SVMCrossVal.git

@@ -11,15 +11,6 @@ timeline.decodeDuration  = header.frameShift.decodeDuration;

 timeLineStart  = timeline.frameShiftStart;

 timeLineEnd    = timeline.frameShiftEnd;

-% for iVoxel = 1:nVoxel

-%     rawdata = [];

-%     for iImage = 1:length(extr);

-%         tmp = extr(iImage);

-%         rawdata = [rawdata tmp.dat(iVoxel)];

-%     end

-%     pst{iVoxel} = calculatePST(timeline,calculatePstOpts,rawdata);

-% end

-

 timePointArgs.pst           = subjectStruct.pst;

 timePointArgs.labelMap      = LabelMap(header.classDef.labelCells,header.classDef.conditionCells);

 timePointArgs.eventList     = header.classDef.eventMatrix;

@@ -30,6 +21,7 @@ decodePerformance = [];

 for index = 1:timeLineEnd-timeLineStart+1

     svmdata      = timePointMatrix{index}(:,2:size(timePointMatrix{index},2));

+    svmdata      = header.timeframeGroupingfunction(svmdata);

     svmlabel     = timePointMatrix{index}(:,1);

     if RANDOMIZE_DATAPOINTS

@@ -1,13 +1,13 @@

-function [extr voxelcount] = calculateImageData(filenameList, coordlist)

+function [extr voxelcount] = calculateImageData(filenameList, coordlist,gFkt)

+%center coordinates

 vox = [];

-% radius = [];

 nCoords = size(coordlist,1);

 for iCoord = 1:nCoords

    vox = [vox ; coordlist(iCoord).coord];

-%    radius = [radius , coordlist(iCoord).rad];

 end

+%maximum extracted voxels over all images

 voxelcount = 0;

 V = filenameList;

@@ -15,14 +15,13 @@ nImage = numel(V);

 nVoxel = nCoords;

 for kImage=1:nImage

     roicenter = round(inv(V(kImage).mat)*[vox, ones(nVoxel,1)]');

-%     x = roicenter(1,:);

-%     y = roicenter(2,:);

-%     z = roicenter(3,:);

     subvoxelcount = 0;

     for iVoxel = 1:nVoxel

+        %radius

         rad = coordlist(iVoxel).rad;

+

         tmp = spm_imatrix(V(kImage).mat);

         vdim = tmp(7:9);

         vxrad = ceil((rad*ones(nVoxel,3))./(ones(nVoxel,1)*vdim))';

@@ -45,12 +44,16 @@ for kImage=1:nImage

         dat = spm_sample_vol(V(kImage), x, y, z,0);

+        %%Implement Spatial grouping here

+        

+        dat = gFkt(dat);

+        

+        % no grouping

         for iSubVoxel = 1:size(dat,1)

             subvoxelcount = subvoxelcount +1;

             extr(kImage).dat(subvoxelcount)    = dat(iSubVoxel);

         end

-%         extr(kImage).mean(iVoxel)     = nanmean(dat);

-%         extr(kImage).nvx(iVoxel)      = numel(dat);

+        

     end

     voxelcount = max(voxelcount,subvoxelcount);

 end   

@@ -1,35 +1,15 @@

 function defineGlobals()

-global SVMCROSSVAL_SPMDIR;

+

 global SVMCROSSVAL_TOOLBOXPATH;

-SVMCROSSVAL_TOOLBOXPATH = fullfile(SVMCROSSVAL_SPMDIR,'toolbox','SVMCrossVal');

+SVMCROSSVAL_TOOLBOXPATH = fullfile(getSpmPath,'toolbox','SVMCrossVal');

 global SVMCROSSVAL_STUDYDIR;

 SVMCROSSVAL_STUDYDIR = fullfile(SVMCROSSVAL_TOOLBOXPATH,'study');

-%define global constants

-global SVMCROSSVAL_USE_DRIVE_CHECK_HACK;

-%enables subroutine to check if image path starts with 'D'

-SVMCROSSVAL_USE_DRIVE_CHECK_HACK                  = 1;  

-

-global SVMCROSSVAL_CROSSVAL_METHOD_DEF;

