Predicting brain activity using a Bayesian spatial model

Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
Statistical Methods in Medical Research (Impact Factor: 4.47). 06/2012; 22(4). DOI: 10.1177/0962280212448972
Source: PubMed


Increasing the clinical applicability of functional neuroimaging technology is an emerging objective, e.g. for diagnostic and treatment purposes. We propose a novel Bayesian spatial hierarchical framework for predicting follow-up neural activity based on an individual's baseline functional neuroimaging data. Our approach attempts to overcome some shortcomings of the modeling methods used in other neuroimaging settings, by borrowing strength from the spatial correlations present in the data. Our proposed methodology is applicable to data from various imaging modalities including functional magnetic resonance imaging and positron emission tomography, and we provide an illustration here using positron emission tomography data from a study of Alzheimer's disease to predict disease progression.

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    • "Related prior work Spatiotemporal models that pool the temporal covariance structure across spatial locations have been successfully used in the functional neuroimaging literature (Bowman, 2007; Bowman et al., 2008; Derado et al., 2012; Friston et al., 2005; Gossl et al., 2004; Guo et al., 2008; Woolrich et al., 2004). In practice, it has been demonstrated that this approach can increase the precision of parameter estimates . "
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    NeuroImage 05/2013; 81. DOI:10.1016/j.neuroimage.2013.05.049 · 6.36 Impact Factor
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    Statistical Methods in Medical Research 05/2012; 22(4). DOI:10.1177/0962280212448973 · 4.47 Impact Factor
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    ABSTRACT: The aim of this paper is to develop a spatial Gaussian predictive process (SGPP) framework for accurately predicting neuroimaging data by using a set of covariates of interest, such as age and diagnostic status, and an existing neuroimaging data set. To achieve better prediction, we not only delineate spatial association between neuroimaging data and covariates, but also explicitly model spatial dependence in neuroimaging data. The SGPP model uses a functional principal component model to capture medium-to-long-range (or global) spatial dependence, while SGPP uses a multivariate simultaneous autoregressive model to capture short-range (or local) spatial dependence as well as cross-correlations of different imaging modalities. We propose a three-stage estimation procedure to simultaneously estimate varying regression coefficients across voxels and the global and local spatial dependence structures. Furthermore, we develop a predictive method to use the spatial correlations as well as the cross-correlations by employing a cokriging technique, which can be useful for the imputation of missing imaging data. Simulation studies and real data analysis are used to evaluate the prediction accuracy of SGPP and show that SGPP significantly outperforms several competing methods, such as voxel-wise linear model, in prediction. Although we focus on the morphometric variation of lateral ventricle surfaces in a clinical study of neurodevelopment, it is expected that SGPP is applicable to other imaging modalities and features.
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