文章基本信息

标题：Spatial Bayesian Variable Selection Models on Functional Magnetic Resonance Imaging Time-Series Data
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作者：Kuo-Jung Lee ; Galin L. Jones ; Brian S. Caffo 等
期刊名称：Bayesian Analysis
印刷版ISSN：1931-6690
电子版ISSN：1936-0975
出版年度：2014
卷号：9
期号：3
页码：699-732
DOI：10.1214/14-BA873
语种：English
出版社：International Society for Bayesian Analysis
摘要：A common objective of fMRI (functional magnetic resonance imaging) studies is to determine subject-specific areas of increased blood oxygenation level dependent (BOLD) signal contrast in response to a stimulus or task, and hence to infer regional neuronal activity. We posit and investigate a Bayesian approach that incorporates spatial and temporal dependence and allows for the task-related change in the BOLD signal to change dynamically over the scanning session. In this way, our model accounts for potential learning effects in addition to other mechanisms of temporal drift in task-related signals. We study the properties of the model through its performance on simulated and real data sets.