Characterizing Frequency-Selective Network Vulnerability for Alzheimer's Disease by Identifying Critical Harmonic Patterns

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Characterizing Frequency-Selective Network Vulnerability for Alzheimer's Disease by Identifying Critical Harmonic Patterns


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Characterizing Frequency-Selective Network Vulnerability for Alzheimer's Disease by Identifying Critical Harmonic Patterns

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Alzheimer's disease (AD) is a multi-factor neurodegenerative disease that selectively affects certain regions of the brain while other areas remain unaffected. The underlying mechanisms of this selectivity, however, are still largely elusive. To address this challenge, we propose a novel longitudinal network analysis method employing sparse logistic regression to identify frequency-specific oscillation patterns which contribute to the selective network vulnerability for patients at risk of advancing to the more severe stage of dementia. We fit and apply our statistical method to more than 100 longitudinal brain networks, and validate it on synthetic data. A set of critical connectome pathways are identified that exhibit strong association to the progression of AD.

Alzheimer's disease (AD) is a multi-factor neurodegenerative disease that selectively affects certain regions of the brain while other areas remain unaffected. The underlying mechanisms of this selectivity, however, are still largely elusive. To address this challenge, we propose a novel longitudinal network analysis method employing sparse logistic regression to identify frequency-specific oscillation patterns which contribute to the selective network vulnerability for patients at risk of advancing to the more severe stage of dementia. We fit and apply our statistical method to more than 100 longitudinal brain networks, and validate it on synthetic data. A set of critical connectome pathways are identified that exhibit strong association to the progression of AD.