A machine learning approach for potential Super-Agers identification using neuronal functional connectivity networks.

Mohammad FiliParvin MohammadiarvejehBrandon S KlinedinstQian WangShannin MoodyNeil BarnettAmy PollpeterBrittany LarsenTianqi LiSara A WilletteJonathan P MochelKarin AllenspachGuiping HuAuriel A Willette

Published in: Alzheimer's & dementia (Amsterdam, Netherlands) (2024)

Design an algorithm to distinguish between a Positive-Ager and a Cognitive-Decliner.Introduce a mathematical definition for cognitive classes based on cognitive tests.Accurate Positive-Ager identification using rsfMRI and demographic data (AUC = 0.88).Posterior default mode network has the highest impact on Positive-Aging odds ratio.

Keyphrases

functional connectivity
resting state
machine learning
deep learning
electronic health record
artificial intelligence
high resolution
risk assessment
blood brain barrier
subarachnoid hemorrhage
data analysis
network analysis
cerebral ischemia
neural network