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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2022: Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 pp 271–281Cite as

Nonlinear Conditional Time-Varying Granger Causality of Task fMRI via Deep Stacking Networks and Adaptive Convolutional Kernels

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13431))

Abstract

Time-varying Granger causality refers to patterns of causal relationships that vary over time between brain functional time series at distinct source and target regions. It provides rich information about the spatiotemporal structure of brain activity that underlies behavior. Current methods for this problem fail to quantify nonlinear relationships in source-target relationships, and require ad hoc setting of relationship time lags. This paper proposes deep stacking networks (DSNs), with adaptive convolutional kernels (ACKs) as component parts, to address these challenges. The DSNs use convolutional neural networks to estimate nonlinear source-target relationships, ACKs allow these relationships to vary over time, and time lags are estimated by analysis of ACKs coefficients. When applied to synthetic data and data simulated by the STANCE fMRI simulator, the method identified ground-truth time-varying causal relationships and time lags more robustly than competing methods. The method also identified more biologically-plausible causal relationships in a real-world task fMRI dataset than a competing method. Our method is promising for modeling complex functional relationships within brain networks.

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Author information

Authors and Affiliations

  1. Medical Physics Graduate Program, Louisiana State University, Baton Rouge, LA, USA

    Kai-Cheng Chuang

  2. Biomedical Imaging Center, Pennington Biomedical Research Center, Baton Rouge, LA, USA

    Kai-Cheng Chuang, Sreekrishna Ramakrishnapillai & Owen Carmichael

  3. Department of Epidemiology, Tulane School of Public Health and Tropical Medicine, New Orleans, LA, USA

    Lydia Bazzano

Authors
  1. Kai-Cheng Chuang
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  2. Sreekrishna Ramakrishnapillai
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  3. Lydia Bazzano
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  4. Owen Carmichael
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Corresponding authors

Correspondence to Kai-Cheng Chuang or Owen Carmichael .

Editor information

Editors and Affiliations

  1. Rochester Institute of Technology, Rochester, NY, USA

    Linwei Wang

  2. Chinese University of Hong Kong, Hong Kong, Hong Kong

    Qi Dou

  3. University of Virginia, Charlottesville, VA, USA

    P. Thomas Fletcher

  4. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  5. Case Western Reserve University, Cleveland, OH, USA

    Shuo Li

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Chuang, KC., Ramakrishnapillai, S., Bazzano, L., Carmichael, O. (2022). Nonlinear Conditional Time-Varying Granger Causality of Task fMRI via Deep Stacking Networks and Adaptive Convolutional Kernels. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13431. Springer, Cham. https://doi.org/10.1007/978-3-031-16431-6_26

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  • DOI: https://doi.org/10.1007/978-3-031-16431-6_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16430-9

  • Online ISBN: 978-3-031-16431-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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