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Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data

Literature suggests functional brain connectivity difference between AD and normal aging. Existing functional connectivity studies have limitations: -Correlation-based methods capture only pair-wise info. -Most approaches are confirmative, not exploratory.

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Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data

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  1. Literature suggests functional brain connectivity difference between AD and normal aging. • Existing functional connectivity studies have limitations: • -Correlation-based methods capture only pair-wise info. • -Most approaches are confirmative, not exploratory. • -A small number of brain regions are focused on. • -Thorough comparison between AD, MCI, and normal aging • with statistical significant assessment is lacking. • -fMRI data are mostly used, not PET • Build functional brain connectivity models of AD, MCI, and normal controls using a machine learning technique, called inverse covariance, based on ADNI-PET data. • Assess statistical significance of the connectivity difference and summarize the results. Learning Brain Connectivity of Alzheimer's Disease from Neuroimaging Data This work was sponsored by the NSF. 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 Introduction Objective Approach & Monotone Property Sparse Inverse Covariance Estimation (SICE) 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 Observations: • 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. AD NC MCI Strong Connectivity Mild Connectivity Weak Connectivity AD MCI NC AD MCI NC AD MCI NC Observations: • 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.

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