1. Bayesian modeling of multiple structural connectivity networks during the progression of Alzheimer's disease
- Author
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James D. Doecke, Nathan Osborne, Francesco C. Stingo, Christine B. Peterson, Pierrick Bourgeat, and Marina Vannucci
- Subjects
FOS: Computer and information sciences ,Statistics and Probability ,Computer science ,Bayesian probability ,Inference ,Disease ,Bayesian inference ,Machine learning ,computer.software_genre ,Statistics - Applications ,01 natural sciences ,General Biochemistry, Genetics and Molecular Biology ,Article ,Methodology (stat.ME) ,010104 statistics & probability ,03 medical and health sciences ,Alzheimer Disease ,Prior probability ,Humans ,Applications (stat.AP) ,Cognitive Dysfunction ,Graphical model ,0101 mathematics ,Statistics - Methodology ,030304 developmental biology ,0303 health sciences ,General Immunology and Microbiology ,business.industry ,Applied Mathematics ,Bayes Theorem ,Neurodegenerative Diseases ,General Medicine ,Magnetic Resonance Imaging ,Disease Progression ,Artificial intelligence ,Enhanced Data Rates for GSM Evolution ,General Agricultural and Biological Sciences ,business ,Occipital lobe ,computer - Abstract
Alzheimer's disease is the most common neurodegenerative disease. The aim of this study is to infer structural changes in brain connectivity resulting from disease progression using cortical thickness measurements from a cohort of participants who were either healthy control, or with mild cognitive impairment, or Alzheimer's disease patients. For this purpose, we develop a novel approach for inference of multiple networks with related edge values across groups. Specifically, we infer a Gaussian graphical model for each group within a joint framework, where we rely on Bayesian hierarchical priors to link the precision matrix entries across groups. Our proposal differs from existing approaches in that it flexibly learns which groups have the most similar edge values, and accounts for the strength of connection (rather than only edge presence or absence) when sharing information across groups. Our results identify key alterations in structural connectivity which may reflect disruptions to the healthy brain, such as decreased connectivity within the occipital lobe with increasing disease severity. We also illustrate the proposed method through simulations, where we demonstrate its performance in structure learning and precision matrix estimation with respect to alternative approaches., Accepted to Biometrics January 2020
- Published
- 2018