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

被引:2
|
作者
Wang, Le-Mei [1 ]
Huang, Yi-Hua [2 ]
Chou, Po-Han [3 ]
Wang, Yi-Min [1 ]
Chen, Chung-Ming [2 ]
Sun, Chia-Wei [1 ]
机构
[1] Natl Yang Ming Chiao Tung Univ, Dept Photon, Coll Elect & Comp Engn, Biomed Opt Imaging Lab, Hsinchu, Taiwan
[2] Natl Taiwan Univ, Dept Biomed Engn, Taipei, Taiwan
[3] China Med Univ, China Med Univ Hsinchu Hosp, Dept Psychiat, Hsinchu, Taiwan
关键词
brain connectivity; convolutional neural network; functional near-infrared spectroscopy; machine learning; verbal fluency test; PREFRONTAL CORTEX CONNECTIVITY; ALZHEIMERS-DISEASE; FRONTAL-LOBE; ACTIVATION; SCHIZOPHRENIA; SEVERITY; DISORDER; FNIRS; TASK; NIRS;
D O I
10.1002/jbio.202100180
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
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 (Delta HB) 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 Delta HB mapping, which is beneficial to improve the understanding of brain functional connections.
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页数:8
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