├── README.md
├── S1CreateIndividualROIs.m
├── S2ROISignalExtract.m
├── S2tLIofActivation.m
├── S2tWaveletDecompose.m
├── S3CommunityMatrix.m
├── S4ModularityMeasures.m
├── S5RandomCommunity.m
└── S6NullModelMeasures.m


/README.md:
--------------------------------------------------------------------------------
 1 | # DynamicFunctionalConnectivity
 2 | Codes for dynamic functional connectivity on task and resting fMRI for PNAS submission:
 3 | 
 4 | "Disrupted dynamic network reconfiguration of the language system in temporal lobe epilepsy"
 5 | 
 6 | Xiaosong He1, Danielle S Bassett2, Chaitanya Ganne1, Lauren Kozlowski1, Michael R Sperling1, and Joseph I Tracy1*
 7 | 
 8 | 1Department of Neurology, Thomas Jefferson University, Philadelphia, PA, 19107
 9 | 2Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104
10 | 
11 | Note1: Codes for the "generalized Louvain" method used in the paper are available at: http://netwiki.amath.unc.edu/GenLouvain/GenLouvain
12 | 
13 | Note2: Codes for dynamic network statistics are available at: http://commdetect.weebly.com/
14 | 


--------------------------------------------------------------------------------
/S1CreateIndividualROIs.m:
--------------------------------------------------------------------------------
 1 | % Create individual masks and convert to .mat format
 2 | clear all;
 3 | sbj=importdata('E:\VerbGeneration_network\sbj.txt');
 4 | ROI=importdata('E:\VerbGeneration_network\ROIs.txt');
 5 | for t=1:length(sbj)
 6 |     if exist(['E:\VerbGeneration_network\4IndividualROIs\'  sbj{t} '\'],'dir')==7
 7 |         rmdir(['E:\VerbGeneration_network\4IndividualROIs\'  sbj{t} '\'],'s');
 8 |         mkdir(['E:\VerbGeneration_network\4IndividualROIs\'  sbj{t} '\']);
 9 |     else
10 |         mkdir(['E:\VerbGeneration_network\4IndividualROIs\'  sbj{t} '\']);
11 |     end
12 |     clear V Y;
13 |     V=spm_vol(['E:\VerbGeneration_network\3FirstLevelAnalysis\' sbj{t} '\spmT_0001.nii']);
14 |     Y=spm_read_vols(V);
15 |     for k=1:length(ROI)
16 |         clear Vm Ym Yt s Thr vol;
17 |         Vm=spm_vol(['E:\VerbGeneration_network\SelectedROIs\res_' ROI{k} '.img']);
18 |         Ym=spm_read_vols(Vm);
19 | 		Yt=Y./Ym;
20 | 		Yt(Yt==-Inf)=Inf;
21 | 		s=sort(Yt(abs(Yt)<Inf),1,'descend');
22 |         Thr=s(round(length(s)*0.1));
23 | 		Yt(Yt<Thr)=Inf;
24 |         Yt(Yt<Inf)=1;
25 | 		Yt(Yt==Inf)=0;
26 | 		Yt(isnan(Yt))=0;
27 |         vol=V;
28 |         vol.fname=['E:\VerbGeneration_network\4IndividualROIs\' sbj{t} '\' ROI{k} '.img'];
29 |         vol.private.dat.fname=vol.fname;
30 |         spm_write_vol(vol,Yt);
31 | 		mars_img2rois(['E:\VerbGeneration_network\4IndividualROIs\' sbj{t} '\' ROI{k} '.img'],['E:\VerbGeneration_network\4IndividualROIs\' sbj{t} '\'],ROI{k},'i');
32 |         clear Vm Ym Yt s Thr vol;
33 |     end
34 |     clear V Y;
35 | end
36 | 


--------------------------------------------------------------------------------
/S2ROISignalExtract.m:
--------------------------------------------------------------------------------
 1 | % Extract ROI Signal
