Artificial and convolutional neural networks for assessing functional connectivity in resting-state functional near infrared spectroscopy

Behboodi, Bahareh; Lim, Sung‐Ho; Luna, Miguel; Jeon, Hyeon-Ae; Choi, Ji‐Woong

doi:10.1177/0967033519836623

Cited by 5 publications

(3 citation statements)

References 40 publications

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“…Behboodi et al. 84 used fNIRS to record the resting-state functional connectivity (RSFC) of the sensorimotor and motor regions of the brain. In this study, four methods were used, seed-based, ICA, ANN, and CNN.…”

Section: Applications In Fnirsmentioning

confidence: 99%

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Deep learning in fNIRS: a review

Eastmond

Subedi

et al. 2022

Neurophoton.

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Significance: Optical neuroimaging has become a well-established clinical and research tool to monitor cortical activations in the human brain. It is notable that outcomes of functional nearinfrared spectroscopy (fNIRS) studies depend heavily on the data processing pipeline and classification model employed. Recently, deep learning (DL) methodologies have demonstrated fast and accurate performances in data processing and classification tasks across many biomedical fields.Aim: We aim to review the emerging DL applications in fNIRS studies.Approach: We first introduce some of the commonly used DL techniques. Then, the review summarizes current DL work in some of the most active areas of this field, including braincomputer interface, neuro-impairment diagnosis, and neuroscience discovery.Results: Of the 63 papers considered in this review, 32 report a comparative study of DL techniques to traditional machine learning techniques where 26 have been shown outperforming the latter in terms of the classification accuracy. In addition, eight studies also utilize DL to reduce the amount of preprocessing typically done with fNIRS data or increase the amount of data via data augmentation. Conclusions:The application of DL techniques to fNIRS studies has shown to mitigate many of the hurdles present in fNIRS studies such as lengthy data preprocessing or small sample sizes while achieving comparable or improved classification accuracy.

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Section: Applications In Fnirsmentioning

confidence: 99%

“…As a result, there is an interest in using neuroimaging to understand the functional connectivity of the brain. Behboodi et al 84 used fNIRS to record the resting-state functional connectivity (RSFC) of the sensorimotor and motor regions of the brain. In this study, four methods were used, seed-based, ICA, ANN, and CNN.…”

Section: Analysis Of Cortical Activationsmentioning

confidence: 99%

Deep learning in fNIRS: a review

Eastmond

Subedi

et al. 2022

Neurophoton.

View full text Add to dashboard Cite

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“…In addition, DL has been largely adopted in brain connectivity studies [272], which has become prevalent for deciphering the brain circuitry [273] and diagnostic purposes [274]. Similar to MRI [275], DL is expected to play a critical role in next generation functional brain connectivity studies [276]. Still, numerous challenges lie ahead to implement full end‐to‐end solutions in data processing and classification.…”

Section: Applications In Biomedical Opticsmentioning

confidence: 99%

Deep Learning in Biomedical Optics

et al. 2021

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This article reviews deep learning applications in biomedical optics with a particular emphasis on image formation. The review is organized by imaging domains within biomedical optics and includes microscopy, fluorescence lifetime imaging, in vivo microscopy, widefield endoscopy, optical coherence tomography, photoacoustic imaging, diffuse tomography, and functional optical brain imaging. For each of these domains, we summarize how deep learning has been applied and highlight methods by which deep learning can enable new capabilities for optics in medicine. Challenges and opportunities to improve translation and adoption of deep learning in biomedical optics are also summarized. Lasers Surg. Med. © 2021 Wiley Periodicals LLC

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Convolutional neural networks can detect orthostatic hypotension in Parkinson's disease using resting‐state functional near‐infrared spectroscopy data

Lee,

Paik,

Kang

et al. 2024

Journal of Biophotonics

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Neurological disorders such as Parkinson's disease (PD) often adversely affect the vascular system, leading to alterations in blood flow patterns. Functional near‐infrared spectroscopy (fNIRS) is used to monitor hemodynamic changes via signal measurement. This study investigated the potential of using resting‐state fNIRS data through a convolutional neural network (CNN) to evaluate PD with orthostatic hypotension. The CNN demonstrated significant efficacy in analyzing fNIRS data, and it outperformed the other machine learning methods. The results indicate that judicious input data selection can enhance accuracy by over 85%, while including the correlation matrix as an input further improves the accuracy to more than 90%. This study underscores the promising role of CNN‐based fNIRS data analysis in the diagnosis and management of the PD. This approach enhances diagnostic accuracy, particularly in resting‐state conditions, and can reduce the discomfort and risks associated with current diagnostic methods, such as the head‐up tilt test.

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Artificial and convolutional neural networks for assessing functional connectivity in resting-state functional near infrared spectroscopy

Cited by 5 publications

References 40 publications

Deep learning in fNIRS: a review

Deep learning in fNIRS: a review

Deep Learning in Biomedical Optics

Convolutional neural networks can detect orthostatic hypotension in Parkinson's disease using resting‐state functional near‐infrared spectroscopy data

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