Probabilistic Mixed-Effects Graph Autoencoding for Individualized Multi-Paradigm Functional Connectome Modeling

Repeated observations of the same individual are common in biomedical data, and multi-paradigm fMRI is a prominent example: every scan reflects the current task while sharing a stable individual signature with the participant’s other scans. Most representation-learning methods treat scans as independent samples or leave this shared component implicit. We formulate functional connectome reconstruction under repeated measurements as a conditional neural mixed-effects problem and propose P-MEGA, a probabilistic mixed-effects graph autoencoder that decomposes each functional connectome into a scan-specific population-level prediction and a low-rank participant-specific random effect shared across scans, which we refer to as the participant’s brain fingerprint. A signed multi-window graph encoder and a shared edge decoder form the population-level pathway, while a permutation-invariant set encoder parameterizes an amortized Gaussian posterior over the participant-specific effect from an arbitrary set of support scans, enabling inference for unseen participants with all model parameters shared across the cohort. We evaluate P-MEGA on two large multi-paradigm cohorts widely used to study individual differences and brain development: the Human Connectome Project (HCP; 865 young adults, 9 paradigms) and the Philadelphia Neurodevelopmental Cohort (PNC; 1,272 youths aged 8–21, 3 paradigms). P-MEGA attains the best out-of-fold reconstruction among the evaluated baselines. A support-swap test, which fixes the target scan and changes only whose scans are used to infer the fingerprint, shows that fingerprints inferred from the same participant’s other paradigms explain the target’s residual connectivity better than those from demographically matched or random participants in both cohorts. The inferred fingerprints are reproducible across training seeds and folds and show network-level associations with cognition and age.