Machine learning assessment of cognitive reserve using functional near-infrared spectroscopy in older adults with cognitive frailty
Wanrui Wei, Shuaifang Wei, Wei Han, Kairong Wang, Huan Zhang, Gabriella Engstrom, Azita Emami, Zheng Li (2025) Machine learning assessment of cognitive reserve using functional near-infrared spectroscopy in older adults with cognitive frailty Geroscience (IF: 5.4)Abstract
Cognitive reserve mitigates aging-related cognitive decline and frailty, yet current assessments lack neurobiological specificity. We aimed to develop a noninvasive, functional near infrared spectroscopy (fNIRS)-based machine learning model to classify cognitive reserve levels in older adults with cognitive frailty. Seventy-one community-dwelling adults underwent resting-state and task-based (Stroop, n-back) fNIRS scans. Graph theory metrics and task-related β-values were extracted. Support vector machine classifiers were trained on 70% of the dataset and tested on 30%. Models incorporating β-values from significantly activated channels during the Stroop, 0-back, and 1-back tasks achieved the best performance (accuracy = 0.727, recall = 0.857, area under the curve [AUC] = 0.829). Resting-state features alone yielded lower performance (AUC = 0.714), while combining both resting-state and task-based features improved it moderately (AUC = 0.790). fNIRS-based modeling enables objective classification of cognitive reserve levels in older adults with cognitive frailty. This approach offers a portable, scalable, real-time strategy for early risk stratification and may support precision interventions in both clinical and community settings.© 2025. The Author(s), under exclusive licence to American Aging Association.
Links
http://www.ncbi.nlm.nih.gov/pubmed/41076505http://dx.doi.org/10.1007/s11357-025-01918-w
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