AIDAconnect Version 1.1

Aswendt-Lab · Dec 2, 2021 · 7eee895 · 7eee895
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commit 7eee895
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Showing 12 changed files with 74 additions and 27 deletions.
diff --git a/.DS_Store b/.DS_Store
diff --git a/AIDAconnect_Manual_v1.0.pdf → AIDAconnect_Manual_v1.1.pdf b/AIDAconnect_Manual_v1.0.pdf → AIDAconnect_Manual_v1.1.pdf
diff --git a/DTI/GraphEval/getMergedDTI_data.m b/DTI/GraphEval/getMergedDTI_data.m
@@ -1,4 +1,4 @@
-function getMergedDTI_data(dtiStruct,thres)
+function getMergedDTI_data(dtiStruct,thres_type,thres)
 
 %% getMergedDTI_data
 % This function is used by mergeFMRIdata_input.m and is not meant to be
@@ -39,6 +39,7 @@ function getMergedDTI_data(dtiStruct,thres)
         charPathLength =            nan(1,length(matFile_cur));
         charPathLength_rand =       nan(1,length(matFile_cur));
         charPathLength_normalized = nan(1,length(matFile_cur));
+        overallConnectivity =       zeros(1,length(matFile_cur));
 
         if length(matFile_cur)<1
             error('There is no content in the given path!');
@@ -66,14 +67,20 @@ function getMergedDTI_data(dtiStruct,thres)
             current_matAll(current_matAll<=0) = 0;
 
             % Apply the threshold for the number of fibers
-            threshold = mean(current_matAll)*thres;
-            current_matAll(current_matAll<threshold) = 0;
+            switch(thres_type)
+                case 0 % Fixed threshold
+                    threshold = mean(current_matAll)*thres;
+                    current_matAll(current_matAll<threshold) = 0;
+                case 1 % Density-based threshold
+                    current_matAll(:,:,i) = threshold_proportional(current_matAll(:,:,i),thres);
+            end
 
             % Ensure there are no self-connections
             for ii = 1:size(current_matAll,1)
                 current_matAll(ii,ii,i) = 0;
             end 
             current_mat = current_matAll(:,:,i);
+            overallConnectivity(i) = mean(current_mat, 'all');
 
             % Hint: 
             % The calculations of the following metrics using BCT
@@ -171,6 +178,7 @@ function getMergedDTI_data(dtiStruct,thres)
         clustercoef_normalized(:,i) = nanmean(clustercoef(:,i),2)./nanmean(clustercoef_rand(:,i),2);
         charPathLength_normalized(i) = charPathLength(i)/charPathLength_rand(i);        
         infoDTI.smallWorldness = (clustercoef_normalized(:,i)/charPathLength_normalized(i))';
+        infoDTI.overallConnectivity = overallConnectivity;
 
         %infoDTI.smallWorldness = (nanmean(clustercoef(:,i),2)/charPathLength(i))';
 

diff --git a/DTI/mergeDTIdata_input.m b/DTI/mergeDTIdata_input.m
@@ -13,8 +13,13 @@
 % Groups (e.g. "Sham" etc.)
 inputDTI.groups = ["Group1", "Group2"];
 
+% Threshold Type (0: Fixed, 1: Density-based)
+thres_type = 1;
+
 % Threshold (0-1)
-thres = 0;
+% For the Density-based threshold, this is the proportion of
+% the biggest weights to preserve
+thres = 0.8;
 
 % Output path 
 inputDTI.out_path = "/Volumes/path/to/desired/output";
@@ -29,7 +34,7 @@
 addpath('./GraphEval/')
 if ~exist(inputDTI.out_path,'dir')
     mkdir(inputDTI.out_path)
-    getMergedDTI_data(inputDTI,thres);
+    getMergedDTI_data(inputDTI,thres_type,thres);
 end
 [graphCell,matrixValues,ids]=graphAnalysis_DTI(inputDTI);
 acronyms = load('../Tools/infoData/acronyms_splitted.mat').acronyms;
diff --git a/DTI/plotDistribution.m b/DTI/plotDistribution.m
@@ -4,17 +4,19 @@ function plotDistribution(inputDTI, graphCell, strParameter, day)
 % Displays the distribution either of the node degree or of the 
 % node strength (mean over all subjects) 
 % for all groups at a given time point. 
-% You can specify a value range in lines 16 and 17 to additionally output 
-% the region's names whose degree/strength values are within that range.
+% You can specify a value range in lines 16 and 17 to limit the range 
+% in the plot. Region's names whose degree/strength values are within that
+% range are furthermore displayed in the Command Window.
 
