An Overview, Challenges, and Future Directions - SAMSI

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Experiments Models Connectivity Prediction Summary Methods High-dimensional Data Methods Develop statistical models, which pool predictive strength across multiple data modalities Proposed statistical techniques Penalized likelihood approaches: l p (β, λ) = −l(β) + ∑ k λ kP k (β k ) l(β) = ∑ i [y ilogπ i + (1 − y i )log(1 − π i ] Variable selection or shrinkage Novel joint multimodal probability models Model development, training, testing, and validation F. D. Bowman (Emory University) SAMSI: NDA RTP, NC 73 / 77
Experiments Models Connectivity Prediction Summary Bayesian Spatial Prediction Model We blindly classify subjects as either PD or HC from multimodal imaging data (and other patient information) Two-level parcellation: AAL Regions → Subregions Spatial correlations: Between AAL regions: Unstructured covariance matrix Borrow strength between subregions within each AAL region: CAR model Between voxels within each subregion: Exchangeable structure F. D. Bowman (Emory University) SAMSI: NDA RTP, NC 74 / 77
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An Overview, Challenges, and Future Directions - SAMSI

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