nirs.modules.MixedEffectsConnectivity

See also nirs.modules.MixedEffects

Untitled

nirs.modules.MixedEffectsConnectivity

This module runs second-level (or group) statistical models for connectivity analysis. This modules has similar explanation (Wilkinson-Roger’s notation, continuous variable, categorical variable, etc) with nirs.modules.MixedEffects.
Example Usage (Continuous variable):
for i = 1:5
raw(i) = nirs.testing.simData_connectivity_shortsep;
end
 
job = nirs.modules.Resample();
job.Fs = 1; %For speed
job = nirs.modules.OpticalDensity(job);
job = nirs.modules.BeerLambertLaw(job);
hb = job.run(raw);
 
job = nirs.modules.Connectivity();
job.AddShortSepRegressors = true; %false if the SS-data is not available
ConnStats = job.run(hb);
removing short-seperation noise 1 of 5 …………………………..2 of 5 …………………………..3 of 5 …………………………..4 of 5 …………………………..5 of 5 …………………………..Finished 1 of 5 Finished 2 of 5 Finished 3 of 5 Finished 4 of 5 Finished 5 of 5
 
job = nirs.modules.MixedEffectsConnectivity();
GroupConnStats = job.run(ConnStats);
GroupConnStats =
sFCStats with properties: type: @(data)nirs.sFC.ar_corr(data,’4xFs’,true) variables: [] description: ‘Group Level Connectivity’ probe: [1×1 nirs.core.Probe] demographics: [] conditions: {‘rest’} R: [50×50 double] dfe: 4 p: [50×50 double] Z: [50×50 double] q: [50×50 double] t: [50×50 double] ishyperscan: 0 ishypersymm: 0 numunique: 1225
 
GroupConnStats.draw(‘R’,[],‘pvalue<0.05’)