Brain network extraction from probabilistic ICA using functional Magnetic Resonance Images and advanced template matching techniques

Abstract

The human brain is a complicated network made-up of a large number of regions, which are structurally and/or functionally connected. Recently, neuroimaging studies using functional Magnetic Resonance Imaging have revealed that certain neural structures are highly active during periods of rest. Amongst several methods that have been developed to analyze resting-state fMRI data, Probabilistic Independent Component Analysis (PICA) is currently the most popular technique. The major challenge of using PICA is that resting-state networks are split into several components and visually extracting them can be difficult. In this paper, we propose a fast and precise algorithm based on advanced template matching in spatial domain such as Normalized Cross Correlation adapted to functional images in order to automatically extract the Default Mode Network (DMN) which is the task independent resting state network in the brain using PICA. We create a DMN template covering all reported regions in literature using two standard atlases. Ultimately, we reconstruct an image of the extracted DMN from PICA using an optimized decision making. Our approach was effective given that our algorithm results correlated highly with the DMN template.