Series Title: Adaptive Neuroregulation Ontology Series Foundation This paper serves as the foundational work for the research series Adaptive Neuroregulatory Ontology. The series develops a conceptual and analytical framework for understanding mental health through the classification of neurocognitive states rather than through traditional diagnostic categories. The central premise of this research program is that mental health phenomena can be described in terms of dynamic neurocognitive states and their transitions. By focusing on states rather than diagnoses, the framework aims to support alternative approaches to conceptualizing cognitive and emotional functioning, allowing for more flexible interpretation of mental processes, variability, and contextual change. This initial paper introduces the core motivation for the framework and outlines the conceptual basis for non-diagnostic classification. It establishes the foundational terminology, theoretical orientation, and research questions that guide the subsequent papers in the series. Research Series Structure The following papers expand upon the concepts introduced in this work and progressively develop the framework: Adaptive Neuroregulation Ontology (ANO) – Dynamic Regulatory Framework for Non-Diagnostic Neurocognitive State Classification. DOI: 10.5281/zenodo.18865248 Parallel Domain Architectures (PDA) – Extending Adaptive Neuroregulation Ontology Across Functional. DOI: 10.5281/zenodo.18939854 Triadic Tic Architecture (TTA) – Extending Adaptive Neuroregulation Ontology Tourette’s Spectrum. DOI: 10.5281/zenodo.18940510 Memory Processing Architecture (MPA) – Extending Adaptive Neuroregulation Ontology Behavioral Expression. DOI: 10.5281/zenodo.18941138 Additional papers may further extend the framework and explore methodological, clinical, or research applications. Citation Guidance This paper should be cited as the foundational reference for Adaptive Neuroregulatory Ontology. Subsequent papers in the series build upon the concepts introduced here and reference this work as the primary theoretical basis.

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.