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Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data Shui Huang1, Jing Li1, Liang Sun1, Jun Liu1, Teresa Wu1, Kewei Chen2, Adam Fleisher2, Eric Reiman2, Jieping Ye1 1: Arizona State University, 2: Banner Alzheimer’s Institute This work was sponsored by the NSF. Introduction Sparse Inverse Covariance Estimation (SICE) Objective Approach & Monotone Property Monotone Property Let and be the sets of all the connectivity components of with and respectively. If , then . Intuitively, if two regions are connected (either directly or indirectly) at one level of sparseness, they will be connected at all lower levels of sparseness. Results AD MCI NC AD: between-lobe connectivity weaker than within-lobe con. AD: left-right same region connectivity much weaker. MCI: patterns not as distinct from normal controls as AD. Observations: AD MCI NC AD MCI NC AD MCI NC Strong Connectivity Mild Connectivity Weak Connectivity Temporal: decreased connectivity in AD, decrease not significant in MCI. • Frontal: increased connectivity in AD (compensation), increase not significant in MCI. • Parietal, occipital: no significant difference. • Parietal-occipital: increased weak/mild con. in AD. • Frontal-occipital: decreased weak/mild con. in MCI. • Left-right: decreased strong con. in AD, not MCI. Observations: