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

Abstract

Abstract INTRODUCTION Aging is often associated with cognitive decline. Understanding neural factors that distinguish adults in midlife with superior cognitive abilities (Positive‐Agers) may offer insight into how the aging brain achieves resilience. The goals of this study are to (1) introduce an optimal labeling mechanism to distinguish between Positive‐Agers and Cognitive Decliners, and (2) identify Positive‐Agers using neuronal functional connectivity networks data and demographics. METHODS In this study, principal component analysis initially created latent cognitive trajectories groups. A hybrid algorithm of machine learning and optimization was then designed to predict latent groups using neuronal functional connectivity networks derived from resting state functional magnetic resonance imaging. Specifically, the Optimal Labeling with Bayesian Optimization (OLBO) algorithm used an unsupervised approach, iterating a logistic regression function with Bayesian posterior updating. This study encompassed 6369 adults from the UK Biobank cohort. RESULTS OLBO outperformed baseline models, achieving an area under the curve of 88% when distinguishing between Positive‐Agers and cognitive decliners. DISCUSSION OLBO may be a novel algorithm that distinguishes cognitive trajectories with a high degree of accuracy in cognitively unimpaired adults. Highlights 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.

Keywords

• Bayesian optimization
• classification
• cognitive decliners
• resting state functional MRI (rsfMRI)
• Super‐Agers