Author:

Keywords:

Science & Technology, Technology, Computer Science, Artificial Intelligence, Engineering, Electrical & Electronic, Computer Science, Engineering, fMRI, functional connectivity, image segmentation, FUNCTIONAL MRI, ENERGY MINIMIZATION, NEURAL ACTIVITY, SETS, HOMOGENEITY, Algorithms, Animals, Brain, Brain Mapping, Cluster Analysis, Haplorhini, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Models, Statistical, Multivariate Analysis, 0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences, Artificial Intelligence & Image Processing, 4603 Computer vision and multimedia computation, 4607 Graphics, augmented reality and games

Abstract:

In this paper, we describe a new methodology for defining brain regions-of-interset (ROIs) in functional magnetic resonance imaging (fMRI) data. The ROIs are defined based on their functional connectivity to other ROIs, i.e., ROIs are defined as sets of voxels with similar connectivity patterns to other ROIs. The method relies on 1) a spatially regularized canonical correlation analysis for identifying maximally correlated signals, which are not due to correlated noise; 2) a test for merging ROIs which have similar connectivity patterns to the other ROIs; and 3) a graph-cuts optimization for assigning voxels to ROIs. Since our method is fully connectivity-based, the extracted ROIs and their corresponding time signals are ideally suited for a subsequent brain connectivity analysis.