Optimising 7T-fMRI for Imaging Regions of Magnetic Susceptibility.

Code ↔ Paper

22 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 22 matches · 7 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted

[1] § Methods › Data Analysis › Preprocessing ↔ sub_7Tpilot_ME.sh, lines 94–170 · score 0.97 · phase encoding direction, antsRegistrationSyN.sh, antsApplyTransforms, rigid body, native T1, native EPI
[2] § Methods › Data Analysis › Preprocessing ↔ sub_7Tpilot_SE.sh, lines 47–131 · score 0.96 · phase encoding direction, antsRegistrationSyN.sh, antsApplyTransforms, rigid body, native T1, native EPI
[3] § Methods › Data Analysis › Preprocessing ↔ sub_7Tpilot_SE.sh, lines 47–131 · score 0.92 · dDespike, dTshift, dAllineate, brain mask, AFNI, ANTs
[4] § Methods › Data Analysis › Preprocessing ↔ sub_7Tpilot_ME.sh, lines 47–92 · score 0.84 · dDespike, dTshift, dAllineate, AFNI, FSL, dvolreg
[5] § Methods › Data Analysis › Preprocessing ↔ sub_7Tpilot_ME_slice_leakage.sh, lines 94–144 · score 0.81 · t2smap, native EPI space, brain mask, slice leakage, optimal, coregistration
[6] § Methods › Data Analysis › Preprocessing ↔ matlab/mp2rage_scripts/DemoRemoveBackgroundNoise.m, the whole file · a weak match · score 0.77 · background noise, MP2RAGE T1w, T1w image, segmentation, bias, brain
[7] § Methods › Data Analysis › Preprocessing ↔ matlab/mp2rage_proc.m, the whole file · a weak match · score 0.76 · skull stripped, fMRIprep, CAT12, segmentation, SPM12, bias
[8] § Methods › Data Analysis › Preprocessing ↔ sub_7Tpilot_ME.sh, lines 94–170 · score 0.76 · tedana workflow, native EPI space, brain mask, optimal, coregistration, preprocessing
[9] § Methods › Data Analysis › First‐Level (Within‐Participant) GLM ↔ firstlevel_glm.m, lines 272–334 · score 0.74 · odd volumes, single band, ICA denoised, multi echo, GLM, linear
[10] § Methods › Data Analysis › First‐Level (Within‐Participant) GLM ↔ firstlevel_glm_mvpa.m, lines 55–112 · score 0.72 · high pass filter, beta image, implicitly, AR, onset, regressors
[11] § Methods › Data Analysis › First‐Level (Within‐Participant) GLM ↔ firstlevel_glm_ernst.m, lines 112–185 · score 0.70 · high pass filter, motion parameters, implicitly, onset, regressors, SPM
[12] § Methods › Data Analysis › Second‐Level (Across‐Participant) GLM › Slice Leakage Analysis ↔ MC_sliceLeakage_find_masks_all.m, the whole file · a weak match · score 0.69 · spherical ROI, artefact location, shift, radius, peak, native
[13] § Results › Region‐of‐Interest Analysis › Univariate Analysis ↔ secondlevel_glm_roi.m, lines 1549–1624 · score 0.68 · MBodd, LIFGpt, LpMTG, LvATL, interaction, RITG
[14] § Methods › Data Analysis › Second‐Level (Across‐Participant) GLM › Whole‐Brain Analysis ↔ secondlevel_glm.m, lines 97–222 · score 0.66 · batch spm anova, factorial designs, band, MESB, SEMB, MEMB
[15] § Methods › Data Analysis › First‐Level (Within‐Participant) GLM ↔ secondlevel_glm_roi.m, lines 1549–1624 · score 0.66 · SEMBodd, MEMBodd, match, band, GLM, model
[16] § Methods › Data Analysis › Second‐Level (Across‐Participant) GLM › Slice Leakage Analysis ↔ MC_sliceLeakage_extract_ROIs_all.m, lines 13–81 · score 0.65 · slice leakage, artefact regions, control sequence, peaks, SB, seed
[17] § Results › Region‐of‐Interest Analysis ↔ secondlevel_glm_roi.m, lines 232–291 · score 0.61 · LmMTG, LIFGpt, LpMTG, LvATL, LTP, LFP
[18] § Methods › Image Acquisition ↔ matlab/mp2rage_scripts/func/T1M0estimateMP2RAGE.m, the whole file · a weak match · score 0.60 · partial Fourier, flip angle, neuroimaging, slabs, pulse, TR
[19] § Methods › Image Acquisition ↔ matlab/mp2rage_scripts/func/T1estimateMP2RAGE.m, the whole file · a weak match · score 0.60 · partial Fourier, flip angle, neuroimaging, slabs, pulse, TR
[20] § Methods › Data Analysis › Second‐Level (Across‐Participant) GLM › Slice Leakage Analysis ↔ MC_sliceLeakage_find_masks_all.m, the whole file · a weak match · score 0.60 · artefact location, slice leakage, shift, peaks, GRAPPA, seed
[21] § Results › Excluded Participants ↔ motion_outliers.m, the whole file · a weak match · score 0.57 · absolute translation, absolute rotation, motion, volumes
[22] § Methods › Data Analysis › Second‐Level (Across‐Participant) GLM › Region‐of‐Interest (ROI) Analysis › Exploratory Multivariate Pattern Analysis (MVPA) ↔ secondlevel_glm_roi.m, lines 909–968 · score 0.56 · cosine distance, beta images, zero, block, MVPA, ROI

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The paper is loaded by your browser, with JavaScript: read it at doi.org or on Europe PMC.

