Attention deficit/hyperactivity disorder Classification based on deep spatio-temporal features of functional Magnetic Resonance Imaging

doi:10.1016/j.bspc.2021.103239

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	Attention deficit/hyperactivity disorder Classification based on deep spatio-temporal features of functional Magnetic Resonance Imaging
	Liu, Shuaiqi 1,2,3,6; Zhao, Ling 1,2,5; Zhao, Jie 1,2,5; Li, Bing 6; Wang, Shui-Hua 4
刊名	BIOMEDICAL SIGNAL PROCESSING AND CONTROL
	2022
卷号	71 页码:10
关键词	Attention deficit hyperactivity disorder Resting functional magnetic resonance imaging Classification Convolutional gated recurrent unit
ISSN号	1746-8094
DOI	10.1016/j.bspc.2021.103239
通讯作者	Zhao, Ling(lingzhao_hbu@163.com)
英文摘要	Attention deficit/hyperactivity disorder is a neurological disorder characterized by inattention, hyperactivity and impulsivity. Since the resting functional magnetic resonance imaging (rs-fMRI) can reflect the brain activity state, automatic diagnosis of attention deficit/hyperactivity disorder (ADHD) based on re-fMRI has become a critical method which can relieve the pressure of doctors meanwhile improve the efficiency and accuracy of diagnosis. Up to now, the traditional diagnosis of ADHD based on rs-fMRI converts rs-fMRI into functional connectivity or low-dimensional data for classification which will lead to original information loss since rs-fMRI contains 3D spatial and 1D temporal information. In this paper, we propose a deep learning model consisting of the spatio-temporal feature extraction and classifier. The nested residual convolutional denoising autoencoder (NRCDAE) is exploited to reduce the spatial dimension of rs-fMRI and extract the 3D spatial features. Then, the 3D convolutional gated recurrent unit (GRU) is adopted to extract the spatial and temporal features simultaneously. The spatio-temporal features extracted are sent into the sigmoid classifier for classification. The training and testing are carried out across five sites of ADHD-200 and the results of experimental show that the proposed method can effectively improve the classification performance between ADHD and typically developing (TD) in the cross-site test set from 1.14% to 10.9% compared with other methods. To our knowledge, the 3D convolutional GRU is first employed to extract the features of the fMRI and the model has better generalization than other models.
资助项目	National Natural Science Foundation of China[62172139] ; Natural Science Foundation of Hebei Province[F2020201025] ; Natural Science Foundation of Hebei Province[F2019201151] ; Natural Science Foundation of Hebei Province[F2018210148] ; Science Research Project of Hebei Province[BJ2020030] ; National Key RD Plan[2020AAA0106800] ; Natural Science Foundation of China[U1936204] ; Natural Science Foundation of China[U1803119] ; Beijing Natural Science Foundation[JQ18018] ; Foundation of President of Hebei University[XZJJ201909] ; Natural Science Foundation of Hebei University[2014-303] ; Natural Science Foundation of Hebei University[8012605] ; Open Foundation of Guangdong Key Laboratory of Digital Signal and Image Processing Technology[2020GDDSIPL-04] ; High-Performance Computing Center of Hebei University
WOS关键词	FMRI ; NETWORK
WOS研究方向	Engineering
语种	英语
出版者	ELSEVIER SCI LTD
WOS记录号	WOS:000718886600005
资助机构	National Natural Science Foundation of China ; Natural Science Foundation of Hebei Province ; Science Research Project of Hebei Province ; National Key RD Plan ; Natural Science Foundation of China ; Beijing Natural Science Foundation ; Foundation of President of Hebei University ; Natural Science Foundation of Hebei University ; Open Foundation of Guangdong Key Laboratory of Digital Signal and Image Processing Technology ; High-Performance Computing Center of Hebei University
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46482]
专题	自动化研究所_模式识别国家重点实验室_视频内容安全团队
通讯作者	Zhao, Ling
作者单位	1.Hebei Univ, Coll Elect & Informat Engn, Baoding 071002, Peoples R China 2.Key Lab Digital Med Engn Hebei Prov, Baoding 071002, Peoples R China 3.Machine Vis Engn Res Ctr Hebei Prov, Baoding 071002, Peoples R China 4.Univ Leicester, Sch Math & Actuarial Sci, Leicester LE1 7RH, Leics, England 5.Hebei Univ, Sch Qual & Tech Supervis, Baoding 071002, Peoples R China 6.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Liu, Shuaiqi,Zhao, Ling,Zhao, Jie,et al. Attention deficit/hyperactivity disorder Classification based on deep spatio-temporal features of functional Magnetic Resonance Imaging[J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL,2022,71:10.
APA	Liu, Shuaiqi,Zhao, Ling,Zhao, Jie,Li, Bing,&Wang, Shui-Hua.(2022).Attention deficit/hyperactivity disorder Classification based on deep spatio-temporal features of functional Magnetic Resonance Imaging.BIOMEDICAL SIGNAL PROCESSING AND CONTROL,71,10.
MLA	Liu, Shuaiqi,et al."Attention deficit/hyperactivity disorder Classification based on deep spatio-temporal features of functional Magnetic Resonance Imaging".BIOMEDICAL SIGNAL PROCESSING AND CONTROL 71(2022):10.