOM_ParseFileParam(masks.white); + +if masks.white.OK == -1 + SOM_LOG('FATAL ERROR : You specified an white mask that doesn''t exist'); + return +end + +% CSF ROI? + +if isfield(masks,'csf') == 0 + masks.csf = []; +end + +masks.csf = SOM_ParseFileParam(masks.csf); + +if masks.csf.OK == -1 + SOM_LOG('FATAL ERROR : You specified an csf mask that doesn''t exist'); + return +end + +% If no common epi mask then we will use one create on the fly. + +if isfield(masks,'epi') == 0 + masks.epi = []; +else + masks.epi = SOM_ParseFileParam(masks.epi); + if masks.epi.OK == -1 + SOM_LOG('FATAL ERROR : You specified an epi mask that doesn''t exist'); + return + end +end + +% Okay, we made it this far so all good. + +masks.OK = 1; + +% +% all done. +% \ No newline at end of file diff --git a/som/SOM_CheckOutput.m b/som/SOM_CheckOutput.m new file mode 100755 index 00000000..4744c720 --- /dev/null +++ b/som/SOM_CheckOutput.m @@ -0,0 +1,127 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% +% INPUT +% +% parameters -- see SOM_PreProcesData +% +% .rois +% .Output +% .correlation - 'maps' - save a single correlation matrix +% 'images' - save a single correlation image +% per ROI +% .directory - full directory path to output +% .name - name of output file (generic) +% .description - comment to add to image files. +% +% +% OUTPUT +% +% Output +% +% .OK = 1 all okay, other wise not. +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function Output = SOM_CheckOutput(parameters) + +% Default is error + +Output.OK = -1; + +% check for the "rois" field; + +if isfield(parameters,'Output') == 0 + SOM_LOG('FATAL ERROR : You need to specify ".Output" definitions'); + return +end + +Output = parameters.Output; + +Output.OK = -1; + +% +% Make sure they have asked for "maps" or "images". +% + +if isfield(Output,'correlation') + if ischar(Output.correlation) + Output.correlation = lower(Output.correlation); + switch lower(Output.correlation(1)) + case 'm' + Output.type = 0; % Correlation map/mat file. This require more than 1 ROI! + case 'i' + Output.type = 1; % r-image and z-image. + otherwise + SOM_LOG('FATAL ERROR : Output type no specified'); + return + end + else + SOM_LOG('FATAL ERROR : Output type needs to be specified as string'); + return + end +else + SOM_LOG('FATAL ERROR : Correlation output type is missing'); + return +end + +% +% Now check for the output directory, it must exist +% + +if isfield(Output,'directory') + if exist(Output.directory,'dir') == 0 + SOM_LOG('WARNING : Output directory is not there, but will attempt to create'); + [mkS mkM mkID] = mkdir(Output.directory); + if mkS + SOM_LOG('WARNING : Success'); + else + SOM_LOG('FATAL WARNING : Can not create'); + return + end + end +else + SOM_LOG('FATAL WARNING : Can not create'); + return +end + +if isfield(Output,'name') == 0 + SOM_LOG('WARNING : Missing output name, will use generic'); + Outputname = 'rmap'; +else + Outputname = Output.name; +end + +% Now make sure there are no special characters in name, but allow '_'. + +CHAROK = isstrprop(Outputname, 'alphanum'); + +Output.name = []; + +for iCHAR = 1:length(CHAROK) + if CHAROK(iCHAR) | strcmp(Outputname(iCHAR),'_') + Output.name = [ Output.name Outputname(iCHAR) ] ; + else + SOM_LOG(sprintf('WARNING : Removing special character "%s" from Output.name',Outputname(iCHAR))); + end +end + +if isfield(Output,'description') == 0 + Output.description = 'Correlaton map'; + SOM_LOG('STATUS : Using generic file comment description.'); +end + +Output.OK = 1; + +return + +% +% All done +% + + diff --git a/som/SOM_CheckROIParameters.m b/som/SOM_CheckROIParameters.m new file mode 100644 index 00000000..9f929fa1 --- /dev/null +++ b/som/SOM_CheckROIParameters.m @@ -0,0 +1,326 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% +% See SOM_MakeSphereROI if you want sizes other than those below. +% +% Check the ROI parameters. +% +% INPUT +% +% parameters -- see SOM_PreProcesData +% +% .rois +% [ specify one or the other: "mni" or "files"] +% .mni +% .coordinates - table of n coordinates (x,y,z) +% .size - which size, 1, 7, 19, 27 voxels +% (default is 19) +% .XROI - optional array of user specficied size. +% .YROI see below on how to build it. +% .ZROI +% .files - table of ROI files +% +% .mask +% .File - full directory path and name to file. +% .MaskFLAG - 0 no mask, 1 mask +% +% OUTPUT +% +% rois +% +% .OK = 1 all okay, other wise not. +% +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + +function rois = SOM_CheckROIParameters(parameters); + +%save SOM_CheckROIParameters_01 parameters + +% Some initialization of tables we need. + +% Single voxel + +XROI{1} = [ +0 ]; +YROI{1} = [ +0 ]; +ZROI{1} = [ +0 ]; + +% Intersectiong plus signs. + +XROI{2} = [ +0 +0 -1 +0 +1 +0 +0]; +YROI{2} = [ +0 -1 +0 +0 +0 +1 +0]; +ZROI{2} = [ -1 +0 +0 +0 +0 +0 +1]; + +% Neighbors in a cube, but don't include the corners. + +XROI{3} = [ +0 -1 +0 +1 +0 -1 +0 +1 -1 +0 +1 -1 +0 +1 +0 -1 +0 +1 +0]; +YROI{3} = [ -1 +0 +0 +0 +1 -1 -1 -1 +0 +0 +0 +1 +1 +1 -1 +0 +0 +0 +1]; +ZROI{3} = [ -1 -1 -1 -1 -1 +0 +0 +0 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1]; + +% Neighbors in a cube, but include the corners. + +XROI{4} = [-1 +0 +1 -1 +0 +1 -1 +0 +1 -1 +0 +1 -1 +0 +1 -1 +0 +1 -1 +0 +1 -1 +0 +1 -1 +0 +1 ]; +YROI{4} = [-1 -1 -1 +0 +0 +0 +1 +1 +1 -1 -1 -1 +0 +0 +0 +1 +1 +1 -1 -1 -1 +0 +0 +0 +1 +1 +1 ]; +ZROI{4} = [-1 -1 -1 -1 -1 -1 -1 -1 -1 +0 +0 +0 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1 +1 +1 +1 +1 ]; + +SIZENAMES = ['XROI';'YROI';'ZROI']; + +SIZEOPTIONS = [1 7 19 27]; + +% Default is error + +rois.OK = -1; + +% check for the "rois" field; + +if isfield(parameters,'rois') == 0 + SOM_LOG('FATAL ERROR : You need to specify ROI definitions'); + return +end + +rois = parameters.rois; + +rois.OK = -1; + +% Okay, let's determine if specifying mni coordinates or images. + +if isfield(rois,'mni') == 0 + if isfield(rois,'files') == 0 + SOM_LOG('FATAL ERROR : You need to specify at least ".mni" or ".files"'); + return + else + rois.type = 1; + end +else + rois.type = 0; +end + +% Now see if they specified a mask to constrain the ROI's +% The rois will ALSO be constrained by the mask as defined in +% parameters.masks.epi + +if isfield(rois,'mask') == 0 + rois.mask = []; +end + +rois.mask = SOM_ParseFileParam(rois.mask); + +if rois.mask.OK == -1 + SOM_LOG('FATAL ERROR : You specified a mask that does not exist'); + return +end + +% +% If the mask wasn't specified we will use the first image of the time series +% data as a mask as we need to make sure that the ROIS are in the brain. +% + +if isempty(rois.mask.File) + try + PMask = parameters.data.run(1).P(1,:); + SOM_LOG(sprintf('STATUS : Using %s as a masking image',parameters.data.run(1).P(1,:))); + catch + SOM_LOG('FATAL ERROR : No time series data specified yet.'); + return + end +else + PMask = rois.mask.File; +end + +% +% Now make sure that the mask specified matchs our data. +% + +PMaskHDR = spm_vol(PMask); + +if SOM_SpaceVerify(parameters.data.run(1).hdr,PMaskHDR) ~= 1 + SOM_LOG('FATAL ERROR : Error with consistent image space (ROI Mask) definition.'); + return +end + +% Now work through the configuration of the ROIs + +switch rois.type + + case 0 + + % + % this case is mni coordinates + % + + % Must have specified the array of MNI coordinates in the + % structure. + if isfield(rois.mni,'coordinates') == 0 + SOM_LOG('FATAL ERROR : You need to specificy MNI coordinates, ".coordinates" array is missing'); + return + end + + % And of course they need to be numeric. + if isnumeric(rois.mni.coordinates) == 0 + SOM_LOG('FATAL ERROR : You must specify numerical MNI coordinates'); + return + end + + % And the array needs to be N x 3 + if size(rois.mni.coordinates,2) ~= 3 + SOM_LOG('FATAL ERROR : Incorrect size of MNI ".coordinates" array'); + SOM_LOG('FATAL ERROR : You need to specify 3 coordinates (x,y,z) for each entry'); + return + end + + % Next, how big is each ROI, they will all be the same. + if isfield(rois.mni,'size') == 0 + SOM_LOG(sprintf('STATUS : size of ROIS not specified, defaulting to %d voxels',SOM.defaults.roi.mni.size)); + rois.mni.size = SOM.defaults.roi.mni.size; + end + + % You can either specify the size, or you can attach structure + % indicating the size of the ROI with the ".XROI", ".YROI" and + % ".ZROI" sizes. + if isnumeric(rois.mni.size) + whichSIZE = find(SIZEOPTIONS==rois.mni.size(1)); + if length(whichSIZE) < 1 + whichSIZE = 3; + SOM_LOG('WARNING : roi ".size" is not recognized, defaulting to 19 voxels'); + end + rois.mni.size = []; + rois.mni.size.XROI = XROI{whichSIZE}; + rois.mni.size.YROI = YROI{whichSIZE}; + rois.mni.size.ZROI = ZROI{whichSIZE}; + rois.mni.size.size = length(rois.mni.size.XROI); + else + for iXYZ = 1:3 + if