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Using modular connectome-based predictive modeling to reveal brain-behavior relationships of individual differences in working memory

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Abstract

Working memory plays a crucial role in our daily lives, and brain imaging has been used to predict working memory performance. Here, we present an improved connectome-based predictive modeling approach for building a predictive model of individual working memory performance from whole-brain functional connectivity. The model was built using n-back task-based fMRI and resting-state fMRI data from the Human Connectome Project. Compared to prior models, our model was more interpretable, demonstrated a closer connection to the known anatomical and functional network. The model also demonstrates strong generalization on nine other cognitive behaviors from the HCP database and can well predict the working memory performance of healthy individuals in external datasets. By comparing the differences in prediction effects of different brain networks and anatomical feature analysis on n-back tasks, we found the essential role of some networks in differentiating between high and low working memory loads conditions.

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Data availability

The data that support the findings of this study are openly available in Human Connectome Project S1200 release at [http://www.humanconnectome.org/] and OpenfMRI database at [https://www.openfmri.org/] (accession number is ds000115).

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Funding

This research was supported by grants from NSFC (62176045), by Sichuan Science and Technology Program (2023YFS0191),111 project (B12027), and the Fundamental Research Funds for the Central Universities (ZYGX2020FRJH014).

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Authors and Affiliations

  1. MOE Key Lab for NeuroInformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Huayi Yang, Junjun Zhang, Zhenlan Jin & Ling Li

  2. Department of Physiology, McGill University, Montréal, QC, H3G 1Y6, Canada

    Huayi Yang & Pouya Bashivan

  3. Mila, University of Montreal, Montréal, QC, H2S 3H1, Canada

    Pouya Bashivan

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  1. Huayi Yang
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  2. Junjun Zhang
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  4. Pouya Bashivan
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  5. Ling Li
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HY wrote the main manuscript text. All authors reviewed the manuscript and made revisions to the manuscript.

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Correspondence to Ling Li.

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Yang, H., Zhang, J., Jin, Z. et al. Using modular connectome-based predictive modeling to reveal brain-behavior relationships of individual differences in working memory. Brain Struct Funct 228, 1479–1492 (2023). https://doi.org/10.1007/s00429-023-02666-3

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  • DOI: https://doi.org/10.1007/s00429-023-02666-3

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