 2 | % Order: LIFGorb, LIFG, LMFG, LAT, LMAT, LMPT, LPT, LAG, RIFGorb, RIFG, RMFG, RAT, RMAT, RMPT, RPT, RAG
 3 | clear all;
 4 | sbj=importdata('E:\VerbGeneration_network\sbj.txt');
 5 | parfor i=1:length(sbj)
 6 |     roi_filest = spm_get('files',['E:\VerbGeneration_network\4IndividualROIs\' sbj{i}],'*roi.mat');
 7 |     roi_files=roi_filest([4 3 5 2 6:8 1 12 11 13 10 14:16 9],:);
 8 |     ImgData=['E:\VerbGeneration_network\1Preprocessing\FunImgARCWS\' sbj{i} '\swCovRegressed_4DVolume.nii']; 
 9 |     pathD=['E:\VerbGeneration_network\5ROISignals\' sbj{i} '.txt'];
10 |     extractROIsignals(roi_files,ImgData,pathD);
11 | end
12 | 
13 | function extractROIsignals(roi_files,ImgData,pathD)
14 | rois = maroi('load_cell', roi_files);
15 | mY=get_marsy(rois{:},ImgData,'mean');
16 | y = summary_data(mY);
17 | save(pathD,'y','-ascii','-double');
18 | end
19 | 


--------------------------------------------------------------------------------
/S2tLIofActivation.m:
--------------------------------------------------------------------------------
  1 | % Calculate the LI for Activation in two ways
  2 | % Fuction: (L-R)/(L+R)
  3 | clear all;
  4 | sbj=importdata('E:\VerbGeneration_network\sbj.txt');
  5 | maskname = importdata('E:\VerbGeneration_network\ROIs_LI.txt');
  6 | for i=1:length(sbj)
  7 |     i
  8 | 	clear roi_files rois mY y y1;
  9 |     % Using Individual ROI
 10 |     % Single ROI
 11 | 	roi_files = spm_get('files',['E:\VerbGeneration_network\4IndividualROIs\' sbj{i}],'*roi.mat');
 12 | 	rois = maroi('load_cell', roi_files);
 13 | 	mY=get_marsy(rois{:},['E:\VerbGeneration_network\3FirstLevelAnalysis\' sbj{i} '\spmT_0001.nii'],'mean');
 14 | 	y1 = summary_data(mY);
 15 |     % Combined ROI
 16 |     clear roi_files rois mY y2;
 17 | 	roi_files = spm_get('files',['E:\VerbGeneration_network\4tLI_ROIs\' sbj{i}],'*roi.mat');
 18 | 	rois = maroi('load_cell', roi_files);
 19 | 	mY=get_marsy(rois{:},['E:\VerbGeneration_network\3FirstLevelAnalysis\' sbj{i} '\spmT_0001.nii'],'mean');
 20 | 	y2 = summary_data(mY);
 21 |     y = [y1(1:8) y2(1:3) y1(9:16) y2(4:6)];
 22 | 	save(['E:\VerbGeneration_network\4tLIofActivation\T_statistics\' sbj{i} '.txt'],'y','-ascii','-double');
 23 | %     LI_ind(i,:)=(y(1:11)-y(12:22))./(y(1:11)+y(12:22));
 24 |     clear roi_files rois mY y1 y2 y;
 25 |     
 26 |     % Using Predefined ROI
 27 |     for k=1:length(maskname)
 28 |         clear out;
 29 |         if exist(['E:\VerbGeneration_network\3FirstLevelAnalysis\' sbj{i} '\li_' maskname{k} '_boot.txt'],'file')
 30 |             delete(['E:\VerbGeneration_network\3FirstLevelAnalysis\' sbj{i} '\li_' maskname{k} '_boot.txt']);
 31 |             delete(['E:\VerbGeneration_network\3FirstLevelAnalysis\' sbj{i} '\li_' maskname{k} '_boot.mat']);
 32 |         end
 33 |         out=struct('A',['E:\VerbGeneration_network\3FirstLevelAnalysis\' sbj{i} '\spmT_0001.nii'],...
 34 |             'B1',['E:\VerbGeneration_network\SelectedROIs\LI_' maskname{k} '.img'],...
 35 |             'C1',2,...
 36 |             'thr1',-5,...
 37 |             'pre',1,...
