Brain-Cognitive Gaps in relation to Dopamine and Health-related Factors: Insights from AI-Driven Functional Connectome Predictions
A key question in human neuroscience is to understand how individual differences in brain function are related to cognitive differences. However, the optimal condition of brain function to study between-person differences in cognition remains unclear. Additionally, there is a lack of objective biomarkers to accurately predict cognitive function, with brain age emerging as a potential candidate. Recent research suggests that brain age offers minimal additional information on cognitive decline beyond what chronological age provides, prompting a shift toward approaches focused directly on cognitive prediction. Using a novel deep learning approach, we evaluated the predictive power of the functional connectome during various states (resting state, movie-watching, and n-back) on episodic memory and working memory performance. Our findings show that while task-based connectomes, especially during movie watching, better predict working memory, resting state connectomes are equally effective in predicting episodic memory. Furthermore, individuals with a negative brain-cognition gap (where brain predictions underestimate actual performance) exhibited lower physical activity, lower education, and higher cardiovascular risk compared to those with a positive gap. This shows that knowledge of the brain-cognition gap provides insights into factors contributing to cognitive resilience. Further lower PET-derived measures of dopamine binding were linked to a greater brain-cognition gap, mediated by regional functional variability. Together, our study introduces the brain-cognitive gap, as a new marker, modulated by the dopamine system, to identify individuals at risk of compromised brain function.