Quasi-Periodic Patterns of Neural Activity improve Classification of Alzheimer's Disease in Mice.

ABSTRACT: Resting state (rs)fMRI allows measurement of brain functional connectivity and has identified default mode (DMN) and task positive (TPN) network disruptions as promising biomarkers for Alzheimer's disease (AD). Quasi-periodic patterns (QPPs) of neural activity describe recurring spatiotemporal patterns that display DMN with TPN anti-correlation. We reasoned that QPPs could provide new insights into AD network dysfunction and improve disease diagnosis. We therefore used rsfMRI to investigate QPPs in old TG2576 mice, a model of amyloidosis, and age-matched controls. Multiple QPPs were determined and compared across groups. Using linear regression, we removed their contribution from the functional scans and assessed how they reflected functional connectivity. Lastly, we used elastic net regre

SUBMITTER: Belloy ME 

PROVIDER: S-EPMC6030071 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature