期刊
IEEE TRANSACTIONS ON MEDICAL IMAGING
卷 30, 期 7, 页码 1401-1416出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMI.2011.2122341
关键词
Bayesian inference; functional neuroimaging; Gaussian processes; meta-analysis
类别
资金
- U.K. Research Councils
- GlaxoSmithKline
- MRC [G0900908] Funding Source: UKRI
- Medical Research Council [G0900908] Funding Source: researchfish
The purpose of neuroimaging meta-analysis is to localize the brain regions that are activated consistently in response to a certain intervention. As a commonly used technique, current coordinate-based meta-analyses (CBMA) of neuroimaging studies utilize relatively sparse information from published studies, typically only using (x,y,z) coordinates of the activation peaks. Such CBMA methods have several limitations. First, there is no way to jointly incorporate deactivation information when available, which has been shown to result in an inaccurate statistic image when assessing a difference contrast. Second, the scale of a kernel reflecting spatial uncertainty must be set without taking the effect size (e.g., Z-stat) into account. To address these problems, we employ Gaussian-process regression (GPR), explicitly estimating the unobserved statistic image given the sparse peak activation coordinate and standardized effect-size estimate data. In particular, our model allows estimation of effect size at each voxel, something existing CBMA methods cannot produce. Our results show that GPR outperforms existing CBMA techniques and is capable of more accurately reproducing the (usually unavailable) full-image analysis results.
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