Abstract

Regions of interests (ROIs) for defining nodes of brain network are of great importance in brain network analysis of fMRI data. The ROIs are typically identified using prior anatomical information, seed region based correlation analysis, clustering analysis, region growing or ICA based methods. In this paper, we propose a novel method to identify subject specific and functional consistent ROIs for brain network analysis using semi-supervised learning. Specifically, a graph theory based semi-supervised learning method is adopted to optimize ROIs defined using prior knowledge with a constraint of local and global functional consistency, yielding subject specific ROIs with enhanced functional connectivity. Experiments using simulated fMRI data have demonstrated that functional consistent ROIs can be identified effectively from data with different signal to noise ratios (SNRs). Experiments using resting state fMRI data of 25 normal subjects for identifying ROIs of the default mode network have demonstrated that the proposed method is capable of identifying subject specific ROIs with stronger functional connectivity and higher consistency across subjects than existing alternative techniques, indicating that the proposed method can better identify brain network ROIs with intrinsic functional connectivity.