 38 |             'outfile',['E:\VerbGeneration_network\3FirstLevelAnalysis\' sbj{i} '\li_' maskname{k} '_boot.txt']);
 39 |         LI(out);
 40 |         clear out;
 41 |     end
 42 | end
 43 | 
 44 | 
 45 | 
 46 | 
 47 | 
 48 | % % Build ROIs
 49 | % % Create individual ROI for left/right frontal, left/right temporal, and left/right hemisphere
 50 | % clear all;
 51 | % sbj=importdata('E:\VerbGeneration_network\sbj.txt');
 52 | % for i=1:length(sbj)
 53 | %     if exist(['E:\VerbGeneration_network\4tLI_ROIs\'  sbj{i} '\'],'dir')==7
 54 | %         rmdir(['E:\VerbGeneration_network\4tLI_ROIs\'  sbj{i} '\'],'s');
 55 | %         mkdir(['E:\VerbGeneration_network\4tLI_ROIs\'  sbj{i} '\']);
 56 | %     else
 57 | %         mkdir(['E:\VerbGeneration_network\4tLI_ROIs\'  sbj{i} '\']);
 58 | %     end
 59 | %     i
 60 | % 	clear r* k;
 61 | %     roi_files = spm_get('files',['E:\VerbGeneration_network\4IndividualROIs\' sbj{i}],'*roi.mat');
 62 | % 	rois = maroi('load_cell', roi_files);
 63 | %     for k = 1:length(rois)
 64 | %         eval(sprintf('r%d = rois{%d};', k, k));
 65 | %     end
 66 | %     % left frontal
 67 | %     clear func o roi_fname varargout;
 68 | %     func='r3|r4|r5';
 69 | %     eval(['o=' func ';']);
 70 | %     o = label(o, 'leftFrontal');
 71 | %     roi_fname = maroi('filename', ['E:\VerbGeneration_network\4tLI_ROIs\' sbj{i} '\leftFrontal_1_roi.mat']);
 72 | %     varargout = {saveroi(o, roi_fname)};
 73 | %     % right frontal
 74 | %     clear func o roi_fname varargout;
 75 | %     func='r11|r12|r13';
 76 | %     eval(['o=' func ';']);
 77 | %     o = label(o, 'rightFrontal');
 78 | %     roi_fname = maroi('filename', ['E:\VerbGeneration_network\4tLI_ROIs\' sbj{i} '\rightFrontal_1_roi.mat']);
 79 | %     varargout = {saveroi(o, roi_fname)};
 80 | %     % left temporal
 81 | %     clear func o roi_fname varargout;
 82 | %     func='r1|r2|r6|r7|r8';
 83 | %     eval(['o=' func ';']);
 84 | %     o = label(o, 'leftTemporal');
 85 | %     roi_fname = maroi('filename', ['E:\VerbGeneration_network\4tLI_ROIs\' sbj{i} '\leftTemporal_1_roi.mat']);
 86 | %     varargout = {saveroi(o, roi_fname)};
 87 | %     % right temporal
 88 | %     clear func o roi_fname varargout;
 89 | %     func='r9|r10|r14|r15|r16';
 90 | %     eval(['o=' func ';']);
 91 | %     o = label(o, 'rightTemporal');
 92 | %     roi_fname = maroi('filename', ['E:\VerbGeneration_network\4tLI_ROIs\' sbj{i} '\rightTemporal_1_roi.mat']);
 93 | %     varargout = {saveroi(o, roi_fname)};
 94 | %     % left hemisphere
 95 | %     clear func o roi_fname varargout;
 96 | %     func='r1|r2|r3|r4|r5|r6|r7|r8';
 97 | %     eval(['o=' func ';']);
 98 | %     o = label(o, 'zleftHemi');
 99 | %     roi_fname = maroi('filename', ['E:\VerbGeneration_network\4tLI_ROIs\' sbj{i} '\leftzHemi_1_roi.mat']);
100 | %     varargout = {saveroi(o, roi_fname)};
101 | %     % right hemisphere
102 | %     clear func o roi_fname varargout;
103 | %     func='r9|r10|r11|r12|r13|r14|r15|r16';
104 | %     eval(['o=' func ';']);
105 | %     o = label(o, 'zrightHemi');
106 | %     roi_fname = maroi('filename', ['E:\VerbGeneration_network\4tLI_ROIs\' sbj{i} '\rightzHemi_1_roi.mat']);
107 | %     varargout = {saveroi(o, roi_fname)};
108 | %     clear r* k func o roi_fname varargout;
