Adaptive Neuroregulation Ontology (ANO)

This addresses the gap in existing neurocognitive classification systems, which often rely on static categorical diagnostic frameworks rather than dynamic process-level models. The proposed computational framework introduces a state-space approach to classify and monitor neurocognitive regulatory states by integrating the activation dynamics and regulatory effectiveness. It quantifies neurocognitive function as the interplay between activation levels and regulatory control capacity, which is modeled as dynamically coupled dimensions. Multimodal inputs are transformed into coherence indices (reflecting functional integration), fragmentation measures (capturing coordination inconsistency), and decay metrics (quantifying temporal stability). Regulatory states are algorithmically identified using coherence–fragmentation thresholds, bypassing symptom-based categories. A dynamic calibration reference derived from stable integration parameters adapts to individual regulatory profiles rather than normative benchmarks. A system that detects state transitions, oscillations, and degradation trajectories, distinguishing reversible imbalances from capacity loss. Conceptually, it advances computational psychiatry by emphasizing the dynamic structure over diagnostic classification. However, its scope is limited to regulatory states, relying on multimodal data and excluding specific clinical disorders such as schizophrenia. The framework offers a dimensional and descriptive model of neurocognitive variability as dynamic system states.