Abstract
Superagers, older adults with memory performance similar to middle-aged individuals, were studied to identify key neural networks responsible for their brain function connectivity. Using a previously published resting-state fMRI (rs-fMRI) dataset from 31 participants (14 superagers and 17 controls) examined at 3 and 7 Tesla (T) scanners, we cross-validated the findings from an Elastic Net regression model using a Random Forest algorithm. Important nodes were identified based on Mean Decrease Gini and Mean Decrease Accuracy measures. Superagers were initially phenotyped in six key preselected networks and then across eleven whole-brain networks. The study confirmed the importance of the salience and default mode networks in classifying superagers, identifying significant nodes in the precuneus, posterior cingulate cortex, prefrontal cortex, temporo-occipital junction, and extrastriate superior cortex. Whole-brain analysis highlighted novel relevant networks, including auditory, visual-lateral, and visual-medial networks. Results showed that 7T rs-fMRI provided more discriminative nodes and better predictive performance than 3T. The findings underscore the role of particular brain regions and networks related to memory and cognition in superagers and suggest that additional nodes in auditory and visual networks contribute to their cognitive resilience. These insights enhance understanding of brain resilience and preserved cognitive abilities in older adults.