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PROJECT NEURO-ORIGINIS™

A Phase-Gated Systems Biology Framework for the Biological Origins of Cognitive and Behavioural Syndromes

Article type: Viewpoint / Conceptual Framework

Affiliation: SCF Advanced Medicine Clinic — Research & Development Division

SUMMARY

Psychiatric and cognitive disorders remain major contributors to global disease burden, yet prevention and durable recovery remain limited. Current diagnostic frameworks prioritise symptom description and late-stage intervention, often after substantial biological dysfunction has occurred. We propose NEURO-ORIGINIS™, a phase-gated systems biology framework that conceptualises cognitive and behavioural syndromes as late-stage manifestations of progressive biological dysregulation rather than primary psychological disorders. Integrating psychoepigenetics, immune phase biology, neuroenergetics, structural integrity, and large-scale brain network dynamics, the model defines a continuous progression from early vulnerability to clinical syndrome. Earlier phases are characterised by regulatory bias and compensation, while later phases involve structural embedding and network rigidity. By reframing psychiatric illness as a staged biological process, NEURO-ORIGINIS™ provides a coherent explanatory bridge between psychological phenomena and systems biology, with implications for early detection, prevention-oriented care, and phase-appropriate therapeutic development.

INTRODUCTION

Psychiatric disorders account for a substantial proportion of global morbidity and disability, yet advances in prevention and long-term outcomes have lagged behind those seen in other medical disciplines. Although diagnostic systems have improved reliability, they remain largely descriptive, offering limited insight into disease origin, progression, or biological staging. Consequently, intervention typically begins after symptoms are entrenched, often corresponding to advanced biological dysfunction.

Conditions such as trauma-related syndromes and early cognitive decline illustrate this limitation. By the time a formal diagnosis is made, immune dysregulation, metabolic strain, and large-scale network changes are frequently established. This temporal mismatch between disease onset and clinical recognition contributes to treatment resistance, relapse, and chronicity.

Growing evidence links psychiatric disorders to immune activation, altered energy metabolism, epigenetic modulation, and network-level brain dysfunction. However, these findings are often studied in isolation, without a unifying framework that explains how such processes unfold over time. NEURO-ORIGINIS™ was developed to address this gap by introducing a phase-gated, biologically anchored model of cognitive and behavioural illness that precedes symptom-based classification.

CONCEPTUAL FRAMEWORK

NEURO-ORIGINIS™ is founded on the premise that cognition, emotion, and behaviour reflect underlying biological state rather than constituting primary disease mechanisms. Psychological phenomena are understood as phenotypic outputs of interacting biological systems responsible for regulation, adaptation, and recovery.

The framework integrates five core domains: psychoepigenetic regulation, immune phase signalling, neuroenergetic allocation, structural neural integrity, and large-scale network modularity. Dysfunction across these domains does not occur simultaneously but unfolds progressively, producing predictable transitions between phases.

This approach aligns with, but extends beyond, existing dimensional and staging models in psychiatry by explicitly anchoring each stage to biological processes and decision boundaries. Rather than replacing psychological constructs, NEURO-ORIGINIS™ embeds them within a systems-level causal architecture.

THE PHASE-GATED MODEL

NEURO-ORIGINIS™ defines seven continuous phases.

Phase 0 represents vulnerability and meaning-based priming, in which repeated stress or environmental pressures bias interpretation and prioritisation without measurable pathology.

Phase 1 marks the emergence of psychoepigenetic and immune bias, where stress-responsive regulatory systems begin to encode persistent patterns, rendering disease progression forecastable but highly reversible.

Phase 2 is characterised by compensated dysfunction. Biological systems preserve outward function through energetic diversion and autonomic engagement, masking progression while accumulating metabolic debt.

Phase 3 represents a critical inflection point. Compensation fails, persistent immune signalling emerges, and neural networks reorganise around threat-dominant states. Dysfunction becomes self-reinforcing.

Phase 4 involves structural injury and biological embedding, including myelin stress, synaptic loss, and impaired clearance mechanisms. Reversibility is reduced but not eliminated.

Phase 5 is defined by network collapse and rigid dominance, where adaptive modularity is lost and behaviour becomes inflexible and context-insensitive.