isfield(rois.mni.size,SIZENAMES(iXYZ,:)) == 0 + SOM_LOG('FATAL ERROR : You need to specify either a size, or your own XROI, YROI, ZROI'); + end + end + rois.mni.size.size = length(rois.mni.size.XROI); + end + + % + % Now make sure that the sizes are all the same + % + + if any(size(rois.mni.size.XROI)-size(rois.mni.size.YROI)) + SOM_LOG('FATAL ERROR : size of XROI, YROI do not match'); + return + end + if any(size(rois.mni.size.XROI)-size(rois.mni.size.ZROI)) + SOM_LOG('FATAL ERROR : size of XROI, ZROI do not match'); + return + end + + % + % Now see if any of the coordinates are inside the masking image. + % + + % How many rois were passed? + rois.nroisRequested = size(rois.mni.coordinates,1); + + rois.mni.inIDX = SOM_roiPointsInMask(PMask,rois.mni.coordinates); + + rois.ROIOK = zeros(rois.nroisRequested,1); + + % For now each ROI will contain the same number of voxels, however, + % this will later get masked again so make sure that the ROI true + % extent doesn't go beyond the time series masking image. + rois.nvoxels = rois.mni.size.size*ones(rois.nroisRequested,1); + + % Mark which ROI's are good. + rois.ROIOK(rois.mni.inIDX) = 1; + + % How many valid ROIS really? + rois.nrois = length(rois.mni.inIDX); + + case 1 + + % + % this case is roi-files + % + + % How many rois were passed? + rois.nroisRequested = size(rois.files,1); + rois.nvoxels = zeros(rois.nroisRequested,1); + + % + % For now the voxel dimensionality of the ROI files must strictly + % adhere to the size of the input data. + % + + try + MVol = spm_read_vols(spm_vol(PMask)); + MVol = MVol > 0; + catch + SOM_LOG('FATAL ERROR : Can not figure out the mask'); + return + end + + %DATAHDR = spm_vol(parameters.data.run(1).P(1,:)); + + for iFILE = 1:rois.nroisRequested + thisFILE = strtrim(rois.files(iFILE,:)); + if exist(thisFILE,'file') == 0 + SOM_LOG(sprintf('FATAL ERROR : ROI file:%s does not exist.',thisFILE)); + return + end + % + % If the file doesn't adhere to SPM standards this will produce + % a fatal error. + % + try + rois.hdr(iFILE) = spm_vol(thisFILE); + + if SOM_SpaceVerify(parameters.data.run(1).hdr,rois.hdr(iFILE)) ~= 1 + SOM_LOG('FATAL ERROR : Error with consistent roi image space definition.'); + return + end + + % + % Count how many voxels in this ROI + % + SOM_LOG(sprintf('STATUS : read hdr for %s\n',thisFILE)); + thisVOL = spm_read_vols(rois.hdr(iFILE)); + rois.nvoxels(iFILE) = sum(sum(sum((thisVOL>0).*MVol))); + catch + SOM_LOG(sprintf('FATAL ERROR : spm could not read file %s',thisFILE)); + return + end + end + + rois.ROIOK = zeros(rois.nroisRequested,1); + rois.ROIOK(find(rois.nvoxels)) = 1; + + % How many valid ROIS really? + rois.nrois = length(find(rois.nvoxels)); +end + +% +% In either method, did any rois survive the masking? +% + +if rois.nrois < 1 + SOM_LOG('FATAL WARNING : No rois survived masking.'); + return +end + +SOM_LOG(sprintf('STATUS : %d rois survived masking',rois.nrois)); + +% +% If we got this far, then the ROI definitions are good. +% Other logic testing takes place in SOM_CalculateCorrelations. +% + +rois.OK = 1; + +return + +% +% All done +% + diff --git a/som/SOM_CheckRegressFLAGS.m b/som/SOM_CheckRegressFLAGS.m new file mode 100755 index 00000000..0a9349a3 --- /dev/null +++ b/som/SOM_CheckRegressFLAGS.m @@ -0,0 +1,148 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% Validate the FLAG parameters +% pass for SOM_PreProcessData +% +% RegressFLAGS. +% +% prinComp = 0 use average if available +% # use [N] principle components specified +% +% motion = 0 no motion regression +% 1 motion regression (default if MotionParameters +% are present) +% THIS IS AN INTERNAL FLAG +% +% order = alphabetic order of signal processing. +% D = detrend +% G = global +% C = CSF +% W = white matter +% M = motion +% B = band pass +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +% Modified Nov 8, 2011 to have nTIME be part of data.run +% structure, previously it was part of the TIME.run structure. + +function RegressFLAGS = SOM_CheckRegressFLAGS(parameters) + +global SOM + +if isfield(parameters,'RegressFLAGS') == 0 + SOM_LOG('WARNING : No regression flags specified, going to build defaults'); + RegressFLAGS = []; + RegressFLAGS.prinComp = SOM.defaults.RegressFLAGS.prinComp; + RegressFLAGS.motion = 1; + for iRUN = 1:length(parameters.data.run) + % + % If motion parameters are missing for any run then we turn + % the regression off for all runs. + % + % + if isfield(parameters.data.run(iRUN),'MotionParameters') == 0 + parameters.data.run(iRUN).MotionParameters = []; + RegressFLAGS.motion = 0; + SOM_LOG(sprintf('WARNING : No motion parameters for run %d, setting RegressFLAGS.motion = 0',iRUN)); + end + % + % Fatal error if motion parameters are not sufficient. + % + if ~isnumeric(parameters.data.run(iRUN).MotionParameters) | ... + parameters.data.run(iRUN).nTIME > size(parameters.data.MotionParameters,1) + SOM_LOG(sprintf('FATAL ERROR : Problem with motion parameters for run %d',iRUN)); + return + else + RegressFLAGS.motion = 1; + end + end + % Default the order. + RegressFLAGS.order = SOM.defaults.RegressFLAGS.order; + RegressFLAGS.OK = 1; + return +end + +RegressFLAGS = parameters.RegressFLAGS; + +% Default to being okay. + +RegressFLAGS.OK = 1; + +if isfield(RegressFLAGS,'prinComp') == 0 + SOM_LOG('WARNING : prinComp FLAG no set, using default of 5'); + RegressFLAGS.prinComp = SOM.defaults.RegressFLAGS.prinComp; +end + +%if isfield(RegressFLAGS,'global') == 0 +% SOM_LOG('WARNING : global FLAG no set, using default of 0'); +% RegressFLAGS.global = SOM.defaults.RegressFLAGS.global; +%end + +%if isfield(RegressFLAGS,'csf') == 0 +% SOM_LOG('WARNING : csf FLAG no set, using default of parameters.masks.csf.MaskFLAG'); +% RegressFLAGS.csf = parameters.masks.csf.MaskFLAG; +%end + +%if isfield(RegressFLAGS,'white') == 0 +% SOM_LOG('WARNING : white FLAG no set, using default of parameters.masks.white.MaskFLAG'); +% RegressFLAGS.white = parameters.masks.white.MaskFLAG; +%end + +% Check the motion parameters for each run + +for iRUN = 1:length(parameters.data.run) + if isfield(parameters.data.run(iRUN),'MotionParameters') == 0 | length(parameters.data.run(iRUN).MotionParameters) < 1 + SOM_LOG('WARNING : Motion parameters not specified'); + MotionParameters = []; + RegressFLAGS.motion = 0; + else + MotionParameters = parameters.data.run(iRUN).MotionParameters; + + if ~isnumeric(MotionParameters) | ... + parameters.data.run(iRUN).nTIME > size(MotionParameters,1) + SOM_LOG('FATAL ERROR : motion parameters missing, or mismatched, not using.'); + return + else + if isfield(RegressFLAGS,'motion') == 0 + SOM_LOG('WARNING : motion flag not there, but motion parameters present, setting flag to 1.'); + RegressFLAGS.motion = 1; + end + end + end +end + +% The regression order. + +if isfield(RegressFLAGS,'order') == 0 + SOM_LOG(sprintf('WARNING : Assuming regression order of : %s',SOM.defaults.RegressFLAGS.order)); + RegressFLAGS.order = SOM.defaults.RegressFLAGS.order; +else + if ischar(RegressFLAGS.order) == 0 + SOM_LOG('FATAL : Regression order must be a character array'); + RegressFLAGS.OK = -1; + return + end + nOrder = ''; + for iOrder = 1:length(RegressFLAGS.order) + if length(findstr(upper(RegressFLAGS.order(iOrder)),'DGCWMB')) > 0 + nOrder = [nOrder upper(RegressFLAGS.order(iOrder))]; + else + SOM_LOG(sprintf('FATAL : I don''t recognize the regression order symbol : %s',RegressFLAGS.order(iOrder))); + RegressFLAGS.OK = -1; + return + end + end + RegressFLAGS.order = nOrder; +end + +return + +% +% All done. +% diff --git a/som/SOM_CheckTimeParams.m b/som/SOM_CheckTimeParams.m new file mode 100755 index 00000000..53cb9760 --- /dev/null +++ b/som/SOM_CheckTimeParams.m @@ -0,0 +1,139 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% Validate the time parameters +% pass for SOM_PreProcessData +% +% TIME = SOM_CheckTimeParams(parameters) +% +% TIME.run(iRUN). +% TR = repetition time +% BandFLAG = 0 no band pass filter +% 1 apply bandpass filter +% TrendFLAG < 0 no linear detrending +% # use [N]-order polynomial to detrend. +% LowF = low frequency band cut +% HiF = high frequency band cut +% gentle = 0, no rolling +% 1, rolling +% padding = # time points to pad on left/right +% whichFilter = 0, use the MATLAB filter +% #, use SOM_Filter_FFT +% +% fraction = fraction of variance for principle components +% analysis. Default 1. +% +% Returne TIME.OK = 1 if all okay, else -1 if bad. +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + +% Modified Nov 8, 2011 to have nTIME be part of data.run +% structure, previously it was part of the TIME.run structure. + +function TIME = SOM_CheckTimeParams(parameters) + +global SOM + +% Figure out if parameters are okay. + +TIME = []; + +if isfield(parameters,'TIME') == 0 + SOM_LOG('WARNING : Missing TIME parameters'); +else + TIME = parameters.TIME; +end + +% Default to be all OK. + +TIME.OK = 1; + +if isfield(TIME,'run') == 0 + SOM_LOG('WARNING : Missing run specific timing information'); + SOM_LOG('WARNING : I will create using defaults for each run.'); + for iRUN = 1:length(parameters.data.run) + TIME.run(iRUN) = SOM.defaults.TIME; + end + return +end + +% If the number of runs specified by TIME.run == 1, but we have +% more runs than that specified by data.run, then we will replicate +% for each run. + +if length(TIME.run) == 1 & length(parameters.data.run) > 1 + SOM_LOG('STATUS : Replicating timing parameters for all runs.