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DeepGraphDMD: Interpretable Spatio-Temporal Decomposition of Non-linear Functional Brain Network Dynamics

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 (MICCAI 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14227))

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Abstract

Functional brain dynamics is supported by parallel and overlapping functional network modes that are associated with specific neural circuits. Decomposing these network modes from fMRI data and finding their temporal characteristics is challenging due to their time-varying nature and the non-linearity of the functional dynamics. Dynamic Mode Decomposition (DMD) algorithms have been quite popular for solving this decomposition problem in recent years. In this work, we apply GraphDMD—an extension of the DMD for network data—to extract the dynamic network modes and their temporal characteristics from the fMRI time series in an interpretable manner. GraphDMD, however, regards the underlying system as a linear dynamical system that is sub-optimal for extracting the network modes from non-linear functional data. In this work, we develop a generalized version of the GraphDMD algorithm—DeepGraphDMD—applicable to arbitrary non-linear graph dynamical systems. DeepGraphDMD is an autoencoder-based deep learning model that learns Koopman eigenfunctions for graph data and embeds the non-linear graph dynamics into a latent linear space. We show the effectiveness of our method in both simulated data and the HCP resting-state fMRI data. In the HCP data, DeepGraphDMD provides novel insights into cognitive brain functions by discovering two major network modes related to fluid and crystallized intelligence.

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Notes

  1. 1.

    https://github.com/mturja-vf-ic-bd/DeepGraphDMD.git.

  2. 2.

    https://www.humanconnectome.org/storage/app/media/documentation/s1200/HCP1200-DenseConnectome+PTN+Appendix-July2017.pdf.

  3. 3.

    sklearn.decomposition.PCA.

  4. 4.

    sklearn.decomposition.FastICA.

  5. 5.

    https://mathlab.github.io/PyDMD/dmd.html.

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Authors and Affiliations

  1. Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, USA

    Md Asadullah Turja & Martin Styner

  2. Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, USA

    Guorong Wu

Authors
  1. Md Asadullah Turja
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  2. Martin Styner
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  3. Guorong Wu
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Corresponding author

Correspondence to Md Asadullah Turja .

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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Turja, M.A., Styner, M., Wu, G. (2023). DeepGraphDMD: Interpretable Spatio-Temporal Decomposition of Non-linear Functional Brain Network Dynamics. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. https://doi.org/10.1007/978-3-031-43993-3_35

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  • DOI: https://doi.org/10.1007/978-3-031-43993-3_35

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  • Online ISBN: 978-3-031-43993-3

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