Predicting Real-Life Cognitive Scores From Functional Connectivity

Over the past decade, functional connectivity patterns, derived from functional magnetic resonance imaging (fMRI), have been widely used to predict various cognitive traits. However, most studies have focused on measures assessed under controlled laboratory conditions, which may not fully reflect the complexity of the traits in real-world environments. In this study, we investigated connectome-based predictions of cognitive performance in ecologically valid, real-world settings. Participants (n = 194) performed the Psychometric Entrance Test, a standardized exam used for admission to higher education institutions in Israel and a strong predictor of undergraduate academic success. Using functional connectivity patterns, we significantly predicted overall test performance, as well as its three cognitive-specific domains: quantitative reasoning, verbal reasoning, and proficiency in a foreign language. Significant predictions were consistent across four different prediction approaches, demonstrating the robustness of the relations between functional connectivity and cognition. Additionally, we examined which connectivity features mostly contributed to predictions, analyzing both edge- and node-level contributions. We found that different cognitive abilities (i.e., quantitative skills vs. language-related skills) were primarily predicted by unique connectivity patterns. Yet, predictive features were more similar for scores that were more strongly correlated at the behavioral level. Last, we implemented a transfer learning approach in which the predicted cognitive-specific scores were used as features for prediction of the global score, resulting in an improved prediction compared to that derived directly from functional connectivity. Overall, our results demonstrate that the functional connectome captures real-world variability in both global and domain-specific cognitive abilities, emphasizing its potential to serve as an objective marker of real-world cognitive performance.