\n'); + % Make the passed the new defaults. + SOM.defaults.TIME = TIME.run(1); + TIME = rmfield(TIME,'run'); + %TIME.run = []; + for iRUN = 1:length(parameters.data.run) + TIME.run(iRUN) = SOM.defaults.TIME; + end + return +end + +% If they have sufficient then we just check each parameter to make +% sure it is present. + +for iRUN = 1:length(TIME.run) + if isfield(TIME.run(iRUN),'TR') == 0 + SOM_LOG(sprintf('WARNING : Missing TIME.run(%d).TR, using default of TR=2',iRUN)); + TIME.run(iRUN).TR = SOM.defaults.TIME.TR; + end + + if isfield(TIME.run(iRUN),'BandFLAG') == 0 + SOM_LOG(sprintf('WARNING : Missing TIME.run(%d).BandFLAG, setting to 1',iRUN)); + TIME.run(iRUN).BandFLAG = SOM.defaults.TIME.BandFLAG; + end + + if isfield(TIME.run(iRUN),'TrendFLAG') == 0 + SOM_LOG(sprintf('WARNING : Missing TIME.run(%d).TrendFLAG, setting to 1',iRUN)); + TIME.run(iRUN).TrendFLAG = SOM.defaults.TIME.TrendFLAG; + end + + if isfield(TIME.run(iRUN),'LowF') == 0 + SOM_LOG(sprintf('WARNING : Missing TIME.run(%d).LowF, setting to 0.01',iRUN)); + TIME.run(iRUN).LowF = SOM.defaults.TIME.LowF; + end + + if isfield(TIME.run(iRUN),'HiF') == 0 + SOM_LOG(sprintf('WARNING : Missing TIME.run(%d).HiF, setting to 0.10',iRUN)); + TIME.run(iRUN).HiF = SOM.defaults.TIME.HiF; + end + + if isfield(TIME.run(iRUN),'padding') == 0 + SOM_LOG(sprintf('WARNING : Missing TIME.run(%d).padding, setting to 10',iRUN)); + TIME.run(iRUN).padding = SOM.defaults.TIME.padding; + end + + if isfield(TIME.run(iRUN),'gentle') == 0 + SOM_LOG(sprintf('WARNING : Missing TIME.run(%d).gentle, setting to 1',iRUN)); + TIME.run(iRUN).gentle = SOM.defaults.TIME.gentle; + end + + if isfield(TIME.run(iRUN),'whichFilter') == 0 + SOM_LOG(sprintf('WARNING : Missing TIME.run(%d).whichFilter, setting to 1',iRUN)); + TIME.run(iRUN).whichFilter = SOM.defaults.TIME.whichFilter; + end + + if isfield(TIME.run(iRUN),'fraction') == 0 + SOM_LOG(sprintf('WARNING : Missing TIME.run(%d).fraction, setting to 1',iRUN)); + TIME.run(iRUN).fraction = SOM.defaults.TIME.fraction; + end + + if TIME.run(iRUN).LowF >= TIME.run(iRUN).HiF + SOM_LOG(sprintf('FATAL ERROR : low frequency equal or exceed high frequency cut off. run:%d',iRUN)); + TIME.OK = -1; + end + +end + +% All done. + +return diff --git a/som/SOM_CostFunction.c b/som/SOM_CostFunction.c new file mode 100755 index 00000000..32d8dd5b --- /dev/null +++ b/som/SOM_CostFunction.c @@ -0,0 +1,572 @@ +/*----------------- +% +% Copyright Robert C. Welsh, Ann Arbor, MI, 2006 +% +% A routine to calculate the cost metrix +% between U and V where U are data (nVoxels,nTime) and +% V is SOM Exemplars (nTime,nVoxels); +% +% results = SOM_CostFunction(theData,SelfOMap,[whichCOST]); +% +% Input: +% +% theData = theData(nVoxels,nTime) +% SelfOMap = SelfOMap(nTime,nSOM); +% +% Optional: +% +% whichCOST = 0 - U.V (opening angle) (default) +% 1 - |U-V| (normalized euclidean distance). +% 2 - |U-V|^2 +% 3 - Mutual Information +% Output: +% +% results = results(nVoxels,nSOM) -> Cost Function. +% +% To compile do: +% +% mex [-DSOMDEBUG] [-DSOMDEBUG2] SOM_CostFunction.c +% +% where -DSOMDEBUG[2] is a debug flag to the compiler. +% +% +% -Robert Welsh, 2006-12-12. +% +%------------------*/ + +#include +#include "mex.h" + +/*// Define a ^2 function to use in Euclidean Distance to avoid using math routine "pow", it's too slow!*/ +#define SQR(a) (a*a) + +#define EPS 1e-10 + +/* + Results area - make it global so we can access it for multiple calls. +*/ + +static mxArray *resultsMX=NULL; + +static mxArray *theDataTMX=NULL; + +static double *timeSeries=NULL; + +/* + Exit routine - need to use "clear SOM_CostFunction" to free up the memory. +*/ + +static void SOM_ExitCost(void) +{ + + mexPrintf("SOM_ExitCost has been called.\n"); + if (resultsMX != NULL) + { + mexPrintf("Destroying persistent\n"); + mxDestroyArray(resultsMX); + resultsMX = NULL; + } + if (theDataTMX != NULL) + { + mexPrintf("Destroying persistent 'theDataTMX'\n"); + mxDestroyArray(theDataTMX); + theDataTMX = NULL; + } + if (timeSeries != NULL) + { + mexPrintf("Removing 'timeSeries'\n"); + mxFree(timeSeries); + timeSeries = NULL; + } +} + +/* + + Routine to calculate the Mutual Inforation Cost Function. + Based loosely on some code from Luis Hernandez. + + *** WARNING *** Mutual Information calculation is really slow!!! + +*/ + +float SOM_MutualInformation(double data[], double som[], int npnts) +{ + + int iPnt; + + double minx, maxx, deltax, miny, maxy, deltay; + + int nx, nxx; + + int iX, iY; + + double MI; + + /* Dynamically create the histograms: joint, x, and y */ + + double *JH, *HX, *HY; + + double histIncr; + + /* Make sure we have enough data points to histogram */ + + if (npnts < 2) + { + mexPrintf("The number of times points < 2, silly.\n"); + mexErrMsgTxt("Aborting."); + }; + + /* Ok, determine limits of histograms for calculating entropy */ + + minx = 1e9; + miny = 1e9; + maxx = -1e9; + maxy = -1e9; + + /* Find the limits of the histograms. */ + + for (iPnt = 0;iPnt < npnts;iPnt++) + { + minx = ( (minxdata[iPnt]) ? maxx : data[iPnt] ); + maxy = ( (maxx>som[iPnt]) ? maxy : som[iPnt] ); + } + + /* Determine number of elements in each histogram direction */ + + nx = (int) ( pow( (float)npnts , (float)(1./3.) ) + .5); + + deltax = (maxx-minx)/(nx-1); + deltay = (maxy-miny)/(nx-1); + + /* now add a bin for underflows and overflows */ + + nxx = nx + 2; + + if ( nx < 2 ) + { + mexPrintf("\nSOM_CostFunction.c:\n Histogram size needs to be at least 2x2\n\n"); + mexErrMsgTxt("Aborting."); + } + + /* Request memory from matlab heap for histograms */ + + JH = mxCalloc((nxx*nxx),sizeof(double)); + HX = mxCalloc(nxx,sizeof(double)); + HY = mxCalloc(nxx,sizeof(double)); + + /* Accumulate the histograms, use the following + for the histogram increment, that way it will be + unit normalized already */ + + histIncr = (double) (1.0/( (double) npnts)); + + for (iPnt = 0; iPnt < npnts; iPnt ++) + { + /* Calculate index and min and max it.*/ + + iX = (int) ((data[iPnt]-minx)/deltax + 1.5); + iY = (int) ((som[iPnt]-miny)/deltay + 1.5); + +#if SOMDEBUG2 + mexPrintf("%d %d\n",iX,iY); +#endif + + iX = (iX < 0) ? 0 : iX; + iY = (iY < 0) ? 0 : iY; + + iX = (iX > nx+1) ? nx+1 : iX; + iY = (iY > nx+1) ? nx+1 : iY; + + HX[iX] += histIncr; + HY[iY] += histIncr; + JH[iX*nxx+iY] += histIncr; + } + +#if SOMDEBUG2 + for (iX = 0; iX <= nx+1; iX ++) + { + for (iY = 0; iY <= nx+1 ; iY++) + mexPrintf("%f ",JH[iX*nxx+iY]); + mexPrintf("\n"); + } + mexPrintf("\n"); +#endif + + /* Now calculate the entropy, include overflow and underflow bins */ + + MI = 0; + + for (iX = 0; iX < nxx; iX ++) + for (iY = 0; iY < nxx ; iY++) + if ( HX[iX] > 0 && HY[iY] > 0 ) + { +#if SOMDEBUG2 + mexPrintf("%f %f %f %d %d\n",JH[iX*nxx+iY],HX[iX],HY[iY],iX,iY); +#endif + MI += JH[iX*nxx+iY]*log( ( (JH[iX*nxx+iY]+EPS)/HX[iX]/HY[iY] ) ); + } + +#if SOMDEBUG + mexPrintf("min/max : %f %f %f\n",minx,maxx,deltax); + mexPrintf("min/max : %f %f %f\n",miny,maxy,deltay); + mexPrintf("\n"); +#endif + + /* Free up the memory */ + + mxFree(JH); + mxFree(HX); + mxFree(HY); + +#if SOMDEBUG + mexPrintf("MI : %f\n",MI); +#endif + + return MI; + +} + +void SOM_CostFunctionUsage() +{ + mexPrintf("\nUsage : results = SOM_CostFunction(theData,SelfOMap,whichCOST)\n\n"); + mexPrintf(" theData -> theData(nVoxels,nTime)\n"); + mexPrintf(" SelfOMap -> theData(nTime,nSOM)\n"); + mexPrintf(" whichCOST -> 0=U.V, 1=|U-V|, 2=|U-V|^2, 3=Mutual Information\n\n"); +} + +/* + This is the main function that is called by MATLAB + + The routine will return a matrix of the cost-function evaluation. + + All of the cost functions are contained in here, except mutual information + which is lengthy code-wise and hence is above. + +*/ + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + + mxArray *theDataMX; + mxArray *SelfOMapMX; + + /*mxArray *theDataTMX; /* Needed for mutual information calculation.*/ + + mxArray *tmpMX[2]; + + mxArray *whichCOSTMX; + + double *whichCOST; + + double *results; + + double *theData; + double *SelfOMap; + + double *theDataT; /* Needed for mutual information */ + + int nTime; + int nTimeSOM; + + int nSOM; + int nVoxels; + + int iSOM; + int iTime; + int iVoxel; + + int i1; + + int iPnt; + int COSTFLAG; + + double tmp1; + double tmp2; + + int idx1; + int idx2; + + /*// Now the function.