-% supported classification methods

-SVMCROSSVAL_CROSSVAL_METHOD_DEF.svmcrossval       = 'svm crossval';

-SVMCROSSVAL_CROSSVAL_METHOD_DEF.classPerformance  = 'svm class performance';

-SVMCROSSVAL_CROSSVAL_METHOD_DEF.crossSubject      = 'svm across subject testing';

-SVMCROSSVAL_CROSSVAL_METHOD_DEF.somTraining       = 'som Training';

-

-global SVMCROSSVAL_VOXEL_SELECTION_MODE_DEF;

-% supported voxel selection methods

-SVMCROSSVAL_VOXEL_SELECTION_MODE_DEF.manualGui    = 'manually defined in GUI';

-SVMCROSSVAL_VOXEL_SELECTION_MODE_DEF.roiImage     = 'use ROI image by pop-up image selector';

-

 global SVMCROSSVAL_SUBJECT_PREFIX;

-% internally used to prefix subject-ids starting with numbers.

 SVMCROSSVAL_SUBJECT_PREFIX                        = 'subject';

 global SVMCROSSVAL_SUBJECTSTRUCT_NAME;

 SVMCROSSVAL_SUBJECTSTRUCT_NAME                    = 'subjectStruct';

-global SVMCROSSVAL_PREPROCESSED_DATA_NAME;

-SVMCROSSVAL_PREPROCESSED_DATA_NAME                = 'preprocessedData';

 end

\ No newline at end of file

@@ -0,0 +1,5 @@

+function fkt = getSpatialGroupingFunction(model)

+fields = get(model.selRoiGrouping,'UserData');

+value = get(model.selRoiGrouping,'Value');

+fkt = fields{value};

+end

@@ -0,0 +1,5 @@

+function fkt = getTemporalGroupingFunction(model)

+fields = get(model.selTimeframeGrouping,'UserData');

+value = get(model.selTimeframeGrouping,'Value');

+fkt = fields{value};

+end

@@ -31,7 +31,7 @@ psthOpts.psthNorm      = getPsthNormalizationMethod(model);

 switch task

     case 'COORD'

-        disp('COORD');

+        disp('LUT Approach');

         out = struct;

         out.header = struct;

@@ -48,11 +48,12 @@ switch task

         coordargs.coords        = parseCoordinateTextField(model);

         coordargs.mask          = mask;

         coordargs.psthOpts      = psthOpts;

+        coordargs.groupingFkt   = getSpatialGroupingFunction(model);

         out.subjectdata = runCoordTable(coordargs);

     case 'ROI'

-        disp('ROI');

+        disp('ROI Image Approach');

         out = struct;

         out.header = struct;

         out.header.type = 'ROI';

@@ -117,6 +118,7 @@ end

 warn = warning('off','all');

 header            = preprocessedData.header;

 header.frameShift = getFrameShiftParams(model);

+header.timeframeGroupingfunction = getTemporalGroupingFunction(model);

 data              = preprocessedData.subjectdata;

 switch task

@@ -1,7 +1,5 @@

 function subjectData = runCoordTable(args)

-% global SVMCROSSVAL_SUBJECTSTRUCT_NAME;

-

     disp('run coord table')

     subjects = args.subjects;

@@ -12,20 +10,21 @@ function subjectData = runCoordTable(args)

     disp(sprintf('batch processing %g subjects.',nSubjects));

     for s = 1:nSubjects

+        % load SPM Design 

         subjectStruct{s}.dir = fullfile(args.basedir,cell2mat(subjects(s)));

         d = load(fullfile(subjectStruct{s}.dir,'results','SPM.mat'));

         subjectStruct{s}.des = d.SPM;

         subjectStruct{s}.name = cell2mat(subjects(s));

-        

+        % load ROI look-up table

         map = load(fullfile(subjectStruct{s}.dir,'results','roi','coord_map.mat'));

         subjectStruct{s}.coords = getSubjectCoordinates(args.coords,map);

-%         nVoxel = size(subjectStruct{s}.coords,1);

-        

+        %preload images

         disp('fetching volume definitions, please wait');

         subjectStruct{s}.volumes = spm_vol(getImageFileList(subjectStruct{s}.dir,sessionlist,args.mask));

