FMRI DATA CLASSIFICATION BASED ON HYBRID TEMPORAL AND SPATIAL SPARSE REPRESENTATION

Task-based functional magnetic resonance imaging (tfMRI) is widely used to localize brain regions or networks in response to various cognitive tasks. However, given two groups of tfMRI data acquired under distinct task paradigms, it is not clear whether there exist intrinsic inter-group differences in signal composition patterns, and if so, whether these differences could be used for data discrimination. The major challenges originate from the high dimensionality and low signal-to-noise ratio of fMRI data. In this paper, we proposed a novel framework using hybrid temporal and spatial sparse representation to tackle above challenges. We applied the proposed framework to the Human Connectome Project (HCP) tfMRI data. Our experimental results demonstrated that the task types of fMRI data can be successfully classified, achieving a 100% classification accuracy. We also showed that both task-related components and resting state networks (RSNs) can be reliably identified. Our study provides a novel data-driven approach to detecting discriminative inter-group differences in fMRI data based on signal composition patterns, and thus potentially can be used to control fMRI data quality and to infer biomarkers in brain disorders.