*/ + + /* Register the exit function */ + + mexAtExit(SOM_ExitCost); + + /*// Did they pass in enough arguements?*/ + + COSTFLAG = 0; + + if (nrhs < 2 | nrhs > 3) + { + SOM_CostFunctionUsage(); + mexErrMsgTxt("Error, wrong number of input parameters."); + } + + /*// get the cost function option. 0 = U.V, 1 = |U-V|*/ + + if (nrhs == 3) + { + whichCOSTMX = prhs[2]; + if (mxGetM(whichCOSTMX) != 1 | mxGetN(whichCOSTMX) != 1) + { + SOM_CostFunctionUsage(); + mexErrMsgTxt("Error, 'whichCOST' must be a scaler."); + } + whichCOST = mxGetPr(whichCOSTMX); + switch ((int) whichCOST[0]) + { + case 3: + { +#if SOMDEBUG + mexPrintf("Using mutual information cost function.\n"); +#endif + COSTFLAG=3; + } + break; + case 2: + { +#if SOMDEBUG + mexPrintf("Using |U-V|^2 cost function.\n"); +#endif + COSTFLAG=2; + } + break; + case 1: + { +#if SOMDEBUG + mexPrintf("Using |U-V| cost function.\n"); +#endif + COSTFLAG=1; + } + break; + default: + { +#if SOMDEBUG + mexPrintf("Using U.V cost function.\n"); +#endif + COSTFLAG=0; + } + break; + } + } + + /*// Get the pointers to the data and self-organizing map.*/ + + theDataMX = prhs[0]; + SelfOMapMX = prhs[1]; + + /*// Now get the dimensions of each.*/ + + nVoxels = mxGetM(theDataMX); + nTime = mxGetN(theDataMX); + + nTimeSOM = mxGetM(SelfOMapMX); + nSOM = mxGetN(SelfOMapMX); + + theData = mxGetPr(theDataMX); + + SelfOMap = mxGetPr(SelfOMapMX); + + /*// Make sure the dimensions are good.*/ + if (nTime != nTimeSOM) + { + SOM_CostFunctionUsage(); + mexPrintf("Error, time points in 'SelfOMap' and time points of 'theData' don't match!\n"); + mexErrMsgTxt("Aborting."); + }; + +#if SOMDEBUG + mexPrintf("theData(%d,%d), SelfOMap(%d,%d)\n",nVoxels,nTime,nTimeSOM,nSOM); + mexPrintf("number of returns : %d\n",nlhs); +#endif + + /*// Results returned in a new matrix.*/ + + if (resultsMX == NULL) + { + mexPrintf("resultsMX is unknown\n"); + resultsMX = mxCreateDoubleMatrix(nVoxels,nSOM,mxREAL); + mexMakeArrayPersistent(resultsMX); + mexPrintf("Created persistent resultsMX array.\n"); + timeSeries = mxMalloc(sizeof(double)*nTime); + mexMakeMemoryPersistent(timeSeries); + mexPrintf("Created 'timeSeries' array\n"); + } + else + { + /* Check it's size */ + if (mxGetM(resultsMX) != nVoxels || mxGetN(resultsMX) != nSOM) + { + mexPrintf("resultsMX is known, %d\n",resultsMX); + mexPrintf("but, wrong size and must destroy persistent array and recreate.\n"); + mxDestroyArray(resultsMX); + resultsMX = mxCreateDoubleMatrix(nVoxels,nSOM,mxREAL); + mexMakeArrayPersistent(resultsMX); + mxFree(timeSeries); + timeSeries = mxMalloc(sizeof(double)*nTime); + mexMakeMemoryPersistent(timeSeries); + mexPrintf("Recreated 'timeSeries' array\n"); + } + } + + plhs[0] = resultsMX; + +#if SOMDEBUG + mexPrintf("Created output array of size %d x %d\n",mxGetM(plhs[0]),mxGetN(plhs[0])); +#endif + + /*// get the pointer to the data area of the output array.*/ + + results = mxGetPr(plhs[0]); + + /*// Now run the cost function.*/ + +#if SOMDEBUG + mexPrintf("COSTFLAG:%d\n",COSTFLAG); +#endif + switch (COSTFLAG) + { + case 0: + /* + simple U.V multiplication + */ +#if SOMDEBUG + mexPrintf("U.V\n"); +#endif + tmpMX[0] = theDataMX; + tmpMX[1] = SelfOMapMX; + /* + Just let matlab do the calculation + In a way this is silly but at least + then the interface for SOM_CostFunction + is the same. When we do the multiplication + ourselves it is too slow. + */ + mexCallMATLAB(1,&plhs[0],2,tmpMX,"*"); + break; + case 1: + /* + Euclidean distance. + */ +#if SOMDEBUG + mexPrintf("|U-V|\n"); +#endif + for (iVoxel = 0; iVoxel < nVoxels; iVoxel++) + { + /* Pull the time series for this voxel since we keep using it */ + for ( iTime = 0; iTime < nTime; iTime++) + timeSeries[iTime] = theData[iVoxel+iTime*nVoxels]; + /* */ + for (iSOM = 0; iSOM < nSOM; iSOM++) + { + idx1 = iSOM*nTime; + idx2 = nVoxels*iSOM; + tmp1 = 0; + for ( iTime = 0; iTime < nTime; iTime++) + { +#if SOMDEBUG2 + mexPrintf("|%f - %f|^2\n",theData[iVoxel+iTime*nVoxels],SelfOMap[idx1+iTime]); +#endif + tmp2 = timeSeries[iTime]-SelfOMap[idx1+iTime]; + tmp1 += tmp2*tmp2; + } + /* Now store the answer*/ + results[idx2+iVoxel] = sqrt(tmp1); +#if SOMDEBUG2 + mexPrintf("\n"); +#endif + } + } + break; + case 2: + /* + Euclidean distance squared. + */ +#if SOMDEBUG + mexPrintf("|U-V|^2\n"); +#endif + for (iVoxel = 0; iVoxel < nVoxels; iVoxel++) + { + /* Pull the time series for this voxel since we keep using it */ + for ( iTime = 0; iTime < nTime; iTime++) + timeSeries[iTime] = theData[iVoxel+iTime*nVoxels]; + /* */ + for (iSOM = 0; iSOM < nSOM; iSOM++) + { + idx1 = iSOM*nTime; + idx2 = nVoxels*iSOM; + tmp1 = 0; + for ( iTime = 0; iTime < nTime; iTime++) + { +#if SOMDEBUG2 + mexPrintf("|%f - %f|^2\n",theData[iVoxel+iTime*nVoxels],SelfOMap[idx1+iTime]); +#endif + tmp2 = timeSeries[iTime]-SelfOMap[idx1+iTime]; + tmp1 += tmp2*tmp2; + } + /* Now store the answer*/ + results[idx2+iVoxel] = tmp1; + +#if SOMDEBUG2 + mexPrintf("\n"); +#endif + } + } + break; + case 3: + /* + Mutual Information. + */ +#if SOMDEBUG + mexPrintf("M.I.\n"); +#endif + /* Transpose the data matrix to be able to index it via c like indexing.*/ + + /* Does the transpose area already exist, if so the right size. If so just use + the allocated space. This helps memory management.*/ + + if (theDataTMX == NULL) + { + mexPrintf("theDataTMX is unknown\n"); + theDataTMX = mxCreateDoubleMatrix(nVoxels,nSOM,mxREAL); + mexMakeArrayPersistent(theDataTMX); + mexPrintf("Created persistent theDataTMX array.\n"); + } + else + { + if ( mxGetM(theDataTMX) != mxGetN(theDataMX) || mxGetN(theDataTMX) != mxGetM(theDataMX) ) + { + mexPrintf("theDataTMX is known, but needs redefinition: %d\n",theDataTMX); + mxDestroyArray(theDataTMX); + theDataTMX = mxCreateDoubleMatrix(nVoxels,nSOM,mxREAL); + mexMakeArrayPersistent(theDataTMX); + } + } + + /* Now transpose it so we can look up time-series data for calculation of M.I. between + data and SOM */ + + mexCallMATLAB(1,&theDataTMX,1,&theDataMX,"'"); + theDataT = mxGetPr(theDataTMX); + for (iVoxel = 0; iVoxel < nVoxels; iVoxel++) + { + for (iSOM = 0; iSOM < nSOM; iSOM++) + { + results[nVoxels*iSOM+iVoxel] = SOM_MutualInformation(&theDataT[iVoxel*nTime],&SelfOMap[iSOM*nTime],nTime); +#if SOMDEBUG2 + mexPrintf("\n"); +#endif + } + } + break; + default: + SOM_CostFunctionUsage(); + mexErrMsgTxt("Error - whichCOST unknown"); + } +} + + +/*// All done.*/ + diff --git a/som/SOM_CostFunction.m b/som/SOM_CostFunction.m new file mode 100755 index 00000000..f6fe669d --- /dev/null +++ b/som/SOM_CostFunction.m @@ -0,0 +1,46 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2005-2007 +% +% +% function results = SOM_CostFunction(slot,theSOM) +% +% IT IS ASSUMED THAT THE DATA AND SOM ARE UNIT NORMED +% +% global SOMMem +% SOMMem.theData = theData(nSpace,nTime); +% +% theSOM = theSOM(nTime,nSOM); +% +% idx = array of indices for best matching SOM vector. +% wts = cos(angle) or euclidean distance. +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function SOM_CostFunction(slot,theSOM,CF); + +global SOMMem + +switch CF + case 0 + SOMMem{slot}.dataBySOM = SOMMem{slot}.theData*theSOM; + case 1 + SOMMem{slot}.dataBySOM = 2*(1.-SOMMem{slot}.theData*theSOM); + case 2 + SOMMem{slot}.dataBySOM = 4*(1.-SOMMem{slot}.theData*theSOM).^2; + case 3 + fprintf('Too slow to do mutual information at the moment !\n'); + SOMMem{slot}.dataBySOM = SOM_CostFunctionMI(SOMMem{slot}.theData,theSOM); + return +end + +% Free up memory - hopefully. + +clear theSOM + +return + +% +% All done. +% diff --git a/som/SOM_CostFunctionMI.c b/som/SOM_CostFunctionMI.c new file mode 100755 index 00000000..d1511369 --- /dev/null +++ b/som/SOM_CostFunctionMI.c @@ -0,0 +1,494 @@ +/*----------------- +% +% Copyright Robert C. Welsh, Ann Arbor, MI, 2006 +% +% A routine to calculate the Mutual Information Cost Metric +% between U and V, where U are data (nVoxels,nTime) and +% V is SOM Exemplars (nTime,nVoxels); +% +% results = SOM_CostFunctionMI(theData,SelfOMap); +% +% Input: +% +% theData = theData(nVoxels,nTime) +% SelfOMap = SelfOMap(nTime,nSOM); +% +% This will explicitly calculate the Mutual Information +% as the cost function. +% +% Output: +% +% results = results(nVoxels,nSOM) -> Cost Function. +% +% To compile do: +% +% mex [-DSOMDEBUG] [-DSOMDEBUG2] SOM_CostFunctionMI.c +% +% where -DSOMDEBUG[2] is a debug flag to the compiler. +% +% +% -Robert Welsh, 2006-12-12. +% +%------------------*/ + +#include +#include "mex.h" + + +#define EPS 1e-10 + +/* + Results area - make it global so we can access it for multiple calls. +*/ + +static mxArray *resultsMX=NULL; + +static int nPntsSaved=0; + +/*static mxArray *theDataTMX=NULL;*/ + + /* Dynamically create the histograms: joint, x, and y */ + +static double *JH=NULL, *HX=NULL, *HY=NULL; + +/* + Exit routine - need to use "clear SOM_CostFunction" to free up the memory. +*/ + +static void SOM_ExitCost(void) +{ + + mexPrintf("SOM_ExitCost has been called.