Multi-task fMRI data classification via group-wise hybrid temporal and spatial sparse representations.

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Abstract

Task-based functional magnetic resonance imaging (tfMRI) has been widely used to induce functional brain activities corresponding to various cognitive tasks. A relatively under-explored question is whether there exist fundamental differences in fMRI signal composition patterns that can effectively classify the task states of tfMRI data, furthermore, whether there exist key functional components in characterizing the diverse tfMRI signals. Recently, fMRI signal composition patterns of multiple tasks have been investigated via deep learning models, where relatively large populations of fMRI datasets are indispensable and the neurological meaning of their results is elusive. Thus, the major challenges arise from the high dimensionality, low signal-to-noise ratio, inter-individual variability, a small sample size of fMRI data, and the explainability of classification results. To address the above challenges, we proposed a computational framework based on group-wise hybrid temporal and spatial sparse representations (HTSSR) to identify and differentiate multi-task fMRI signal composition patterns. Using relatively small cohorts of Human Connectome Project (HCP) tfMRI data as test-bed, the experimental results demonstrated that the multi-task of fMRI data can be successfully classified with an average accuracy of 96.67%, where the key components in differentiating the multi-task can be characterized, suggesting the effectiveness and explainability of the proposed method. Moreover, both task-related components and resting state networks (RSNs) can be reliably detected. Therefore, our study proposed a novel framework that identifies the interpretable and discriminative fMRI composition patterns and can be potentially applied for controlling fMRI data quality and inferring biomarkers in brain disorders with small sample neuroimaging datasets.Significance StatementTask-based functional magnetic resonance imaging (tfMRI) is known to be able to induce functional brain activities corresponding to various cognitive tasks. However, the neuroscience mechanism of inherent functional differences that can effectively classify the multi-tfMRI data and the key functional components in composition patterns have been rarely tapped. Our proposed framework can uncover the fundamental differences in fMRI signal composition patterns and classify the multi-task fMRI data with an average accuracy of 96.67%. In addition, our framework can effectively identify the key components with greater capacity in multi-task classification and disclose the underlying network mechanism of these key components.Copyright © 2022 Song et al.