Region-of-Interest (ROI)

for i = 1:10

raw(i) = nirs.testing.simData([], @(t)nirs.testing.blockedStimDesign(t,15,25,2));

% task-based 15s duration, 25s between onset, and 2 conditions

end

 

% Adding demographic manually

% Let’s assume that the SubjectID is in the correct order

for i = 1:10

raw(i).demographics(‘Subject’) = [‘S’ num2str(i)];

end

 

%Let’s make up the table contain SubjectID, Group, AgeMonths, ADHD

for i = 1:10; Subject{i,1} = [‘S’ num2str(i)]; end

for i = 1:5; Group{i,1} = [‘S1’]; end

for i = 6:10; Group{i,1} = [‘S2’]; end

AgeMonths = [60; 65; 68; 60; 69; 72; 61; 68; 70; 66];

ADHD = [2; 6; 3; 9; 5; 7; 3; 7; 5; 4];

demo = table(Subject, Group, AgeMonths, ADHD);

 

job=nirs.modules.AddDemographics;

job.allowMissing = true;

job.demoTable=demo;

job.varToMatch=‘Subject’;

raw=job.run(raw);

 

nirs.createDemographicsTable(raw);

 

% Get hb data

job = nirs.modules.RenameStims();

job.listOfChanges = {‘A’, ‘TaskA’; ‘B’, ‘TaskB’};

job = nirs.modules.Resample(job);

job.Fs = 1; %For speed

job = nirs.modules.OpticalDensity(job);

job = nirs.modules.BeerLambertLaw(job);

hb = job.run(raw);

 

% First Level

job = nirs.modules.GLM();

Stats = job.run(hb);

Finished 10 of 10.

 

% Second-level or group-level

job = nirs.modules.MixedEffects();

job.formula = ‘beta ~ -1 + cond + cond:ADHD + (1|Subject)’;

job.robust = true; %Recommended to use the robust version

job.include_diagnostics = true; %to see scatter plot

GroupStats1 = job.run(Stats);

 

GroupStats1.probe.draw

 

% Let’s devide this probe into 4 region

% Region1: Any probe connect to D1 and D2

% Region2: Any probe connect to D3 and D4

% Region3: Any probe connect to D5 and D6

% Region4: Any probe connect to D7 and D8

 

MeasList = [NaN 1; NaN 2];

Region{1} = table(MeasList(:,1),MeasList(:,2),‘VariableNames’,{‘source’,‘detector’});

MeasList = [NaN 3; NaN 4];

Region{2} = table(MeasList(:,1),MeasList(:,2),‘VariableNames’,{‘source’,‘detector’});

MeasList = [NaN 5; NaN 6];

Region{3} = table(MeasList(:,1),MeasList(:,2),‘VariableNames’,{‘source’,‘detector’});

MeasList = [NaN 7; NaN 8];

Region{4} = table(MeasList(:,1),MeasList(:,2),‘VariableNames’,{‘source’,‘detector’});

 

% This example to do the ROI in group level

[ROIGrouptable, ROIGroupstats] = nirs.util.roiAverage(GroupStats1,Region,{‘Region1’,‘Region2’,‘Region3’,‘Region4’});

ROIGroupstats.probe.link

ans = 8×2 table

	ROI	type
1	‘Region1’	‘hbo’
2	‘Region1’	‘hbr’
3	‘Region2’	‘hbo’
4	‘Region2’	‘hbr’
5	‘Region3’	‘hbo’
6	‘Region3’	‘hbr’
7	‘Region4’	‘hbo’
8	‘Region4’	‘hbr’

% In the table, you will see the group stats in 4 regions

 

%Let’s say that you want to see the scatter plot from Region 3 for hbo

ROIGroupstats.scatterplot(5, 0.95, true) %Plot channel 1 (hbo): see Group1.probe.link

 

• ROI in hb data: We can see the time series of that ROI results
• ROI after the first level
• ROI in the group level

 

%There are many option to do the ROI

% ROI in hb data: We can see the time series in that ROI

% ROI after the first level

% ROI in the group level (done)

 

% Let’s see the ROI in the hb data

ROIhb = nirs.util.roiAverage(hb,Region,{‘Region1’,‘Region2’,‘Region3’,‘Region4’});

%Check this:

ROIhb(1).probe.link

ans = 8×2 table

	ROI	type
1	‘Region1’	‘hbo’
2	‘Region1’	‘hbr’
3	‘Region2’	‘hbo’
4	‘Region2’	‘hbr’
5	‘Region3’	‘hbo’
6	‘Region3’	‘hbr’
7	‘Region4’	‘hbo’
8	‘Region4’	‘hbr’

%You will see, this variable only has 10 data (5 regions X hb)

figure; ROIhb(10).draw(5) %hbo data from subject 10, region 3

 

% Then, after this, you can run the first level, then the group level

 

 

% New function: ROI from tstat

roitbl=nirs.util.roi_from_tstat(GroupStats1,{‘TaskA:ADHD’,‘TaskB:ADHD’});

roitbl.weight(roitbl.weight<0)=0;

[~,roi]=nirs.util.roiAverage(Stats,roitbl);

 

job = nirs.modules.MixedEffects;

job.formula = ‘beta ~ -1 + cond + cond:ADHD + (1|Subject)’; %continuous

job.robust = true;

job.include_diagnostics = true;

Group1roi = job.run(roi); %only 1 roi average from that tstat

 

Group1roi.scatterplot(1, 0.95, true) %hbo data for TaskA:ADHD