The authors' code

MATLAB · 1,624 lines · 71 KB · no license · 4 matches

addpath('/group/mlr-lab/AH/Projects/spm12/');
addpath('/group/mlr-lab/AH/Projects/toolboxes/');
addpath('/group/mlr-lab/AH/Projects/toolboxes/Violinplot/');
addpath('/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/riksneurotools-master/Util');
% the above line adds the function roi_extract.m to path. Before using this
% % function, check that lines 195-196 read:
% ROI(nr,s).mean = nanmean(d,2);
% ROI(nr,s).median = nanmedian(d,2);
addpath('/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts');
root = ['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/derivatives'];
cd(root);
% exclusions: 013 and 017 for excessive head motion, and 004 because the
% pTx run was not acquired.
subs=[{'001'},{'002'},{'003'},{'005'},{'006'},{'007'},{'008'},{'009'},{'010'},{'011'},{'012'},{'014'},{'015'},{'016'},{'017'},{'018'},{'019'}];
%% comparing pTx to SESB
% setup ROIs. Plot the cluster-corrected main effect (all conditions set to
% 1) and use only ROIs that overlap with this main effect (comment out
% those that do not).
R = struct();
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--51_9_-36.nii'];% temporalpole
R.ROIfiles{1}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--42_-24_-27.nii']; % vATL
R.ROIfiles{2}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-45_-33_-21.nii'];% rITG
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--3_57_-15.nii'];% frontal pole
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--48_-21_-9.nii'];% mMTG
R.ROIfiles{3}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--55_-43_-7.nii'];% pMTG
R.ROIfiles{4}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--54_27_6.nii'];% IFGptri
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-51_-27_-3.nii'];% rSTG
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-60_0_33.nii'];%r PCG
% extract ROIs from con images (expressing model fit)
imgs = cell(1,length(subs));
con = struct();
for s=1:length(subs)
imgs{s}{1} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_SESB/con_0003.nii'];
imgs{s}{2} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_ptx8ms/con_0003.nii'];
end
% extract data from each ROI
con.Datafiles = imgs;
con.output_raw = 1;
con.ROIfiles=R.ROIfiles;
con.ROI = roi_extract(con);
% extract ROIs from spmT images (expressing model precision)
imgs = cell(1,length(subs));
spmT = struct();
for s=1:length(subs)
imgs{s}{1} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_SESB/spmT_0003.nii'];
imgs{s}{2} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_ptx8ms/spmT_0003.nii'];
end
% extract data from each ROI
spmT.Datafiles = imgs;
spmT.output_raw = 1;
spmT.ROIfiles=R.ROIfiles;
spmT.ROI = roi_extract(spmT);
%remove NaN voxels from data
% for each participant
for s=1:length(subs)
% for each protocol
for c=1:length(imgs{1,1})
% for each ROI
for n=1:length(con.ROIfiles)
% find NaNs in the contrast
x=isnan(con.ROI(n,s).rawdata(1,:));
% get indices of these NaNs
ind=find(x);
% remove the NaNs from the data and the corresponding values
% from the spmT data
con.ROI(n,s).rawdata(:,ind)=[];
spmT.ROI(n,s).rawdata(:,ind)=[];
% remove the coordinates of the NaNs from the matrix of
% coordinates, so that everything lines up
con.ROI(n,s).XYZ(:,ind)=[];
spmT.ROI(n,s).XYZ(:,ind)=[];
% extract the mean and median for each ROI (for each contrast)
con_collate_median{n}(s,:)=con.ROI(n,s).median';
con_collate_mean{n}(s,:)=con.ROI(n,s).mean';
spmT_collate_median{n}(s,:)=spmT.ROI(n,s).median';
spmT_collate_mean{n}(s,:)=spmT.ROI(n,s).mean';
end
end
end
% violin plot - con images
roiname=[{'LvATL'},{'RITG'},{'LpMTG'},{'LIFGpt'}];
cond={'SESB';'pTx'};
figure;
colours = get(gca,'colororder'); % colours: 1 = dark blue, 2 = orange, 3 = yellow, 4 = purple, 5 = green, 6 = pale blue, 7 = dark pink/red
% for every ROI
for i=1:length(R.ROIfiles)
% get data from that ROI and make it into a table
tmp=con_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% make a violin plot
subplot(2,2,i);
v=violinplot(tmp);
% set title and y-axis label
title(roiname{1,i});
ylabel('Contrast value','FontSize',20);
% Set violin colours, which should be consistent across all plots in the paper. SESB = dark pink, pTx = purple
v(1,1).ViolinColor = colours(7,:);
v(1,2).ViolinColor = colours(4,:);
set(gca,'FontSize',16)
end
% make the plots a standard size
set(gcf,'PaperUnits','centimeters','PaperSize',[20 40])
% make the axes of all subplots match
ax1 = subplot(2,2,1);
ax2 = subplot(2,2,2);
ax3 = subplot(2,2,3);
ax4 = subplot(2,2,4);
linkaxes([ax1,ax2,ax3,ax4],'y')
% violin plots - spmT images
figure;
% for every ROI
for i=1:length(R.ROIfiles)
% get data from that ROI and make it into a table
tmp=spmT_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% make a violin plot
subplot(2,2,i);
v=violinplot(tmp);
% set title and y-axis label
title(roiname{1,i});
ylabel('spmT value','FontSize',20);
% Set violin colours, which should be consistent across all plots in the paper. SESB = dark pink, pTx = purple
v(1,1).ViolinColor = colours(7,:);
v(1,2).ViolinColor= colours(4,:);
set(gca,'FontSize',16)
end
% make the plots a standard size
set(gcf,'PaperUnits','centimeters','PaperSize',[20 40])
% make the axes of all subplots match
ax1 = subplot(2,2,1);
ax2 = subplot(2,2,2);
ax3 = subplot(2,2,3);
ax4 = subplot(2,2,4);
linkaxes([ax1,ax2,ax3,ax4],'y')
% statistics - post-hoc t-tests
for n=1:length(R.ROIfiles)
% SESB > pTx
% conduct paired t-test on con values
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,2)],[con_collate_median{1,n}(:,1)],'tail','left');
% get p-values
con_p_val(1,n)=tmp2;
% also get t-values
con_t_val(1,n)=tmp4.tstat;
% repeat for spmT values
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,2)],[spmT_collate_median{1,n}(:,1)],'tail','left');
spmT_p_val(1,n)=tmp2;
spmT_t_val(1,n)=tmp4.tstat;
% pTx > SESB
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,1)],[con_collate_median{1,n}(:,2)],'tail','left');
con_p_val(2,n)=tmp2;
con_t_val(2,n)=tmp4.tstat;
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,1)],[spmT_collate_median{1,n}(:,2)],'tail','left');
spmT_p_val(2,n)=tmp2;
spmT_t_val(2,n)=tmp4.tstat;
end
%% 2x2 Factorial design - echo and band
clear con_collate_median con_collate_mean con_p_val con_t_val spmT_collate_median spmT_collate_mean spmT_p_val spmT_t_val
% setup ROIs. Plot the cluster-corrected main effect (all conditions set to
% 1) and use only ROIs that overlap with this main effect (comment out
% those that do not).
R = struct();
R.ROIfiles{1}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--51_9_-36.nii'];% temporalpole
R.ROIfiles{2}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--42_-24_-27.nii']; % vATL
R.ROIfiles{3}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-45_-33_-21.nii'];% rITG
R.ROIfiles{4}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--3_57_-15.nii'];% frontal pole
R.ROIfiles{5}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--48_-21_-9.nii'];% mMTG
R.ROIfiles{6}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--55_-43_-7.nii'];% pMTG
R.ROIfiles{7}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--54_27_6.nii'];% IFGptri
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-51_-27_-3.nii'];% rSTG
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-60_0_33.nii'];%r PCG
% extract ROIs from con images (expressing model fit)
imgs = cell(1,length(subs));
con = struct();
for s=1:length(subs)
imgs{s}{1} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_SESB/con_0003.nii'];
imgs{s}{2} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_SEMB/con_0003.nii'];
imgs{s}{3} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MESB/con_0003.nii'];
imgs{s}{4} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MEMB/con_0003.nii'];
end
% extract data from each ROI
con.Datafiles = imgs;
con.output_raw = 1;
con.ROIfiles=R.ROIfiles;
con.ROI = roi_extract(con);
% extract ROIs from spmT images (expressing model precision)
imgs = cell(1,length(subs));
spmT = struct();
for s=1:length(subs)
imgs{s}{1} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_SESB/spmT_0003.nii'];
imgs{s}{2} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_SEMB/spmT_0003.nii'];
imgs{s}{3} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MESB/spmT_0003.nii'];
imgs{s}{4} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MEMB/spmT_0003.nii'];
end
% extract data from each ROI
spmT.Datafiles = imgs;
spmT.output_raw = 1;
spmT.ROIfiles=R.ROIfiles;
spmT.ROI = roi_extract(spmT);
%remove NaN voxels from data
% for each participant
for s=1:length(subs)
% for each protocol
for c=1:length(imgs{1,1})
% for each ROI
for n=1:length(con.ROIfiles)
% find NaNs in the contrast
x=isnan(con.ROI(n,s).rawdata(1,:));
% get indices of these NaNs
ind=find(x);
% remove the NaNs from the data and the corresponding values
% from the spmT data
con.ROI(n,s).rawdata(:,ind)=[];
spmT.ROI(n,s).rawdata(:,ind)=[];
% remove the coordinates of the NaNs from the matrix of
% coordinates, so that everything lines up
con.ROI(n,s).XYZ(:,ind)=[];
spmT.ROI(n,s).XYZ(:,ind)=[];
% extract the mean and median for each ROI (for each contrast)
con_collate_median{n}(s,:)=con.ROI(n,s).median';
con_collate_mean{n}(s,:)=con.ROI(n,s).mean';
spmT_collate_median{n}(s,:)=spmT.ROI(n,s).median';
spmT_collate_mean{n}(s,:)=spmT.ROI(n,s).mean';
end
end
end
% violin plot - con images
roiname=[{'LTP'},{'LvATL'},{'RITG'},{'LFP'},{'LmMTG'},{'LpMTG'},{'LIFGpt'}];
cond={'SESB';'SEMB';'MESB';'MEMB'};
figure;
a = get(gca,'colororder'); % colours: 1 = dark blue, 2 = orange, 3 = yellow, 4 = purple, 5 = green, 6 = pale blue, 7 = dark pink/red
for i=1:length(R.ROIfiles)
% get data from that ROI and make it into a table
tmp=con_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% make a violin plot
subplot(2,4,i);
v=violinplot(tmp);
% set title and y-axis label
title(roiname{1,i},'FontSize',20);
ylabel('Contrast value','FontSize',16)
% Set violin colours, which should be consistent across all plots in the paper. SESB = dark red; SEMB = green; MESB = orange; MEMB = dark blue
v(1,1).ViolinColor = a(7,:);
v(1,2).ViolinColor = a(5,:);
v(1,3).ViolinColor = a(2,:);
v(1,4).ViolinColor = a(1,:);
% set axis font size
set(gca,'FontSize',16)
end
% make the plots a standard size
set(gcf,'PaperUnits','centimeters','PaperSize',[20 40])
% make the axes of all subplots match
ax1 = subplot(2,4,1);
ax2 = subplot(2,4,2);
ax3 = subplot(2,4,3);
ax4 = subplot(2,4,4);
ax5 = subplot(2,4,5);
ax6 = subplot(2,4,6);
ax7 = subplot(2,4,7);
linkaxes([ax1,ax2,ax3,ax4,ax5,ax6,ax7],'y')
% violin plots - spmT images
figure;
for i=1:length(R.ROIfiles)
% get data from that ROI and make it into a table
tmp=spmT_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% make a violin plot
subplot(2,4,i);
v=violinplot(tmp);
% set title and y-axis label
title(roiname{1,i},'FontSize',20);
ylabel('spmT value','FontSize',16)
% Set violin colours, which should be consistent across all plots in the paper. SESB = dark red; SEMB = green; MESB = orange; MEMB = dark blue
v(1,1).ViolinColor = a(7,:);
v(1,2).ViolinColor = a(5,:);
v(1,3).ViolinColor = a(2,:);
v(1,4).ViolinColor = a(1,:);
% set axis font size
set(gca,'FontSize',16)
end
% make the plots a standard size
set(gcf,'PaperUnits','centimeters','PaperSize',[20 40])
% make the axes of all subplots match
ax1 = subplot(2,4,1);
ax2 = subplot(2,4,2);
ax3 = subplot(2,4,3);
ax4 = subplot(2,4,4);
ax5 = subplot(2,4,5);
ax6 = subplot(2,4,6);
ax7 = subplot(2,4,7);
linkaxes([ax1,ax2,ax3,ax4,ax5,ax6,ax7],'y')
% statistics - anova
for i=1:length(con.ROIfiles)
% get data from that ROI and make it into a table
tmp=con_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% setup
w = table(categorical([1 1 2 2].'), categorical([1 2 1 2].'), 'VariableNames', {'Echo', 'Band'});
% fit repeated measures model
rm = fitrm(tmp, 'MEMB-SESB ~ 1', 'WithinDesign', w);
% run anova
anova_con{i}=ranova(rm, 'withinmodel', 'Echo*Band');
% get p-values
anova_con_p(:,i) = [anova_con{i}.pValue(1,1),anova_con{i}.pValue(3,1),anova_con{i}.pValue(5,1),anova_con{i}.pValue(7,1)];
% get data from that ROI and make it into a table
tmp=spmT_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% setup
w = table(categorical([1 1 2 2].'), categorical([1 2 1 2].'), 'VariableNames', {'Echo', 'Band'});
% fit repeated measures model
rm = fitrm(tmp, 'MEMB-SESB ~ 1', 'WithinDesign', w);
% run anova
anova_spmT{i}=ranova(rm, 'withinmodel', 'Echo*Band');
