A Bayesian probit model with spatially varying coefficients for brain decoding using fMRI data
Publication in refereed journal



摘要Recent advances in human neuroimaging have shown that it is possible to accurately decode how the brain perceives information based only on non-invasive functional magnetic resonance imaging measurements of brain activity. Two commonly used statistical approaches, namely, univariate analysis and multivariate pattern analysis often lead to distinct patterns of selected voxels. One current debate in brain decoding concerns whether the brain's representation of sound categories is localized or distributed. We hypothesize that the distributed pattern of voxels selected by most multivariate pattern analysis models can be an artifact due to the spatial correlation among voxels. Here, we propose a Bayesian spatially varying coefficient model, where the spatial correlation is modeled through the variance-covariance matrix of the model coefficients. Combined with a proposed region selection strategy, we demonstrate that our approach is effective in identifying the truly localized patterns of the voxels while maintaining robustness to discover truly distributed pattern. In addition, we show that localized or clustered patterns can be artificially identified as distributed if without proper usage of the spatial correlation information in fMRI data. Copyright (c) 2016 John Wiley & Sons, Ltd.
著者Zhang FQ, Jiang WX, Wong P, Wang JP
期刊名稱Statistics in Medicine
頁次4380 - 4397
關鍵詞brain decoding; classification; fMRI; multivariate pattern analysis; variable selection
Web of Science 學科類別Mathematical & Computational Biology; Mathematics; Medical Informatics; Medicine, Research & Experimental; Public, Environmental & Occupational Health; Research & Experimental Medicine; Statistics & Probability

上次更新時間 2020-26-03 於 00:53