Loading FSL data

+rsa/+fsl/getDataFromFSL.m

@@ -0,0 +1,32 @@
+function [betas,residuals] = getDataFromFSL(userOptions,subjectName)
+
+%load mask
+curmaskPath=rsa.util.replaceWildcards(userOptions.maskPath, ...
+        '[[subjectName]]',subjectName, ...
+        '[[maskName]]',userOptions.maskNames{1});
+mask=load_nii(curmaskPath);
+
+%load copes
+betas=[];
+residuals=[];
+for r = userOptions.run_names
+    curfeatsPath=rsa.util.replaceWildcards(userOptions.featsPath, ...
+        '[[subjectName]]',subjectName, ...
+        '[[featPrefix]]',userOptions.featsPrefix, ...
+        '[[runName]]',r{1}, ...
+        '[[featSuffix]]',userOptions.featsSuffix);
+    for c = userOptions.copes
+        beta=load_nii(fullfile(curfeatsPath,'stats',['cope' int2str(c{1})' '.nii.gz']));
+        betas=cat(2,betas,beta.img(boolean(mask.img)));
+    end
+
+    res=load_nii(fullfile(curfeatsPath,'stats','res4d.nii.gz'));
+    res1=res.img(repmat(boolean(mask.img),[1 1 1 size(res.img,4)]));
+    residuals=cat(2,residuals,reshape(res1,[length(res1)/size(res.img,4) size(res.img,4)]));
+    %ress=cat(2,ress,res.img(boolean(mask.img)));
+end
+
+residuals=residuals';
+betas=betas';
+
+end

+rsa/+fsl/noiseNormalizeBetaFSL.m

@@ -0,0 +1,17 @@
+function [u_hat] = noiseNormalizeBetaFSL(Beta,Res,Partition)
+
+[T,P] = size(Res);
+[Q,P] = size(Beta);
+
+Nrun= max(Partition);
+partT = kron(1:Nrun,ones(1,T/Nrun))'; 
+partQ = Partition;
+
+for i=1:Nrun
+    idxT=partT==i;
+    idxQ=partQ==i;
+    Sw_hat(:,:,i)=rsa.stat.covdiag(Res(idxT,:));       %%% regularize Sw_hat through optimal shrinkage
+    u_hat(idxQ,:)=Beta(idxQ,:)*Sw_hat(:,:,i)^(-1/2);   %%% multivariate noise normalization
+end;
+
+end

Demos/FSLData/Readme.md

@@ -0,0 +1,3 @@
+# Download instructions
+Downlad FSLData From:https://www.dropbox.com/s/usjjz31tf3q5kwx/FSLData.zip?dl=0
+and extract here

Demos/defineUserOptions.m

@@ -8,7 +8,7 @@
 %  a new name, so all will not be lost if you don't do this).
 %
 %  For a guide to how to fill out the fields in this file, consult
-%  the documentation folder (particularly the userOptions_guide.m)
+%  the docu mentation folder (particularly the userOptions_guide.m)
 %
 %  Cai Wingfield 11-2009
 %__________________________________________________________________________

Demos/fsl_demo.m

@@ -0,0 +1,17 @@
+function distance=fsl_demo()
+
+userOptions=projectOptions_fsl_demo;
+
+[betas,residuals]=rsa.fsl.getDataFromFSL(userOptions,'T05');
+
+partQ = kron(1:length(userOptions.run_names),ones(1,length(userOptions.copes)))';  %runNumber (partition)
+partK = repmat(1:length(userOptions.copes),[1 length(userOptions.run_names)])';    %condNumber (CondVec)
+
+u_hat   = rsa.fsl.noiseNormalizeBetaFSL(betas,residuals,partQ);   % Get noise normalised betas
+distance  = rsa.distanceLDC(u_hat,partQ,partK);
+
+figure; imagesc(squareform(distance));
+
+
+
+end

Demos/projectOptions_fsl_demo.m

@@ -0,0 +1,78 @@
+function userOptions = projectOptions_fsl_demo()
+% projectOptions_fsl_demo is an options file for the FSL demo tutorial
+%
+% Roni Maimon 9-2017 
+%__________________________________________________________________________
+% Copyright (C)
+
+%% Project details
+%Current folder
+curFolder=fileparts(which('projectOptions_fsl_demo.m'));
+
+% This name identifies a collection of files which all belong to the same run of a project.
+userOptions.analysisName = 'DEMO_FSL'; % this is renamed in the code for demos 3-4.
+
+% This is the root directory of the project.
+userOptions.rootPath = [curFolder,filesep,'DEMO_FSL'];
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%
+%% EXPERIMENTAL SETUP %%
+%%%%%%%%%%%%%%%%%%%%%%%%
+
+
+% The list of subjects to be included in the study.
+userOptions.subjectNames = { ...
+	'T05', ...
+	'T06', ...
+	};
+
+%% FSL Specific parameters
+userOptions.run_names = { ...
+    'A1b','A2b','A3b','A4b' ...
+    };
+userOptions.featsPrefix = '5_';
+userOptions.featsSuffix = '';
+userOptions.featsPath = [curFolder,filesep,'FSLData',filesep,'[[subjectName]]',...
+                        filesep,'models',filesep,'glm',filesep,'fingers', ...
+                        filesep, '[[featPrefix]]','[[runName]]','[[featSuffix]]','.feat'];
+
+userOptions.copes = { ...
+    1,2,3,4,5 ...
+    };
+
+%% End of FSL specific parameters
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% ANALYSIS PREFERENCES %%
+%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+%% First-order analysis
+% Text lables which may be attached to the conditions for MDS plots.
+userOptions.conditionLabels = { ...
+	'F1', ...
+	'F2', ...
+    'F3', ...
+	'F4', ...
+    'F5'
+	};
+
+userOptions.useAlternativeConditionLabels = false;
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%% FEATUERS OF INTEREST SELECTION OPTIONS %%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+	%% %% %% %% %%
+	%% fMRI  %% Use these next three options if you're working in fMRI native space:
+	%% %% %% %% %%
+
+	% The path to a stereotypical mask data file is stored (not including subject-specific identifiers).
+	% "[[subjectName]]" should be used as a placeholder to denote an entry in userOptions.subjectNames
+	% "[[maskName]]" should be used as a placeholder to denote an entry in userOptions.maskNames
+	userOptions.maskPath = [curFolder,filesep,'FSLData',filesep,'[[subjectName]]',filesep,'models',filesep,'masks',filesep,'[[maskName]].nii.gz'];
+
+		% The list of mask filenames (minus .hdr extension) to be used.
+	userOptions.maskNames = { ...
+			'S1_handknob_alldigits'...
+			};
+end%function