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VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis.

Sulantha Mathotaarachchi1, Seqian Wang2, Monica Shin2

  • 1Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill UniversityMontreal, QC, Canada.

Frontiers in Neuroinformatics
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Summary

VoxelStats is a new computational framework for voxel-wise multimodal neuroimaging analysis. It enables advanced statistical modeling of brain structure and function relationships at a detailed level, overcoming previous computational limits.

Keywords:
Alzheimer's diseaseROC analysisgeneralized linear modellongitudinal analysismixed effect modelmultimodal analysisvoxel-wise analysis

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biostatistics

Background:

  • Brain structure and neurochemical phenotypes correlate with behavioral outcomes in healthy individuals.
  • Cognitive decline in neurodegenerative diseases is linked to regional brain atrophy, neurochemical changes, and abnormal protein aggregates.
  • Modeling multiple regional abnormalities' effects on cognitive decline at the voxel level is computationally challenging and underexplored in multimodal imaging.

Purpose of the Study:

  • To introduce VoxelStats, a voxel-wise computational framework designed to overcome computational limitations in multimodal neuroimaging analysis.
  • To enable statistical operations on multiple scalar variables and imaging modalities at the voxel level.
  • To facilitate advanced regional association metrics estimation.

Main Methods:

  • VoxelStats is a Matlab-based framework supporting Nifti-1, ANALYZE, and MINC v2 imaging formats.
  • It offers prebuilt functions for voxel-wise general and generalized linear models, mixed effect models, and receiver operating characteristic analysis.
  • The framework accommodates scalar values or image volumes as response variables and volumetric covariates, including interaction effects.

Main Results:

  • Validation confirmed identical linear regression functionality compared to existing toolboxes like glim_image and RMINC.
  • Additional functionalities were demonstrated through feature case assessments, generating t-statistics, odds ratio, and true positive rate maps.
  • VoxelStats successfully expands multimodal imaging analysis capabilities.

Conclusions:

  • VoxelStats provides a computationally efficient solution for voxel-wise multimodal neuroimaging analysis.
  • The framework allows for the estimation of advanced regional association metrics at the voxel level.
  • It enhances the ability to model complex relationships between brain structure/function and clinical outcomes.