Partial correlation-based functional connectivity analysis for functional near-infrared spectroscopy signals

Akın, Ata

doi:10.1117/1.jbo.22.12.126003

Cited by 12 publications

(29 citation statements)

References 80 publications

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“…The validity of this probe design and its ability to detect brain tissue were discussed in our previous study 9 as well as its efficacy in providing cognition related signals. 12,15 –17,22,25…”

Section: Methodsmentioning

confidence: 99%

“…In my previous paper, I have outlined the analysis steps in detail. 12 As a quick summary, I developed a signal processing pipeline to compute the functional connectivity matrices from [HbO] signals by using a partial correlation method, rather than the conventional pearson correlation analysis. Then these matrices were used to compute the global efficiency values.…”

Section: Methodsmentioning

confidence: 99%

“…[30][31][32] Global efficiency is one of the many metrics of graph-based network analysis and has been used in brain connectivity studies. 12,[33][34][35] We considered the channels as a set of vertices V and the partial correlation coefficients as assigned weights on the set of edges E, between vertices to construct an undirected complete weighted graph G = (V, E). 36-38 FC matrices are computed for each type o stimuli.…”

Section: Global Efficiencymentioning

confidence: 99%

“…I have decided to consider these properties in proposing an improved post-processing approach to data obtained from fNIRS recordings over my last paper. 12 The sole aim was to converge on a data analysis pipeline that will be accepted and adapted easily by “fellow fNIRSians” . The proposed algorithm should have a common denominator, a base, for all where anyone can build upon it.…”

Section: Introductionmentioning

confidence: 99%

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fNIRS-derived Neurocognitive Ratio as a Biomarker for Neuropsychiatric Diseases

Akın

2021

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Clinical use of fNIRS derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. The aim in this study is to investigate the classification accuracy of a 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. 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). 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 (NCR) over all stimuli were computed, they showed high sensitivity (100%), specificity (95.5%), and accuracy (96.3%) for all subjects groups. NCR can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Global Efficiencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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fNIRS-derived Neurocognitive Ratio as a Biomarker for Neuropsychiatric Diseases

Akın

2021

Preprint

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“…The analysis of functional connectivity used in the current study was based on the procedure described by Racz et al (2017). Unlike those authors, our analysis of functional connectivity was based upon a matrix of partial correlation coefficients calculated between each available channel of CBSI_HbO, that is, partial correlation coefficients represent association between two channels of CBSI_HbO while controlling for the effect of the other 13 channels (Akın, 2017;Dadgostar et al, 2016). A matrix of partial correlation coefficients (partial r) was calculated for all 15 channels of CBSI_HbO for each of the six periods of the simulation (watch1, watch2, watch3, watch4, decision2, and decision2) for each participant.…”

Section: Fnirs Analysis Ii: Functional Connectivitymentioning

confidence: 99%

The Role of the Prefrontal Cortex and Functional Connectivity during Maritime Operations: An fNIRS study

et al. 2020

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Introduction Watchkeeping is a significant activity during maritime operations, and failures of sustained attention and decision‐making can increase the likelihood of a collision. Methods A study was conducted in a ship bridge simulator where 40 participants (20 experienced/20 inexperienced) performed: (1) a 20‐min period of sustained attention to locate a target vessel and (2) a 10‐min period of decision‐making/action selection to perform an evasive maneuver. Half of the participants also performed an additional task of verbally reporting the position of their vessel. Activation of the prefrontal cortex (PFC) was captured via a 15‐channel functional near‐infrared spectroscopy (fNIRS) montage, and measures of functional connectivity were calculated frontal using graph‐theoretic measures. Results Neurovascular activation of right lateral area of the PFC decreased during sustained attention and increased during decision‐making. The graph‐theoretic analysis revealed that density declined during decision‐making in comparison with the previous period of sustained attention, while local clustering declined during sustained attention and increased when participants prepared their evasive maneuver. A regression analysis revealed an association between network measures and behavioral outcomes, with respect to spotting the target vessel and making an evasive maneuver. Conclusions The right lateral area of the PFC is sensitive to watchkeeping and decision‐making during operational performance. Graph‐theoretic measures allow us to quantify patterns of functional connectivity and were predictive of safety‐critical performance.

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