fixed paths

Author: mangstad

s01_Load.m

@@ -2,13 +2,13 @@
 %utility funcions
 addpath /home/slab/users/mangstad/repos/Misc_utils/
 
-Exp = '/net/pepper/ABCD/CIFTI/';
+Exp = '/net/pepper/ABCD/CIFTI/Scripts/rest_neurocognition/';
 
 %%
 %Setup files
-DataFile = [Exp '/Scripts/rest_neurocognition/Data/ABCD_rest.csv'];
-CorrTemplate = [Exp '/Rest/Gordon_Sub_Cere/[Subject].txt'];
-NetsFile = [Exp '/Scripts/rest_neurocognition/Data/gordon_sub_cere_parcels.csv'];
+DataFile = [Exp '/Data/ABCD_rest.csv'];
+CorrTemplate = [Exp '/Data/Gordon_Sub_Cere/[Subject].txt'];
+NetsFile = [Exp '/Data/gordon_sub_cere_parcels.csv'];
 
 %%
 %setup

s02_BBS.m

@@ -1,6 +1,4 @@
 %%
-%utility funcions
-addpath /home/slab/users/mangstad/repos/Misc_utils/
 
 NumComp = 250;
 Perms = 10000;
@@ -68,42 +66,7 @@
 results_g_wo_perms = mc_bbs_perm(featuremat(subset2,:),mainpheno(subset2,:),nuisance(subset2,:),folds_sub2,NumComp,Perms,'LOSOPheno',0,'Scores',results_g_white.Aa);
 results_g_wo_nih_perms = mc_bbs_perm(featuremat(subset2,:),nihpheno(subset2,:),nuisance(subset2,:),folds_sub2,NumComp,Perms,'LOSOPheno',0,'Scores',results_g_white.Aa);
 
-results_g_all_perms_dsg = mc_bbs_perm(featuremat,losophenoG,nuisance,folds,NumComp,Perms,'LOSOPheno',0,'Scores',results_g_all.Aa);
-results_g_all_perms_dss1 = mc_bbs_perm(featuremat,losophenoS1,nuisance,folds,NumComp,Perms,'LOSOPheno',0,'Scores',results_g_all.Aa);
-results_g_all_perms_dss2 = mc_bbs_perm(featuremat,losophenoS2,nuisance,folds,NumComp,Perms,'LOSOPheno',0,'Scores',results_g_all.Aa);
-results_g_all_perms_dss3 = mc_bbs_perm(featuremat,losophenoS3,nuisance,folds,NumComp,Perms,'LOSOPheno',0,'Scores',results_g_all.Aa);
-
-%now calculate proper dropsite performance, since LOSOPheno isn't working
-%at the moment for permutations
-
-
-
-
-%permute g pheno and measure dmntpn
-inmask = zeros(418,418);
-for i = 1:max(nets)
-    inmask(nets==i,nets==i) = 1;
-end
-inmask = inmask==1;
-
-dmntpn = zeros(418,418);
-idx = [3,5,8,11,12];
-for i = 1:numel(idx)
-    for j = 1:numel(idx)
-        dmntpn(nets==idx(i),nets==idx(j)) = 1;
-        dmntpn(nets==idx(j),nets==idx(i)) = 1;
-    end
-end
-dmntpn = (dmntpn - inmask)>0;
-inmask = mc_flatten_upper_triangle(inmask);
-dmntpn = mc_flatten_upper_triangle(dmntpn);
-
-mdl_true = fitlm([Aall(good1,1:250) nuisance(good1,:)],dat.G_lavaan(good1));
-zcons = zscore(coeffall(:,1:250)*mdl_true.Coefficients.Estimate(2:251));
-
-supra = abs(zcons)>2;
-supra = supra';
-100*sum(inmask)/numel(inmask)
-100*sum(dmntpn)/numel(dmntpn)
-100*sum(supra.*inmask)/sum(supra)
-100*sum(supra.*dmntpn)/sum(supra)
+results_g_all_perms_dsg = mc_bbs_perm(featuremat,losophenoG,nuisance,folds,NumComp,Perms,'LOSOPheno',1,'Scores',results_g_all.Aa);
+results_g_all_perms_dss1 = mc_bbs_perm(featuremat,losophenoS1,nuisance,folds,NumComp,Perms,'LOSOPheno',1,'Scores',results_g_all.Aa);
+results_g_all_perms_dss2 = mc_bbs_perm(featuremat,losophenoS2,nuisance,folds,NumComp,Perms,'LOSOPheno',1,'Scores',results_g_all.Aa);
+results_g_all_perms_dss3 = mc_bbs_perm(featuremat,losophenoS3,nuisance,folds,NumComp,Perms,'LOSOPheno',1,'Scores',results_g_all.Aa);

s03_FullPCA.m

@@ -1,6 +1,6 @@
 %%
 %Setup output file
-OutputFile = [Exp '/Scripts/rest_neurocognition/Results/ABCD_rest_CIFTI_expressions.csv'];
+OutputFile = [Exp '/Results/ABCD_rest_CIFTI_expressions.csv'];
 NumComp = 250;
 
 %%
@@ -36,7 +36,7 @@
 
 %%
 %consensus
-betas = load([Exp '/CIFTI/Scripts/rest_neurocognition/Results/mm_betas.csv']);
+betas = load([Exp '/Results/mm_betas.csv']);
 cons = coeffall(:,1:NumComp)*betas;
 
 %%
@@ -45,16 +45,16 @@
 
 close all
 plot_jica_component(cons(:,1)',1,1,2,nets,'Mixed Model Consensus',[1:16]);
-print([Exp '/Scripts/rest_neurocognition/Results/consensus.pdf'],'-dpdf','-r600');
+print([Exp '/Results/consensus.pdf'],'-dpdf','-r600');
 
 tmp = zcons(:,1)';
 tmp = mc_unflatten_upper_triangle(tmp);
 tmp = tmp + tmp';
-save([Exp '/Scripts/rest_neurocognition/Results/consensus_z_square.txt'],'tmp','-ASCII');
+save([Exp '/Results/consensus_z_square.txt'],'tmp','-ASCII');
 
 close all
 plot_jica_component(cons(:,1)',1,1,2,nets,'Mixed Model Consensus',[1:16]);
-print([Exp '/Scripts/rest_neurocognition/Results/consensus.svg'],'-dsvg','-r600');
+print([Exp '/Results/consensus.svg'],'-dsvg','-r600');
 
 
 dmntpn = zeros(418,418);

s05_Variance.m

@@ -93,4 +93,4 @@
 network1 = NetNames(ind(u,1));
 network2 = NetNames(ind(u,2));
 output = table(network1,network2,posneg,g,s1,s2,s3,cardsort,flanker,list,pattern,picture,picvocab,reading,ravlt_sd,ravlt_ld,wiscv,lmt);
-writetable(output,'Results/network_21_suprathreshold.csv');
+writetable(output,[Exp '/Results/network_21_suprathreshold.csv']);