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

Wenyuan Qiu,Liang Ma,Tianzi Jiang,Yu Zhang

doi:10.3389/fnins.2022.838347

Abstract

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.

Highlights

Brain atlas has been an important tool to understand the neural basis of human cognition and to study the functional organization of the human brain (Ungerleider and Desimone, 1986; Felleman and Van Essen, 1991; Amunts and Zilles, 2015)
The masked semi-supervised graph convolutional neural network (MSGCN) model was evaluated using 1,022 subjects from Human Connectome Project (HCP) S1200 dataset with two functional magnetic resonance imaging (fMRI) sessions for each subject, among which 40 subjects were randomly chosen for model training, 10 subjects for validation, and the rest of the datasets used for model testing
We proposed a masked semi-supervised graph neural networks (GNN) model for individualized brain parcellation taking into account the homogeneity of functional connectivity profiles, alignment of topographic organization across subjects, as well as the reliability of test–retest data on individual brains

Summary

Introduction

Brain atlas has been an important tool to understand the neural basis of human cognition and to study the functional organization of the human brain (Ungerleider and Desimone, 1986; Felleman and Van Essen, 1991; Amunts and Zilles, 2015). The majority of current approaches focused on the group representative functional mapping of the cerebral cortex, which may provide useful insights into the intrinsic organizational principles of the human brain (Buckner et al, 2013; Wig, 2017), but ignore the variability of individual brains in areal size, location, spatial arrangement, and connectivity patterns (Mueller et al, 2013; Zuo and Xing, 2014). The precise mapping of individualized functional areas is a critical step toward better understanding of the structural–functional relationship of the human brain that underlying cognition and behavior (Wang et al, 2015; Kong et al, 2019, 2021) as well as for personalized localization diagnosis and treatment of neurological disorders (Mueller et al, 2015; Wang et al, 2020)

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Conclusion