\n"); + if (resultsMX != NULL) + { + mexPrintf("Destroying persistent\n"); + mxDestroyArray(resultsMX); + resultsMX = NULL; + } + /* + if (theDataTMX != NULL) + { + mexPrintf("Destroying persistent 'theDataTMX'\n"); + mxDestroyArray(theDataTMX); + theDataTMX = NULL; + } +*/ + if ( JH != NULL ) + { + mexPrintf("Destroying persistent 'JH'\n"); + mxFree(JH); + JH=NULL; + } + if ( HX != NULL ) + { + mexPrintf("Destroying persistent 'HX'\n"); + mxFree(HX); + HX=NULL; + } + if ( HY != NULL ) + { + mexPrintf("Destroying persistent 'HY'\n"); + mxFree(HY); + HY=NULL; + } +} + +/* + + Routine to calculate the Mutual Inforation Cost Function. + Based loosely on some code from Luis Hernandez. + + *** WARNING *** Mutual Information calculation is really slow!!! + +*/ + +double SOM_MutualInformation(double data[], double som[], int npnts) +{ + + int iPnt; + + double minx, maxx, deltax, miny, maxy, deltay; + + int nx, nxx; + + int iX, iY; + + double MI; + + double histIncr; + + /* Make sure we have enough data points to histogram */ + + if (npnts < 2) + { + mexPrintf("The number of times points < 2, silly.\n"); + mexErrMsgTxt("Aborting."); + }; + + /* Ok, determine limits of histograms for calculating entropy */ + + minx = 1e9; + miny = 1e9; + maxx = -1e9; + maxy = -1e9; + + /* Find the limits of the histograms. */ + + for (iPnt = 0;iPnt < npnts;iPnt++) + { + minx = ( (minxdata[iPnt]) ? maxx : data[iPnt] ); + maxy = ( (maxx>som[iPnt]) ? maxy : som[iPnt] ); + } + +#if SOMDEBUG3 + mexPrintf("%f %f %f %f\n",minx,maxx,miny,maxy); +#endif + + /* Determine number of elements in each histogram direction */ + + nx = (int) ( pow( (float)npnts , (float)(1./3.) ) + .5); + + deltax = (maxx-minx)/(nx-1); + deltay = (maxy-miny)/(nx-1); + + /* now add a bin for underflows and overflows */ + + nxx = nx + 2; + + if ( nx < 2 ) + { + mexPrintf("\nSOM_CostFunction.c:\n Histogram size needs to be at least 2x2\n\n"); + mexErrMsgTxt("Aborting."); + } + + + /* If previously created can be reuse? */ + + if ( nPntsSaved != npnts ) + { + if ( JH != NULL) mxFree(JH); + if ( HX != NULL) mxFree(HX); + if ( HY != NULL) mxFree(HY); + JH = NULL; + HX = NULL; + HY = NULL; + } + + /* Request memory from matlab heap for histograms */ + + if ( JH == NULL) + { + JH = mxCalloc((nxx*nxx),sizeof(double)); + mexMakeMemoryPersistent(JH); + } + if ( HX == NULL ) + { + HX = mxCalloc(nxx,sizeof(double)); + mexMakeMemoryPersistent(HX); + } + if ( HY == NULL ) + { + HY = mxCalloc(nxx,sizeof(double)); + mexMakeMemoryPersistent(HY); + } + + /* Accumulate the histograms, use the following + for the histogram increment, that way it will be + unit normalized already */ + + histIncr = (double) (1.0/( (double) npnts)); + + for (iPnt = 0; iPnt < npnts; iPnt ++) + { + /* Calculate index and min and max it.*/ + + iX = (int) ((data[iPnt]-minx)/deltax + 1.5); + iY = (int) ((som[iPnt]-miny)/deltay + 1.5); + +#if SOMDEBUG4 + mexPrintf("%+f:%d \t %+f:%d\n",data[iPnt],iX,som[iPnt],iY); +#endif + + iX = (iX < 0) ? 0 : iX; + iY = (iY < 0) ? 0 : iY; + + iX = (iX > nx+1) ? nx+1 : iX; + iY = (iY > nx+1) ? nx+1 : iY; + + HX[iX] += histIncr; + HY[iY] += histIncr; + JH[iX*nxx+iY] += histIncr; + } + +#if SOMDEBUG2 + for (iX = 0; iX <= nx+1; iX ++) + { + for (iY = 0; iY <= nx+1 ; iY++) + mexPrintf("%f ",JH[iX*nxx+iY]); + mexPrintf("\n"); + } + mexPrintf("\n"); +#endif + + /* Now calculate the entropy, include overflow and underflow bins */ + + MI = 0; + + for (iX = 0; iX < nxx; iX ++) + for (iY = 0; iY < nxx ; iY++) + if ( HX[iX] > 0 && HY[iY] > 0 ) + { +#if SOMDEBUG2 + mexPrintf("%f %f %f %d %d\n",JH[iX*nxx+iY],HX[iX],HY[iY],iX,iY); +#endif + MI += JH[iX*nxx+iY]*log( ( (JH[iX*nxx+iY]+EPS)/HX[iX]/HY[iY] ) ); + } + +#if SOMDEBUG2 + mexPrintf("min/max : %f %f %f\n",minx,maxx,deltax); + mexPrintf("min/max : %f %f %f\n",miny,maxy,deltay); + mexPrintf("\n"); +#endif + + /* Free up the memory */ + + /* mxFree(JH); + mxFree(HX); + mxFree(HY); + */ +#if SOMDEBUG2 + mexPrintf("MI : %f\n",MI); +#endif + + return MI; + +} + +void SOM_CostFunctionUsage() +{ + mexPrintf("\nUsage : results = SOM_CostFunctionMI(theData,SelfOMap)\n\n"); + mexPrintf(" theData -> theData(nVoxels,nTime)\n"); + mexPrintf(" SelfOMap -> theData(nTime,nSOM)\n"); +} + +/* + This is the main function that is called by MATLAB + + The routine will return a matrix of the cost-function evaluation. + + All of the cost functions are contained in here, except mutual information + which is lengthy code-wise and hence is above. + +*/ + +void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) +{ + + mxArray *theDataTMX; + mxArray *theDataMX; + mxArray *SelfOMapMX; + + double *results; + + double *theData; + double *SelfOMap; + + double *theDataT; /* Needed for mutual information */ + + int nTime; + int nTimeSOM; + + int nSOM; + int nVoxels; + + int iSOM; + int iTime; + int iVoxel; + + int i1; + + int iPnt; + int COSTFLAG; + + double tmp1; + double tmp2; + + int idx1; + int idx2; + + /*// Now the function.*/ + + /* Register the exit function */ + + mexAtExit(SOM_ExitCost); + + /*// Did they pass in enough arguements?*/ + + if (nrhs != 2) + { + SOM_CostFunctionUsage(); + mexErrMsgTxt("Error, wrong number of input parameters."); + } + + /*// Get the pointers to the data and self-organizing map.*/ + + theDataMX = prhs[0]; + SelfOMapMX = prhs[1]; + + /*// Now get the dimensions of each.*/ + + nVoxels = mxGetM(theDataMX); + nTime = mxGetN(theDataMX); + + nTimeSOM = mxGetM(SelfOMapMX); + nSOM = mxGetN(SelfOMapMX); + + theData = mxGetPr(theDataMX); + + SelfOMap = mxGetPr(SelfOMapMX); + + /*// Make sure the dimensions are good.*/ + if (nTime != nTimeSOM) + { + SOM_CostFunctionUsage(); + mexPrintf("Error, time points in 'SelfOMap' and time points of 'theData' don't match!\n"); + mexErrMsgTxt("Aborting."); + }; + +#if SOMDEBUG + mexPrintf("theData(%d,%d), SelfOMap(%d,%d)\n",nVoxels,nTime,nTimeSOM,nSOM); + mexPrintf("number of returns : %d\n",nlhs); +#endif + + /*// Results returned in a new matrix.*/ + + if (resultsMX == NULL) + { + mexPrintf("resultsMX is unknown\n"); + resultsMX = mxCreateDoubleMatrix(nVoxels,nSOM,mxREAL); + mexMakeArrayPersistent(resultsMX); + mexPrintf("Created persistent resultsMX array.\n"); + } + else + { + /* Check it's size */ + if (mxGetM(resultsMX) != nVoxels || mxGetN(resultsMX) != nSOM) + { + mexPrintf("resultsMX is known, %d\n",resultsMX); + mexPrintf("but, wrong size and must destroy persistent array and recreate.\n"); + mxDestroyArray(resultsMX); + resultsMX = mxCreateDoubleMatrix(nVoxels,nSOM,mxREAL); + mexMakeArrayPersistent(resultsMX); + } + } + + plhs[0] = resultsMX; + +#if SOMDEBUG + mexPrintf("Created output array of size %d x %d\n",mxGetM(plhs[0]),mxGetN(plhs[0])); +#endif + + /*// get the pointer to the data area of the output array.*/ + + results = mxGetPr(plhs[0]); + + /*// Now run the cost function.*/ + + /* + Mutual Information. + */ +#if SOMDEBUG + mexPrintf("M.I.\n"); +#endif + /* Transpose the data matrix to be able to index it via c like indexing.*/ + + /* Does the transpose area already exist, if so the right size. If so just use + the allocated space. This helps memory management.*/ + + /* if (theDataTMX == NULL) + { + mexPrintf("theDataTMX is unknown\n"); + theDataTMX = mxCreateDoubleMatrix(nTime,nVoxels,mxREAL); + mexMakeArrayPersistent(theDataTMX); + mexPrintf("Created persistent theDataTMX(%d,%d) array.\n",mxGetM(theDataTMX),mxGetN(theDataTMX)); + } + else + { + if ( mxGetM(theDataTMX) != mxGetN(theDataMX) || mxGetN(theDataTMX) != mxGetM(theDataMX) ) + { + mexPrintf("theDataTMX (%d,%d) ~= (%d,%d)' is known, but needs redefinition: %d\n", + mxGetM(theDataTMX), + mxGetN(theDataTMX), + mxGetM(theDataMX), + mxGetN(theDataMX), + theDataTMX); + mxDestroyArray(theDataTMX); + mexPrintf("Destroyed.\n"); + theDataTMX = mxCreateDoubleMatrix(nTime,nVoxels,mxREAL); + mexPrintf("Created.\n"); + mexMakeArrayPersistent(theDataTMX); + mexPrintf("Made persistent.\n"); + } + } + + */ + /* Now transpose it so we can look up time-series data for calculation of M.I. between + data and SOM */ + + theDataTMX = mxCreateDoubleMatrix(nTime,nVoxels,mxREAL); + /*mexPrintf("Created theDataTMX(%d,%d) array.