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

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Subject: search.filters.subject.global efficiency

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    Graph theoretical approach to functional connectivity in prefrontal cortex via fNIRS
    (SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS, 2017-01-01) Einalou, Zahra; Maghooli, Keivan; Setarehdan, Seyaed Kamaledin; Akin, Ata
    Functional near-infrared spectroscopy (fNIRS) has been proposed as an affordable, fast, and robust alternative to many neuroimaging modalities yet it still has long way to go to be adapted in the clinic. One request from the clinicians has been the delivery of a simple and straightforward metric (a so-called biomarker) from the vast amount of data a multichannel fNIRS system provides. We propose a simple-straightforward signal processing algorithm derived from fNIRS-HbO(2) data collected during a modified version of the color-word matching Stroop task that consists of three different conditions. The algorithm starts with a wavelet-transform-based preprocessing, then uses partial correlation analysis to compute the functional connectivity matrices at each condition and then computes the global efficiency values. To this end, a continuous wave 16 channels fNIRS device (ARGES Cerebro, Hemosoft Inc., Turkey) was used to measure the changes in HbO(2) concentrations from 12 healthy volunteers. We have considered 10\% of strongest connections in each network. A strong Stroop interference effect was found between the incongruent against neutral condition (p = 0.01) while a similar significance was observed for the global efficiency values decreased from neutral to congruent to incongruent conditions {[}F(2
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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