% get p-values
anova_spmT_p(:,i) = [anova_spmT{i}.pValue(1,1),anova_spmT{i}.pValue(3,1),anova_spmT{i}.pValue(5,1),anova_spmT{i}.pValue(7,1)];
end
% statistics - post-hoc t-tests
for n=1:length(R.ROIfiles)
% ME > SE
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,1);con_collate_median{1,n}(:,2)],[con_collate_median{1,n}(:,3);con_collate_median{1,n}(:,4)],'tail','left');
% get p-values
con_p_val(1,n)=tmp2;
% also get t-values
con_t_val(1,n)=tmp4.tstat;
% repeat for spmT values
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,1);spmT_collate_median{1,n}(:,2)],[spmT_collate_median{1,n}(:,3);spmT_collate_median{1,n}(:,4)],'tail','left');
spmT_p_val(1,n)=tmp2;
spmT_t_val(1,n)=tmp4.tstat;
% SE > ME
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,3);con_collate_median{1,n}(:,4)],[con_collate_median{1,n}(:,1);con_collate_median{1,n}(:,2)],'tail','left');
con_p_val(2,n)=tmp2;
con_t_val(2,n)=tmp4.tstat;
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,3);spmT_collate_median{1,n}(:,4)],[spmT_collate_median{1,n}(:,1);spmT_collate_median{1,n}(:,2)],'tail','left');
spmT_p_val(2,n)=tmp2;
spmT_t_val(2,n)=tmp4.tstat;
% MB > SB
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,1);con_collate_median{1,n}(:,3)],[con_collate_median{1,n}(:,2);con_collate_median{1,n}(:,4)],'tail','left');
con_p_val(3,n)=tmp2;
con_t_val(3,n)=tmp4.tstat;
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,1);spmT_collate_median{1,n}(:,3)],[spmT_collate_median{1,n}(:,2);spmT_collate_median{1,n}(:,4)],'tail','left');
spmT_p_val(3,n)=tmp2;
spmT_t_val(3,n)=tmp4.tstat;
% SB > MB
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,2);con_collate_median{1,n}(:,4)],[con_collate_median{1,n}(:,1);con_collate_median{1,n}(:,3)],'tail','left');
con_p_val(4,n)=tmp2;
con_t_val(4,n)=tmp4.tstat;
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,2);spmT_collate_median{1,n}(:,4)],[spmT_collate_median{1,n}(:,1);spmT_collate_median{1,n}(:,3)],'tail','left');
spmT_p_val(4,n)=tmp2;
spmT_t_val(4,n)=tmp4.tstat;
% Interaction
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,2);con_collate_median{1,n}(:,3)],[con_collate_median{1,n}(:,1);con_collate_median{1,n}(:,4)],'tail','left');
con_p_val(5,n)=tmp2;
con_t_val(5,n)=tmp4.tstat;
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,2);spmT_collate_median{1,n}(:,3)],[spmT_collate_median{1,n}(:,1);spmT_collate_median{1,n}(:,4)],'tail','left');
spmT_p_val(5,n)=tmp2;
spmT_t_val(5,n)=tmp4.tstat;
end
%% Effect of denoising
clear con_collate_median con_collate_mean con_p_val con_t_val spmT_collate_median spmT_collate_mean spmT_p_val spmT_t_val
% setup ROIs. Plot the cluster-corrected main effect (all conditions set to
% 1) and use only ROIs that overlap with this main effect (comment out
% those that do not).
R = struct();
R.ROIfiles{1}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--51_9_-36.nii'];% temporalpole
R.ROIfiles{2}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--42_-24_-27.nii']; % vATL
R.ROIfiles{3}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-45_-33_-21.nii'];% rITG
R.ROIfiles{4}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--3_57_-15.nii'];% frontal pole
R.ROIfiles{5}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--48_-21_-9.nii'];% mMTG
R.ROIfiles{6}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--55_-43_-7.nii'];% pMTG
R.ROIfiles{7}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--54_27_6.nii'];% IFGptri
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-51_-27_-3.nii'];% rSTG
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-60_0_33.nii'];%r PCG
% extract ROIs from con images (expressing model fit)
imgs = cell(1,length(subs));
con = struct();
for s=1:length(subs)
imgs{s}{1} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MESB/con_0003.nii'];
imgs{s}{2} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MESB_dn/con_0003.nii'];
imgs{s}{3} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MEMB/con_0003.nii'];
imgs{s}{4} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MEMB_dn/con_0003.nii'];
end
% extract data from each ROI
con.Datafiles = imgs;
con.output_raw = 1;
con.ROIfiles=R.ROIfiles;
con.ROI = roi_extract(con);
% extract ROIs from spmT images (expressing model precision)
imgs = cell(1,length(subs));
spmT = struct();
for s=1:length(subs)
imgs{s}{1} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MESB/spmT_0003.nii'];
imgs{s}{2} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MESB_dn/spmT_0003.nii'];
imgs{s}{3} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MEMB/spmT_0003.nii'];
imgs{s}{4} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MEMB_dn/spmT_0003.nii'];
end
% extract data from each ROI
spmT.Datafiles = imgs;
spmT.output_raw = 1;
spmT.ROIfiles=R.ROIfiles;
spmT.ROI = roi_extract(spmT);
%remove NaN voxels from data
% for each participant
for s=1:length(subs)
% for each protocol
for c=1:length(imgs{1,1})
% for each ROI
for n=1:length(con.ROIfiles)
% find NaNs in the contrast
x=isnan(con.ROI(n,s).rawdata(1,:));
% get indices of these NaNs
ind=find(x);
% remove the NaNs from the data and the corresponding values
% from the spmT data
con.ROI(n,s).rawdata(:,ind)=[];
spmT.ROI(n,s).rawdata(:,ind)=[];
% remove the coordinates of the NaNs from the matrix of
% coordinates, so that everything lines up
con.ROI(n,s).XYZ(:,ind)=[];
spmT.ROI(n,s).XYZ(:,ind)=[];
% extract the mean and median for each ROI (for each contrast)
con_collate_median{n}(s,:)=con.ROI(n,s).median';
con_collate_mean{n}(s,:)=con.ROI(n,s).mean';
spmT_collate_median{n}(s,:)=spmT.ROI(n,s).median';
spmT_collate_mean{n}(s,:)=spmT.ROI(n,s).mean';
end
end
end
% violin plot - con images
roiname=[{'LTP'},{'LvATL'},{'RITG'},{'LFP'},{'LmMTG'},{'LpMTG'},{'LIFGpt'}];
cond={'MESB';'MESBdn';'MEMB';'MEMBdn'};
figure;
a = get(gca,'colororder'); % colours: 1 = dark blue, 2 = orange, 3 = yellow, 4 = purple, 5 = green, 6 = pale blue, 7 = dark pink/red
for i=1:length(R.ROIfiles)
% get data from that ROI and make it into a table
tmp=con_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% make a violin plot
subplot(2,4,i);
v=violinplot(tmp);
% set title and y-axis label
title(roiname{1,i},'FontSize',20);
ylabel('Contrast value','FontSize',16)
% Set violin colours, which should be consistent across all plots in
% the paper. MESB = orange; MESBdn = yellow; MEMB = dark blue; MEMBdn =
% pale blue
v(1,1).ViolinColor = a(2,:);
v(1,2).ViolinColor = a(3,:);
v(1,3).ViolinColor = a(1,:);
v(1,4).ViolinColor = a(6,:);
% set axis font size
set(gca,'FontSize',16)
end
% make the plots a standard size
set(gcf,'PaperUnits','centimeters','PaperSize',[20 40])
% make the axes of all subplots match
ax1 = subplot(2,4,1);
ax2 = subplot(2,4,2);
ax3 = subplot(2,4,3);
ax4 = subplot(2,4,4);
ax5 = subplot(2,4,5);
ax6 = subplot(2,4,6);
ax7 = subplot(2,4,7);
linkaxes([ax1,ax2,ax3,ax4,ax5,ax6,ax7],'y')
% violin plots - spmT images
figure;
for i=1:length(R.ROIfiles)
% get data from that ROI and make it into a table
tmp=spmT_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% make a violin plot
subplot(2,4,i);
v=violinplot(tmp);
% set title and y-axis label
title(roiname{1,i},'FontSize',20);
ylabel('spmT value','FontSize',16)
% Set violin colours, which should be consistent across all plots in the paper. MESB = orange; MESBdn = yellow; MEMB = dark blue; MEMBdn =
% pale blue
v(1,1).ViolinColor = a(2,:);
v(1,2).ViolinColor = a(3,:);
v(1,3).ViolinColor = a(1,:);
v(1,4).ViolinColor = a(6,:);
% set axis font size
set(gca,'FontSize',16)
end
% make the plots a standard size
set(gcf,'PaperUnits','centimeters','PaperSize',[20 40])
% make the axes of all subplots match
ax1 = subplot(2,4,1);
ax2 = subplot(2,4,2);
ax3 = subplot(2,4,3);
ax4 = subplot(2,4,4);
ax5 = subplot(2,4,5);
ax6 = subplot(2,4,6);
ax7 = subplot(2,4,7);
linkaxes([ax1,ax2,ax3,ax4,ax5,ax6,ax7],'y')
% statistics - anova
for i=1:length(con.ROIfiles)
% get data from that ROI and make it into a table
tmp=con_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% setup
w = table(categorical([1 1 2 2].'), categorical([1 2 1 2].'), 'VariableNames', {'Band','denoisedEcho'});
% fit repeated measures model
rm = fitrm(tmp, 'MEMBdn-MESB ~ 1', 'WithinDesign', w);
% run anova
anova_con{i}=ranova(rm, 'withinmodel', 'Band*denoisedEcho');
% get p-values
anova_con_p(:,i) = [anova_con{i}.pValue(1,1),anova_con{i}.pValue(3,1),anova_con{i}.pValue(5,1),anova_con{i}.pValue(7,1)];
% get data from that ROI and make it into a table
tmp=spmT_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% setup
w = table(categorical([1 1 2 2].'), categorical([1 2 1 2].'), 'VariableNames', {'Band','denoisedEcho'});
% fit repeated measures model
rm = fitrm(tmp, 'MEMBdn-MESB ~ 1', 'WithinDesign', w);
% run anova
anova_spmT{i}=ranova(rm, 'withinmodel', 'Band*denoisedEcho');
% get p-values
anova_spmT_p(:,i) = [anova_spmT{i}.pValue(1,1),anova_spmT{i}.pValue(3,1),anova_spmT{i}.pValue(5,1),anova_spmT{i}.pValue(7,1)];
end
% statistics - post-hoc t-tests
for n=1:length(R.ROIfiles)
% standard > denoised
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,2);con_collate_median{1,n}(:,4)],[con_collate_median{1,n}(:,1);con_collate_median{1,n}(:,3)],'tail','left');
con_p_val(1,n)=tmp2;
con_t_val(1,n)=tmp4.tstat;
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,2);spmT_collate_median{1,n}(:,4)],[spmT_collate_median{1,n}(:,1);spmT_collate_median{1,n}(:,3)],'tail','left');
spmT_p_val(1,n)=tmp2;
spmT_t_val(1,n)=tmp4.tstat;
% denoised > standard
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,1);con_collate_median{1,n}(:,3)],[con_collate_median{1,n}(:,2);con_collate_median{1,n}(:,4)],'tail','left');
con_p_val(2,n)=tmp2;
con_t_val(2,n)=tmp4.tstat;
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,1);spmT_collate_median{1,n}(:,3)],[spmT_collate_median{1,n}(:,2);spmT_collate_median{1,n}(:,4)],'tail','left');
spmT_p_val(2,n)=tmp2;
spmT_t_val(2,n)=tmp4.tstat;
% Interaction
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,2);con_collate_median{1,n}(:,3)],[con_collate_median{1,n}(:,1);con_collate_median{1,n}(:,4)],'tail','left');
con_p_val(3,n)=tmp2;
con_t_val(3,n)=tmp4.tstat;
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,2);spmT_collate_median{1,n}(:,3)],[spmT_collate_median{1,n}(:,1);spmT_collate_median{1,n}(:,4)],'tail','left');
spmT_p_val(3,n)=tmp2;
spmT_t_val(3,n)=tmp4.tstat;
end
%% Odd volumes
clear con_collate_median con_collate_mean con_p_val con_t_val spmT_collate_median spmT_collate_mean spmT_p_val spmT_t_val
% setup ROIs. Plot the cluster-corrected main effect (all conditions set to
% 1) and use only ROIs that overlap with this main effect (comment out
% those that do not).
R = struct();
R.ROIfiles{1}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--51_9_-36.nii'];% temporalpole
R.ROIfiles{2}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--42_-24_-27.nii']; % vATL
R.ROIfiles{3}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-45_-33_-21.nii'];% rITG
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--3_57_-15.nii'];% frontal pole
R.ROIfiles{4}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--48_-21_-9.nii'];% mMTG
R.ROIfiles{5}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--55_-43_-7.nii'];% pMTG
R.ROIfiles{6}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--54_27_6.nii'];% IFGptri
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-51_-27_-3.nii'];% rSTG
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-60_0_33.nii'];%r PCG
% extract ROIs from con images (expressing model fit)
imgs = cell(1,length(subs));
con = struct();
for s=1:length(subs)
imgs{s}{1} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_SESB/con_0003.nii'];
imgs{s}{2} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_SEMB_odd/con_0003.nii'];
imgs{s}{3} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MESB/con_0003.nii'];
imgs{s}{4} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MEMB_odd/con_0003.nii'];
end
% extract data from each ROI
con.Datafiles = imgs;
con.output_raw = 1;
con.ROIfiles=R.ROIfiles;
con.ROI = roi_extract(con);
% extract ROIs from spmT images (expressing model precision)
imgs = cell(1,length(subs));
spmT = struct();
for s=1:length(subs)
imgs{s}{1} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_SESB/spmT_0003.nii'];
imgs{s}{2} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_SEMB_odd/spmT_0003.nii'];
imgs{s}{3} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MESB/spmT_0003.nii'];