\n",mxGetM(theDataTMX),mxGetN(theDataTMX));*/ + + mexCallMATLAB(1,&theDataTMX,1,&theDataMX,"'"); + +#if SOMDEBUG + mexPrintf("Transposed with result of (%d,%d)\n",mxGetM(theDataTMX),mxGetN(theDataTMX)); +#endif + theDataT = mxGetPr(theDataTMX); + for (iVoxel = 0; iVoxel < nVoxels; iVoxel++) + { + for (iSOM = 0; iSOM < nSOM; iSOM++) + { + results[nVoxels*iSOM+iVoxel] = SOM_MutualInformation(&theDataT[iVoxel*nTime],&SelfOMap[iSOM*nTime],nTime); + +#if SOMDEBUG2 + mexPrintf("\n"); +#endif + + } + } +#if SOMDEBUG + mexPrintf("Finished calculation theDataTMX(%d,%d)\n",mxGetM(theDataTMX),mxGetN(theDataTMX)); +#endif + /* + mexPrintf("Destroying persistent 'theDataTMX'\n"); + mxDestroyArray(theDataTMX); + */ + theDataTMX = NULL; + + /* + if ( JH != NULL ) + { + mexPrintf("Destroying persistent 'JH'\n"); + mxFree(JH); + JH=NULL; + } + if ( HX != NULL ) + { + mexPrintf("Destroying persistent 'HX'\n"); + mxFree(HX); + HX=NULL; + } + if ( HY != NULL ) + { + mexPrintf("Destroying persistent 'HY'\n"); + mxFree(HY); + HY=NULL; + } + */ +} + + +/*// All done.*/ + diff --git a/som/SOM_CostFunctionMI.m b/som/SOM_CostFunctionMI.m new file mode 100755 index 00000000..f8eef983 --- /dev/null +++ b/som/SOM_CostFunctionMI.m @@ -0,0 +1,54 @@ +function results = SOM_CostFunctionMI(theData, theSOM) + +nVoxels = size(theData,1); +nTime = size(theData,2); +nSOM = size(theSOM,2); + +minx = min(theData,[],2); +miny = min(theSOM,[],1); +minx_rep = repmat(minx,1,nTime); +miny_rep = repmat(miny,nTime,1); +maxx = max(theData,[],2); +maxy = max(theSOM,[],1); + +nx = floor(nTime^(1/3)+.5); +deltax = (maxx-minx)/(nx-1); +deltay = (maxy-miny)/(nx-1); +deltax_rep = repmat(deltax,1,nTime); +deltay_rep = repmat(deltay,nTime,1); + +iX = floor((theData-minx_rep)./deltax_rep+1.5); +iY = floor((theSOM-miny_rep)./deltay_rep+1.5); + +histIncr = 1/nTime; + +HX = zeros(nVoxels,nx); +HY = zeros(nSOM,nx); +for ii=1:nx + HX(:,ii) = sum(iX==ii,2)*histIncr; + HY(:,ii) = sum(iY==ii,1)*histIncr; +end +clear deltax +clear deltax_rep +clear maxx +clear minx +clear minx_rep +%keyboard; +%tic +for ii=1:nx + eval(sprintf('HY_rep%d = log(repmat(HY(:,%d)'',nVoxels,1));',ii,ii)); +end +%HY_rep = log(repmat(HY(:,1)',nVoxels,1)); +results = zeros(nVoxels,nSOM); +for ii=1:nx + HX_rep = repmat(HX(:,ii),1,nSOM); + HX_rep = log(HX_rep); + for jj=1:nx + JH = single(iX==ii)*histIncr*(iY==jj); + eval(sprintf('results = results + JH.*(log((JH+1e-10))-HX_rep-HY_rep%d);',jj)); + end + ii +end +%toc + +%keyboard; \ No newline at end of file diff --git a/som/SOM_CreateMask.m b/som/SOM_CreateMask.m new file mode 100755 index 00000000..cffb9dc9 --- /dev/null +++ b/som/SOM_CreateMask.m @@ -0,0 +1,182 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2005 +% +% Routine to create analysis mask. +% +% function [hdr, results, analyzeFMT] = SOM_CreateMask(P) +% +% P - a list of files to create the mask from. +% +% hdr - Analyze hdr for the mask. +% results - a binary image of the mask +% analyzeFMT - a flag, 1 if analyze used, 0 if plain mat files. +% +% If you are using plain files, then make sure that the only +% item contained in the file is a single volume time point. +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function [hdr, results, analyzeFMT] = SOM_CreateMask(P) + +global SOM + +if ~isfield(SOM,'maskThresh') + SOM.maskThresh = .125; +else + if SOM.maskThresh > 1; + SOM.maskThresh = 1/SOM.maskThresh; + fprintf('Making SOM.maskThresh < 1 : %f\n',SOM.maskThresh); + end +end + +fprintf('Create mask...\n'); + +% Determine whether to use spm or read in mat files? + +[fPath fName fExt] = fileparts(P(1,:)); + +if strcmp(strtrim(lower(fExt)),'.img') + analyzeFMT=1 +else + if (strcmp(strtrim(lower(fExt)),'.nii')) + analyzeFMT=2; + else + analyzeFMT=0; + end +end + +if analyzeFMT == 1 + som_mask = spm_read_vols(spm_vol(P(1,:))); +else + if analyzeFMT == 2 + mtx = spm_read_vols(spm_vol(P(1,:))); + som_mask = ones(size(mtx,1),size(mtx,2),size(mtx,3)); + else + tmpVol = load(P(1,:)); + fldNm = fieldnames(tmpVol); + if length(fldNm) ~= 1 + fprintf('\nError - the mat file has more than one variable.\n'); + fprintf('File : %s\n',P(1,:)); + hdr = []; + results = []; + analyzeFMT = []; + return + end + som_mask = getfield(tmpVol,fldNm{1}); + end +end +volSIZE = size(som_mask); + +som_mask = ones(size(som_mask)); + +spm('defaults','fmri'); +global defaults; + +if analyzeFMT == 2 + V = spm_vol(P(1,:)); + for iV = 1:size(V,1) + vol = spm_read_vols(V(iV)); + t(iV) = spm_global(V(iV)); + som_mask = som_mask.*(vol>(defaults.mask.thresh * t(iV))); + % + % Check to see if the volumes being read are all the same size. + % + if any(volSIZE - size(vol)) + fprintf('\nVolumes are of different size!.\n'); + fprintf('%s\n',P(iP,:)); + hdr = []; + results = []; + analyzeFMT= []; + return + end + clear vol; + end + +else + for iP = 1:size(P,1) + fprintf('\b\b\b%03d',iP) + if analyzeFMT == 1 + vol = spm_read_vols(spm_vol(P(iP,:))); + t(iP) = spm_global(spm_vol(P(iP,:))); + else + tmpVol = load(P(iP,:)); + fldNm = fieldnames(tmpVol); + if length(fldNm) ~= 1 + fprintf('\nError - the mat file has more than one variable.\n'); + fprintf('File : %s\n',P(iP,:)); + hdr = []; + results = []; + analyzeFMT = []; + return + end + vol = getfield(tmpVol,fldNm{1}); + mvol = mean(mean(mean(vol)))*SOM.maskThresh; % Just like SPM. + t(iP) = mean(vol(find(vol>mvol))); + + end + som_mask = som_mask.*(vol>(defaults.mask.thresh * t(iP))); + % + % Check to see if the volumes being read are all the same size. + % + if any(volSIZE - size(vol)) + fprintf('\nVolumes are of different size!.\n'); + fprintf('%s\n',P(iP,:)); + hdr = []; + results = []; + analyzeFMT= []; + return + end + clear vol; + end +end + + +fprintf('\b\b\bdone\n'); + +iMask = find(som_mask); + +if analyzeFMT == 1 + hdr = spm_vol(P(1,:)); + [pn fn] = fileparts(hdr.fname); + nhdr.fname = fullfile(pn,'som_mask.img'); + nhdr.dim(1:3) = hdr.dim(1:3); + nhdr.mat = hdr.mat; + nhdr.descrip = ['SOM Created Masked based on SPM:',spm('ver')]; + if strcmp(spm('ver'),'SPM5') | strcmp(spm('ver'),'SPM8') + nhdr.dt = [4 0]; + else + nhdr.dim(4) = 4; + end + spm_write_vol(nhdr,som_mask); + hdr = nhdr; +else + if analyzeFMT == 2 + hdr = spm_vol(P(1,:)); + hdr = hdr(1); + [pn fn] = fileparts(hdr.fname); + nhdr.fname = fullfile(pn,'som_mask.img'); + nhdr.dim(1:3) = hdr.dim(1:3); + nhdr.mat = hdr.mat; + nhdr.descrip = ['SOM Created Masked based on SPM:',spm('ver')]; + if (strcmp(spm('ver'),'SPM5') | strcmp(spm('ver'),'SPM8')) + nhdr.dt = [4 0]; + else + nhdr.dim(4) = 4; + end + spm_write_vol(nhdr,som_mask); + hdr = nhdr; + else + + hdr = []; + save(fullfile(fPath,'som_mask'),'som_mask'); + end +end + +results = som_mask; + +clear som_mask; + +% +% All done. +% diff --git a/som/SOM_Detrend.m b/som/SOM_Detrend.m new file mode 100644 index 00000000..e726fe04 --- /dev/null +++ b/som/SOM_Detrend.m @@ -0,0 +1,65 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2011 +% +% Ann Arbor, MI +% +% +% newData = SOM_Detrend(data,polyorder); +% +% +% Input Parameters that we need for preparing the data +% +% polyorder = order of the polynomial, 0 is mean centered. +% +% data = data (space x time); +% +% This we operate on the 2nd dimension. +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_Detrend(Y,polyorder) + +% Check to see if they specified the polynomial order. + +if nargin == 1 + polyorder = 0; +end + +% +% If polyorder = 0 then we are just mean centering the data. +% +% Okay they specified something, and actually we only recommend +% linear detrending due to the definition of zero in time. +% + +X = zeros(polyorder+1,size(Y,2)); + +% This handles the mean. + +X(1,:) = 1; + +% Now the other terms. + +for iP = 1:polyorder + X(iP+1,:) = ([1:size(X,2)].^iP); +end + +% We solve the GLM that is written in matrix form as +% +% Y = Beta X +% +% Beta = space x regressor +% X = regressor x time +% +% Beta = Y * pinv(X) +% +% and thus +% +% Yp = Y - Beta X = Y - ( Y pinv(X) ) X +% + +results = Y - ( Y * pinv(X) ) * X; + +return diff --git a/som/SOM_Ex2SC.m b/som/SOM_Ex2SC.m new file mode 100755 index 00000000..2392bcda --- /dev/null +++ b/som/SOM_Ex2SC.m @@ -0,0 +1,66 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2007 +% +% A routine to remap the indices of the voxel solutions from +% the exemplars in the full SOM to the exemplars of the +% super clustered SOM +% +% IDX = SOM_Ex2SC(SOMResults.IDX,SCResults.IDX) +% +% Input : +% +% SOMResults.IDX - the indices pointing to the exemplars +% SCResults.IDX - indices of exemplars to super-cluster +% exemplars. +% +% Output : +% +% IDX - pointers to super-cluster exemplars for +% for each voxel. +% +% +% You would use this for datamining based on ROI's. Operationally, +% you would troll the data space looking for the super-cluster +% that occupied your ROI, then you would display that super-cluster +% exemplar cost-function, or rho-value or z-map. +% +% See SOM_CostFunction +% SOM_Pearson +% SOM_Rho2Z +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function IDX = SOM_Ex2SC(IDX1,IDX2) + +IDX = []; + +% +% Make sure things are in range. +% + +nExemplars = size(IDX2); + +maxIDX = max(IDX1); + +if maxIDX > nExemplars + fprintf('The exemplar indices are bigger than the number of exemplars in the super-clusters.