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METHODS article

Front. Neurosci.
Sec. Brain Imaging Methods
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1385847
This article is part of the Research Topic Methods and Applications of Diffusion MRI Tractometry View all 12 articles

A practical guide for combining functional regions of interest and white matter bundles

Provisionally accepted
Steven L. Meisler Steven L. Meisler 1,2*Emily Kubota Emily Kubota 3*Mareike Grotheer Mareike Grotheer 4,5John D. Gabrieli John D. Gabrieli 1,6Kalanit Grill-Spector Kalanit Grill-Spector 3,7
  • 1 Department of Brain and Cognitive Sciences, School of Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
  • 2 Division of Medical Sciences, Harvard Medical School, Boston, Massachusetts, United States
  • 3 Department of Psychology, School of Humanities and Sciences, Stanford University, Stanford, California, United States
  • 4 Department of Psychology, University of Marburg, Marburg, Hesse, Germany
  • 5 Center for Mind, Brain and Behavior (CMBB), Marburg, Hesse, Germany
  • 6 McGovern Institute for Brain Research, School of Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
  • 7 Wu Tsai Neurosciences Institute, Stanford University, Stanford, California, United States

The final, formatted version of the article will be published soon.

    Diffusion-weighted imaging (DWI) is the primary method to investigate macro-and microstructure of neural white matter in vivo. DWI can be used to identify and characterize individual-specific white matter bundles, enabling precise analyses on hypothesis-driven connections in the brain and bridging the relationships between brain structure, function, and behavior. However, cortical endpoints of bundles may span larger areas than what a researcher is interested in, challenging presumptions that bundles are specifically tied to certain brain functions. Functional MRI (fMRI) can be integrated to further refine bundles such that they are restricted to functionally-defined cortical regions. Analyzing properties of these Functional Sub-Bundles (FSuB) increases precision and interpretability of results when studying neural connections supporting specific tasks. Several parameters of DWI and fMRI analyses, ranging from data acquisition to processing, can impact the efficacy of integrating functional and diffusion MRI. Here, we discuss the applications of the FSuB approach, suggest best practices for acquiring and processing neuroimaging data towards this end, and introduce the FSuB-Extractor, a flexible open-source software for creating FSuBs. We demonstrate our processing code and the FSuB-Extractor on an openly-available dataset, the Natural Scenes Dataset.

    Keywords: DWI, fMRI, white matter, structural connectivity, open-source software

    Received: 13 Feb 2024; Accepted: 11 Jul 2024.

    Copyright: © 2024 Meisler, Kubota, Grotheer, Gabrieli and Grill-Spector. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Steven L. Meisler, Department of Brain and Cognitive Sciences, School of Science, Massachusetts Institute of Technology, Cambridge, 02139-4307, Massachusetts, United States
    Emily Kubota, Department of Psychology, School of Humanities and Sciences, Stanford University, Stanford, CA 94305-2130, California, United States

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.