imgs{s}{4} = [root,'/GLM/first_mni/sub-',subs{s},'/6sm_MEMB_odd/spmT_0003.nii'];
end
% extract data from each ROI
spmT.Datafiles = imgs;
spmT.output_raw = 1;
spmT.ROIfiles=R.ROIfiles;
spmT.ROI = roi_extract(spmT);
%remove NaN voxels from data
% for each participant
for s=1:length(subs)
% for each protocol
for c=1:length(imgs{1,1})
% for each ROI
for n=1:length(con.ROIfiles)
% find NaNs in the contrast
x=isnan(con.ROI(n,s).rawdata(1,:));
% get indices of these NaNs
ind=find(x);
% remove the NaNs from the data and the corresponding values
% from the spmT data
con.ROI(n,s).rawdata(:,ind)=[];
spmT.ROI(n,s).rawdata(:,ind)=[];
% remove the coordinates of the NaNs from the matrix of
% coordinates, so that everything lines up
con.ROI(n,s).XYZ(:,ind)=[];
spmT.ROI(n,s).XYZ(:,ind)=[];
% extract the mean and median for each ROI (for each contrast)
con_collate_median{n}(s,:)=con.ROI(n,s).median';
con_collate_mean{n}(s,:)=con.ROI(n,s).mean';
spmT_collate_median{n}(s,:)=spmT.ROI(n,s).median';
spmT_collate_mean{n}(s,:)=spmT.ROI(n,s).mean';
end
end
end
% violin plot - con images
roiname=[{'LTP'},{'LvATL'},{'RITG'},{'LmMTG'},{'LpMTG'},{'LIFGpt'}];
cond={'SESB';'SEMBodd';'MESB';'MEMBodd'};
figure;
a = get(gca,'colororder'); % colours: 1 = dark blue, 2 = orange, 3 = yellow, 4 = purple, 5 = green, 6 = pale blue, 7 = dark pink/red
for i=1:length(R.ROIfiles)
% get data from that ROI and make it into a table
tmp=con_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% make a violin plot
subplot(2,3,i);
v=violinplot(tmp);
% set title and y-axis label
title(roiname{1,i},'FontSize',20);
ylabel('Contrast value','FontSize',16)
% Set violin colours, which should be consistent across all plots in the paper. SESB = dark red; SEMB = green; MESB = orange; MEMB = dark blue
v(1,1).ViolinColor = a(7,:);
v(1,2).ViolinColor = a(5,:);
v(1,3).ViolinColor = a(2,:);
v(1,4).ViolinColor = a(1,:);
% set axis font size
set(gca,'FontSize',16)
end
% make the plots a standard size
set(gcf,'PaperUnits','centimeters','PaperSize',[20 40])
% make the axes of all subplots match
ax1 = subplot(2,3,1);
ax2 = subplot(2,3,2);
ax3 = subplot(2,3,3);
ax4 = subplot(2,3,4);
ax5 = subplot(2,3,5);
ax6 = subplot(2,3,6);
linkaxes([ax1,ax2,ax3,ax4,ax5,ax6],'y')
% violin plots - spmT images
figure;
for i=1:length(R.ROIfiles)
% get data from that ROI and make it into a table
tmp=spmT_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% make a violin plot
subplot(2,3,i);
v=violinplot(tmp);
% set title and y-axis label
title(roiname{1,i},'FontSize',20);
ylabel('spmT value','FontSize',16)
% Set violin colours, which should be consistent across all plots in the paper. SESB = dark red; SEMB = green; MESB = orange; MEMB = dark blue
v(1,1).ViolinColor = a(7,:);
v(1,2).ViolinColor = a(5,:);
v(1,3).ViolinColor = a(2,:);
v(1,4).ViolinColor = a(1,:);
% set axis font size
set(gca,'FontSize',16)
end
% make the plots a standard size
set(gcf,'PaperUnits','centimeters','PaperSize',[20 40])
% make the axes of all subplots match
ax1 = subplot(2,3,1);
ax2 = subplot(2,3,2);
ax3 = subplot(2,3,3);
ax4 = subplot(2,3,4);
ax5 = subplot(2,3,5);
ax6 = subplot(2,3,6);
linkaxes([ax1,ax2,ax3,ax4,ax5,ax6],'y')
% statistics - anova
for i=1:length(con.ROIfiles)
% get data from that ROI and make it into a table
tmp=con_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% setup
w = table(categorical([1 1 2 2].'), categorical([1 2 1 2].'), 'VariableNames', {'Echo','oddBand'});
% fit repeated measures model
rm = fitrm(tmp, 'MEMBodd-SESB ~ 1', 'WithinDesign', w);
% run anova
anova_con{i}=ranova(rm, 'withinmodel', 'Echo*oddBand');
% get p-values
anova_con_p(:,i) = [anova_con{i}.pValue(1,1),anova_con{i}.pValue(3,1),anova_con{i}.pValue(5,1),anova_con{i}.pValue(7,1)];
% get data from that ROI and make it into a table
tmp=spmT_collate_median{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% setup
w = table(categorical([1 1 2 2].'), categorical([1 2 1 2].'), 'VariableNames', {'Echo','oddBand'});
% fit repeated measures model
rm = fitrm(tmp, 'MEMBodd-SESB ~ 1', 'WithinDesign', w);
% run anova
anova_spmT{i}=ranova(rm, 'withinmodel', 'Echo*oddBand');
% get p-values
anova_spmT_p(:,i) = [anova_spmT{i}.pValue(1,1),anova_spmT{i}.pValue(3,1),anova_spmT{i}.pValue(5,1),anova_spmT{i}.pValue(7,1)];
end
% statistics - post-hoc t-tests
for n=1:length(R.ROIfiles)
% SB > MBodd
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,2);con_collate_median{1,n}(:,4)],[con_collate_median{1,n}(:,1);con_collate_median{1,n}(:,3)],'tail','left');
con_p_val(1,n)=tmp2;
con_t_val(1,n)=tmp4.tstat;
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,2);spmT_collate_median{1,n}(:,4)],[spmT_collate_median{1,n}(:,1);spmT_collate_median{1,n}(:,3)],'tail','left');
spmT_p_val(1,n)=tmp2;
spmT_t_val(1,n)=tmp4.tstat;
% MBodd > SB
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,1);con_collate_median{1,n}(:,3)],[con_collate_median{1,n}(:,2);con_collate_median{1,n}(:,4)],'tail','left');
con_p_val(2,n)=tmp2;
con_t_val(2,n)=tmp4.tstat;
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,1);spmT_collate_median{1,n}(:,3)],[spmT_collate_median{1,n}(:,2);spmT_collate_median{1,n}(:,4)],'tail','left');
spmT_p_val(2,n)=tmp2;
spmT_t_val(2,n)=tmp4.tstat;
% Interaction
[tmp1,tmp2,tmp3,tmp4]=ttest([con_collate_median{1,n}(:,2);con_collate_median{1,n}(:,3)],[con_collate_median{1,n}(:,1);con_collate_median{1,n}(:,4)],'tail','left');
con_p_val(3,n)=tmp2;
con_t_val(3,n)=tmp4.tstat;
[tmp1,tmp2,tmp3,tmp4]=ttest([spmT_collate_median{1,n}(:,2);spmT_collate_median{1,n}(:,3)],[spmT_collate_median{1,n}(:,1);spmT_collate_median{1,n}(:,4)],'tail','left');
spmT_p_val(3,n)=tmp2;
spmT_t_val(3,n)=tmp4.tstat;
end
%% Exploratory MVPA
% Comparing pTx to SESB
% setup ROIs. Plot the cluster-corrected main effect (all conditions set to
% 1) and use only ROIs that overlap with this main effect (comment out
% those that do not).
R = struct();
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--51_9_-36.nii'];% temporalpole
R.ROIfiles{1}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--42_-24_-27.nii']; % vATL
R.ROIfiles{2}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-45_-33_-21.nii'];% rITG
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--3_57_-15.nii'];% frontal pole
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--48_-21_-9.nii'];% mMTG
R.ROIfiles{3}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--55_-43_-7.nii'];% pMTG
R.ROIfiles{4}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--54_27_6.nii'];% IFGptri
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-51_-27_-3.nii'];% rSTG
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-60_0_33.nii'];%r PCG
% extract ROIs from beta images
cond={'SESB';'ptx8ms'};
imgs = cell(1,length(subs));
con_mvpa = struct();
% for each protocol
for c = 1:length(cond)
% for each participant
for s = 1:length(subs)
% setup beta image structure
imgs{s}{1} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0001.nii'];
imgs{s}{2} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0002.nii'];
imgs{s}{3} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0003.nii'];
imgs{s}{4} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0004.nii'];
imgs{s}{5} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0005.nii'];
imgs{s}{6} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0006.nii'];
imgs{s}{7} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0007.nii'];
imgs{s}{8} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0008.nii'];
imgs{s}{9} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0009.nii'];
imgs{s}{10} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0010.nii'];
imgs{s}{11} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0011.nii'];
imgs{s}{12} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0012.nii'];
imgs{s}{13} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0013.nii'];
imgs{s}{14} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0014.nii'];
imgs{s}{15} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0015.nii'];
imgs{s}{16} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0016.nii'];
imgs{s}{17} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0017.nii'];
imgs{s}{18} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0018.nii'];
imgs{s}{19} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0019.nii'];
imgs{s}{20} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0020.nii'];
imgs{s}{21} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0021.nii'];
imgs{s}{22} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0022.nii'];
imgs{s}{23} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0023.nii'];
imgs{s}{24} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0024.nii'];
end
% extract data from each ROI and store in one big struct
field = cond{c};
con_mvpa.(field).Datafiles = imgs;
con_mvpa.(field).output_raw = 1;
con_mvpa.(field).ROIfiles = R.ROIfiles;
con_mvpa.(field).ROI = roi_extract(con_mvpa.(field));
end
% remove NaN voxels from data
% for each participant
for s=1:length(subs)
% for each protocol
for c=1:length(cond)
% for each ROI
for n=1:length(R.ROIfiles)
field = cond{c};
% find NaNs in the beta images
x=isnan(con_mvpa.(field).ROI(n,s).rawdata(1,:));
% get indices of these NaNs
ind=find(x);
% remove the NaNs from the data
con_mvpa.(field).ROI(n,s).rawdata(:,ind)=[];
% remove the coordinates of the NaNs from the matrix of
% coordinates, so that everything lines up
con_mvpa.(field).ROI(n,s).XYZ(:,ind)=[];
end
end
end
% for each participant
for s=1:length(subs)
% for each protocol
for c=1:length(cond)
% for each ROI
for n=1:length(R.ROIfiles)
field = cond{c};
% extract data (this matrix is beta images x nonzero voxels)
x=con_mvpa.(field).ROI(n,s).rawdata;
% Each block (12 semantic and 12 control) is one row of x.
% Calculate cosine distance between each pair of blocks (This
% is stored as the upper triangle of the similarity matrix).
con_mvpa.(field).ROI(n,s).dissimilarity=triu(squareform(pdist(x,'cosine')));
% convert zeros in the matrix to NaNs
con_mvpa.(field).ROI(n,s).dissimilarity(con_mvpa.(field).ROI(n,s).dissimilarity==0)=nan;
% calculate the mean dissimilarity between pairs of blocks in
% the same condition (i.e. mean dissimilarity between pairs of
% semantic blocks or pairs of control blocks)
con_mvpa.(field).ROI(n,s).mvpa_within_mean=nanmean([reshape(con_mvpa.(field).ROI(n,s).dissimilarity([1:12],[1:12]),[],1);reshape(con_mvpa.(field).ROI(n,s).dissimilarity([13:24],[13:24]),[],1)]);
% calculate the mean dissimilarity between pairs of blocks in
% different conditions (i.e. mean dissimilarity between one
% semantic block and one control block)
con_mvpa.(field).ROI(n,s).mvpa_between_mean=nanmean(reshape(con_mvpa.(field).ROI(n,s).dissimilarity([1:12],[13:24]),[],1));
% calculate the difference in means
con_mvpa.(field).ROI(n,s).mvpa_comparison_mean=[con_mvpa.(field).ROI(n,s).mvpa_between_mean-con_mvpa.(field).ROI(n,s).mvpa_within_mean];
% collate results
con_mvpa_collate_mean{n}(s,c)=con_mvpa.(field).ROI(n,s).mvpa_comparison_mean;
end
end
end
% violin plot - beta images
roiname=[{'LvATL'},{'RITG'},{'LpMTG'},{'LIFGpt'}];
cond={'SESB';'pTx'};
figure;
colours = get(gca,'colororder'); % colours: 1 = dark blue, 2 = orange, 3 = yellow, 4 = purple, 5 = green, 6 = pale blue, 7 = dark pink/red
% for every ROI
for i=1:length(R.ROIfiles)
% get data from that ROI and make it into a table
tmp=con_mvpa_collate_mean{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% make a violin plot
subplot(2,2,i);
v=violinplot(tmp);
% set title and y-axis label
title(roiname{1,i});
ylabel('Difference in cosine distance','FontSize',20);
% Set violin colours, which should be consistent across all plots in the paper. SESB = dark pink, pTx = purple
v(1,1).ViolinColor = colours(7,:);
v(1,2).ViolinColor= colours(4,:);
set(gca,'FontSize',16)
end
% make the plots a standard size
set(gcf,'PaperUnits','centimeters','PaperSize',[20 40])
% make the axes of all subplots match
ax1 = subplot(2,2,1);
ax2 = subplot(2,2,2);
ax3 = subplot(2,2,3);
ax4 = subplot(2,2,4);
linkaxes([ax1,ax2,ax3,ax4],'y')
% statistics - post-hoc t-tests
for n=1:length(R.ROIfiles)
% SESB > pTx
% conduct paired t-test on con values
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,2)],[con_mvpa_collate_mean{1,n}(:,1)],'tail','left');
% get p-values
con_mvpa_p_val(1,n)=tmp2;
% also get t-values
con_mvpa_t_val(1,n)=tmp4.tstat;
% pTx > SESB
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,1)],[con_mvpa_collate_mean{1,n}(:,2)],'tail','left');