109 | % end
110 | 
111 | 
112 | 
113 | 
114 | 
115 | % % % No need to be done at every time
116 | % % % Build ROIs for LI analysis    
117 | % % ROI=importdata('E:\VerbGeneration_network\ROIs_LI.txt');
118 | % % for k=1:length(ROI)
119 | % %     clear V1 Y1 V2 Y2 Y vol;
120 | % %     V1=spm_vol(['E:\VerbGeneration_network\SelectedROIs\res_Left' ROI{k} '.img']);
121 | % %     Y1=spm_read_vols(V1);
122 | % %     V2=spm_vol(['E:\VerbGeneration_network\SelectedROIs\res_Right' ROI{k} '.img']);
123 | % %     Y2=spm_read_vols(V2);
124 | % %     Y=double(or(Y1,Y2));
125 | % %     vol=V1;
126 | % %     vol.fname=['E:\VerbGeneration_network\SelectedROIs\LI_' ROI{k} '.img'];
127 | % %     vol.private.dat.fname=vol.fname;
128 | % %     spm_write_vol(vol,Y);
129 | % %     clear V1 Y1 V2 Y2 Y vol;
130 | % % end
131 | % % % Frontal
132 | % % clear all;
133 | % % V1=spm_vol('E:\VerbGeneration_network\SelectedROIs\LI_IFG.img');
134 | % % Y1=spm_read_vols(V1);
135 | % % V2=spm_vol('E:\VerbGeneration_network\SelectedROIs\LI_IFGorb.img');
136 | % % Y2=spm_read_vols(V2);
137 | % % V3=spm_vol('E:\VerbGeneration_network\SelectedROIs\LI_MFG.img');
138 | % % Y3=spm_read_vols(V3);
139 | % % Y=double(Y1|Y2|Y3);
140 | % % vol=V1;
141 | % % vol.fname='E:\VerbGeneration_network\SelectedROIs\LI_Frontal.img';
142 | % % vol.private.dat.fname=vol.fname;
143 | % % spm_write_vol(vol,Y);
144 | % % % Temporal
145 | % % clear all;
146 | % % V1=spm_vol('E:\VerbGeneration_network\SelectedROIs\LI_AntTemp.img');
147 | % % Y1=spm_read_vols(V1);
148 | % % V2=spm_vol('E:\VerbGeneration_network\SelectedROIs\LI_MidAntTemp.img');
149 | % % Y2=spm_read_vols(V2);
150 | % % V3=spm_vol('E:\VerbGeneration_network\SelectedROIs\LI_MidPostTemp.img');
151 | % % Y3=spm_read_vols(V3);
152 | % % V4=spm_vol('E:\VerbGeneration_network\SelectedROIs\LI_PostTemp.img');
153 | % % Y4=spm_read_vols(V4);
154 | % % V5=spm_vol('E:\VerbGeneration_network\SelectedROIs\LI_AngG.img');
155 | % % Y5=spm_read_vols(V5);
156 | % % Y=double(Y1|Y2|Y3|Y4|Y5);
157 | % % vol=V1;
158 | % % vol.fname='E:\VerbGeneration_network\SelectedROIs\LI_Temporal.img';
159 | % % vol.private.dat.fname=vol.fname;
160 | % % spm_write_vol(vol,Y);
161 | % % % Hemisphere
162 | % % clear all;
163 | % % V1=spm_vol('E:\VerbGeneration_network\SelectedROIs\LI_Frontal.img');
164 | % % Y1=spm_read_vols(V1);
165 | % % V2=spm_vol('E:\VerbGeneration_network\SelectedROIs\LI_Temporal.img');
166 | % % Y2=spm_read_vols(V2);
167 | % % Y=double(or(Y1,Y2));
168 | % % vol=V1;
169 | % % vol.fname='E:\VerbGeneration_network\SelectedROIs\LI_Hemi.img';
170 | % % vol.private.dat.fname=vol.fname;
171 | % % spm_write_vol(vol,Y);
172 | 


--------------------------------------------------------------------------------
/S2tWaveletDecompose.m:
--------------------------------------------------------------------------------
 1 | % DO THIS UNDER R2014 with WMTSA toolbox (https://atmos.washington.edu/wmtsa/)
 2 | % using the maximal overlap discrete wavelet transform (MODWT) to decompose the time series
 3 | % Out of the top 4 bandpath: (1) 0.1-0.2Hz; (2) 0.05-0.1Hz; (3) 0.025-0.05Hz; (4) 0.0125-0.025Hz;
 4 | % We'd use (2) and (3) because (1) is not typically used in resting, and (4) is too slow for a window of 40s (0.025Hz)
