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

摘要: Resting state (rs)fMRI allows measurement of brain functional connectivity and has identified default mode (DMN) task positive (TPN) network disruptions as promising biomarkers for Alzheimer's disease (AD). Quasi-periodic patterns (QPPs) neural activity describe recurring spatiotemporal that display DMN with TPN anti-correlation. We reasoned QPPs could provide new insights into AD dysfunction improve diagnosis. therefore used rsfMRI to investigate in old TG2576 mice, a model amyloidosis, age-matched controls. Multiple were determined compared across groups. Using linear regression, we removed their contribution from the scans assessed how they reflected connectivity. Lastly, elastic net regression determine if improved classification. present three prominent findings: (1) Compared controls, mice marked by opposing dynamics which areas anti-correlated displayed diminished anti-correlation TPN. (2) lowered revealed significantly decreased DMN-TPN anti-correlations. (3) QPP-derived measures classification conventional measures. Altogether, our findings insight aberrant indicate might serve translational diagnostic tool.