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

Author

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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)

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.

Suggested Citation

  • Youzhi Qu & Kai Fu & Linjing Wang & Yu Zhang & Haiyan Wu & Quanying Liu, 2024. "Hypergraph-Based Multitask Feature Selection with Temporally Constrained Group Sparsity Learning on fMRI," Mathematics, MDPI, vol. 12(11), pages 1-16, June.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:11:p:1733-:d:1407401
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    References listed on IDEAS

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    1. Nikos K. Logothetis, 2008. "What we can do and what we cannot do with fMRI," Nature, Nature, vol. 453(7197), pages 869-878, June.
    2. Feng Wang & Feng Hu & Rumeng Chen & Naixue Xiong, 2023. "HLEGF: An Effective Hypernetwork Community Detection Algorithm Based on Local Expansion and Global Fusion," Mathematics, MDPI, vol. 11(16), pages 1-17, August.
    3. Evan M. Gordon & Roselyne J. Chauvin & Andrew N. Van & Aishwarya Rajesh & Ashley Nielsen & Dillan J. Newbold & Charles J. Lynch & Nicole A. Seider & Samuel R. Krimmel & Kristen M. Scheidter & Julia Mo, 2023. "A somato-cognitive action network alternates with effector regions in motor cortex," Nature, Nature, vol. 617(7960), pages 351-359, May.
    4. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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