medRxiv, vol.5, no.10, pp.1-14, 2021 (Non-Refreed Journal)
Article / Article
Title of Journal :
Significance Clinical use of fNIRS derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. This article provides an algorithm to extract cognition related features by eliminating the effect of background signal contamination; hence, improves the classification accuracy.
Aim The aim in this study is to investigate the classification accuracy of an fNIRS derived biomarker based on global efficiency. To this end, fNIRS data were collected during a computerized Stroop Task from healthy controls, and patients with migraine, obsessive compulsive disorder, and schizophrenia.
Approach Functional connectivity (FC) maps were computed from [HbO] time series data for Neutral, Congruent and Incongruent stimuli using the partial correlation approach. Reconstruction of FC matrices with optimal choice of principal components yielded two independent networks: Cognitive Mode Network (CM) and Default Mode Network(DM).
Results Global Efficiency (GE) values computed for each FC matrix after applying principal component analysis yielded strong statistical significance leading to a higher specificity and accuracy. A new index, Neurocognitive Ratio (NCR), was computed by multiplying the Cognitive Quotients (CQ) and ratio of GE of CM to GE of DM. When mean values of NCR over all stimuli were computed, they showed high sensitivity (100%), specificity (95.5%), and accuracy (96.3%) for all subjects groups.
Conclusions can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients.