VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis.
VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis.
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DOI：
10.3389/fninf.2016.00020
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发表时间：
2016
影响因子：
3.5
通讯作者：
Rosa-Neto P
中科院分区：
文献类型：
--
作者：
Mathotaarachchi S;Wang S;Shin M;Pascoal TA;Benedet AL;Kang MS;Beaudry T;Fonov VS;Gauthier S;Labbe A;Rosa-Neto P
在健康个体中，行为结果与大脑区域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萎缩的程度、神经化学物质下降或大脑区域异常蛋白聚集的浓度有关。然而，多模态成像研究在体素水平上对多个区域异常作为认知衰退决定因素的影响进行建模，在很大程度上仍未被探索，因为从各种成像模式估计每个单个体素的回归模型的计算成本很高。VoxelStats是一个体素计算框架，克服了这些计算限制，并在体素水平上对多个标量变量和成像模式执行统计操作。VoxelStats软件包已在Matlab®中开发，并支持成像格式，如Nifti-1， ANALYZE和MINC v2。VoxelStats中的预构建功能使用户能够执行体素一般和广义线性模型以及具有多个体积协变量的混合效果模型。重要的是，VoxelStats可以识别标量值或图像体积作为响应变量，并且可以容纳体积统计协变量以及它们与其他变量的交互影响。此外，该软件包还包括内置功能，可执行体素接收器工作特性分析以及配对和非配对组对比分析。通过将线性回归功能与现有的工具箱（如glim_image和RMINC）进行比较，对VoxelStats进行验证。验证结果与现有方法相同，并且通过生成特征案例评估（t统计量、比值比和真阳性率图）演示了额外的功能。总之，VoxelStats扩展了当前的多模态成像分析方法，允许在体素级别估计高级区域关联指标。
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.