Cognitive aging clock based on Explainable artificial intelligence

Biological clocks aim to estimate the physiological state of an organism, or of specific organs or systems, from the perspective of aging. The inferred individual age acceleration is presumably linked to increased mortality and morbidity risks. Here, we introduce a cognitive aging clock based on a battery of five tests (sensomotor arithmetic, sensomotor mirror letter, 1-back, campimetry and the Stroop), capturing sensory and verbal perception, working memory, and decision-making in the context of cognitive conflict. The developed LightGBM machine learning model has the cognitive age MAE of 7.6 years. SHAP analysis yields the overall and individualized interpretations of accelerated aging. In particular, we demonstrate the dominant contribution of Stroop and campimetry test variables to cognitive age. The cognitive age is accelerated in the patients with dementia, whereas moderate cognitive impairment also shows the acceleration trend, but lacks statistical significance. MoCA test score is negatively associated with cognitive age deviation.