AIDAconnect v1.0

Aswendt-Lab · Apr 23, 2021 · 48f0405 · 48f0405
1 parent c9767a7
commit 48f0405
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Showing 10 changed files with 30 additions and 3 deletions.
diff --git a/.DS_Store b/.DS_Store
diff --git a/AIDAconnect_Manual.pdf b/AIDAconnect_Manual.pdf
diff --git a/DTI/.DS_Store b/DTI/.DS_Store
diff --git a/DTI/GraphEval/getMergedDTI_data.m b/DTI/GraphEval/getMergedDTI_data.m
@@ -24,6 +24,7 @@ function getMergedDTI_data(dtiStruct,thres)
         clustercoef =               zeros(length(matFile_cur),numOfRegions);  
         clustercoef_rand =          zeros(length(matFile_cur),numOfRegions);
         clustercoef_normalized =    zeros(length(matFile_cur),numOfRegions);
+        participationcoef =         zeros(length(matFile_cur),numOfRegions);
         degrees =                   zeros(length(matFile_cur),numOfRegions); 
         strengths =                 zeros(length(matFile_cur),numOfRegions); 
         betweenness =               zeros(length(matFile_cur),numOfRegions); 
@@ -95,6 +96,11 @@ function getMergedDTI_data(dtiStruct,thres)
             % Local parameters for each region using graph theory (BCT)
             clustercoef(i,:) =  clustering_coef_wu(current_mat_normalized);
             clustercoef_rand(i,:) = clustering_coef_wu(randomNetwork);
+            [communityAffiliationVector,~] = community_louvain(current_mat,1);
+            % The community affiliation vector assigns nodes to specific,
+            % non-overlapping modules and is a necessary parameter for the
+            % participation coefficient
+            participationcoef(i,:) = participation_coef(current_mat,communityAffiliationVector,0);
             degrees(i,:) =      degrees_und(current_mat)';
             strengths(i,:)=     strengths_und(current_mat)';
             betweenness(i,:) =  betweenness_wei(current_mat_inverse);
@@ -142,6 +148,7 @@ function getMergedDTI_data(dtiStruct,thres)
         infoDTI.matrix = coMat;
         infoDTI.labels = labels;
         infoDTI.clustercoef = clustercoef;
+        infoDTI.participationcoef = participationcoef;
         infoDTI.degrees = degrees;
         infoDTI.strengths = strengths;
         infoDTI.betw_centrality = betweenness;

diff --git a/DTI/GraphEval/graphAnalysis_DTI.m b/DTI/GraphEval/graphAnalysis_DTI.m
@@ -59,6 +59,7 @@
             G.Nodes.allEigenvector(:,mIdx) = tempFile.infoDTI.eign_centrality(mIdx,:);
             G.Nodes.allBetweenness(:,mIdx) = tempFile.infoDTI.betw_centrality(mIdx,:); 
             G.Nodes.allClustercoef(:,mIdx) = tempFile.infoDTI.clustercoef(mIdx,:);
+            G.Nodes.allParticipationcoef(:,mIdx) = tempFile.infoDTI.participationcoef(mIdx,:);
             G.Nodes.allEfficiency(:,mIdx) = tempFile.infoDTI.localEfficiency(mIdx,:);
             G.Nodes.FA0(:,mIdx) = tempFile.infoDTI.FA0(mIdx,:);
             G.Nodes.AD(:,mIdx) = tempFile.infoDTI.AD(mIdx,:);

diff --git a/DTI/plotLocalParameter.m b/DTI/plotLocalParameter.m
@@ -7,7 +7,8 @@ function plotLocalParameter(inputDTI, graphCell, strNodeName, strParameter)
 % inputDTI and graphCell from mergeDTIdata_input.m
 % strNodeName = Region to examine (as String)
 % strParameter = 'Degree', 'Eigenvector', 'Betweenness', 'Strength', 
-%                'Clustercoefficient', 'Efficiency',
+%                'Clustercoefficient', 'Participationcoefficient',
+%                'Efficiency', 
 %                'FA0', 'AD', 'MD', 'RD'
 
 %% Example
@@ -39,6 +40,8 @@ function plotLocalParameter(inputDTI, graphCell, strNodeName, strParameter)
                 valuesGroup1(animal,dIdx) = graphCell{1,dIdx}.Nodes.allBetweenness(b,animal);
             case 'clustercoefficient' 
                 valuesGroup1(animal,dIdx) = graphCell{1,dIdx}.Nodes.allClustercoef(b,animal);
+            case 'participationcoefficient'
+                valuesGroup1(animal,dIdx) = graphCell{1,dIdx}.Nodes.allParticipationcoef(b,animal);               
             case 'efficiency' 
                 valuesGroup1(animal,dIdx) = graphCell{1,dIdx}.Nodes.allEfficiency(b,animal);
             case 'fa0'
@@ -79,6 +82,8 @@ function plotLocalParameter(inputDTI, graphCell, strNodeName, strParameter)
                 valuesGroup2(animal,dIdx) = graphCell{2,dIdx}.Nodes.allBetweenness(b,animal);
             case 'clustercoefficient' 
                 valuesGroup2(animal,dIdx) = graphCell{2,dIdx}.Nodes.allClustercoef(b,animal);
+            case 'participationcoefficient'
+                valuesGroup2(animal,dIdx) = graphCell{2,dIdx}.Nodes.allParticipationcoef(b,animal);   
             case 'efficiency' 
                 valuesGroup2(animal,dIdx) = graphCell{2,dIdx}.Nodes.allEfficiency(b,animal);
             case 'fa0'

diff --git a/fMRI/.DS_Store b/fMRI/.DS_Store
diff --git a/fMRI/plotLocalParameter.m b/fMRI/plotLocalParameter.m
@@ -7,7 +7,8 @@ function plotLocalParameter(inputFMRI, graphCell, strNodeName, strParameter)
 % inputFMRI and graphCell from mergeFMRIdata_input.m
 % strNodeName = Region to examine (as String)
 % strParameter = 'Degree', 'Eigenvector', 'Betweenness', 'Strength',
-%                'Clustercoefficient', 'Efficiency'
+%                'Clustercoefficient', 'Participationcoefficient',
+%                'Efficiency'
 