con_mvpa_p_val(2,n)=tmp2;
con_mvpa_t_val(2,n)=tmp4.tstat;
end
% 2x2 factorial design - echo and band
clear con_mvpa con_mvpa_collate_mean con_mvpa_p_val con_mvpa_t_val
% setup ROIs. Plot the cluster-corrected main effect (all conditions set to
% 1) and use only ROIs that overlap with this main effect (comment out
% those that do not).
R = struct();
R.ROIfiles{1}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--51_9_-36.nii'];% temporalpole
R.ROIfiles{2}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--42_-24_-27.nii']; % vATL
R.ROIfiles{3}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-45_-33_-21.nii'];% rITG
R.ROIfiles{4}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--3_57_-15.nii'];% frontal pole
R.ROIfiles{5}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--48_-21_-9.nii'];% mMTG
R.ROIfiles{6}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--55_-43_-7.nii'];% pMTG
R.ROIfiles{7}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--54_27_6.nii'];% IFGptri
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-51_-27_-3.nii'];% rSTG
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-60_0_33.nii'];%r PCG
% extract ROIs from beta images
cond={'SESB';'SEMB';'MESB';'MEMB'};
imgs = cell(1,length(subs));
con_mvpa = struct();
% for each protocol
for c = 1:length(cond)
% for each participant
for s = 1:length(subs)
% setup beta image structure
imgs{s}{1} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0001.nii'];
imgs{s}{2} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0002.nii'];
imgs{s}{3} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0003.nii'];
imgs{s}{4} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0004.nii'];
imgs{s}{5} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0005.nii'];
imgs{s}{6} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0006.nii'];
imgs{s}{7} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0007.nii'];
imgs{s}{8} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0008.nii'];
imgs{s}{9} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0009.nii'];
imgs{s}{10} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0010.nii'];
imgs{s}{11} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0011.nii'];
imgs{s}{12} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0012.nii'];
imgs{s}{13} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0013.nii'];
imgs{s}{14} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0014.nii'];
imgs{s}{15} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0015.nii'];
imgs{s}{16} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0016.nii'];
imgs{s}{17} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0017.nii'];
imgs{s}{18} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0018.nii'];
imgs{s}{19} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0019.nii'];
imgs{s}{20} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0020.nii'];
imgs{s}{21} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0021.nii'];
imgs{s}{22} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0022.nii'];
imgs{s}{23} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0023.nii'];
imgs{s}{24} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0024.nii'];
end
% extract data from each ROI and store in one big struct
field = cond{c};
con_mvpa.(field).Datafiles = imgs;
con_mvpa.(field).output_raw = 1;
con_mvpa.(field).ROIfiles = R.ROIfiles;
con_mvpa.(field).ROI = roi_extract(con_mvpa.(field));
end
% remove NaN voxels from data
% for each participant
for s=1:length(subs)
% for each protocol
for c=1:length(cond)
% for each ROI
for n=1:length(R.ROIfiles)
field = cond{c};
% find NaNs in the beta images
x=isnan(con_mvpa.(field).ROI(n,s).rawdata(1,:));
% get indices of these NaNs
ind=find(x);
% remove the NaNs from the data
con_mvpa.(field).ROI(n,s).rawdata(:,ind)=[];
% remove the coordinates of the NaNs from the matrix of
% coordinates, so that everything lines up
con_mvpa.(field).ROI(n,s).XYZ(:,ind)=[];
end
end
end
% for each participant
for s=1:length(subs)
% for each protocol
for c=1:length(cond)
% for each ROI
for n=1:length(R.ROIfiles)
field = cond{c};
% extract data (this matrix is beta images x nonzero voxels)
x=con_mvpa.(field).ROI(n,s).rawdata;
% Each block (12 semantic and 12 control) is one row of x.
% Calculate cosine distance between each pair of blocks (This
% is stored as the upper triangle of the similarity matrix).
con_mvpa.(field).ROI(n,s).dissimilarity=triu(squareform(pdist(x,'cosine')));
% convert zeros in the matrix to NaNs
con_mvpa.(field).ROI(n,s).dissimilarity(con_mvpa.(field).ROI(n,s).dissimilarity==0)=nan;
% calculate the mean dissimilarity between pairs of blocks in
% the same condition (i.e. mean dissimilarity between pairs of
% semantic blocks or pairs of control blocks)
con_mvpa.(field).ROI(n,s).mvpa_within_mean=nanmean([reshape(con_mvpa.(field).ROI(n,s).dissimilarity([1:12],[1:12]),[],1);reshape(con_mvpa.(field).ROI(n,s).dissimilarity([13:24],[13:24]),[],1)]);
% calculate the mean dissimilarity between pairs of blocks in
% different conditions (i.e. mean dissimilarity between one
% semantic block and one control block)
con_mvpa.(field).ROI(n,s).mvpa_between_mean=nanmean(reshape(con_mvpa.(field).ROI(n,s).dissimilarity([1:12],[13:24]),[],1));
% calculate the difference in means
con_mvpa.(field).ROI(n,s).mvpa_comparison_mean=[con_mvpa.(field).ROI(n,s).mvpa_between_mean-con_mvpa.(field).ROI(n,s).mvpa_within_mean];
% collate results
con_mvpa_collate_mean{n}(s,c)=con_mvpa.(field).ROI(n,s).mvpa_comparison_mean;
end
end
end
% violin plot - beta images
roiname=[{'LTP'},{'LvATL'},{'RITG'},{'LFP'},{'LmMTG'},{'LpMTG'},{'LIFGpt'}];
cond={'SESB';'SEMB';'MESB';'MEMB'};
figure;
a = get(gca,'colororder'); % colours: 1 = dark blue, 2 = orange, 3 = yellow, 4 = purple, 5 = green, 6 = pale blue, 7 = dark pink/red
for i=1:length(R.ROIfiles)
% get data from that ROI and make it into a table
tmp=con_mvpa_collate_mean{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% make a violin plot
subplot(2,4,i);
v=violinplot(tmp);
% set title and y-axis label
title(roiname{1,i},'FontSize',20);
ylabel('Difference in cosine distance','FontSize',16)
% Set violin colours, which should be consistent across all plots in the paper. SESB = dark red; SEMB = green; MESB = orange; MEMB = dark blue
v(1,1).ViolinColor = a(7,:);
v(1,2).ViolinColor = a(5,:);
v(1,3).ViolinColor = a(2,:);
v(1,4).ViolinColor = a(1,:);
% set axis font size
set(gca,'FontSize',16)
end
% make the plots a standard size
set(gcf,'PaperUnits','centimeters','PaperSize',[20 40])
% make the axes of all subplots match
ax1 = subplot(2,4,1);
ax2 = subplot(2,4,2);
ax3 = subplot(2,4,3);
ax4 = subplot(2,4,4);
ax5 = subplot(2,4,5);
ax6 = subplot(2,4,6);
ax7 = subplot(2,4,7);
linkaxes([ax1,ax2,ax3,ax4,ax5,ax6,ax7],'y')
% statistics - anova
for i=1:7
% get data from that ROI and make it into a table
tmp=con_mvpa_collate_mean{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% setup
w = table(categorical([1 1 2 2].'), categorical([1 2 1 2].'), 'VariableNames', {'Echo', 'Band'});
% fit repeated measures model
rm = fitrm(tmp, 'MEMB-SESB ~ 1', 'WithinDesign', w);
% run anova
anova_mvpa{i}=ranova(rm, 'withinmodel', 'Echo*Band');
% get p-values
anova_mvpa_p(:,i) = [anova_mvpa{i}.pValue(1,1),anova_mvpa{i}.pValue(3,1),anova_mvpa{i}.pValue(5,1),anova_mvpa{i}.pValue(7,1)];
end
% statistics - post-hoc t-tests
for n=1:length(R.ROIfiles)
% ME > SE
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,1);con_mvpa_collate_mean{1,n}(:,2)],[con_mvpa_collate_mean{1,n}(:,3);con_mvpa_collate_mean{1,n}(:,4)],'tail','left');
% get p-values
con_mvpa_p_val(1,n)=tmp2;
% also get t-values
con_mvpa_t_val(1,n)=tmp4.tstat;
% SE > ME
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,3);con_mvpa_collate_mean{1,n}(:,4)],[con_mvpa_collate_mean{1,n}(:,1);con_mvpa_collate_mean{1,n}(:,2)],'tail','left');
con_mvpa_p_val(2,n)=tmp2;
con_mvpa_t_val(2,n)=tmp4.tstat;
% MB > SB
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,1);con_mvpa_collate_mean{1,n}(:,3)],[con_mvpa_collate_mean{1,n}(:,2);con_mvpa_collate_mean{1,n}(:,4)],'tail','left');
con_mvpa_p_val(3,n)=tmp2;
con_mvpa_t_val(3,n)=tmp4.tstat;
% SB > MB
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,2);con_mvpa_collate_mean{1,n}(:,4)],[con_mvpa_collate_mean{1,n}(:,3);con_mvpa_collate_mean{1,n}(:,1)],'tail','left');
con_mvpa_p_val(4,n)=tmp2;
con_mvpa_t_val(4,n)=tmp4.tstat;
% Interaction
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,2);con_mvpa_collate_mean{1,n}(:,3)],[con_mvpa_collate_mean{1,n}(:,1);con_mvpa_collate_mean{1,n}(:,4)],'tail','left');
con_mvpa_p_val(5,n)=tmp2;
con_mvpa_t_val(5,n)=tmp4.tstat;
end
% Effect of denoising
clear con_mvpa con_mvpa_collate_mean con_mvpa_p_val con_mvpa_t_val
% setup ROIs. Plot the cluster-corrected main effect (all conditions set to
% 1) and use only ROIs that overlap with this main effect (comment out
% those that do not).
R = struct();
R.ROIfiles{1}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--51_9_-36.nii'];% temporalpole
R.ROIfiles{2}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--42_-24_-27.nii']; % vATL
R.ROIfiles{3}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-45_-33_-21.nii'];% rITG
R.ROIfiles{4}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--3_57_-15.nii'];% frontal pole
R.ROIfiles{5}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--48_-21_-9.nii'];% mMTG
R.ROIfiles{6}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--55_-43_-7.nii'];% pMTG
R.ROIfiles{7}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--54_27_6.nii'];% IFGptri
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-51_-27_-3.nii'];% rSTG
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-60_0_33.nii'];%r PCG
% extract ROIs from beta images
cond={'MESB';'MESB_dn';'MEMB';'MEMB_dn'};
imgs = cell(1,length(subs));
con_mvpa = struct();
% for each protocol
for c = 1:length(cond)
% for each participant
for s = 1:length(subs)
% setup beta image structure
imgs{s}{1} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0001.nii'];
imgs{s}{2} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0002.nii'];
imgs{s}{3} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0003.nii'];
imgs{s}{4} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0004.nii'];
imgs{s}{5} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0005.nii'];
imgs{s}{6} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0006.nii'];
imgs{s}{7} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0007.nii'];
imgs{s}{8} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0008.nii'];
imgs{s}{9} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0009.nii'];
imgs{s}{10} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0010.nii'];
imgs{s}{11} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0011.nii'];
imgs{s}{12} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0012.nii'];
imgs{s}{13} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0013.nii'];
imgs{s}{14} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0014.nii'];
imgs{s}{15} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0015.nii'];
imgs{s}{16} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0016.nii'];
imgs{s}{17} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0017.nii'];
imgs{s}{18} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0018.nii'];
imgs{s}{19} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0019.nii'];
imgs{s}{20} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0020.nii'];
imgs{s}{21} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0021.nii'];
imgs{s}{22} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0022.nii'];
imgs{s}{23} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0023.nii'];
imgs{s}{24} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0024.nii'];
end
% extract data from each ROI and store in one big struct
field = cond{c};
con_mvpa.(field).Datafiles = imgs;
con_mvpa.(field).output_raw = 1;
con_mvpa.(field).ROIfiles = R.ROIfiles;
con_mvpa.(field).ROI = roi_extract(con_mvpa.(field));
end
% remove NaN voxels from data
% for each participant
for s=1:length(subs)
% for each protocol
for c=1:length(cond)
% for each ROI
for n=1:length(R.ROIfiles)
field = cond{c};
% find NaNs in the beta images
x=isnan(con_mvpa.(field).ROI(n,s).rawdata(1,:));
% get indices of these NaNs
ind=find(x);
% remove the NaNs from the data
con_mvpa.(field).ROI(n,s).rawdata(:,ind)=[];
% remove the coordinates of the NaNs from the matrix of
