Mathotaarachchi Sulantha, Wang Seqian, Shin Monica, Pascoal Tharick A, Benedet Andrea L, Kang Min Su, Beaudry Thomas, Fonov Vladimir S, Gauthier Serge, Labbe Aurélie, Rosa-Neto Pedro
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.
Translational Neuroimaging Laboratory, Departments of Neurology and Neurosurgery, McGill University Research Centre for Studies in Aging, Douglas Research Institute, McGill University Montreal, QC, Canada.
Front Neuroinform. 2016 Jun 15;10:20. doi: 10.3389/fninf.2016.00020. eCollection 2016.
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.
在健康个体中，行为结果与脑区结构或神经化学表型的变异性高度相关。同样，在神经退行性疾病的背景下，神经影像学显示认知能力下降与脑区萎缩程度、神经化学物质减少或异常蛋白质聚集体浓度有关。然而，鉴于从各种成像模态为每个体素估计回归模型的计算成本很高，多模态成像研究在很大程度上尚未探索将多个区域异常的影响建模为体素水平认知能力下降的决定因素。VoxelStats是一个逐体素计算框架，用于克服这些计算限制，并在体素水平对多个标量变量和成像模态执行统计操作。VoxelStats软件包是在Matlab(®)中开发的，支持Nifti-1、ANALYZE和MINC v2等成像格式。VoxelStats中的预建函数使用户能够执行具有多个体积协变量的逐体素一般线性模型、广义线性模型和混合效应模型。重要的是，VoxelStats可以将标量值或图像体积识别为响应变量，并可以容纳体积统计协变量及其与其他变量的交互作用。此外，该软件包还包括执行逐体素接收器操作特征分析以及配对和非配对组对比分析的内置功能。通过将线性回归功能与glim_image和RMINC等现有工具箱进行比较，对VoxelStats进行了验证。验证结果与现有方法相同，并通过生成特征病例评估（t统计量、优势比和真阳性率图）展示了其附加功能。总之，VoxelStats通过允许在体素水平估计高级区域关联指标，扩展了当前多模态成像分析的方法。
