NPBayes-fMRI: Non-parametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data
NPBayes-fMRI: Non-parametric Bayesian General Linear Models for Single- and Multi-Subject fMRI Data
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DOI：
10.1007/s12561-017-9205-0
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发表时间：
2019-04-01
影响因子：
1
通讯作者：
Vannucci, Marina
中科院分区：
文献类型：
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作者：
Kook, Jeong Hwan;Guindani, Michele;Vannucci, Marina
在本文中，我们介绍NPBayes-fMRI，一个用户友好的MATLAB GUI，实现了一个统一的，概率连贯的非参数贝叶斯框架的任务相关的fMRI数据分析从多学科实验。建模方法是基于时空线性回归模型，该模型通过空间信息多受试者非参数变量选择先验，具体说明了受试者之间神经元活动的异质性。该方法的一个特征是，它导致受试者聚类成以相似的大脑反应为特征的子组，同时产生组级和受试者级激活图。这是不同于两阶段的组分析方法，传统上认为在功能磁共振成像文献中，独立的推断在人口水平上的推断对个人的功能磁共振成像时间进程。在这里，我们首先描述的模型和变分贝叶斯算法的后验推理。接下来，我们将介绍工具箱，并通过一个示例说明其功能。
In this paper, we introduce NPBayes-fMRI, a user-friendly MATLAB GUI that implements a unified, probabilistically coherent non-parametric Bayesian framework for the analysis of task-related fMRI data from multi-subject experiments. The modeling approach is based on a spatio-temporal linear regression model that specifically accounts for the between-subjects heterogeneity in neuronal activity via a spatially informed multi-subject non-parametric variable selection prior. A characteristic feature of the approach is that it results in a clustering of the subjects into subgroups characterized by similar brain responses, while simultaneously producing group-level as well as subject-level activation maps. This is distinct from two-stage group analysis approaches traditionally considered in the fMRI literature, which separate the inference on the individual fMRI time courses from the inference at the population level. Here, we first describe the models and a Variational Bayes algorithm for posterior inference. Next, we introduce the toolbox and illustrate its features via an example.