% coordinates, so that everything lines up
con_mvpa.(field).ROI(n,s).XYZ(:,ind)=[];
end
end
end
% for each participant
for s=1:length(subs)
% for each protocol
for c=1:length(cond)
% for each ROI
for n=1:length(R.ROIfiles)
field = cond{c};
% extract data (this matrix is beta images x nonzero voxels)
x=con_mvpa.(field).ROI(n,s).rawdata;
% Each block (12 semantic and 12 control) is one row of x.
% Calculate cosine distance between each pair of blocks (This
% is stored as the upper triangle of the similarity matrix).
con_mvpa.(field).ROI(n,s).dissimilarity=triu(squareform(pdist(x,'cosine')));
% convert zeros in the matrix to NaNs
con_mvpa.(field).ROI(n,s).dissimilarity(con_mvpa.(field).ROI(n,s).dissimilarity==0)=nan;
% calculate the mean dissimilarity between pairs of blocks in
% the same condition (i.e. mean dissimilarity between pairs of
% semantic blocks or pairs of control blocks)
con_mvpa.(field).ROI(n,s).mvpa_within_mean=nanmean([reshape(con_mvpa.(field).ROI(n,s).dissimilarity([1:12],[1:12]),[],1);reshape(con_mvpa.(field).ROI(n,s).dissimilarity([13:24],[13:24]),[],1)]);
% calculate the mean dissimilarity between pairs of blocks in
% different conditions (i.e. mean dissimilarity between one
% semantic block and one control block)
con_mvpa.(field).ROI(n,s).mvpa_between_mean=nanmean(reshape(con_mvpa.(field).ROI(n,s).dissimilarity([1:12],[13:24]),[],1));
% calculate the difference in means
con_mvpa.(field).ROI(n,s).mvpa_comparison_mean=[con_mvpa.(field).ROI(n,s).mvpa_between_mean-con_mvpa.(field).ROI(n,s).mvpa_within_mean];
% collate results
con_mvpa_collate_mean{n}(s,c)=con_mvpa.(field).ROI(n,s).mvpa_comparison_mean;
end
end
end
% violin plots - beta images
roiname=[{'LTP'},{'LvATL'},{'RITG'},{'LFP'},{'LmMTG'},{'LpMTG'},{'LIFGpt'}];
cond={'MESB';'MESBdn';'MEMB';'MEMBdn'};
figure;
a = get(gca,'colororder'); % colours: 1 = dark blue, 2 = orange, 3 = yellow, 4 = purple, 5 = green, 6 = pale blue, 7 = dark pink/red
for i=1:length(R.ROIfiles)
% get data from that ROI and make it into a table
tmp=con_mvpa_collate_mean{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% make a violin plot
subplot(2,4,i);
v=violinplot(tmp);
% set title and y-axis label
title(roiname{1,i},'FontSize',20);
ylabel('Difference in cosine distance','FontSize',16)
% Set violin colours, which should be consistent across all plots in
% the paper. MESB = orange; MESBdn = yellow; MEMB = dark blue; MEMBdn =
% pale blue
v(1,1).ViolinColor = a(2,:);
v(1,2).ViolinColor = a(3,:);
v(1,3).ViolinColor = a(1,:);
v(1,4).ViolinColor = a(6,:);
% set axis font size
set(gca,'FontSize',16)
end
% make the plots a standard size
set(gcf,'PaperUnits','centimeters','PaperSize',[20 40])
% make the axes of all subplots match
ax1 = subplot(2,4,1);
ax2 = subplot(2,4,2);
ax3 = subplot(2,4,3);
ax4 = subplot(2,4,4);
ax5 = subplot(2,4,5);
ax6 = subplot(2,4,6);
ax7 = subplot(2,4,7);
linkaxes([ax1,ax2,ax3,ax4,ax5,ax6,ax7],'y')
% statistics - anova
for i=1:7
% get data from that ROI and make it into a table
tmp=con_mvpa_collate_mean{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% setup
w = table(categorical([1 1 2 2].'), categorical([1 2 1 2].'), 'VariableNames', {'Band','denoisedEcho'});
% fit repeated measures model
rm = fitrm(tmp, 'MEMBdn-MESB ~ 1', 'WithinDesign', w);
% run anova
anova_mvpa{i}=ranova(rm, 'withinmodel', 'Band*denoisedEcho');
% get p-values
anova_mvpa_p(:,i) = [anova_mvpa{i}.pValue(1,1),anova_mvpa{i}.pValue(3,1),anova_mvpa{i}.pValue(5,1),anova_mvpa{i}.pValue(7,1)];
end
% statistics - post-hoc t-tests
for n=1:length(R.ROIfiles)
% standard > denoised
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,2);con_mvpa_collate_mean{1,n}(:,4)],[con_mvpa_collate_mean{1,n}(:,1);con_mvpa_collate_mean{1,n}(:,3)],'tail','left');
con_mvpa_p_val(1,n)=tmp2;
con_mvpa_t_val(1,n)=tmp4.tstat;
% denoised > standard
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,1);con_mvpa_collate_mean{1,n}(:,3)],[con_mvpa_collate_mean{1,n}(:,2);con_mvpa_collate_mean{1,n}(:,4)],'tail','left');
con_mvpa_p_val(2,n)=tmp2;
con_mvpa_t_val(2,n)=tmp4.tstat;
% Interaction
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,2);con_mvpa_collate_mean{1,n}(:,3)],[con_mvpa_collate_mean{1,n}(:,1);con_mvpa_collate_mean{1,n}(:,4)],'tail','left');
con_mvpa_p_val(3,n)=tmp2;
con_mvpa_t_val(3,n)=tmp4.tstat;
end
% Odd volumes
clear con_mvpa con_mvpa_collate_mean con_mvpa_p_val con_mvpa_t_val
% setup ROIs. Plot the cluster-corrected main effect (all conditions set to
% 1) and use only ROIs that overlap with this main effect (comment out
% those that do not).
R = struct();
R.ROIfiles{1}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--51_9_-36.nii'];% temporalpole
R.ROIfiles{2}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--42_-24_-27.nii']; % vATL
R.ROIfiles{3}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-45_-33_-21.nii'];% rITG
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--3_57_-15.nii'];% frontal pole
R.ROIfiles{4}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--48_-21_-9.nii'];% mMTG
R.ROIfiles{5}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--55_-43_-7.nii'];% pMTG
R.ROIfiles{6}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8--54_27_6.nii'];% IFGptri
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-51_-27_-3.nii'];% rSTG
% R.ROIfiles{n}=['/imaging/projects/cbu/wbic-p00567-7Tmultiecho/main/scripts/HumphreysPNAS2015/PNAS_semantic_sphere_8-60_0_33.nii'];%r PCG
% extract ROIs from beta images
cond={'SESB';'SEMB_odd';'MESB';'MEMB_odd'};
imgs = cell(1,length(subs));
con_mvpa = struct();
% for each protocol
for c = 1:length(cond)
% for each participant
for s = 1:length(subs)
% setup beta image structure
imgs{s}{1} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0001.nii'];
imgs{s}{2} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0002.nii'];
imgs{s}{3} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0003.nii'];
imgs{s}{4} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0004.nii'];
imgs{s}{5} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0005.nii'];
imgs{s}{6} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0006.nii'];
imgs{s}{7} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0007.nii'];
imgs{s}{8} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0008.nii'];
imgs{s}{9} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0009.nii'];
imgs{s}{10} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0010.nii'];
imgs{s}{11} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0011.nii'];
imgs{s}{12} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0012.nii'];
imgs{s}{13} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0013.nii'];
imgs{s}{14} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0014.nii'];
imgs{s}{15} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0015.nii'];
imgs{s}{16} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0016.nii'];
imgs{s}{17} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0017.nii'];
imgs{s}{18} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0018.nii'];
imgs{s}{19} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0019.nii'];
imgs{s}{20} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0020.nii'];
imgs{s}{21} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0021.nii'];
imgs{s}{22} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0022.nii'];
imgs{s}{23} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0023.nii'];
imgs{s}{24} = [root,'/GLM/first_mni_mvpa/sub-',subs{s},'/6sm_',cond{c},'/beta_0024.nii'];
end
% extract data from each ROI and store in one big struct
field = cond{c};
con_mvpa.(field).Datafiles = imgs;
con_mvpa.(field).output_raw = 1;
con_mvpa.(field).ROIfiles = R.ROIfiles;
con_mvpa.(field).ROI = roi_extract(con_mvpa.(field));
end
% remove NaN voxels from data
% for each participant
for s=1:length(subs)
% for each protocol
for c=1:length(cond)
% for each ROI
for n=1:length(R.ROIfiles)
field = cond{c};
% find NaNs in the beta images
x=isnan(con_mvpa.(field).ROI(n,s).rawdata(1,:));
% get indices of these NaNs
ind=find(x);
% remove the NaNs from the data
con_mvpa.(field).ROI(n,s).rawdata(:,ind)=[];
% remove the coordinates of the NaNs from the matrix of
% coordinates, so that everything lines up
con_mvpa.(field).ROI(n,s).XYZ(:,ind)=[];
end
end
end
% for each participant
for s=1:length(subs)
% for each protocol
for c=1:length(cond)
% for each ROI
for n=1:length(R.ROIfiles)
field = cond{c};
% extract data (this matrix is beta images x nonzero voxels)
x=con_mvpa.(field).ROI(n,s).rawdata;
% Each block (12 semantic and 12 control) is one row of x.
% Calculate cosine distance between each pair of blocks (This
% is stored as the upper triangle of the similarity matrix).
con_mvpa.(field).ROI(n,s).dissimilarity=triu(squareform(pdist(x,'cosine')));
% convert zeros in the matrix to NaNs
con_mvpa.(field).ROI(n,s).dissimilarity(con_mvpa.(field).ROI(n,s).dissimilarity==0)=nan;
% calculate the mean dissimilarity between pairs of blocks in
% the same condition (i.e. mean dissimilarity between pairs of
% semantic blocks or pairs of control blocks)
con_mvpa.(field).ROI(n,s).mvpa_within_mean=nanmean([reshape(con_mvpa.(field).ROI(n,s).dissimilarity([1:12],[1:12]),[],1);reshape(con_mvpa.(field).ROI(n,s).dissimilarity([13:24],[13:24]),[],1)]);
% calculate the mean dissimilarity between pairs of blocks in
% different conditions (i.e. mean dissimilarity between one
% semantic block and one control block)
con_mvpa.(field).ROI(n,s).mvpa_between_mean=nanmean(reshape(con_mvpa.(field).ROI(n,s).dissimilarity([1:12],[13:24]),[],1));
% calculate the difference in means
con_mvpa.(field).ROI(n,s).mvpa_comparison_mean=[con_mvpa.(field).ROI(n,s).mvpa_between_mean-con_mvpa.(field).ROI(n,s).mvpa_within_mean];
% collate results
con_mvpa_collate_mean{n}(s,c)=con_mvpa.(field).ROI(n,s).mvpa_comparison_mean;
end
end
end
% violin plot - con images
roiname=[{'LTP'},{'LvATL'},{'RITG'},{'LmMTG'},{'LpMTG'},{'LIFGpt'}];
cond={'SESB';'SEMBodd';'MESB';'MEMBodd'};
figure;
a = get(gca,'colororder'); % colours: 1 = dark blue, 2 = orange, 3 = yellow, 4 = purple, 5 = green, 6 = pale blue, 7 = dark pink/red
for i=1:length(R.ROIfiles)
% get data from that ROI and make it into a table
tmp=con_mvpa_collate_mean{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% make a violin plot
subplot(2,3,i);
v=violinplot(tmp);
% set title and y-axis label
title(roiname{1,i},'FontSize',20);
ylabel('Difference in cosine distance','FontSize',16)
% Set violin colours, which should be consistent across all plots in the paper. SESB = dark red; SEMB = green; MESB = orange; MEMB = dark blue
v(1,1).ViolinColor = a(7,:);
v(1,2).ViolinColor = a(5,:);
v(1,3).ViolinColor = a(2,:);
v(1,4).ViolinColor = a(1,:);
% set axis font size
set(gca,'FontSize',16)
end
% make the plots a standard size
set(gcf,'PaperUnits','centimeters','PaperSize',[20 40])
% make the axes of all subplots match
ax1 = subplot(2,3,1);
ax2 = subplot(2,3,2);
ax3 = subplot(2,3,3);
ax4 = subplot(2,3,4);
ax5 = subplot(2,3,5);
ax6 = subplot(2,3,6);
linkaxes([ax1,ax2,ax3,ax4,ax5,ax6],'y')
clear anova_mvpa anova_mvpa_p
% statistics - anova
for i=1:6
% get data from that ROI and make it into a table
tmp=con_mvpa_collate_mean{1,i};
tmp=array2table(tmp,'VariableNames',cond);
% setup
w = table(categorical([1 1 2 2].'), categorical([1 2 1 2].'), 'VariableNames', {'Echo','oddBand'});
% fit repeated measures model
rm = fitrm(tmp, 'MEMBodd-SESB ~ 1', 'WithinDesign', w);
% run anova
anova_mvpa{i}=ranova(rm, 'withinmodel', 'Echo*oddBand');
% get p-values
anova_mvpa_p(:,i) = [anova_mvpa{i}.pValue(1,1),anova_mvpa{i}.pValue(3,1),anova_mvpa{i}.pValue(5,1),anova_mvpa{i}.pValue(7,1)];
end
% statistics - post-hoc t-tests
for n=1:6
% SB > MBodd
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,2);con_mvpa_collate_mean{1,n}(:,4)],[con_mvpa_collate_mean{1,n}(:,1);con_mvpa_collate_mean{1,n}(:,3)],'tail','left');
con_mvpa_p_val(1,n)=tmp2;
con_mvpa_t_val(1,n)=tmp4.tstat;
% MBodd > SB
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,1);con_mvpa_collate_mean{1,n}(:,3)],[con_mvpa_collate_mean{1,n}(:,2);con_mvpa_collate_mean{1,n}(:,4)],'tail','left');
con_mvpa_p_val(2,n)=tmp2;
con_mvpa_t_val(2,n)=tmp4.tstat;
% Interaction
[tmp1,tmp2,tmp3,tmp4]=ttest([con_mvpa_collate_mean{1,n}(:,2);con_mvpa_collate_mean{1,n}(:,3)],[con_mvpa_collate_mean{1,n}(:,1);con_mvpa_collate_mean{1,n}(:,4)],'tail','left');
con_mvpa_p_val(3,n)=tmp2;
con_mvpa_t_val(3,n)=tmp4.tstat;
end

