Similarity analysis of functional connectivity with functional near-infrared spectroscopy

Dalmış, Mehmet Ufuk; Akın, Ata

doi:10.1117/1.jbo.20.8.086012

Cited by 10 publications

(11 citation statements)

References 32 publications

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“…Several studies have demonstrated that fNIRS is a suitable tool to evaluate brain functional connectivity [15][16][17][18]. Besides supporting the feasibility of fNIRS for the study of meaningful functional connectivity variations, these previous findings also suggest the possibility of assessing individual connetome profiles [17].…”

Section: Introductionmentioning

confidence: 66%

Identifying individuals using fNIRS-based cortical connectomes

Rodrigues

Ribeiro

Sato

et al. 2019

Biomed. Opt. Express

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The fMRI-based functional connectome was shown to be sufficiently unique to allow individual identification (fingerprinting). We aimed to test whether a fNIRS-based connectome could also be used to identify individuals. Forty-four participants performed experimental protocols that consisted of two periods of resting-state interleaved by a cognitive task period. Connectome identification was performed for all possible pairwise combinations of the three periods. The influence of hemodynamic global variation was tested using global signal regression and principal component analysis. High identification accuracies well-above chance level (2.3%) were observed overall, being particularly high (93%) to the oxyhemoglobin signal between resting conditions. Our results suggest that fNIRS is a suitable technique to assess connectome fingerprints.

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Section: Introductionmentioning

confidence: 66%

Identifying individuals using fNIRS-based cortical connectomes

Rodrigues

Ribeiro

Sato

et al. 2019

Biomed. Opt. Express

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“…CBF has been dynamically measured in the resting state, and fluctuates in a similar manner to BOLD (Chen et al, 2015b; Tak et al, 2014), shows stronger coupling to BOLD in networks (using static rsfMRI methods) (Liang et al, 2013; Tak et al, 2014), and coupling between CBF and functional connectivity strength may be modulated by tasks similar to dynamic rsfMRI (as shown in the top section of Table 1) (Tak et al, 2015). Measuring CBF and CMRO 2 using NIRS, functional dynamics can be observed during tasks (Dalmis and Akin, 2015; Harrivel et al, 2016) and at rest using a sliding window approach similar to dynamic rsfMRI (Li et al, 2015b). Dynamic measurements of CBV also show large-scale waves similar to BOLD, though at a different peak frequency (Magnuson et al, 2010).…”

Section: 3 Dynamic Rsfmri Correlates With Neural and Metabolic Actimentioning

confidence: 99%

Neural and metabolic basis of dynamic resting state fMRI

Thompson

2018

NeuroImage

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Resting state fMRI (rsfMRI) as a technique showed much initial promise for use in psychiatric and neurological diseases where diagnosis and treatment were difficult. To realize this promise, many groups have moved towards examining "dynamic rsfMRI," which relies on the assumption that rsfMRI measurements on short time scales remain relevant to the underlying neural and metabolic activity. Many dynamic rsfMRI studies have demonstrated differences between clinical or behavioral groups beyond what static rsfMRI measured, suggesting a neurometabolic basis. Correlative studies combining dynamic rsfMRI and other physiological measurements have supported this. However, they also indicate multiple mechanisms and, if using correlation alone, it is difficult to separate cause and effect. Hypothesis-driven studies are needed, a few of which have begun to illuminate the underlying neurometabolic mechanisms that shape observed differences in dynamic rsfMRI. While the number of potential noise sources, potential actual neurometabolic sources, and methodological considerations can seem overwhelming, dynamic rsfMRI provides a rich opportunity in systems neuroscience. Even an incrementally better understanding of the neurometabolic basis of dynamic rsfMRI would expand rsfMRI's research and clinical utility, and the studies described herein take the first steps on that path forward.

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“…Parts of these data were published by our group and coworkers. [13][14][15][16][17][18][19][20][21][22] Consents were obtained from all subjects and they were all informed about the study before the experiment. Subjects were seated in a dimly illuminated insulated room and they were told to look at a computer screen placed in front of them.…”

Section: Subjects and Experimental Proceduresmentioning

confidence: 99%

fNIRS-derived Neurocognitive Ratio as a Biomarker for Neuropsychiatric Diseases

Akın

2021

Preprint

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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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Similarity analysis of functional connectivity with functional near-infrared spectroscopy

Cited by 10 publications

References 32 publications

Identifying individuals using fNIRS-based cortical connectomes

Identifying individuals using fNIRS-based cortical connectomes

Neural and metabolic basis of dynamic resting state fMRI

fNIRS-derived Neurocognitive Ratio as a Biomarker for Neuropsychiatric Diseases

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