Temporal concatenated sparse coding of resting state fMRI data reveal network interaction changes in mTBI

Abstract

Resting state fMRI (rsfMRI) has been a useful imaging modality for network level understanding and diagnosis of brain diseases,such as mild traumatic brain injury (mTBI). However,there call for effective methodologies which can detect group-wise and longitudinal changes of network interactions in mTBI. The major challenges are two folds: (1) There lacks an individualized and common network system that can serve as a reference platform for statistical analysis; (2) Networks and their interactions are usually not modeled in the same algorithmic structure,which results in bias and uncertainty. In this paper,we propose a novel temporal concatenated sparse coding (TCSC) method to address these challenges. Based on the sparse graph theory the proposed method can model the commonly shared spatial maps of networks and the local dynamics of the networks in each subject in one algorithmic structure. Obviously,the local dynamics are not comparable across subjects in rsfMRI or across groups; however,based on the correspondence established by the common spatial profiles,the interactions of these networks can be modeled individually and statistically assessed in a group-wise fashion. The proposed method has been applied on an mTBI dataset with acute and sub-acute stages,and experimental results have revealed meaningful network interaction changes in mTBI.