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Permanent URI for this collectionhttps://hdl.handle.net/11443/932

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    fNIRS-derived neurocognitive ratio as a biomarker for neuropsychiatric diseases
    (SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2021-01-01) Akin, Ata
    Significance: Clinical use of fNIRS -derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. We provide an algorithm to extract cognition-related features by eliminating the effect of background signal contamination, hence improving the classification accuracy. Aim: The aim in this study is to investigate the classification accuracy of an fNIRS-derived biomarker based on global efficiency (GE). 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 {[}Hb0] time series data for neutral (N), congruent (C), and incongruent (I) 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: GE values computed for each FC matrix after applying principal component analysis (PCA) 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 ((NCR) over bar) over all stimuli were computed, they showed high sensitivity (100\%), specificity (95.5\%), and accuracy (96.3\%) for all subjects groups. Conclusions: (NCR) over bar can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License.
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    Partial correlation-based functional connectivity analysis for functional near-infrared spectroscopy signals
    (SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2017-01-01) Akin, Ata
    A theoretical framework, a partial correlation-based functional connectivity (PC-FC) analysis to functional near-infrared spectroscopy (fNIRS) data, is proposed. This is based on generating a common background signal from a high passed version of fNIRS data averaged over all channels as the regressor in computing the PC between pairs of channels. This approach has been employed to real data collected during a Stroop task. The results show a strong significance in the global efficiency (GE) metric computed by the PC-FC analysis for neutral, congruent, and incongruent stimuli (NS, CS, IcS