 % Input Arguments
 % inputDTI and graphCell from mergeDTIdata_input.m
 % strParameter = 'Degree', 'Strength'
 % day = Name of the day to examine as in inputDTI.days (as String)
 
 %% Specifications
-lowerBound = 50;
-upperBound = 60;
+lowerBound = 1;
+upperBound = 90;
+nbins=20;
 
 %% Example
 % plotDistribution(inputDTI, graphCell, 'Degree', 'Baseline') 
@@ -25,7 +27,8 @@ function plotDistribution(inputDTI, graphCell, strParameter, day)
 dayIdx = (days == day);
 groups = inputDTI.groups;
 numOfGroups = size(groups,2);
-
+edges=linspace(lowerBound,upperBound,nbins+1);
+
 figure('Name', 'plotDistribution');
 for i = 1:numOfGroups
     subplot(numOfGroups,1,i);
@@ -39,13 +42,15 @@ function plotDistribution(inputDTI, graphCell, strParameter, day)
         end
     end    
     % Plot the distribution
-    hist = histogram(groupMeanValues{i});
+    histo = histogram(groupMeanValues{i}, edges);
+    binCounts = histo.BinCounts;
+    binEdges = histo.BinEdges;
     title(strrep(groups(i),'_',' ')+" at "+day);
     xlabel(strcat("Mean",plotxLabel));
     ylabel('Frequency of occurrence');
     set(gca,'FontSize',12)
     % Prevent scientific notation
-    axes = ancestor(hist, 'axes');
+    axes = ancestor(histo, 'axes');
     axes.XAxis.Exponent = 0;
     xtickformat('%.0f')
     % Display all regions that have a degree/strength within a specified range
@@ -54,6 +59,9 @@ function plotDistribution(inputDTI, graphCell, strParameter, day)
     regionsValues = groupMeanValues{i}(valueIdxFiltered);
     groupTable = table(regionsNames, regionsValues);
     groupTable.Properties.VariableNames = {'Region', strcat('Mean',plotxLabel)};
+    %h = findobj(gcf, 'Type', 'histogram');
+    %hdata = h(BinEdges);
+    %disp(h.Values);
     disp(strcat("Regions with a mean",lower(plotxLabel)," between ",string(lowerBound)," and ",string(upperBound)," of ",strrep(groups(i),'_',' ')," at ",day,":"));
     if size(groupTable) ~= 0
         disp(groupTable);

diff --git a/DTI/plotGlobalParameter.m b/DTI/plotGlobalParameter.m
@@ -6,7 +6,7 @@ function plotGlobalParameter(inputDTI, graphCell, strParameter)
 % Input Arguments
 % inputDTI and graphCell from mergeDTIdata_input.m
 % strParameter = 'Density', 'Transitivity', 'Efficiency', 'Assortativity', 
-%                'Modularity', 'charPathLength', 'smallWorldness' 
+%                'Modularity', 'charPathLength', 'smallWorldness', 'overallconnectivity' 
 
 %% Example
 % plotGlobalParameter(inputDTI, graphCell, 'Density') 
@@ -53,6 +53,10 @@ function plotGlobalParameter(inputDTI, graphCell, strParameter)
                 tempFile = load(fullfile(path,groups(gIdx),[char(days(dIdx)) '.mat']));
                 currValues = tempFile.infoDTI.smallWorldness;
                 valuesGroup{gIdx}(1:size(currValues,2),dIdx) = currValues;
+            case 'overallconnectivity'
+                tempFile = load(fullfile(path,groups(gIdx),[char(days(dIdx)) '.mat']));
+                currValues = tempFile.infoDTI.overallConnectivity;
+                valuesGroup{gIdx}(1:size(currValues,2),dIdx) = currValues;
             otherwise
                 error('No valid Argument')
         end

diff --git a/T2/.DS_Store b/T2/.DS_Store
diff --git a/Tools/.DS_Store b/Tools/.DS_Store
diff --git a/fMRI/mergeFMRIdata_input.m b/fMRI/mergeFMRIdata_input.m
@@ -13,8 +13,13 @@
 % Groups e.g. “Sham” etc.
 inputFMRI.groups = ["Group1", "Group2"];
 
-% Threshold 
-thres = 0.1;
+% Threshold Type (0: Fixed, 1: Density-based)
+thres_type = 1;
+
+% Threshold (0-1)
+% For the Density-based threshold, this is the proportion of
+% the biggest weights to preserve
+thres = 0.8;
 
 % Output path 
 inputFMRI.out_path = "/Volumes/path/to/desired/output";
@@ -29,7 +34,7 @@
 addpath('./rsfMRI_Processing/')
 if ~exist(inputFMRI.out_path,'dir')
     mkdir(inputFMRI.out_path)  
-    getMergedFMRI_data(inputFMRI,thres);
+    getMergedFMRI_data(inputFMRI,thres_type,thres);
 end
 [graphCell,matrixValues,ids]=graphAnalysis_fMRI(inputFMRI);
 acronyms = load('../Tools/infoData/acronyms_splitted.mat').acronyms;
diff --git a/fMRI/plotDistribution.m b/fMRI/plotDistribution.m
@@ -4,17 +4,19 @@ function plotDistribution(inputFMRI, graphCell, strParameter, day)
 % Displays the distribution either of the node degree or of the 
 % node strength (mean over all subjects) 
 % for all groups at a given time point. 
-% You can specify a value range in lines 16 and 17 to additionally output 
-% the region's names whose degree/strength values are within that range.
+% You can specify a value range in lines 16 and 17 to limit the range 
+% in the plot. Region's names whose degree/strength values are within that
+% range are furthermore displayed in the Command Window.
 
 % Input Arguments
 % inputFMRI and graphCell from mergeFMRIdata_input.m
 % strParameter = 'Degree', 'Strength'
 % day = Name of the day to examine as in inputFMRI.days (as String)
 
 %% Specifications
-lowerBound = 50;
-upperBound = 60;
+lowerBound = 70;
+upperBound = 90;
+nbins=20;
 
 %% Example
 % plotDistribution(inputFMRI, graphCell, 'Degree', 'Baseline') 
@@ -25,7 +27,8 @@ function plotDistribution(inputFMRI, graphCell, strParameter, day)
 dayIdx = (days == day);
 groups = inputFMRI.groups;
 numOfGroups = size(groups,2);
-
+edges=linspace(lowerBound,upperBound,nbins+1);
+
 figure('Name', 'plotDistribution');
 for i = 1:numOfGroups
     subplot(numOfGroups,1,i);
@@ -39,13 +42,15 @@ function plotDistribution(inputFMRI, graphCell, strParameter, day)
         end
     end    
     % Plot the distribution
-    hist = histogram(groupMeanValues{i});
+    histo = histogram(groupMeanValues{i}, edges);
+    binCounts = histo.BinCounts;
+    binEdges = histo.BinEdges;
     title(strrep(groups(i),'_',' ')+" at "+day);
     xlabel(strcat("Mean",plotxLabel));
     ylabel('Frequency of occurrence');
     set(gca,'FontSize',12)
     % Prevent scientific notation
-    axes = ancestor(hist, 'axes');
+    axes = ancestor(histo, 'axes');
     axes.XAxis.Exponent = 0;
     xtickformat('%.0f')
     % Display all regions that have a degree/strength within a specified range
@@ -54,6 +59,9 @@ function plotDistribution(inputFMRI, graphCell, strParameter, day)
     regionsValues = groupMeanValues{i}(valueIdxFiltered);
     groupTable = table(regionsNames, regionsValues);
     groupTable.Properties.VariableNames = {'Region', strcat('Mean',plotxLabel)};
+    %h = findobj(gcf, 'Type', 'histogram');
+    %hdata = h(BinEdges);
+    %disp(h.Values);
     disp(strcat("Regions with a mean",lower(plotxLabel)," between ",string(lowerBound)," and ",string(upperBound)," of ",strrep(groups(i),'_',' ')," at ",day,":"));
     if size(groupTable) ~= 0
         disp(groupTable);

diff --git a/fMRI/plotGlobalParameter.m b/fMRI/plotGlobalParameter.m
@@ -5,7 +5,8 @@ function plotGlobalParameter(inputFMRI, graphCell, strParameter)
 
 % Input Arguments
 % inputFMRI and graphCell from mergeFMRIdata_input.m
-% strParameter = 'Density', 'Transitivity', 'Efficiency', 'Assortativity', 'Modularity', 'charPathLength', 'smallWorldness' 
+% strParameter = 'Density', 'Transitivity', 'Efficiency', 'Assortativity', 
+%                'Modularity', 'charPathLength', 'smallWorldness', 'overallconnectivity' 
 
 %% Example
 % plotGlobalParameter(inputFMRI, graphCell, 'Modularity') 

diff --git a/fMRI/rsfMRI_Processing/getMergedFMRI_data.m b/fMRI/rsfMRI_Processing/getMergedFMRI_data.m
@@ -1,4 +1,4 @@
-function getMergedFMRI_data(fmriStruct,thres)
+function getMergedFMRI_data(fmriStruct,thres_type,thres)
 
 %% getMergedFMRI_data
 % This function is used by mergeFMRIdata_input.m and is not meant to be
@@ -68,10 +68,18 @@ function getMergedFMRI_data(fmriStruct,thres)
 
             [coMat(:,:,i),labels] = matrixMaker_rsfMRI((fullfile(matFile_cur(i).folder,matFile_cur(i).name)));
             current_matAll = abs(coMat); % Absolute values of the matrices
-            current_matAll(current_matAll<thres) = 0; % Threshold for correlations
 
+            % Threshold for correlations
+            switch(thres_type)
+                case 0 % Fixed threshold
+                    current_matAll(current_matAll<thres) = 0; 
+                case 1 % Density-based threshold
+                    current_matAll(:,:,i) = threshold_proportional(current_matAll(:,:,i),thres);
+            end
+
+            % Ensure there are no self-connections
             for ii = 1:size(current_matAll,1)
-                current_matAll(ii,ii,i) = 0; % Ensure there are no self-connections
+                current_matAll(ii,ii,i) = 0; 
             end
             current_mat = current_matAll(:,:,i);
             overallConnectivity(i) = mean(current_mat, 'all');