Semi-supervised clustering for parcellating brain regions based on resting state fMRI data

Hewei Cheng; Yong Fan

doi:10.1117/12.2043467

Abstract

Many unsupervised clustering techniques have been adopted for parcellating brain regions of interest into functionally homogeneous subregions based on resting state fMRI data. However, the unsupervised clustering techniques are not able to take advantage of exiting knowledge of the functional neuroanatomy readily available from studies of cytoarchitectonic parcellation or meta-analysis of the literature. In this study, we propose a semi-supervised clustering method for parcellating amygdala into functionally homogeneous subregions based on resting state fMRI data. Particularly, the semi-supervised clustering is implemented under the framework of graph partitioning, and adopts prior information and spatial consistent constraints to obtain a spatially contiguous parcellation result. The graph partitioning problem is solved using an efficient algorithm similar to the well-known weighted kernel k-means algorithm. Our method has been validated for parcellating amygdala into 3 subregions based on resting state fMRI data of 28 subjects. The experiment results have demonstrated that the proposed method is more robust than unsupervised clustering and able to parcellate amygdala into centromedial, laterobasal, and superficial parts with improved functionally homogeneity compared with the cytoarchitectonic parcellation result. The validity of the parcellation results is also supported by distinctive functional and structural connectivity patterns of the subregions and high consistency between coactivation patterns derived from a meta-analysis and functional connectivity patterns of corresponding subregions.

Citation Download Citation

Hewei Cheng, Hewei Cheng, Yong Fan, Yong Fan, "Semi-supervised clustering for parcellating brain regions based on resting state fMRI data ", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903427 (21 March 2014); doi: 10.1117/12.2043467; https://doi.org/10.1117/12.2043467

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