Add amplitude variability and scoring

 + Add variability in amplitudes to simulations.
 + Test for this as part of scoring procedures.
 + Load inferred PROFUMO amplitudes.

DataGeneration/generateNeuralTimecourses.m

@@ -21,21 +21,19 @@ switch options.An.form
         
 end
 
-%If the option has been specified, turn some networks off
-if isfield(options.An, 'offRate')
-    
-    %Loop over subjects and networks
-    for s = 1:params.S
-        for n = 1:params.N
-            %Randomly eliminate some timecourses
-            if rand < options.An.offRate
-                for r = 1:params.R(s)
-                    An{s}{r}(n,:) = 0;
-                end
-            end
-        end
+% Add amplitude variability
+% And if the option has been specified, turn some networks off
+for s = 1:params.S
+    if isfield(options.An, 'offRate')
+        on = (rand(params.N, 1) > options.An.offRate);
+    else
+        on = ones(params.N, 1);
+    end
+    for r = 1:params.R(s)
+        amp = gamrnd( ...
+            options.An.amp.a, 1.0 / options.An.amp.b, params.N, 1);
+        An{s}{r} = An{s}{r} .* (amp .* on);
     end
-    
 end
 
 %Finally, add a global rescaling such that all timecourses are overall

IO/loadPROFUMO.m

@@ -31,9 +31,14 @@ for s = 1:params.S
         run = sprintf('R%02d',r);
         runDir = fullfile(subjDir, 'Runs', run);
         
+        % Timecourses
         A{s}{r} = h5read(fullfile(runDir, 'TimeCourses.post', ...
             'CleanTimeCourses', 'Means.hdf5'), '/dataset');
             %'Means.hdf5'), '/dataset');
+        % Modulate by amplitudes
+        h = h5read(fullfile(runDir, 'ComponentWeightings.post', ...
+            'Means.hdf5'), '/dataset');
+        A{s}{r} = A{s}{r} .* h;
         
         % Temporal netmats
         precmat = h5read(fullfile(runDir, 'TemporalPrecisionMatrix.post', ...

RunTests.m

@@ -165,8 +165,11 @@ modeOptions.P.registration.form = 'Null';
 %Time courses
 
 options.An.form = 'Freq';
-%options.An.offRate = 0.05 * 1/params.N;  % Useful, but not really with partial correlations...
-options.An.offRate = 0.0;
+% Amplitude variability
+options.An.amp.a = 50.0;
+options.An.amp.b = 50.0;
+% Proportion of modes missing (per subject & mode)
+options.An.offRate = 0.05 * 1/params.N;  % 1/20 subjects has a missing mode
 switch options.An.form
     case 'Freq'
         % Reducing these parameters will increase the

Scoring/calculateDecompositionAccuracy.m

@@ -85,7 +85,6 @@ end
 
 %--------------------------------------------------------------------------
 
-
 % Repeat for the temporal accuracy
 % Take correlation between true and inferred time courses
 scores.A.data   = NaN(sum(params.R), params.N);
@@ -107,6 +106,42 @@ end
 
 %--------------------------------------------------------------------------
 
+% Accuaracy of amplitudes
+% Assemble all elements into a [n, sr] matrix
+aA = NaN(params.N, sum(params.R)); inf_aA = NaN(params.N, sum(params.R));
+sr = 1;
+for s = 1:params.S
+    for r = 1:params.R(s)
+        
+        aA(:,sr) = std(A{s}{r}(gt_inds,:)');
+        inf_aA(:,sr) = std(inf_A{s}{r}(inf_inds,:)');
+        sr = sr + 1;
+        
+    end
+end
+
+% Save the correlations over amplitudes
+% Each subject, across amplitudes (i.e. relative amplitudes similar)
+% ** IGNORED ** Can't really disambiguate this from spatial scale
+%scores.aA.data   = diag(corr(aA, inf_aA));
+% Return scores in order of GT components so comparable across methods
+%scores.aA.data   = scores.aA.data(gt_inds);
+%scores.aA.name   = 'Amplitudes';
+%scores.aA.metric = 'Correlation';
+%scores.aA.range  = [-1.0, 1.0];
+% Each element, across amplitudes (i.e. behavioural prediction)
+scores.aA_xs.data   = diag(corr(aA', inf_aA'));
+scores.aA_xs.name   = 'Amplitudes (cross subject)';
+scores.aA_xs.metric = 'Correlation';
+scores.aA_xs.range  = [-1.0, 1.0];
+
+%figure; imagesc(aA, max(aA(:))*[-1 1]); colorbar();
+%figure; imagesc(inf_aA, max(inf_aA(:))*[-1 1]); colorbar();
+%figure; imagesc(zscore(aA')'); colorbar();
+%figure; imagesc(zscore(inf_aA')'); colorbar();
+
+%--------------------------------------------------------------------------
+
 %Now find how well the mode interactions have been calculated
 
 %First for the spatial correlations