Phase 6 corresponds to clinical syndrome and diagnostic capture, the stage at which most psychiatric conditions are formally identified.

BIOLOGICAL RATIONALE

Early phases are dominated by regulatory bias rather than tissue damage. Psychoepigenetic modulation alters stress responsiveness, immune signalling shifts toward vigilance, and energy metabolism prioritises readiness over repair. Function is maintained through compensation.

Phase 3 marks failure of this compensation. Immune activity becomes persistent and regulatory, while large-scale networks lose flexibility. This inflection predicts rapid progression if unresolved.

Prolonged dysregulation produces structural injury, followed by collapse of network modularity. These later stages correspond to chronic psychiatric and cognitive syndromes marked by rigidity, relapse, and limited spontaneous recovery.

RELATIONSHIP TO DIAGNOSTIC SYSTEMS

NEURO-ORIGINIS™ does not reject existing diagnostic classifications but repositions them as late-stage descriptors rather than causal explanations. Diagnostic labels remain essential for communication and administration but are insufficient for understanding disease origin or guiding early intervention.

This distinction helps explain why many treatments demonstrate modest efficacy and why prevention has historically been limited in psychiatry.

CLINICAL, PUBLIC HEALTH, AND TRANSLATIONAL IMPLICATIONS

Phase-based reasoning enables earlier risk identification, rational sequencing of interventions, and reduced reliance on trial-and-error prescribing. From a public health perspective, the framework supports prevention-oriented strategies for trauma exposure, chronic stress, and early cognitive decline.

For pharmaceutical development, NEURO-ORIGINIS™ provides a structure for phase-specific biomarker development, improved stratification, and more informative trials. The architecture is also compatible with AI-based clinical decision support systems.

LIMITATIONS AND FUTURE DIRECTIONS

NEURO-ORIGINIS™ is a conceptual framework requiring empirical validation. Longitudinal studies, phase-specific biomarkers, and interventional trials are needed to refine thresholds and predictive accuracy. Nonetheless, the model synthesises existing evidence into a coherent structure addressing key limitations of symptom-first psychiatry.

CONCLUSION

NEURO-ORIGINIS™ reframes cognitive and behavioural syndromes as progressive biological phenomena rather than primary psychological disorders. By shifting focus from late-stage diagnosis to early phase detection and intervention, the framework offers a path toward prevention-oriented, biologically coherent mental healthcare.