 5 | % We may test window size of 20, 30, 40s, which actually all falls in (3), and also (2)
 6 | clear all;
 7 | sbj = importdata('E:\VerbGeneration_network\sbj.txt'); % List of your subjects
 8 | 
 9 | for s=1:length(sbj)
10 | 	s
11 | 	clear Dt modDt D2;
12 | 	Dt=importdata(['E:\VerbGeneration_network\5ROISignals\' sbj{s} '.txt']);% Load the ROI signals
13 |     % MODWT
14 |     for i=1:16
15 |         modDt(:,:,i) = modwt(Dt(:,i),'d4',floor(log2(size(Dt,1))),'circular');
16 |     end
17 |     % Select the second bandpath (0.05~0.1)
18 |     D2=squeeze(modDt(:,2,:));     % Select the second bandpath (0.05~0.1)
19 |     D3=squeeze(modDt(:,3,:));     % Select the third bandpath (0.025~0.05)
20 |     % save the components
21 |     save(['E:\VerbGeneration_network\5tDecomposedSignals\' sbj{s} '.mat'],'D2','D3');
22 | end
23 | 


--------------------------------------------------------------------------------
/S3CommunityMatrix.m:
--------------------------------------------------------------------------------
 1 | % Generate the community assignments matrix
 2 | clear all;
 3 | sbj=importdata('E:\VerbGeneration_network\sbj.txt');
 4 | gamma = 1; omega = 0.4; % Based on Chai et al 2016 CC
 5 | 
 6 | parfor t=1:length(sbj)
 7 |     X=load(['E:\VerbGeneration_network\5tDecomposedSignals\' sbj{t} '.mat'],'D2');
 8 |     pathD=['E:\VerbGeneration_network\6CommunityMatrix\' sbj{t} '.mat'];
 9 | 
10 |     communitydetection(gamma,omega,pathD,X.D2);
11 | end
12 | 
13 | function communitydetection(gamma,omega,pathD,d)
14 | % Creating adjacency matrix over time: Window Size = 40 sec (14 windows, 8=half-window)/30 sec (19 windows, 6=half-window)/1 min (9 windows, 12=half-window;
15 | % Overlapping = 50% with matlab new wavelet coherence
16 | SampRate=1/2.5;
17 | for i=1:14
18 |     clear At;
19 |     for k=1:16
20 |         for kk=1:16
21 |             clear wcoh f;
22 |             [wcoh,~,f] = wcoherence(d(((i-1)*8+1):(i+1)*8,k),d(((i-1)*8+1):(i+1)*8,kk),SampRate);
23 |             At(k,kk)=mean(mean(wcoh(find(f>0.05 & f<0.1),:)));
24 |             clear wcoh f;
25 |         end
26 |     end
27 |     At=round(At.*1000000)./1000000;
28 |     A{i,1}=At;
29 |     clear At;
30 | end
31 | 
32 | % % Creating adjacency matrix over time: Window Size = 40 sec (27 windows, (4*i-3):(4*i+12))/30 sec (28 windows, (4*i-3):(4*i+8));
33 | % % Step = 4 TR/10s; with matlab new wavelet coherence
34 | % SampRate=1/2.5;
35 | % for i=1:27
36 | %     clear At;
37 | %     for k=1:16
38 | %         for kk=1:16
39 | %             clear wcoh f;
40 | %             [wcoh,~,f] = wcoherence(d((4*i-3):(4*i+12),k),d((4*i-3):(4*i+12),kk),SampRate);
41 | %             At(k,kk)=mean(mean(wcoh(find(f>0.05 & f<0.1),:)));
42 | %             clear wcoh f;
43 | %         end
44 | %     end
45 | %     At=round(At.*1000000)./1000000;
46 | %     A{i,1}=At;
47 | %     clear At;
48 | % end
49 | 
50 | for ii=1:100 % 100 times of optimization
51 |     % Following based on Example on multilayer network quality function of Mucha et al. 2010
52 |     % (using multilayer cell A with A{s} the adjacency matrix of layer s)
53 |     clear N T B mm PP St;
54 |     N=length(A{1});
55 |     T=length(A);
56 |     [B,mm] = multiord(A,gamma,omega);
57 |     PP = @(S) postprocess_ordinal_multilayer(S,T);