\n'); + return +end + +% +% Now do the remapping. +% + +IDX = 0*IDX1; + +for ii = 1:length(IDX2) + IDX(find(IDX1==ii)) = IDX2(ii); +end + +return + +% +% All done. +% \ No newline at end of file diff --git a/som/SOM_FTest.m b/som/SOM_FTest.m new file mode 100755 index 00000000..1408e7b3 --- /dev/null +++ b/som/SOM_FTest.m @@ -0,0 +1,62 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2007 +% +% +% A routine to calculate a two-sample F-Test. +% +% [FMap Nu] = SOM_Fest(DataSample1,DataSample2); +% +% Input : +% +% DataSample1 = DataSample1(nVoxels,nSubjects1) +% DataSample2 = DataSample2(nVoxels,nSubjects2) +% +% Output: +% +% FMap = TMap(nVoxels) +% Nu - Number of degree of freedom. +% +% The F-Test is calculated such that is an upper one tailed. +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function [FMap Nu] = SOM_FTest(DataSample1,DataSample2) + +FMap = []; +Nu = []; + +% Get the first group information. + +nVoxels1 = size(DataSample1,1); +nSubject1 = size(DataSample1,2); + +% Is there a second group available. + +nVoxels2 = size(DataSample2,1); +nSubject2 = size(DataSample2,2); + +if any ( [nVoxels1] - [nVoxels2] ) + fprintf('Error, you have specified data arrays with different number of voxels.\n'); + return +end + +if nSubject2 < 2 | nSubject1 < 2 + fprintf('You need to have at least 2 subjects per group.\n'); + return +end + +G1Var = var(DataSample1,[],2); +G2Var = var(DataSample2,[],2); + +G1Mu = mean(DataSample1,2); +G2Mu = mean(DataSample2,2); + +FMap = max([G1Var G2Var],[],2)./min([G1Var G2Var],[],2); + +return + +% +% All done. +% \ No newline at end of file diff --git a/som/SOM_Filter.m b/som/SOM_Filter.m new file mode 100755 index 00000000..3e9d8c78 --- /dev/null +++ b/som/SOM_Filter.m @@ -0,0 +1,233 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2005 +% +% +% A routine to bandpass filter data +% +% theData = theData(space,time) (this is the +% standard format being used in this SOM +% implementation). +% +% sample = sample period (TR in fmri language) +% +% lowFreq = low frequency cutoff +% +% highFreq = high frequency cutoff +% +% gentle = 0 - no just a hard cut, 1 - yes. +% gentle is a curve of 0.1, .5, .9, 1.0 on the rising and +% falling edges except for DC. +% = 0 to use fir2 when using the Signal Processing ToolBox +% 1 to use firpm +% +% padding = padding for filtering. (default is 10) +% +% whichFilter = 0 try to use TBX, else use SOM_Filter_FFT +% +% function [results, b, filtParms] = SOM_Filter(theData,sample,lowFreq,highFreq,gentle,padding,whichFilter) +% +% +% If the signal toolbox is present then use that to do the +% filtering. +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function [results, b, filtParms] = SOM_Filter(theData,sample,lowFreq,highFreq,gentle,padding,whichFilter) + +% Save input for posterity. + +filtParms.sample = sample; +filtParms.lowFreq = lowFreq; +filtParms.highFreq = highFreq; +filtParms.gentle = gentle; +filtParms.padding = padding; +filtParms.whichFilter = whichFilter; + +% make sure padding exists. + +if exist('padding') == 0 + SOM_LOG('STATUS : Default padding being set to 10.'); + padding = 10; +end + +% make sure "whichFilter" is existing. + +if exist('whichFilter') == 0 + SOM_LOG('STATUS : Default to trying to use Signal Processing Toolbox.'); + whichFilter = 0; +end + +% If padding is on then we need to pad the beginning and the end +% by "padding" number of average images. These are then +% removed later. + +% Make sure padding is integer and > 0. + +padding = floor(padding); + +if padding < 0 + padding = 0; +end + +theDataMean = mean(theData,2); + +theDataPad = repmat(theDataMean,1,padding); + +theData = [theDataPad theData theDataPad]; + +% Determine if firpm is present and filtfilt if whichFilter == 0 +% Get the fft of the data. + +if whichFilter + SOM_LOG('INFO : Using handmade fft filter method.'); + [results b] = SOM_Filter_FFT(theData,sample,lowFreq,highFreq,gentle); +elseif exist('firpm.m') == 2 & exist('filtfilt.m') == 2 & whichFilter == 0 + SOM_LOG('INFO : Using Signal Processing ToolBox'); + [results b] = SOM_Filter_SIGTBX(theData,sample,lowFreq,highFreq,gentle); +else + SOM_LOG('INFO : Using handmade fft filter method.'); + [results b] = SOM_Filter_FFT(theData,sample,lowFreq,highFreq,gentle); +end + +% Now trim out the padding; + +results = results(:,1+padding:end-padding); + +return + +% +% Using the Signal Processing ToolBox +% + +function [results, b] = SOM_Filter_SIGTBX(theData,sample,lowFreq,highFreq,firOpt); + +%Determine the Nyquist criterion + +nyquist = 1/sample/2; + +% How many time points. + +N = size(theData,2); + +nFilt = floor(N/3)-1; + +if nFilt < 1 + SOM_LOG('FATAL ERROR : insufficient data to actually filter.'); + results = []; + b = []; + return +end + +% Need to put in protection for +% 1) lowFreq <=0 and/or +% 2) highFreq >=nyquist. + +if lowFreq > 0 + fir2Freq = [0 lowFreq/nyquist*.999 lowFreq/nyquist ]; + fir2coef = [0 0 1 ]; +else + fir2Freq = [0 ]; + fir2coef = [1 ]; +end + +if highFreq < nyquist + fir2Freq = [fir2Freq highFreq/nyquist highFreq/nyquist*1.001 1.0 ]; + fir2coef = [fir2coef 1 0 0 ]; +else + fir2Freq = [fir2Freq 1.0 ]; + fir2coef = [fir2coef 1 ]; +end + +if firOpt == 0 + b = fir2(nFilt,[0 lowFreq/nyquist*.999 lowFreq/nyquist highFreq/nyquist ... + highFreq/nyquist*1.001 1.0],[0 0 1 1 0 0]); +else + b = firpm(nFilt,[0 lowFreq/nyquist*.999 lowFreq/nyquist highFreq/nyquist ... + highFreq/nyquist*1.001 1.0],[0 0 1 1 0 0]); +end + +% Filter the data now, forwards and backwards, using +% the transpose of data as time flows down, not across. + +results = (filtfilt(b,1,theData'))'; + +return + + +% +% Using the FFT Method. Problem with Phase? +% + +function [results, b] = SOM_Filter_FFT(theData,sample,lowFreq,highFreq,gentle) + + +%Determine the Nyquist criterion + +nyquist = 1/sample/2; + +% How big is our sample. + +N = size(theData,2); + +% Make a frequency baseline. + +deltaF = nyquist/(floor(N/2)-1); + +freq = (-floor(N/2):floor(N/2)-1)*deltaF; + +% Get the fft of the data. + +ffttheData = fftshift(fft(theData,[],2),2); + +% Find what to remove. + +fftMask = zeros(size(theData)); +maskH = zeros(size(theData)); +maskL = zeros(size(theData)); + +% Need to put in protection for +% 1) lowFreq <=0 and/or +% 2) highFreq >=nyquist. + +ifrqHi = find(abs(freq)<=highFreq); +ifrqLo = find(abs(freq)>=lowFreq); + +maskH(:,ifrqHi) = 1; +maskL(:,ifrqLo) = 1; + +fftMask = maskH.*maskL; + +% Find the transition points and roll a little. + +iup = find(diff(fftMask(1,:))>0); +idn = find(diff(fftMask(1,:))<0); + +if gentle ~= 0 + fftMask(:,iup-1) = .1; + fftMask(:,iup) = .5; + fftMask(:,iup+1) = .9; + fftMask(:,idn+2) = .1; + fftMask(:,idn+1) = .5; + fftMask(:,idn) = .9; +end + +% DC Component is saved. + +dci = floor(N/2)+1; + +fftMask(:,dci) = 1; + +% Filter and return the results - returning the real component. + +results = real(ifft(ifftshift(fftMask.*ffttheData,2),[],2)); + +b = fftMask; + +return + +% +% All done. +% + diff --git a/som/SOM_FindClosest.m b/som/SOM_FindClosest.m new file mode 100755 index 00000000..cfc4df13 --- /dev/null +++ b/som/SOM_FindClosest.m @@ -0,0 +1,112 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2005-2007 +% +% +% function results = SOM_FindClosest(theSOM) +% +% IT IS ASSUMED THAT THE DATA AND SOM ARE UNIT NORMED +% +% global SOMMem +% SOMMem.theData = theData(nSpace,nTime); +% +% theSOM = theSOM(nTime,nSOM); +% +% idx = array of indices for best matching SOM vector. +% wts = cos(angle) or euclidean distance. +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function [idx, wts, didx, dwts] = SOM_FindClosest(theSOM,slot) + +global SOM + +global SOMMem + +idx = []; +wts = []; +didx = []; +dwts = []; + +% make sure data is present in SOMMem + +if isfield(SOMMem{slot},'theData') == 0 + fprintf('Data is missing from SOMMem\n'); + return +end + +% Pre-allocate the dataBySOM, but storing it into SOM? + +if isfield(SOMMem{slot},'dataBySOM') + % Correct size? + if prod(size(SOMMem{slot}.dataBySOM) - [size(SOMMem{slot}.theData,1) size(theSOM,2)]) ~= 0 + SOMMem{slot}.dataBySOM = zeros(size(SOMMem{slot}.theData,1),size(theSOM,2)); + end +else + SOMMem{slot}.dataBySOM = zeros(size(SOMMem{slot}.theData,1),size(theSOM,2)); +end + +% Cosing(opening angle) or Euclidean Distance. + +% SOMMem{slot}.dataBySOM = SOM_CostFunction(slot,theSOM,SOM.Cost); + +% Call to SOM_CostFunction - changed, SOMMem{slot}.dataBySOM is +% filled automatically by the call. + +SOM_CostFunction(slot,theSOM,SOM.Cost); + +% Place the sorted into global to help with memory