secondlevel_glm_roi.m at commit b286716, no license · at the source

Overview

Authors: Saskia L. Frisby¹, Marta M. Correia¹, Minghao Zhang², Christopher T. Rodgers², Timothy T. Rogers^3,4, Matthew A. Lambon Ralph¹, Ajay D. Halai¹

ORCID iDs: Saskia L. Frisby, Marta M. Correia, Minghao Zhang, Christopher T. Rodgers, Timothy T. Rogers, Matthew A. Lambon Ralph, Ajay D. Halai

Institutions: MRC Cognition and Brain Sciences Unit (United Kingdom); University of Cambridge (United Kingdom); University of Wisconsin–Madison (United States); University College London (United Kingdom)

Journal: Human brain mapping, volume 47, issue 11, article e70621

Dates: received 24 February 2026; accepted 27 July 2026; published online 10 August 2026; in print August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1002/hbm.70621 · PMID 42574053 · PMCID PMC13456059 · OpenAlex W7202117783

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: fMRI (modality), human (organism)

Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging, Machine learning

Keywords: 7T‐fMRI, multiband, multi‐echo, orbitofrontal cortex, parallel transmit, temporal lobe

MeSH: Brain*, Brain Mapping*, Image Processing, Computer-Assisted*, Magnetic Resonance Imaging*, Adult, Artifacts, Female, Humans, Male, Oxygen, Signal-To-Noise Ratio (* major topic)

Topic: Advanced MRI Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex

Funding: Medical Research Council (MR/V031481/1, SUAG/019 G116768, MR N013433‐1, MC_UU_00005/18, MR/R023883/1, MR/M008983/1); NIHR Cambridge Biomedical Research Centre (NIHR203312)

Citations: not cited yet (Europe PMC); 94 references in the paper

Abstract

The temporal signal‐to‐noise ratio (tSNR) of functional magnetic resonance imaging (fMRI) is particularly poor in ventral anterior temporal and orbitofrontal regions because of B0 and B1 + magnetic field inhomogeneity, a problem that is exacerbated at higher field strengths. In this 7T‐fMRI study we compared three methods of improving sensitivity in these areas: parallel transmit, which uses multiple transmit elements, controlled independently, to homogenise the flip angle experienced by the tissue; multi‐echo, which entails collection of multiple volumes at different echo times following a single radiofrequency pulse; and multiband, in which multiple slices are acquired simultaneously. We found that parallel transmit and multi‐echo increased the magnitude of the BOLD signal change, but only multi‐echo increased BOLD magnitude in areas prone to susceptibility artefacts. Multiband and denoising of multi‐echo data with independent components analysis (ICA) both improved precision of GLM fit. Exploratory results suggested that multi‐echo and ICA denoising can both benefit multivariate analyses. In conclusion, a multi‐echo, multiband sequence improved fMRI quality in areas prone to susceptibility artefacts while maintaining sensitivity across the whole brain. We recommend this approach for studies investigating the functional roles of ventral temporal and orbitofrontal regions with 7T fMRI.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 22 matches between paragraphs and lines of code.

slfrisby/7TOptimisation

License: none: the authors keep all their rights

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: b2867168d5b684fbd203dd84b9406431edc38e6b, 24 March 2026

Languages: MATLAB (106), Shell (13), Python (2)

Size: 229 files, 121 scripts

Software Heritage: not archived

Found in: the text, “Data Analysis”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: SPM (22 files), Tools for NIfTI and ANALYZE image (MATLAB) (20 files), Statistics and Machine Learning Toolbox (14 files), FSL (10 files), ANTs (8 files), AFNI (5 files), Image Processing Toolbox (2 files), Violinplot-Matlab (2 files), CAT12 (1 file), fMRIPrep (1 file), HeuDiConv (1 file), tedana (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

122 files

00_convert_to_BIDS.sh, Shell, 84 lines
01_anat_proc.sh, Shell, 33 lines
MC_MAP4SL_extracontrols.m, MATLAB, 1,002 lines
MC_sliceLeakage_extract_ROIs_all.m, MATLAB, 160 lines, 1 match
MC_sliceLeakage_find_masks_all.m, MATLAB, 79 lines, 2 matches
NIfTI_20140122/affine.m, MATLAB, 554 lines
NIfTI_20140122/bipolar.m, MATLAB, 94 lines
NIfTI_20140122/bresenham_line3d.m, MATLAB, 189 lines
NIfTI_20140122/clip_nii.m, MATLAB, 115 lines
NIfTI_20140122/collapse_nii_scan.m, MATLAB, 260 lines
NIfTI_20140122/expand_nii_scan.m, MATLAB, 48 lines
NIfTI_20140122/extra_nii_hdr.m, MATLAB, 255 lines
NIfTI_20140122/flip_lr.m, MATLAB, 84 lines
NIfTI_20140122/get_nii_frame.m, MATLAB, 164 lines
NIfTI_20140122/load_nii.m, MATLAB, 198 lines
NIfTI_20140122/load_nii_ext.m, MATLAB, 207 lines
NIfTI_20140122/load_nii_hdr.m, MATLAB, 280 lines
NIfTI_20140122/load_nii_img.m, MATLAB, 392 lines
NIfTI_20140122/load_untouch0_nii_hdr.m, MATLAB, 200 lines
NIfTI_20140122/load_untouch_header_only.m, MATLAB, 187 lines
NIfTI_20140122/load_untouch_nii.m, MATLAB, 191 lines
NIfTI_20140122/load_untouch_nii_hdr.m, MATLAB, 217 lines
NIfTI_20140122/load_untouch_nii_img.m, MATLAB, 468 lines
NIfTI_20140122/make_ana.m, MATLAB, 210 lines
NIfTI_20140122/make_nii.m, MATLAB, 256 lines
NIfTI_20140122/mat_into_hdr.m, MATLAB, 83 lines
NIfTI_20140122/pad_nii.m, MATLAB, 142 lines
NIfTI_20140122/reslice_nii.m, MATLAB, 321 lines
NIfTI_20140122/rri_file_menu.m, MATLAB, 179 lines
NIfTI_20140122/rri_orient.m, MATLAB, 106 lines
NIfTI_20140122/rri_orient_ui.m, MATLAB, 251 lines
NIfTI_20140122/rri_select_file.m, MATLAB, 636 lines
NIfTI_20140122/rri_xhair.m, MATLAB, 92 lines
NIfTI_20140122/rri_zoom_menu.m, MATLAB, 33 lines
NIfTI_20140122/save_nii.m, MATLAB, 286 lines
NIfTI_20140122/save_nii_ext.m, MATLAB, 38 lines
NIfTI_20140122/save_nii_hdr.m, MATLAB, 227 lines
NIfTI_20140122/save_untouch0_nii_hdr.m, MATLAB, 219 lines
NIfTI_20140122/save_untouch_header_only.m, MATLAB, 71 lines
NIfTI_20140122/save_untouch_nii.m, MATLAB, 232 lines
NIfTI_20140122/save_untouch_nii_hdr.m, MATLAB, 207 lines
NIfTI_20140122/save_untouch_slice.m, MATLAB, 580 lines
NIfTI_20140122/unxform_nii.m, MATLAB, 40 lines
NIfTI_20140122/verify_nii_ext.m, MATLAB, 45 lines
NIfTI_20140122/view_nii.m, MATLAB, 4,873 lines
NIfTI_20140122/view_nii_menu.m, MATLAB, 480 lines
NIfTI_20140122/xform_nii.m, MATLAB, 521 lines
accuracy_and_rt_outliers.m, MATLAB, 101 lines
behavioural_statistics.m, MATLAB, 144 lines
cor2mni.m, MATLAB, 35 lines
ernst_angle_calculations.m, MATLAB, 479 lines
firstlevel_glm.m, MATLAB, 335 lines, 1 match
firstlevel_glm_ernst.m, MATLAB, 186 lines, 1 match
firstlevel_glm_mvpa.m, MATLAB, 275 lines, 1 match
firstlevel_glm_native.m, MATLAB, 156 lines
get_label.m, MATLAB, 39 lines
heuristics_main.py, Python, 82 lines
make_contour_map.m, MATLAB, 50 lines
make_design_matrix.m, MATLAB, 13 lines
make_design_matrix_mvpa.m, MATLAB, 101 lines
make_mean_EPI.sh, Shell, 23 lines
matlab/crop/crop_images.m, MATLAB, 89 lines
matlab/crop/nii_clip2bb.m, MATLAB, 147 lines
matlab/crop/nii_setOrigin12x.m, MATLAB, 150 lines
matlab/crop/nii_ungz.m, MATLAB, 38 lines
matlab/mp2rage_proc.m, MATLAB, 135 lines, 1 match
matlab/mp2rage_scripts/DemoCalculateM0estimate.m, MATLAB, 38 lines
matlab/mp2rage_scripts/DemoCalculateT1estimate.m, MATLAB, 33 lines
matlab/mp2rage_scripts/DemoCalculateT1estimate_PSJ.m, MATLAB, 42 lines
matlab/mp2rage_scripts/DemoForR1Correction.m, MATLAB, 62 lines
matlab/mp2rage_scripts/DemoRemoveBackgroundNoise.m, MATLAB, 40 lines, 1 match
matlab/mp2rage_scripts/func/B1mappingSa2RAGElookuptable.m, MATLAB, 42 lines
matlab/mp2rage_scripts/func/MP2RAGE_lookuptable.m, MATLAB, 81 lines
matlab/mp2rage_scripts/func/MPRAGEfunc.m, MATLAB, 145 lines
matlab/mp2rage_scripts/func/Orthoview.m, MATLAB, 96 lines
matlab/mp2rage_scripts/func/RobustCombination.m, MATLAB, 163 lines
matlab/mp2rage_scripts/func/T1B1correctpackage.m, MATLAB, 242 lines
matlab/mp2rage_scripts/func/T1B1correctpackageTFL.m, MATLAB, 301 lines
matlab/mp2rage_scripts/func/T1M0estimateMP2RAGE.m, MATLAB, 68 lines, 1 match
matlab/mp2rage_scripts/func/T1estimateMP2RAGE.m, MATLAB, 55 lines, 1 match
matlab/mp2rage_scripts/func/figureJ.m, MATLAB, 9 lines
matlab/mp2rage_scripts/func/plotMP2RAGEproperties.m, MATLAB, 64 lines
matlab/mp2rage_scripts/nii_func/load_nii_ext.m, MATLAB, 207 lines
matlab/mp2rage_scripts/nii_func/load_nii_hdr.m, MATLAB, 280 lines
matlab/mp2rage_scripts/nii_func/load_untouch0_nii_hdr.m, MATLAB, 200 lines
matlab/mp2rage_scripts/nii_func/load_untouch_nii.m, MATLAB, 191 lines
matlab/mp2rage_scripts/nii_func/load_untouch_nii_hdr.m, MATLAB, 217 lines
matlab/mp2rage_scripts/nii_func/load_untouch_nii_img.m, MATLAB, 468 lines
matlab/mp2rage_scripts/nii_func/save_nii_ext.m, MATLAB, 38 lines
matlab/mp2rage_scripts/nii_func/save_untouch0_nii_hdr.m, MATLAB, 219 lines
matlab/mp2rage_scripts/nii_func/save_untouch_nii.m, MATLAB, 232 lines
matlab/mp2rage_scripts/nii_func/save_untouch_nii_hdr.m, MATLAB, 207 lines
matlab/mp2rage_scripts/nii_func/verify_nii_ext.m, MATLAB, 45 lines
matlab/mp2rage_scripts/removebackgroundnoise.m, MATLAB, 46 lines
matlab/mp2rage_scripts/spm_imcalc_exp.m, MATLAB, 31 lines
mni2cor.m, MATLAB, 39 lines
motion_outliers.m, MATLAB, 55 lines, 1 match
riksneurotools-master/GLM/check_pooled_error.m, MATLAB, 677 lines
riksneurotools-master/GLM/fMRI_GLM_efficiency.m, MATLAB, 352 lines
riksneurotools-master/GLM/fMRI_multitrial_GLMs.m, MATLAB, 431 lines
riksneurotools-master/GLM/glm.m, MATLAB, 147 lines
riksneurotools-master/GLM/repanova.m, MATLAB, 192 lines
riksneurotools-master/GLM/rsfMRI_GLM.m, MATLAB, 685 lines
riksneurotools-master/GLM/t_matrix.m, MATLAB, 207 lines
riksneurotools-master/SPM/batch_spm_anova.m, MATLAB, 359 lines
riksneurotools-master/Util/roi_extract.m, MATLAB, 229 lines
secondlevel_glm.m, MATLAB, 460 lines, 1 match
secondlevel_glm_roi.m, MATLAB, 1,624 lines, 4 matches
slice_leakage_apply_transforms.sh, Shell, 39 lines
slice_leakage_make_volume_mask.m, MATLAB, 10 lines
slice_leakage_statistics.m, MATLAB, 408 lines
slice_out.py, Python, 3 lines
sub_7Tpilot_ME.sh, Shell, 173 lines, 3 matches
sub_7Tpilot_ME_and_SE.sh, Shell, 33 lines
sub_7Tpilot_ME_and_SE_slice_leakage.sh, Shell, 32 lines
sub_7Tpilot_ME_slice_leakage.sh, Shell, 147 lines, 1 match
sub_7Tpilot_SE.sh, Shell, 135 lines, 2 matches
sub_7Tpilot_SE_slice_leakage.sh, Shell, 105 lines
sub_job.sh, Shell, 54 lines
sub_matlabjob.sh, Shell, 19 lines
tSNR.sh, Shell, 37 lines
README.md, Text, 1 line

