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Mode-based morphometry: A multiscale approach to mapping human neuroanatomy.

Trang Cao1, James C Pang1, Ashlea Segal1

  • 1The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.

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|March 6, 2024
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Summary
This summary is machine-generated.

Mode-based morphometry (MBM) offers a novel, multiscale approach to analyzing brain anatomy. This physics-inspired technique characterizes neuroanatomical variations by resonant modes, outperforming traditional methods in accuracy and reliability.

Keywords:
MRIeigenmodesmorphometrymultiscaleneuroanatomy

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Area of Science:

  • Neuroimaging and Computational Anatomy
  • Physics-informed data analysis

Background:

  • Voxel-based morphometry (VBM) and surface-based morphometry (SBM) are standard neuroimaging techniques for brain anatomy analysis.
  • Current methods lack insight into generative processes and are limited to single spatial scales.
  • A need exists for multiscale analysis of neuroanatomical variability.

Purpose of the Study:

  • To develop a novel, physics-based approach for characterizing neuroanatomical variations.
  • To introduce mode-based morphometry (MBM) for multiscale analysis of brain anatomy.
  • To compare MBM with existing vertex-based surface-based morphometry (SBM).

Main Methods:

  • Developed mode-based morphometry (MBM) inspired by classical physics.
  • MBM describes anatomical variations using fundamental resonant modes (eigenmodes) of brain anatomy.
  • Utilized simulated and empirical data to validate the approach against vertex-based SBM.

Main Results:

  • MBM provides a multiscale characterization of neuroanatomical variability.
  • MBM effectively captures differences in cortical thickness maps between experimental groups.
  • MBM demonstrated comparable or superior validity and reliability to vertex-based SBM.

Conclusions:

  • Mode-based morphometry (MBM) is a robust and accurate method for analyzing neuroanatomical variability.
  • MBM offers insights into the spatial frequency content of brain anatomy.
  • The approach links empirical anatomical maps to underlying generative physical processes.