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.
Highlights
Research studies based on multiple neuroimaging modalities in the same individual is becoming increasingly popular due to the widespread availability of imaging techniques such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET)
VoxelStats includes prebuilt functions to perform common statistical operations including general and generalized linear modeling with mixed effects, which can lead to new insights in the analysis of longitudinal neuroimaging data
Its ability to work as an independent Matlab toolbox and the support for Nifti-1, ANALYZE, and MINC v2 format volumes will make VoxelStats immediately useful in the neuroimaging community
Summary
Research studies based on multiple neuroimaging modalities in the same individual (multimodal acquisition) is becoming increasingly popular due to the widespread availability of imaging techniques such as Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Several multimodal imaging studies have evaluated the association between the local amyloid plaque deposition and glucose metabolism using [18F]Florbetapir PET and [18F]FDG PET images in patients in multiple stages of dementia (Engler et al, 2006; Edison et al, 2007; Cohen et al, 2009; Rabinovici et al, 2010; Furst et al, 2012; Ossenkoppele et al, 2012; Lowe et al, 2014; Altmann et al, 2015; Fletcher et al, 2016) These studies were conducted either by focusing on a predefined set of brain regions (Engler et al, 2006; Edison et al, 2007; Rabinovici et al, 2010; Lowe et al, 2014; Altmann et al, 2015) or by using simple voxel wise correlation analysis (Cohen et al, 2009). Studies evaluating the interaction between biomarkers (Pascoal et al, 2016) and genetic factors (Benedet et al, 2015) can take advantage of voxel wise statistical modeling (Furst et al, 2012) with imaging covariates, ; at present, performing voxel-wise statistical analyses and mathematical operations often require utilizing several different specialized toolboxes or modifying the study design to suit the toolboxes available
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