58 |     [St,Q(ii),~] = iterated_genlouvain(B,10000,0,1,'moverandw',[], PP); % tobe saved (Q)
59 |     St = reshape(St, N, T);
60 |     Q(ii)=Q(ii)/mm;
61 |     S(:,:,ii)=St';% tobe saved
62 |     clear N T B mm PP St;
63 | end
64 | if exist(pathD,'file')
65 | delete(pathD);
66 | end
67 | save(pathD,'A','Q','S');
68 | end
69 | 


--------------------------------------------------------------------------------
/S4ModularityMeasures.m:
--------------------------------------------------------------------------------
 1 | % Calculating MA, Flexibility, LI, P, Z, FCw and FCb
 2 | clear all;
 3 | sbj=importdata('E:\VerbGeneration_network\sbj.txt');
 4 | System=[1;1;1;2;2;2;2;2;3;3;3;4;4;4;4;4];% Define System for LI and I, R calculation
 5 | 
 6 | parfor t=1:length(sbj)
 7 |     X=load(['E:\VerbGeneration_network\6CommunityMatrix\' sbj{t} '.mat']);
 8 |     pathD=['E:\VerbGeneration_network\7ModularityMeasures\' sbj{t} '.mat'];
 9 |     ModularityMeasures(System,X.S,X.A,pathD);
10 | end
11 | 
12 | function ModularityMeasures(System,SO,A,pathD)
13 | for ii=1:100
14 |     MA(:,:,ii)=allegiance(SO(:,:,ii));% Module Allegiance
15 |     F(ii,:)=flexibility(SO(:,:,ii));% Flexibility
16 |     Prom(ii,:)=promiscuity(SO(:,:,ii));% Promiscuity
17 |     I_s(ii,:)=integration(MA(:,:,ii),System);% Integration by System (L Broca, L Wernicke, R Broca, R Wernicke)
18 |     R_s(ii,:)=recruitment(MA(:,:,ii),System);% Recruitment by System (L Broca, L Wernicke, R Broca, R Wernicke)
19 |     RI(:,:,ii)=integration_sub(MA(:,:,ii),System);% RI by System (L Broca, L Wernicke, R Broca, R Wernicke)
20 | end
21 | 
22 | save(pathD,'MA','F','Prom','I_s','R_s','RI');
23 | end
24 | 


--------------------------------------------------------------------------------
/S5RandomCommunity.m:
--------------------------------------------------------------------------------
  1 | % Generating Null Models
  2 | 
  3 | clear all;
  4 | sbj=importdata('E:\VerbGeneration_network\sbj.txt');
  5 | gamma = 1; omega = 0.4; % Based on Chai et al 2016 CC
  6 | 
  7 | parfor t=1:length(sbj)
  8 |     X=load(['E:\VerbGeneration_network\5tDecomposedSignals\' sbj{t} '.mat'],'D2');
  9 |     pathD=['E:\VerbGeneration_network\8RandomCommunity\' sbj{t} '.mat'];
 10 |     
 11 |     % Connectional Null Model
 12 |     ConnectionalNullModel(pathD,X.D2,gamma,omega);
 13 | 
 14 |     % Nodal Null Model
 15 |     NodalNullModel(pathD,X.D2,gamma,omega);
 16 | 
 17 |     % Static Null Model
 18 |     StaticNullModel(pathD,X.D2,gamma,omega);
 19 | 	
 20 | end
 21 | 
 22 | % Connectional Null Model
 23 | function ConnectionalNullModel(pathD,d,gamma,omega)
 24 | % Creating adjacency matrix over time: Window Size = 40 sec (14 windows, 8=half-window)/30 sec (19 windows, 6=half-window)/1 min (9 windows, 12=half-window;
 25 | % Overlapping = 50% with matlab new wavelet coherence
 26 | SampRate=1/2.5;
 27 | for i=1:14
 28 |     clear At;
 29 |     for k=1:16
 30 |         for kk=1:16
 31 |             clear wcoh f;
 32 |             [wcoh,~,f] = wcoherence(d(((i-1)*8+1):(i+1)*8,k),d(((i-1)*8+1):(i+1)*8,kk),SampRate);
 33 |             At(k,kk)=mean(mean(wcoh(find(f>0.05 & f<0.1),:)));
 34 |             clear wcoh f;
 35 |         end
 36 |     end
 37 |     At=round(At.*1000000)./1000000;
 38 |     A{i,1}=At;
 39 |     clear At;
 40 | end
