Optimization of fMRI-derived ROIs based on coherent functional interaction patterns

Abstract

Accurate localization of functionally meaningful Regions of Interests (ROIs) from fMRI data is critically important to functional brain imaging. A variety of established approaches such as General Linear Model (GLM) have been widely used in the community. How to determine the optimal location and size of an fMRI-derived ROI, however, remains an open, challenging problem. This paper presents a novel individualized optimization algorithm that simultaneously optimizes the locations and sizes of fMRI-derived ROIs by maximizing the coherences of their functional interaction patterns with respect to the block-based paradigm. As an alternative ROI optimization approach using functional interaction patterns, the algorithm was applied on a working memory task-based fMRI dataset and the experimental results are promising.