An Overview, Challenges, and Future Directions - SAMSI

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Experiments Models Connectivity Prediction Summary Bayesian spatial hierarchical model [Derado et al., 2012, Statistical Methods in Medical Research]. Notation We developed a Bayesian spatial hierarchical model for predicting follow-up neural activity based on baseline functional neuroimaging data and other patient characteristics. Model borrows strength from spatial correlations present in the data. Let i = 1, . . . , n denote subjects, v = 1, . . . , V voxels, g = 1, . . . , G regions. Let Y (v) denote the regional cerebral blood flow (rCBF) (a proxy for brain activity) at voxel v. ( ) T Let Y ig (v) = Y ig (v) (1) , Y ig (v) (2) , (1)=baseline, (2)=follow-up. F. D. Bowman (Emory University) SAMSI: NDA RTP, NC 59 / 77
Experiments Models Connectivity Prediction Summary Spatial dependence: 3D neighborhood For each voxel in the analysis, we define a 3D neighborhood as the 26 immediate neighboring voxels. Borrow strength locally. We consider only within-region neighbors. This information is saved in a connectivity matrix W. F. D. Bowman (Emory University) SAMSI: NDA RTP, NC 60 / 77
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An Overview, Challenges, and Future Directions - SAMSI

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