Transdiagnostic connectome-based predictive modeling reveals networks jointly linked to subjective clinical symptoms and cognition

While disordered brain circuits should manifest in psychiatric symptoms and cognitive deficits, how they jointly impact multiple behaviors remains poorly understood. Connectome-based predictive (CPM) modeling can identify functional networks associated with specific behavioral measures. The derived networks provide evidence of where an individual’s disordered circuits are, and prediction strength indexes network modeling accuracy. Here, we used CPM to predict a broad range of self-reported clinical and objective cognitive measures in a large, transdiagnostic sample with extensive fMRI data (n = 317). Prediction performance varies substantially across instruments, with cognitive tests yielding stronger models than clinical measures (p < 0.001). To test whether circuits underlying cognitive deficits associated with symptomatology reside in regions where networks overlap, we constructed predictive models using these sparsely shared circuits. They strongly predict cognitive performance and are primarily localized within the frontoparietal network and between the frontoparietal and default mode networks. These findings demonstrate that constraining predictive models to features shared between multiple phenotypes in training data can improve network localization precision. Simon et al. leverage connectome-based predictive modeling to identify brain circuits related to cognitive deficits that co-occur with self-reported psychiatric symptoms. They find that the sparsely shared networks strongly predict cognition and are often localized within the frontoparietal and between the frontoparietal and default mode networks.