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Mixed Modeling Frameworks for Analyzing Whole-Brain Network Data

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Biomedical Engineering Technologies

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2393))

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Abstract

Brain network analyses have exploded in recent years and hold great potential in helping us understand normal and abnormal brain function. Network science approaches have facilitated these analyses and our understanding of how the brain is structurally and functionally organized. However, the development of statistical methods that allow relating this organization to health outcomes has lagged behind. We have attempted to address this need by developing mixed modeling frameworks that allow relating system-level properties of brain networks to outcomes of interest. These frameworks serve as a synergistic fusion of multivariate statistical approaches with network science, providing a needed analytic (modeling and inferential) foundation for whole-brain network data. In this chapter we delineate these approaches that have been developed for single-task and multitask (longitudinal) brain network data, illustrate their utility with data applications, detail their implementation with a user-friendly Matlab toolbox, and discuss ongoing work to adapt the methods to (within-task) dynamic network analysis.

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Acknowledgements

This work was supported by National Institute of Biomedical Imaging and Bioengineering R01EB024559, and Wake Forest Clinical and Translational Science Institute (WF CTSI) NCATS UL1TR001420.

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Authors and Affiliations

  1. Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, USA

    Sean L. Simpson

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  1. Sean L. Simpson
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Correspondence to Sean L. Simpson .

Editor information

Editors and Affiliations

  1. Division of Cancer Treatment and Diagnosis, National Cancer Institute, NIH, Bethesda, MD, USA

    Miguel R. Ossandon

  2. Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, USA

    Houston Baker

  3. Division of Cancer Treatment and Diagnosis, National Cancer Institute, Rockville, MD, USA

    Avraham Rasooly

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Simpson, S.L. (2022). Mixed Modeling Frameworks for Analyzing Whole-Brain Network Data. In: Ossandon, M.R., Baker, H., Rasooly, A. (eds) Biomedical Engineering Technologies. Methods in Molecular Biology, vol 2393. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1803-5_30

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  • DOI: https://doi.org/10.1007/978-1-0716-1803-5_30

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  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-1802-8

  • Online ISBN: 978-1-0716-1803-5

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