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An Explainable Autoencoder with Multi-paradigm fMRI Fusion for Identifying Di erences in Dynamic Functional Connectivity During Brain Development

journal contribution
posted on 2023-01-10, 11:23 authored by F Xu, C Qiao, Huiyu Zhou, VD Calhoun, JM Stephen, TW Wilson, Y Wang

Multi-paradigm deep learning models show great potential for dynamic functional connectivity (dFC) analysis by integrating complementary information. However, many of them cannot use information from different paradigms effectively and have poor explainability, that is, the ability to identify significant features that contribute to decision making. In this paper, we propose a multi-paradigm fusion-based explainable deep sparse autoencoder (MF-EDSAE) to address these issues. Considering explainability, the MF-EDSAE is constructed based on a deep sparse autoencoder (DSAE). For integrating information effectively, the MF-EDASE contains the nonlinear fusion layer and multi-paradigm hypergraph regularization. We apply the model to the Philadelphia Neurodevelopmental Cohort and demonstrate it achieves better performance in detecting dynamic FC (dFC) that differ significantly during brain development than the single-paradigm DSAE. The experimental results show that children have more dispersive dFC patterns than adults. The function of the brain transits from undifferentiated systems to specialized networks during brain development. Meanwhile, adults have stronger connectivities between task-related functional networks for a given task than children. As the brain develops, the patterns of the global dFC change more quickly when stimulated by a task.

Funding

This research was supported by the National Natural Science Foundation of China (No. 12090021, 12271429), the National Key Research and Development Program of China (No. 2020AAA0106302), the Natural Science Basic Research Program of Shaanxi, China (No. 2022JM-005) and was partly supported by NIH, United States (No. R01 MH104680, R01 MH121101, R01 MH116782, R01 MH118013, R01 GM109068, and P20 GM144641).

History

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Version

  • AM (Accepted Manuscript)

Published in

Neural Networks

Volume

159

Pagination

185-197

Publisher

Elsevier

issn

0893-6080

Copyright date

2022

Available date

2023-12-22

Language

en

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