-        [extr nExtractedVoxel] = calculateImageData(subjectStruct{s}.volumes,subjectStruct{s}.coords);

+        %extract voxel values

+        [extr nExtractedVoxel] = calculateImageData(subjectStruct{s}.volumes,subjectStruct{s}.coords,args.groupingFkt);

         pstopts.des = subjectStruct{s}.des;

         pstopts.eventList = args.eventList;

@@ -1,7 +1,5 @@

 function subjectStruct  = runROIImageMaskMode(args)

-% global SVMCROSSVAL_SUBJECTSTRUCT_NAME;

-

 subjects = args.subjects;

 nSubjects = numel(subjects);

@@ -5,7 +5,7 @@ function ui_main(varargin)

     frameHeight=450;

     frame = figure('Visible','off','Position',[0,0,frameWidth,frameHeight]);

-    movegui(frame,'west'); % get this thing visible on smaller displays.

+    movegui(frame,'center'); % get this thing visible on smaller displays.

     set(frame,'Name','SVMCrossVal Decode Performance 4 SPM');

     set(frame,'NumberTitle','off');

     set(frame,'MenuBar','none');

@@ -92,7 +92,8 @@ end

 %%%%% ui elements

 function model = createFirstStepPanel(model,parent)

     norm1Model = {'none','mean','minmax'};

-    roiGroupMethods = {'none','mean','max','median'};

+    roiGroupMethodNames = {'none','mean','max','median'};

+    roiGroupMethodFunctions = {@(in)in, @(in)nanmean(in),@(in)nanmax(in),@(in)nanmedian(in)};

     main_grid = cell(2,4);

     main_grid{1,1} = [0 0.7 0.4 0.3];

@@ -243,11 +244,10 @@ function model = createFirstStepPanel(model,parent)

          model.selRoiGrouping = uicontrol(pOptions,'Style','popupmenu',...

                 'Units','normalized',...

                 'Position',cell2mat(optGrid(2,4)),...

-                'String',roiGroupMethods,...

-                'UserData',roiGroupMethods);

+                'String',roiGroupMethodNames,...

+                'UserData',roiGroupMethodFunctions);

          set(model.selRoiGrouping,'BackgroundColor','w');

-        

         % COORD TABLE

         pVoxel = uipanel(parent,'Title','ROI','Position',cell2mat(main_grid(1,3)));

         set(pVoxel,'BackgroundColor','w');

@@ -281,6 +281,9 @@ end

 function model = createSecondStepPanel(model,parent)

 basecolor = 'w';

+TimeframeGroupingStrings = {'none','mean'};%,'sum,'max','median'};

+TimeframeGroupingFunctions = {@(in)in,@(in)nanmean(in,2)};%,'sum',','max','median'};

+

 pTime = uipanel(parent,'Units','normalized','Position',[0.0 0.7 1 0.3]);

     set(pTime,'Title','Decode Timeframe Options');

     set(pTime,'BackgroundColor',basecolor);

@@ -310,14 +313,14 @@ pTime = uipanel(parent,'Units','normalized','Position',[0.0 0.7 1 0.3]);

     model.txtFrameShiftEnd     = createTextField(pTime,cell2mat(time_grid(3,2)),'');

     model.txtFrameShiftDur     = createTextField(pTime,cell2mat(time_grid(2,3)),'');

-    TimeframeGroupingOptions = {'none','sum','mean','max','median'};

+

     model.selTimeframeGrouping = uicontrol(pTime,'Style','popupmenu',...

         'Units','normalized',...

         'Position',cell2mat(time_grid(2,4)),...

-        'String',TimeframeGroupingOptions,...

-        'UserData',TimeframeGroupingOptions);

-    set(model.selTimeframeGrouping,'Enable','off');

+        'String',TimeframeGroupingStrings,...

+        'UserData',TimeframeGroupingFunctions);

+    set(model.selTimeframeGrouping,'Enable','on');

 pSVM = uipanel(parent,'Units','normalized','Position',[0 0.3 0.5 0.4]);

     set(pSVM,'Title','SVM Classification');