Functional connectivity analysis of human brain resting state functional magnetic resonance imaging (rsfMRI) data and resultant functional networks, or RSNs, have drawn increasing interest in both research and clinical applications. A fundamental yet challenging problem is to identify distinct functional regions or regions of interest (ROIs) that have accurate functional correspondence across subjects. This article presents an algorithmic framework to identify ROIs of common RSNs at the individual level. It first employed a dual-sparsity dictionary learning algorithm to extract group connectomic profiles of ROIs and RSNs from noisy and high-dimensional fMRI data, with special attention to the well-known inter-subject variability in anatomy and then identified the ROIs of a given individual by employing both anatomic and group connectomic profile constraints using an energy minimization approach. Applications of this framework demonstrated that it can identify individualized ROIs of RSNs with superior performance over commonly used registration methods in terms of functional correspondence, and a test-retest study revealed that the framework is robust and consistent across both short-interval and long-interval repeated sessions of the same population. These results indicate that our framework can provide accurate substrates for individualized rsfMRI analysis.
Keywords: anatomical variability; connectomic profiles; cortical parcellation; dictionary learning; individualized ROIs/RSNs.