Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders

As AI models move toward clinical decision-making and personalized treatment, understanding what a model learns is important beyond predictive accuracy alone. We investigate whether personalized latent dynamics reveal clinically associated differences even when predictive fit is similar. A lightweight CNN--Transformer EEG foundation model pretrained on the Temple University EEG Corpus (TUEG) extracts segment-level representations. Using the Temple University Epilepsy Corpus (TUEP), representations are mapped to a shared latent-state space, and sparse multinomial logistic transition distributions (mLTD) are fit independently to each subject to obtain personalized transition-dependency graphs Wn. Analyses include n=198 subjects (99 epilepsy / 99 non-epilepsy). At k=4, epilepsy subjects exhibit substantially denser learned dependency structure (p=1.1×10-7), with the same pattern at k=6 (19.90 vs. 13.46; p=5.2×10-5). Graph-derived features provide moderate group discrimination under 5-fold subject-wise cross-validation (AUROC 0.68 at k=4; 0.65 at k=6). In contrast, held-out log-likelihood is nearly identical between groups at k=4 (-0.992 vs. -0.991; p=0.95), with similarly matched next-state prediction (AUROC 0.855 vs. 0.861; p=0.54). Thus, similar predictive fit does not imply similar learned dynamics: groups can be comparably predictable while differing substantially in the internal dynamical structure learned by personalized models. This distinction motivates evaluating learned structure alongside predictive performance in personalized clinical models.