issues? + +% Think about chunking this to cut down on memory load, however, we +% would need to be careful about chunk sizes to make sure we are +% getting into redefining chunck size all of the time. + +if SOM.Cost == 0 | SOM.Cost == 3 + [SOMMem{slot}.wts SOMMem{slot}.idx] = max(SOMMem{slot}.dataBySOM,[],2); + [SOMMem{slot}.dwts SOMMem{slot}.didx] = min(SOMMem{slot}.dataBySOM,[],2); +else + [SOMMem{slot}.dwts SOMMem{slot}.didx] = max(SOMMem{slot}.dataBySOM,[],2); + [SOMMem{slot}.wts SOMMem{slot}.idx] = min(SOMMem{slot}.dataBySOM,[],2); +end + +idx = SOMMem{slot}.idx; +wts = SOMMem{slot}.wts; +didx = SOMMem{slot}.didx; +dwts = SOMMem{slot}.dwts; + +% Find the index to the closest. If cost-function = 0 +% then the closest is given by biggest number. +% If the cost-function = 1 (Euclidean distance), then +% closest is by smallest number. All other cost will use the mex +% code for calculating U.V + +% Find the smallest - which if greatly negative would be deactivation or +% decorrleation? (This is not valid for Euclidean Distance as that +% is a positive definite metric, but fill up anyway. + +% $$$ if SOM.Cost == 0 | SOM.Cost == 3 +% $$$ % Smallest opening angle and mutual information +% $$$ idx = SOMMem{slot}.idx(:,end); +% $$$ wts = SOMMem{slot}.y(:,end); +% $$$ % largest angle, least mutual information. +% $$$ didx = SOMMem{slot}.idx(:,1); +% $$$ dwts = SOMMem{slot}.y(:,1); +% $$$ else +% $$$ % smallest euclidean distance and E.D squared. +% $$$ idx = SOMMem{slot}.idx(:,1); +% $$$ wts = SOMMem{slot}.y(:,1); +% $$$ % largest seperation +% $$$ didx = SOMMem{slot}.idx(:,end); +% $$$ dwts = SOMMem{slot}.y(:,end); +% $$$ end + +% Free up memory - hopefully. + +clear theSOM + +return + +% +% All done. +% diff --git a/som/SOM_FindSCMembers.m b/som/SOM_FindSCMembers.m new file mode 100755 index 00000000..ee374dd8 --- /dev/null +++ b/som/SOM_FindSCMembers.m @@ -0,0 +1,41 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2007 +% +% +% A routine to return indices of SelfOMap exemplars +% that belong to the same supercluster as the passed index. +% +% function [results scnum] = SOM_FindSCMembers(ExemplarIDX,SCIDX) +% +% Input : +% +% ExemplarIDX = Index into SelfOMap (0<#<=nSom) +% +% SCIDX = Index map of the supercluster membership. +% +% Output: +% +% results = list of exemplars that belong to same supercluster +% scnum = supercluster number. +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function [results scnum] = SOM_FindSCMembers(ExemplarIDX,SCIDX) + +if ExemplarIDX < 1 || ExemplarIDX > length(SCIDX) + fprintf('ExemplarIDX is out of range.\n'); + results = []; + return +end + +results = find(SCIDX==SCIDX(ExemplarIDX)); + +scnum = SCIDX(ExemplarIDX); + +return + +% +% All done. +% diff --git a/som/SOM_FitGaussian.m b/som/SOM_FitGaussian.m new file mode 100755 index 00000000..f3b3156b --- /dev/null +++ b/som/SOM_FitGaussian.m @@ -0,0 +1,57 @@ +% +% A function that will take in a list of parameters +% as well as look at global memory and see if there +% is a minimization to perform +% +% function chi2 = SOM_Gaussian(parameters) +% +% fit a variable number of gaussians. + +function chi2 = SOM_FitGaussian(parameters) + +global SOMGaussian + +SOMGaussian.Parms = parameters; + +% Determine which elements of data to use. Only those with errors > +% 0 + +iIncl = find(SOMGaussian.Ye>0); + +SOMGaussian.nDF = length(iIncl); + +% If there are only two parameters then force mu=0; + +if length(parameters) > 2 + mus = parameters(1); + amps = parameters(2); + sigmas = parameters(3); +else + mus = 0; + amps = parameters(1); + sigmas = parameters(2); +end + +gausses = 0; + +SOMGaussian.Yth = gaussian(SOMGaussian.X,mus,sigmas,amps); + +SOMGaussian.Residuals = SOMGaussian.Yth - SOMGaussian.Y; + +chi2 = sum((SOMGaussian.Residuals(iIncl).^2)./(SOMGaussian.Ye(iIncl).^2)); + +% +% Return +% + +%function retGauss = gaussian(xVals,mu,sigma,amplitude) + +function retGauss = gaussian(xVals,mu,sigma,amplitude) + +retGauss = exp(-.5*((xVals-mu)/sigma).^2); + +retGauss = retGauss/max(retGauss)*amplitude; + +% +% all done +% diff --git a/som/SOM_Fix.m b/som/SOM_Fix.m new file mode 100755 index 00000000..bb084458 --- /dev/null +++ b/som/SOM_Fix.m @@ -0,0 +1,27 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2005 +% Ann Arbor MI. +% +% function results = SOM_Fix(SOMResults,maskName) +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_Fix(SOMResults,maskName) + +maskHDR = spm_vol(maskName); + +maskVOL = spm_read_vols(maskHDR); + +SOMResults.iMask = find(maskVOL); + +SOMResults.header = maskHDR; + +results = SOMResults; + +return + +% +% All done. +% \ No newline at end of file diff --git a/som/SOM_Gaussian.m b/som/SOM_Gaussian.m new file mode 100755 index 00000000..4c55b05b --- /dev/null +++ b/som/SOM_Gaussian.m @@ -0,0 +1,57 @@ +% +% A function that will take in a list of parameters +% as well as look at global memory and see if there +% is a minimization to perform +% +% function chi2 = SOM_Gaussian(parameters) +% +% fit a variable number of gaussians. + +function chi2 = SOM_Gaussian(parameters) + +global SOMGaussian + +SOMGaussian.Parms = parameters; + +% Determine which elements of data to use. Only those with errors > +% 0 + +iIncl = find(SOMGaussian.Ye>0); + +SOMGaussian.nDF = length(iIncl); + +% If there are only two parameters then force mu=0; + +if length(parameters) > 2 + mus = parameters(1); + amps = parameters(2); + sigmas = parameters(3); +else + mus = 0; + amps = parameters(1); + sigmas = parameters(2); +end + +gausses = 0; + +SOMGaussian.Yth = gaussian(SOMGaussian.X,mus,sigmas,amps); + +SOMGaussian.Residuals = SOMGaussian.Yth - SOMGaussian.Y; + +chi2 = sum((SOMGaussian.Residuals(iIncl).^2)./(SOMGaussian.Ye(iIncl).^2)); + +% +% Return +% + +%function retGauss = gaussian(xVals,mu,sigma,amplitude) + +function retGauss = gaussian(xVals,mu,sigma,amplitude) + +retGauss = exp(-.5*((xVals-mu)/sigma).^2); + +retGauss = retGauss/max(retGauss)*amplitude; + +% +% all done +% diff --git a/som/SOM_GlobalCalc.m b/som/SOM_GlobalCalc.m new file mode 100755 index 00000000..08efaf29 --- /dev/null +++ b/som/SOM_GlobalCalc.m @@ -0,0 +1,34 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2005 +% +% +% A program to return the global signal +% from a set of time series data in the SOM +% data analysis. +% +% (You could do this yourself, but just trying to make +% code pretty). +% +% function results = SOM_GlobalCalc(theData) +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function results = SOM_GlobalCalc(theData) + +% Find the mean. + +results = mean(theData,1); + +% Now take the baseline off of the mean. + +results = results - mean(results); + +results = results'; + +return + +% +% All done. +% diff --git a/som/SOM_Grid4SCing.m b/som/SOM_Grid4SCing.m new file mode 100755 index 00000000..f93c1723 --- /dev/null +++ b/som/SOM_Grid4SCing.m @@ -0,0 +1,32 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2010 +% +% A function to take the Time x Space Exemplar map +% and return as a xSpace x ySpace x Time Exemplar map +% +% +% function OutMap = SOM_Grid4SC(InSelfOMap,xGrid,yGrid) +% +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + +function OutMap = SOM_Grid4SC(InSelfOMap,xGrid,yGrid) + +nTime = size(InSelfOMap,1); + +nSpace = xGrid*yGrid; + +if nSpace ~= size(InSelfOMap,2) + fprintf('Error, size mismatch. InSelfOMap : %d x %d\n',size(InSelfOMap)); + OutMap = []; + return +end + +OutMap = reshape(InSelfOMap,[nTime xGrid yGrid]); + +OutMap = permute(OutMap,[2 3 1]); + +% +% All done. +% \ No newline at end of file diff --git a/som/SOM_GridSC.m b/som/SOM_GridSC.m new file mode 100755 index 00000000..737c5f4f --- /dev/null +++ b/som/SOM_GridSC.m @@ -0,0 +1,42 @@ +% - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +% +% Robert C. Welsh +% Copyright 2006 +% +% +% function results = SOM_GridSC(SOMSCResults) +% +% Input: +% +% SOMSCResults - results structure returned by SOM_SuperClsuterEasy +% +% Output: +% +% results - a matrix showing the supercluster membership for the +% SOM. +% +%- - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + +function results = SOM_GridSC(SOMSCResults) + +results = []; + +nGrid = floor(sqrt(length(SOMSCResults.IDX))); + +if nGrid ~= sqrt(length(SOMSCResults.IDX)) + fprintf('Something wrong with nGrid.\n'); + return +end + +results = zeros(nGrid,nGrid); + +for iIDX = 1:max(SOMSCResults.IDX) + results(find(SOMSCResults.IDX == iIDX)) = iIDX; +end + +return + +% +% All done +% diff --git a/som/SOM_HowTo.key b/som/SOM_HowTo.key new file mode 100644 index 0000000000000000000000000000000000000000..e2befa7f9392a1d447c00e97970123edf38b5002 GIT binary patch literal 62338602 zcmeF)3A7#6eee15B5S4B3U*@mOWavW-ilv?ZQL|Y((Yh0+3^atWx(*vb7C70NiY}; 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