 41 | for k=1:100
 42 | 	clear Acr;
 43 | 	for i=1:14
 44 | 		Acr{i}=randmio_und(A{i},20);
 45 | 	end
 46 | 	for ii=1:100 % 100 times of optimization
 47 |         clear N T B mm PP St;
 48 |         N=length(Acr{1});
 49 |         T=length(Acr);
 50 |         [B,mm] = multiord(Acr,gamma,omega);
 51 |         PP = @(S) postprocess_ordinal_multilayer(S,T);
 52 |         [St,Qcr(ii,k),~] = iterated_genlouvain(B,10000,0,1,'moverandw',[], PP); % tobe saved (Qtr)
 53 |         Qcr(ii,k)=Qcr(ii,k)/mm;
 54 |         St = reshape(St, N, T);
 55 |         Scr(:,:,ii,k)=St';% tobe saved Str
 56 |         clear N T B mm PP St;
 57 |     end
 58 | 	clear Acr;
 59 | end
 60 | if exist(pathD,'file')
 61 | delete(pathD);
 62 | end
 63 | save(pathD,'Qcr','Scr');
 64 | end
 65 | 
 66 | % Nodal Null Model
 67 | function NodalNullModel(pathD,d,gamma,omega)
 68 | % Creating adjacency matrix over time: Window Size = 40 sec (14 windows, 8=half-window)/30 sec (19 windows, 6=half-window)/1 min (9 windows, 12=half-window;
 69 | % Overlapping = 50% with matlab new wavelet coherence
 70 | SampRate=1/2.5;
 71 | % Creating 100 null models
 72 | for k=1:100
 73 |     % Creating adjacency matrix over time, with ROIs rotated at each window
 74 |     clear Anr;
 75 |     for i=1:14
 76 |         clear At rd dt tt;
 77 |         rd=randperm(16);
 78 |         % [~,Inr(i,:,k)]=sort(rd);% tobe saved Inr
 79 |         for tt=1:16
 80 |             dt(:,tt)=d(:,rd(tt));
 81 |         end
 82 | %         Coherence
 83 | 		for kk=1:16
 84 | 			for kkk=1:16
 85 | 				clear wcoh f;
 86 | 				[wcoh,~,f] = wcoherence(dt(((i-1)*8+1):(i+1)*8,kk),dt(((i-1)*8+1):(i+1)*8,kkk),SampRate);
 87 | 				At(kk,kkk)=mean(mean(wcoh(find(f>0.05 & f<0.1),:)));
 88 | 				clear wcoh f;
 89 | 			end
 90 | 		end
 91 | 		At=round(At.*1000000)./1000000;
 92 |         Anr{i,1}=At;
 93 |         clear At rd dt tt;
 94 |     end
 95 |     for ii=1:100 % 100 times of optimization
 96 |         clear N T B mm PP St;
 97 |         N=length(Anr{1});
 98 |         T=length(Anr);
 99 |         [B,mm] = multiord(Anr,gamma,omega);
100 |         PP = @(S) postprocess_ordinal_multilayer(S,T);
101 |         [St,Qnr(ii,k),~] = iterated_genlouvain(B,10000,0,1,'moverandw',[], PP); % tobe saved (Qnr)
102 |         Qnr(ii,k)=Qnr(ii,k)/mm;
103 |         St = reshape(St, N, T);
104 |         Snr(:,:,ii,k)=St';% tobe saved Snr
105 |         clear N T B mm PP St;
106 |     end
107 |     clear Anr;
108 | end
109 | save(pathD,'Qnr','Snr','-append');% 'Inr'
110 | end
111 | 
112 | % Static Null Model
113 | function StaticNullModel(pathD,d,gamma,omega)
114 | % Creating adjacency matrix over time: Window Size = 40 sec (14 windows, 8=half-window)/30 sec (19 windows, 6=half-window)/1 min (9 windows, 12=half-window;
115 | % Overlapping = 50% with matlab new wavelet coherence
116 | SampRate=1/2.5;
117 | % Creating 100 null models
118 | for k=1:100
119 | 	% Creating adjacency matrix over time, by duplicating one of the windows
120 | 	clear At As;
121 | 	rd=randperm(14);
122 | %     coherence
123 | 	for kk=1:16
124 | 		for kkk=1:16
125 | 			clear wcoh f;
126 | 			[wcoh,~,f] = wcoherence(d(((rd(1)-1)*8+1):(rd(1)+1)*8,kk),d(((rd(1)-1)*8+1):(rd(1)+1)*8,kkk),SampRate);
127 | 			At(kk,kkk)=mean(mean(wcoh(find(f>0.05 & f<0.1),:)));
