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 relate to cognitive differences. However, the optimal condition of brain function to study between-person differences in cognition remains unclear. While many studies have developed objective biomarkers to accurately predict intelligence and general cognition, consensus on domain-specific markers has not yet emerged. Brain age has been proposed as a potential candidate, but 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 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 connectomes during task, especially during movie watching, better predict both episodic and 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 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 findings highlight the importance of brain state in connectome-based cognitive prediction and introduce the brain cognitive gap as a potentially informative, dopamine-modulated marker of vulnerability to compromise brain function.