Hypergraph-Based Multitask Feature Selection with Temporally Constrained Group Sparsity Learning on fMRI

Youzhi Qu, Kai Fu, Linjing Wang, Yu Zhang, Haiyan Wu and Quanying Liu ()
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Youzhi Qu: Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Kai Fu: Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Linjing Wang: Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Yu Zhang: Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
Haiyan Wu: Centre for Cognitive and Brain Sciences and Department of Psychology, University of Macau, Macau 999078, China
Quanying Liu: Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China

Mathematics, 2024, vol. 12, issue 11, 1-16

Abstract: Localizing the brain regions affected by tasks is crucial to understanding the mechanisms of brain function. However, traditional statistical analysis does not accurately identify the brain regions of interest due to factors such as sample size, task design, and statistical effects. Here, we propose a hypergraph-based multitask feature selection framework, referred to as HMTFS, which we apply to a functional magnetic resonance imaging (fMRI) dataset to extract task-related brain regions. HMTFS is characterized by its ability to construct a hypergraph through correlations between subjects, treating each subject as a node to preserve high-order information of time-varying signals. Additionally, it manages feature selection across different time windows in fMRI data as multiple tasks, facilitating time-constrained group sparse learning with a smoothness constraint. We utilize a large fMRI dataset from the Human Connectome Project (HCP) to validate the performance of HMTFS in feature selection. Experimental results demonstrate that brain regions selected by HMTFS can provide higher accuracy for downstream classification tasks compared to other competing feature selection methods and align with findings from previous neuroscience studies.

Keywords: hypergraph; multitask learning; feature selection; fMRI (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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