128 | 			clear wcoh f;
129 | 		end
130 | 	end
131 |     At=round(At.*1000000)./1000000;
132 |     for i=1:14
133 |         As{i,1}=At;
134 |     end
135 |     for ii=1:100 % 100 times of optimization
136 |         % Following based on Example on multilayer network quality function of Mucha et al. 2010
137 |         % (using multilayer cell A with A{s} the adjacency matrix of layer s)
138 |         clear N T B mm PP St;
139 |         N=length(As{1});
140 |         T=length(As);
141 |         [B,mm] = multiord(As,gamma,omega);
142 |         PP = @(S) postprocess_ordinal_multilayer(S,T);
143 |         [St,Qs(ii,k),~] = iterated_genlouvain(B,10000,0,1,'moverandw',[], PP); % tobe saved (Qs)
144 |         Qs(ii,k)=Qs(ii,k)/mm;
145 |         St = reshape(St, N, T);
146 |         Ss(:,:,ii,k)=St';% tobe saved Ss
147 |         clear N T B mm PP St;
148 |     end
149 |     clear At As;
150 | end
151 | save(pathD,'Qs','Ss','-append');
152 | end
153 | 


--------------------------------------------------------------------------------
/S6NullModelMeasures.m:
--------------------------------------------------------------------------------
 1 | % Calculating Flexibility and MA
 2 | clear all;
 3 | sbj=importdata('E:\VerbGeneration_network\sbj.txt');
 4 | System=[1;1;1;2;2;2;2;2;3;3;3;4;4;4;4;4];% Define System for LI and I, R calculation
 5 | 
 6 | parfor t=1:length(sbj)
 7 |     x=load(['E:\VerbGeneration_network\8RandomCommunity\' sbj{t} '.mat']);
 8 |     pathD=['E:\VerbGeneration_network\9NullModelMeasures\' sbj{t} '.mat'];
 9 |     % Generating Modularity Measures
10 |     GenModalMeasure(C,System,x.Scr,x.Str,x.Snr,x.Ss,pathD);
11 | end
12 | 
13 | % Generating Modularity Measures
14 | function GenModalMeasure(C,System,Scr,Str,Snr,Ss,pathD)
15 | for k=1:100
16 |     clear SO MAt Ft Promt Perst I_ht I_st R_ht R_st;
17 |     SO(:,:,:,1)=Scr(:,:,:,k);
18 |     SO(:,:,:,2)=Snr(:,:,:,k);
19 |     SO(:,:,:,3)=Ss(:,:,:,k);
20 | 
21 |     for ii=1:100
22 |         for t=1:3
23 |             MAt(:,:,ii,t)=allegiance(SO(:,:,ii,t));% Module Allegiance
24 |             Ft(ii,:,t)=flexibility(SO(:,:,ii,t));% Flexibility
25 |             Promt(ii,:,t)=promiscuity(SO(:,:,ii,t));% Promiscuity
26 |             I_st(ii,:,t)=integration(MAt(:,:,ii),System);% Integration by System (L Broca, L Wernicke, R Broca, R Wernicke)
27 |             R_st(ii,:,t)=recruitment(MAt(:,:,ii),System);% Recruitment by System (L Broca, L Wernicke, R Broca, R Wernicke)
28 |         end
29 |     end
30 |     
31 |     MAcr(:,:,:,k)=MAt(:,:,:,1);MAnr(:,:,:,k)=MAt(:,:,:,2);MAs(:,:,:,k)=MAt(:,:,:,3);
32 |     Fcr(:,:,k)=Ft(:,:,1);Fnr(:,:,k)=Ft(:,:,2);Fs(:,:,k)=Ft(:,:,3);
33 |     Promcr(:,:,k)=Promt(:,:,1);Promnr(:,:,k)=Promt(:,:,2);Proms(:,:,k)=Promt(:,:,3);
34 |     I_scr(:,:,k)=I_st(:,:,1);I_snr(:,:,k)=I_st(:,:,2);I_ss(:,:,k)=I_st(:,:,3);
35 |     R_scr(:,:,k)=R_st(:,:,1);R_snr(:,:,k)=R_st(:,:,2);R_ss(:,:,k)=R_st(:,:,3);
36 | 
37 |     clear SO MAt Ft Promt I_st R_st;
38 | end
39 | 
40 | if exist(pathD,'file')
41 |     delete(pathD);
42 | end
43 | 
44 | save(pathD,'MAcr','MAnr','MAs','Fcr','Fnr','Fs','Promcr','Promnr','Proms','I_scr','I_snr','I_ss','R_hcr','R_hnr','R_hs','R_scr','R_snr','R_ss');
45 | end
46 | 


--------------------------------------------------------------------------------