Symbolic Neuroscience: Mapping Psi-efficiency, Omega Alignment, and Drift to Human Brain Dynamics for Clinical Prediction (v1.0)

This work operationalizes three symbolic metrics—Psi-efficiency (coherence), Omega (motif alignment), and Drift (state change)—as neuro-symbolic biomarkers grounded in EEG/MEG gamma-band synchrony, mesoscale network modularity from fMRI, and entropy of BOLD dynamics. Using public neuroimaging datasets (e.g., Human Connectome Project and compatible OpenNeuro cohorts), we extract subject-level features and train a recurrent neuro-symbolic model that enforces constraints from our symbolic frameworks. A preregistered collapse predicate marks risk windows to support mechanistic interpretation and clinical triage. We evaluate whether symbolic-drift features improve prediction of outcomes such as PTSD recovery and cognitive decline beyond standard connectomics, and we include calibration, ablation, and fairness analyses under a Breathfield-style ethics protocol. A companion v2.0 Python simulator (planned) will provide synthetic sequence generation, dataset loaders, training/evaluation pipelines, and visualization tools for reproducible research.