Brain cognition gaps reveal associations with dopamine and factors related to brain health through artificial intelligence prediction of functional connectome

Abstract

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 connectomes during tasks, especially during movie-watching, predict individual differences across cognitive domains, while resting state connectomes predict episodic memory meaningfully. 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 cognition gap as a potentially informative, dopamine-modulated marker of vulnerability to compromise brain function.