Individualized Functional Parcellation of the Human Amygdala Using a Semi-supervised Clustering Method: A 7T Resting State fMRI Study

• Published in: Frontiers in neuroscience Vol. 12; p. 270
• Main Authors: Zhang, Xianchang, Cheng, Hewei, Zuo, Zhentao, Zhou, Ke, Cong, Fei, Wang, Bo, Zhuo, Yan, Chen, Lin, Xue, Rong, Fan, Yong
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 26.04.2018; Frontiers Media S.A
• Subjects: Algorithms; Alzheimer's disease; Amygdala; Animal cognition; Brain; Brain architecture; Brain mapping; Clustering; Functional magnetic resonance imaging; Laboratories; Medical imaging; Mental disorders; Methods; Neural networks; Neuroscience; NMR; Nuclear magnetic resonance; parcellation; resting-state fMRI; semi-supervised clustering; Studies; Beijing China; China; parcellation; amygdala; resting-state fMRI; 7T; semi-supervised clustering
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2018.00270

The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delineate boundaries of these subregions even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala . In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering (SSC) algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity (FC) patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions.