Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions

doi:10.3389/fnins.2022.838347

	Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions
	Qiu, Wenyuan 1; Ma, Liang2,3 ; Jiang, Tianzi2,3 ; Zhang, Yu 1
发表期刊	FRONTIERS IN NEUROSCIENCE
	2022-03-09
卷号	16 页码:14
通讯作者	Zhang, Yu(yuzhang2bic@gmail.com)
摘要	Brain parcellation helps to understand the structural and functional organization of the cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) and connectivity analysis provide useful information to delineate individual brain parcels in vivo. We proposed an individualized cortical parcellation based on graph neural networks (GNN) to learn the reliable functional characteristics of each brain parcel on a large fMRI dataset and to infer the areal probability of each vertex on unseen subjects. A subject-specific confidence mask was implemented in the GNN model to account for the tradeoff between the topographic alignment across subjects and functional homogeneity of brain parcels on individual brains. The individualized brain parcellation achieved better functional homogeneity at rest and during cognitive tasks compared with the group-registered atlas (p-values < 0.05). In addition, highly reliable and replicable parcellation maps were generated on multiple sessions of the same subject (intrasubject similarity = 0.89), while notable variations in the topographic organization were captured across subjects (intersubject similarity = 0.81). Moreover, the intersubject variability of brain parcellation indicated large variations in the association cortices while keeping a stable parcellation on the primary cortex. Such topographic variability was strongly associated with the functional connectivity variability, significantly predicted cognitive behaviors, and generally followed the myelination, cytoarchitecture, and functional organization of the human brain. This study provides new avenues to the precise individualized mapping of the cortical areas through deep learning and shows high potentials in the personalized localization diagnosis and treatment of neurological disorders.
关键词	functional connectivity cortical parcellation intersubject variability topographic variability resting-state fMRI (rfMRI) test-retest reliability graph neural network
DOI	10.3389/fnins.2022.838347
关键词[WOS]	CONNECTIVITY ; SYSTEMS ; FMRI
收录类别	SCI
语种	英语
资助项目	Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project[2030-Brain] ; Major Scientific Project of Zhejiang Lab[2021ZD0200201] ; Major Scientific Project of Zhejiang Lab[2020ND8AD02] ; [2021ND0PI01]
项目资助者	Science and Technology Innovation 2030-Brain Science and Brain-Inspired Intelligence Project ; Major Scientific Project of Zhejiang Lab
WOS研究方向	Neurosciences & Neurology
WOS类目	Neurosciences
WOS记录号	WOS:000790466900001
出版者	FRONTIERS MEDIA SA
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48429
专题	脑图谱与类脑智能实验室_脑网络组研究
通讯作者	Zhang, Yu
作者单位	1.Zhejiang Lab, Res Ctr Healthcare Data Sci, Hangzhou, Peoples R China 2.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Qiu, Wenyuan,Ma, Liang,Jiang, Tianzi,et al. Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions[J]. FRONTIERS IN NEUROSCIENCE,2022,16:14.
APA	Qiu, Wenyuan,Ma, Liang,Jiang, Tianzi,&Zhang, Yu.(2022).Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions.FRONTIERS IN NEUROSCIENCE,16,14.
MLA	Qiu, Wenyuan,et al."Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions".FRONTIERS IN NEUROSCIENCE 16(2022):14.