nirs.modules.Connectivity

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nirs.modules.Connectivity

This module run connectivity analysis.
By default, this module use AR-whitened robust correlation. This analysis model was corrected for both serially correlated noise (due to systemic physiology) and heavy-tailed noise due to motion artifacts by using a robust statistical (outlier rejection) algorithm on the basis of bisquare weighting [1]. This previously validated model has been demonstrated to show better sensitivity-specificity characteristics and statistically address both increased false discovery rates introduced by serially correlated noise due to physiology in fNIRS and outliers related to motion [2, 3]. We can also utililze short-separation (SS) measurement, if SS data is available [4].
Example Usage (1):
raw = nirs.testing.simData_connectivity_shortsep;
 
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
ConnStats1 = job.run(hb);
removing short-seperation noise 1 of 1 …………………………..Finished 1 of 1
 
ConnStats1.conditions %only has 1 condition: Rest
ans = 1×1 cell array
{‘rest’}
Example Usage (2):
This example shows how to run connectivity analysis for each condition or block
raw = nirs.testing.simData([], @(t)nirs.testing.blockedStimDesign(t,80,0,3));
% task-based 80s duration, 0s between onset, and 3 conditions
 
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.divide_events = true;
ConnStats2 = job.run(hb);
Spliting condition: A 1 of 1 Spliting condition: B 1 of 1 Spliting condition: C 1 of 1 Finished 1 of 1
 
ConnStats2.conditions %only has 3 conditions: ‘A’, ‘B’, and ‘C’
ans = 1×3 cell
‘A’ ‘B’ ‘C’
References
[1] Santosa H, Aarabi A, Perlman SB, Huppert TJ. Characterization and correction of the false-discovery rates in resting state connectivity using functional near-infrared spectroscopy. J Biomed Opt. 2017 May 1;22(5):55002. doi: 10.1117/1.JBO.22.5.055002. PMID: 28492852; PMCID: PMC5424771. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5424771/
[2] Huppert TJ. Commentary on the statistical properties of noise and its implication on general linear models in functional near-infrared spectroscopy. Neurophotonics. 2016 Jan;3(1):010401. doi: 10.1117/1.NPh.3.1.010401. Epub 2016 Mar 2. PMID: 26989756; PMCID: PMC4773699. https://pubmed.ncbi.nlm.nih.gov/26989756/
[3] Santosa H, Zhai X, Fishburn F, Huppert T. The NIRS Brain AnalyzIR Toolbox. Algorithms. 2018 May;11(5):73. doi: 10.3390/a11050073. Epub 2018 May 16. PMID: 38957522; PMCID: PMC11218834. https://pubmed.ncbi.nlm.nih.gov/38957522/
[4] Lanka P, Bortfeld H, Huppert TJ. Correction of global physiology in resting-state functional near-infrared spectroscopy. Neurophotonics. 2022 Jul;9(3):035003. doi: 10.1117/1.NPh.9.3.035003. Epub 2022 Aug 18. PMID: 35990173; PMCID: PMC9386281. https://pubmed.ncbi.nlm.nih.gov/35990173/