 %% Example
 % plotLocalParameter(inputFMRI, graphCell, 'L SSp-ul', 'Degree') 
@@ -38,6 +39,8 @@ function plotLocalParameter(inputFMRI, graphCell, strNodeName, strParameter)
                 valuesGroup1(animal,dIdx) = graphCell{1,dIdx}.Nodes.allBetweenness(b,animal);
             case 'clustercoefficient' 
                 valuesGroup1(animal,dIdx) = graphCell{1,dIdx}.Nodes.allClustercoef(b,animal);
+            case 'participationcoefficient'
+                valuesGroup1(animal,dIdx) = graphCell{1,dIdx}.Nodes.allParticipationcoef(b,animal);  
             case 'efficiency' 
                 valuesGroup1(animal,dIdx) = graphCell{1,dIdx}.Nodes.allEfficiency(b,animal);
             otherwise
@@ -70,6 +73,8 @@ function plotLocalParameter(inputFMRI, graphCell, strNodeName, strParameter)
                 valuesGroup2(animal,dIdx) = graphCell{2,dIdx}.Nodes.allBetweenness(b,animal); 
             case 'clustercoefficient' 
                 valuesGroup2(animal,dIdx) = graphCell{2,dIdx}.Nodes.allClustercoef(b,animal);
+            case 'participationcoefficient'
+                valuesGroup2(animal,dIdx) = graphCell{2,dIdx}.Nodes.allParticipationcoef(b,animal);  
             case 'efficiency' 
                 valuesGroup2(animal,dIdx) = graphCell{2,dIdx}.Nodes.allEfficiency(b,animal);
             otherwise

diff --git a/fMRI/rsfMRI_Processing/getMergedFMRI_data.m b/fMRI/rsfMRI_Processing/getMergedFMRI_data.m
@@ -31,13 +31,15 @@ function getMergedFMRI_data(fmriStruct,thres)
         % All subsequent positions should contain the number of atlas
         % labels, which should always result in the same number.
         % If not, then line 36 will cause an error because the numOfRegions
-        % is not unique anymore.
+        % is not unique anymore. A quick solution is then to specify
+        % a concrete number in line 29 for numOfRegions, e.g. 98.
         infoFMRI = struct();
         coMat = zeros(numOfRegions,numOfRegions,length(matFile_cur));
         namesOfMat = cell(length(matFile_cur),1);
         clustercoef =               zeros(length(matFile_cur),numOfRegions); 
         clustercoef_rand =          zeros(length(matFile_cur),numOfRegions);
         clustercoef_normalized =    zeros(length(matFile_cur),numOfRegions);
+        participationcoef =         zeros(length(matFile_cur),numOfRegions);
         degrees =                   zeros(length(matFile_cur),numOfRegions); 
         strengths =                 zeros(length(matFile_cur),numOfRegions); 
         betweenness =               zeros(length(matFile_cur),numOfRegions); 
@@ -91,6 +93,11 @@ function getMergedFMRI_data(fmriStruct,thres)
             % Local parameters for each region using graph theory (BCT)
             clustercoef(i,:) =  clustering_coef_wu(current_mat);
             clustercoef_rand(i,:) = clustering_coef_wu(randomNetwork);
+            [communityAffiliationVector,~] = community_louvain(current_mat,1);
+            % The community affiliation vector assigns nodes to specific,
+            % non-overlapping modules and is a necessary parameter for the
+            % participation coefficient
+            participationcoef(i,:) = participation_coef(current_mat,communityAffiliationVector,0);
             degrees(i,:) =      degrees_und(current_mat)';
             strengths(i,:) =    strengths_und(current_mat)';
             betweenness(i,:) =  betweenness_wei(current_mat_inverse);           
@@ -120,6 +127,7 @@ function getMergedFMRI_data(fmriStruct,thres)
         infoFMRI.matrix = current_matAll;
         infoFMRI.labels = labels;
         infoFMRI.clustercoef = clustercoef;
+        infoFMRI.participationcoef = participationcoef;
         infoFMRI.degrees = degrees;
         infoFMRI.strengths = strengths;
         infoFMRI.betw_centrality = betweenness;

diff --git a/fMRI/rsfMRI_Processing/graphAnalysis_fMRI.m b/fMRI/rsfMRI_Processing/graphAnalysis_fMRI.m
@@ -59,6 +59,7 @@
             G.Nodes.allEigenvector(:,mIdx) = tempFile.infoFMRI.eign_centrality(mIdx,:);
             G.Nodes.allBetweenness(:,mIdx) = tempFile.infoFMRI.betw_centrality(mIdx,:);
             G.Nodes.allClustercoef(:,mIdx) = tempFile.infoFMRI.clustercoef(mIdx,:);
+            G.Nodes.allParticipationcoef(:,mIdx) = tempFile.infoFMRI.participationcoef(mIdx,:);
             G.Nodes.allEfficiency(:,mIdx) = tempFile.infoFMRI.localEfficiency(mIdx,:);
         end
         graphCell{gIdx,dIdx} = G;