add option to truncate timeseries

src/functional_pipeline/config_functional_default.json

@@ -56,7 +56,7 @@
 		"reconstructionMethod": "corr",
 		"connectivityMatrixFile": "OUTPUTDIR/SUBJECT_connectivity_METHOD_TEMPLATE.mat",
 		"timeSeriesFile": "OUTPUTDIR/SUBJECT_time_series_METHOD_TEMPLATE.mat",
-		"minRepetitionTime": 100,
+        "minRepetitionTime": 100,
 		"regression":{
 			"regressionMask": [24,2,41,251,252,253,254,255],
 			"globalMeanRegression": false
@@ -65,6 +65,7 @@
 			"filter": true,
 			"frequencies": [0.01, 0.1]
 		},
+		"truncateFrames": 5,
 		"scrubbing":{
 			"scrubbing": true,
 			"maxFD": 0.25, 

src/functional_pipeline/reconstruction_functional_network.m

@@ -41,6 +41,9 @@ function reconstruction_functional_network(configParams)
     minRepetitionTime = configParams.(methods{iMethod}).minRepetitionTime;
     bandpass_filter = configParams.(methods{iMethod}).bandpass_filter;
 
+    % Truncation parameters
+    truncateFrames = configParams.(methods{iMethod}).truncateFrames;
+
     % Scrubbing parameters
     scrubbing = configParams.(methods{iMethod}).scrubbing;
     motionMetricsFile = configParams.compute_motion_metrics.motionMetricsFile;
@@ -235,7 +238,7 @@ function reconstruction_functional_network(configParams)
         % Frames with a number of indicators larger or equal to
         % minViolations are labeled as frames with potential motion
         % artifacts and are excluded from further analysis.
-        outlierFrames = sum(violations, 1) >= scrubbing.minViolations;
+        framesToRemove = sum(violations, 1) >= scrubbing.minViolations;
 
         % To accommodate temporal smoothing of data, frames consecutive to
         % frames with labeled motion artifacts are optionally excluded:
@@ -244,29 +247,43 @@ function reconstruction_functional_network(configParams)
         % succeeding frames to be excluded from further analysis.
 
         % This is a convolution, but for clarity let's use a for-loop
-        outlierFramesextended = zeros(size(outlierFrames));
-        for i = 1:length(outlierFrames)
-            if outlierFrames(i) > 0
-                outlierFramesextended(i-scrubbing.backwardNeighbors:i+scrubbing.forwardNeighbors) = 1;
+        framesToRemoveExtended = zeros(size(framesToRemove));
+        for i = 1:length(framesToRemove)
+            if framesToRemove(i) > 0
+                framesToRemoveExtended(i-scrubbing.backwardNeighbors:i+scrubbing.forwardNeighbors) = 1;
             end
         end
-        outlierFrames = outlierFramesextended;
+        framesToRemove = framesToRemoveExtended;
 
         fprintf('- scrubbing:           remove %i frames\n', ...
-            nnz(outlierFrames));
+            nnz(framesToRemove));
 
     else
-        outlierFrames = zeros(1, nTimePoints);
+        framesToRemove = zeros(1, nTimePoints);
     end
 
-    numberOfScrubbedVolumes = nnz(outlierFrames); %#ok
+    %% Truncation
+    % Truncation is advisable when using bandpass filtering as filtering
+    % can potentially introduce artefacts at the beginning and end of the
+    % timeseries.
+
+    if truncateFrames > 0
+        removedPrior = nnz(framesToRemove);
+        framesToRemove = framesToRemove | [true(1, truncateFrames), false(1, nTimePoints-2*truncateFrames), true(1, truncateFrames)];
+        fprintf('- truncating:          remove additional %i frames\n', ...
+            nnz(framesToRemove) - removedPrior);
+    end
+
+    %% Apply scrubbing & truncation
+
+    numberOfScrubbedVolumes = nnz(framesToRemove); %#ok
 
     % calculate average motion metric over remaining frames
-    selectedFrames = find(~outlierFrames);
+    selectedFrames = find(~framesToRemove);
     motionMetrics = mean(motionMetrics(:, selectedFrames(2:end)), 2); %#ok
 
     % Compose final filtered, regressed and scrubbed time series
-    selectedTimeSeries = signalIntensities(:, ~outlierFrames);
+    selectedTimeSeries = signalIntensities(:, ~framesToRemove);
     clear signalIntensities
 
     %% Correlate