Characteristics of brain connectivity during verbal fluency test: Convolutional neural network for functional near‐infrared spectroscopy analysis

• Published in: Journal of biophotonics 2022-01, Vol.15 (1), p.e202100180-n/a
• Main Authors: Wang, Le‐Mei, Huang, Yi‐Hua, Chou, Po‐Han, Wang, Yi‐Min, Chen, Chung‐Ming, Sun, Chia‐Wei
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Brain; Brain - diagnostic imaging; brain connectivity; Brain injury; convolutional neural network; functional near‐infrared spectroscopy; Head injuries; Hemoglobin; Humans; Infrared analysis; Infrared spectroscopy; machine learning; Near infrared radiation; Nervous system; Neural networks; Neural Networks, Computer; Spectroscopy, Near-Infrared; verbal fluency test
• ISSN: 1864-063X; 1864-0648
• DOI: 10.1002/jbio.202100180

Human connectome describes the complicated connection matrix of nervous system among human brain. It also possesses high potential of assisting doctors to monitor the brain injuries and recoveries in patients. In order to unravel the enigma of neuron connections and functions, previous research has strived to dig out the relations between neurons and brain regions. Verbal fluency test (VFT) is a general neuropsychological test, which has been used in functional connectivity investigations. In this study, we employed convolutional neural network (CNN) on a brain hemoglobin concentration changes (ΔHB) map obtained during VFT to investigate the connections of activated brain areas and different mental status. Our results show that feature of functional connectivity can be identified accurately with the employment of CNN on ΔHB mapping, which is beneficial to improve the understanding of brain functional connections. Human connectome describes the complicated connection matrix of nervous system among human brain. Verbal Fluency Test (VFT) is a general neuropsychological test which has been used in functional connectivity investigations. In this study, we employed convolutional neural network (CNN) on a brain hemoglobin concentration changes (ΔHB) map obtained during VFT to investigate the connections of activated brain areas and different mental status. Our results show that feature of functional connectivity can be identified accurately with the employment of CNN on ΔHB mapping, which is beneficial to improve the understanding of brain functional connections.