At the source: github.com/slfrisby/7TOptimisation/

mrc-cbu/riksneurotools

License: GPL-3.0

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: f8b948f0dd465a1af2deedebd050b195ec077a83, 26 January 2026

Languages: MATLAB (17)

Size: 19 files, 17 scripts

Software Heritage: not archived

Found in: the text, “Univariate Analysis”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: SPM (13 files), Statistics and Machine Learning Toolbox (9 files), EEGLAB (1 file), fdr_bh (Benjamini-Hochberg FDR) (1 file), FieldTrip (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

19 files

Conn/connectivity_stats.m, MATLAB, 244 lines
Conn/dcor_dc.m, MATLAB, 28 lines
Conn/dcor_uc.m, MATLAB, 44 lines
Conn/rsfMRI_GLM.m, MATLAB, 685 lines
GLM/check_pooled_error.m, MATLAB, 677 lines
GLM/fMRI_GLM_efficiency.m, MATLAB, 352 lines
GLM/fMRI_multitrial_GLMs.m, MATLAB, 368 lines
GLM/glm.m, MATLAB, 147 lines
GLM/repanova.m, MATLAB, 192 lines
GLM/rsfMRI_GLM.m, MATLAB, 685 lines
GLM/t_matrix.m, MATLAB, 207 lines
MEEG/detect_ICA_artefacts.m, MATLAB, 339 lines
MEEG/fuse_lfp.m, MATLAB, 395 lines
MEEG/get_vertices_fmri.m, MATLAB, 105 lines
MEEG/get_vertices_xyz.m, MATLAB, 77 lines
SPM/batch_spm_anova.m, MATLAB, 361 lines
Util/roi_extract.m, MATLAB, 229 lines
LICENSE, License, 674 lines
README.md, Text, 35 lines

At the source: github.com/MRC-CBU/riksneurotools/blob/master/Util/roi_extract.m

DrMichaelLindner/MAP4SL

License: CC-BY-4.0

State: the link answers, verified on 26 September 2026

Evidence: files inventoried

Commit: 84572d4713df513a6d78cb5c829c1e91288a6138, 7 November 2018

Languages: MATLAB (1)

Size: 4 files, 1 script

Software Heritage: not archived

Found in: the text, “Slice Leakage Analysis”

Holds: README, license file

Not found: CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Statistics and Machine Learning Toolbox (1 file), Tools for NIfTI and ANALYZE image (MATLAB) (1 file)

Availability: 1 check, the latest on 26 September 2026: the link answers

26 September 2026: the link answers

3 files

MAP4SL.m, MATLAB, 918 lines
LICENSE.txt, License, 397 lines
README.md, Text, 93 lines

At the source: github.com/DrMichaelLindner/MAP4SL/

mrc-cbu.cam.ac.uk/publications/opendata

License: none: the authors keep all their rights

State: the link answers, verified on 26 September 2026

Evidence: the link answers

Software Heritage: not checked

Found in: “Data Availability Statement”

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Availability: 1 check, the latest on 26 September 2026: the link answers (HTTP 200)

26 September 2026: the link answers (HTTP 200)

At the source: mrc-cbu.cam.ac.uk/publications/opendata/request/9273/

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
139 scripts, each with its path and the digest of its content;
22 matches between paragraphs of the paper and lines of the code (method lexical-v1);
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

Data are publicly available for scientific research purposes via the MRC Cognition and Brain Sciences Unit Data Repository (https://www.mrc‐cbu.cam.ac.uk/publications/opendata/request/9273/ (https://www.mrc-cbu.cam.ac.uk/publications/opendata/request/9273/)). Code is publicly available at: https://github.com/slfrisby/7TOptimisation/.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 6 keywords, 11 MeSH terms, 2 funders, 91 references.

Cite

This paper

Frisby, S. L., Correia, M. M., Zhang, M., Rodgers, C. T., Rogers, T. T., Lambon Ralph, M. A., & Halai, A. D. (2026). Optimising 7T-fMRI for Imaging Regions of Magnetic Susceptibility. Human brain mapping, 47(11), e70621. https://doi.org/10.1002/hbm.70621

BibTeX

@article{frisby2026optimising, author = {Frisby, Saskia L. and Correia, Marta M. and Zhang, Minghao and Rodgers, Christopher T. and Rogers, Timothy T. and Lambon Ralph, Matthew A. and Halai, Ajay D.}, title = {{Optimising 7T-fMRI for Imaging Regions of Magnetic Susceptibility}}, journal = {Human brain mapping}, year = {2026}, month = aug, volume = {47}, number = {11}, pages = {e70621}, publisher = {Wiley}, issn = {1065-9471}, doi = {10.1002/hbm.70621}, url = {https://doi.org/10.1002/hbm.70621}, pmid = {42574053}, pmcid = {PMC13456059} }

RIS

TY - JOUR AU - Frisby, Saskia L. AU - Correia, Marta M. AU - Zhang, Minghao AU - Rodgers, Christopher T. AU - Rogers, Timothy T. AU - Lambon Ralph, Matthew A. AU - Halai, Ajay D. TI - Optimising 7T-fMRI for Imaging Regions of Magnetic Susceptibility T2 - Human brain mapping J2 - Hum Brain Mapp PY - 2026 DA - 2026/08/01 VL - 47 IS - 11 SP - e70621 SN - 1065-9471 PB - Wiley DO - 10.1002/hbm.70621 UR - https://doi.org/10.1002/hbm.70621 LA - en ER -

CSL-JSON

{ "id": "10.1002/hbm.70621", "type": "article-journal", "title": "Optimising 7T-fMRI for Imaging Regions of Magnetic Susceptibility", "container-title": "Human brain mapping", "author": [ { "family": "Frisby", "given": "Saskia L." }, { "family": "Correia", "given": "Marta M." }, { "family": "Zhang", "given": "Minghao" }, { "family": "Rodgers", "given": "Christopher T." }, { "family": "Rogers", "given": "Timothy T." }, { "family": "Lambon Ralph", "given": "Matthew A." }, { "family": "Halai", "given": "Ajay D." } ], "container-title-short": "Hum Brain Mapp", "volume": "47", "issue": "11", "page": "e70621", "DOI": "10.1002/hbm.70621", "PMID": "42574053", "PMCID": "PMC13456059", "ISSN": "1065-9471", "publisher": "Wiley", "URL": "https://doi.org/10.1002/hbm.70621", "language": "en", "issued": { "date-parts": [ [ 2026, 8, 1 ] ] } }

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.

Optimising 7T-fMRI for Imaging Regions of Magnetic Susceptibility.

Code ↔ Paper

Paper

The authors' code

Overview

Abstract

Repositories

slfrisby/7TOptimisation

mrc-cbu/riksneurotools

DrMichaelLindner/MAP4SL

mrc-cbu.cam.ac.uk/publications/opendata

Tracing map

Data

Data Availability Statement

Versions

Version 1, 27 September 2026: the first record

Cite

This paper

Similar papers

Contribute

Claim this paper

Correct its record

Validate its tracing map

Add the badge to its README

Request its removal

Discussion, reproductions, activity