Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders
Why two brain patterns can predict equally well yet reveal hidden differences
AI models trained on brain activity from epilepsy patients and healthy people made equally accurate predictions about what brain patterns would come next — yet the two groups showed dramatically different internal structures in how the models organized brain data. People with epilepsy had much denser networks of brain state connections (nearly 50% more densely wired), even though the models' raw prediction scores were statistically identical.
As hospitals begin using AI to diagnose and treat brain disorders, this work shows that checking only whether a model predicts well enough is not enough. A model could be clinically misleading if it achieves good accuracy through the wrong internal logic. Examining both what a model predicts and how it thinks could catch these gaps before personalized brain treatments are deployed.