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Front Neuroinform. 2016 Jun 15;10:20. doi: 10.3389/fninf.2016.00020. eCollection 2016.

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

Author information

1
Translational 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.
2
Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill University Montreal, QC, Canada.
3
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University Montreal, QC, Canada.
4
McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada; Douglas Hospital Research Center, Douglas Research Institute, McGill UniversityMontreal, QC, Canada; Department of Psychiatry, McGill UniversityMontreal, QC, Canada; Department of Neurology and Neurosurgery, McGill UniversityMontreal, QC, Canada.
5
Douglas Hospital Research Center, Douglas Research Institute, McGill UniversityMontreal, QC, Canada; Department of Psychiatry, McGill UniversityMontreal, QC, Canada; Department of Epidemiology and Biostatistics, McGill UniversityMontreal, QC, Canada.
6
Translational 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; McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill UniversityMontreal, QC, Canada; Douglas Hospital Research Center, Douglas Research Institute, McGill UniversityMontreal, QC, Canada; Department of Psychiatry, McGill UniversityMontreal, QC, Canada; Department of Neurology and Neurosurgery, McGill UniversityMontreal, QC, Canada.

Abstract

In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab(®) and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.

KEYWORDS:

Alzheimer's disease; ROC analysis; generalized linear model; longitudinal analysis; mixed effect model; multimodal analysis; voxel-wise analysis

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