REFERENCES

  1. GBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9(2):137–150.
  2. Vigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry. 2016;3(2):171–178.
  3. Regier DA, Narrow WE, Clarke DE, et al. DSM-5 field trials in the United States and Canada, Part II: test-retest reliability of selected categorical diagnoses. Am J Psychiatry. 2013;170(1):59–70. doi:10.1176/appi.ajp.2012.12070999.
  4. Hyman SE. The diagnosis of mental disorders: the problem of reification. Annu Rev Clin Psychol. 2010;6:155–179. doi:10.1146/annurev.clinpsy.3.022806.091532.
  5. Kendell R, Jablensky A. Distinguishing between the validity and utility of psychiatric diagnoses. Am J Psychiatry. 2003;160(1):4–12.
  6. Jablensky A. Psychiatric classifications: validity and utility. World Psychiatry. 2016;15(1):26–31.
  7. Insel T, Cuthbert B, Garvey M, et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010;167(7):748–751. doi:10.1176/appi.ajp.2010.09091379.
  8. Cuthbert BN, Insel TR. Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Med. 2013;11:126.
  9. Scott J, Leboyer M, Hickie I, et al. Clinical staging in psychiatry: a cross-cutting model of diagnosis with heuristic and practical value. Br J Psychiatry. 2013;202(4):243–245.
  10. McGorry PD, Hickie IB, Yung AR, Pantelis C, Jackson HJ. Clinical staging of psychiatric disorders: a heuristic framework for choosing earlier, safer and more effective interventions. Aust N Z J Psychiatry. 2006;40(8):616–622.
  11. Cross SPM, Hermens DF, Hickie IB. A clinical staging model for early intervention youth mental health services. Psychiatr Serv. 2014;65(7):939–943.
  12. McGorry PD. Biomarkers and clinical staging in psychiatry. World Psychiatry. 2014;13(3):211–223.
  13. Miller AH, Raison CL. The role of inflammation in depression: from evolutionary imperative to modern treatment target. Nat Rev Immunol. 2016;16(1):22–34. doi:10.1038/nri.2015.5.
  14. Haapakoski R, Mathieu J, Ebmeier KP, Alenius H, Kivimäki M. Cumulative meta-analysis of interleukins 6 and 1β, tumour necrosis factor α and C-reactive protein in patients with major depressive disorder. Brain Behav Immun. 2015;49:206–215.
  15. Osimo EF, Baxter LJ, Lewis G, Jones PB, Khandaker GM. Inflammatory markers in depression: a meta-analysis of mean differences and variability in 5166 patients and 5083 controls. Brain Behav Immun. 2020;87:901–909.
  16. Dantzer R, O’Connor JC, Freund GG, Johnson RW, Kelley KW. From inflammation to sickness and depression: when the immune system subjugates the brain. Nat Rev Neurosci. 2008;9(1):46–56.
  17. Raison CL, Capuron L, Miller AH. Cytokines sing the blues: inflammation and the pathogenesis of depression. Trends Immunol. 2006;27(1):24–31.
  18. Menon V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011;15(10):483–506.
  19. Menon V. The triple network model, insight, and large-scale brain network dynamics in psychopathology. Biol Psychiatry. 2018;84(4):263–265.
  20. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry. 2015;72(6):603–611. doi:10.1001/jamapsychiatry.2015.0071.
  21. Sripada RK, King AP, Welsh RC, et al. Neural dysregulation in posttraumatic stress disorder: evidence for disrupted equilibrium between salience and default mode brain networks. Psychosom Med. 2012;74(9):904–911.
  22. Koenen KC, Ratanatharathorn A, Ng L, et al. Posttraumatic stress disorder in the World Mental Health Surveys. Psychol Med. 2017;47(13):2260–2274.
  23. Kessler RC, Aguilar-Gaxiola S, Alonso J, et al. Trauma and PTSD in the WHO World Mental Health Surveys. Eur J Psychotraumatol. 2017;8(sup5):1353383.
  24. Klengel T, Mehta D, Anacker C, et al. Allele-specific FKBP5 DNA demethylation mediates gene–childhood trauma interactions. Nat Neurosci. 2013;16(1):33–41.
  25. McGowan PO, Sasaki A, D’Alessio AC, et al. Epigenetic regulation of the glucocorticoid receptor in human brain associates with childhood abuse. Nat Neurosci. 2009;12(3):342–348.
  26. Perroud N, Paoloni-Giacobino A, Prada P, et al. Increased methylation of the glucocorticoid receptor gene (NR3C1) in adults with a history of childhood maltreatment: a link with the severity and type of trauma. Transl Psychiatry. 2011;1:e59.
  27. Karabatsiakis A, Böck C, Salinas-Manrique J, et al. Depression, mitochondrial bioenergetics, and immune activation: a translational perspective. Transl Psychiatry. 2020;10:373.
  28. Rappeneau V, Guitton MJ, et al. Molecular correlates of mitochondrial dysfunctions in major depressive disorder: a review. Prog Neuropsychopharmacol Biol Psychiatry. 2020;99:109873.
  29. Giménez-Palomo A, et al. The role of mitochondria in mood disorders. Front Neurosci. 2021;15:706778.
  30. Buckner RL, Andrews-Hanna JR, Schacter DL. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 2008;1124:1–38.
  31. Andrews-Hanna JR, Smallwood J, Spreng RN. The default network and self-generated thought: component processes, dynamic control, and clinical relevance. Ann N Y Acad Sci. 2014;1316:29–52.
  32. Fornito A, Zalesky A, Bullmore ET. Network scaling effects in graph analytic studies of human resting-state fMRI data. Front Syst Neurosci. 2010;4:22.
  33. Bullmore E, Sporns O. The economy of brain network organization. Nat Rev Neurosci. 2012;13(5):336–349.
  34. Buckholtz JW, Meyer-Lindenberg A. Psychopathology and the human connectome: toward a transdiagnostic model of risk for mental illness. Neuron. 2012;74(6):990–1004.
  35. Khandaker GM, Cousins L, Deakin J, Lennox BR, Yolken R, Jones PB. Inflammation and immunity in schizophrenia: implications for pathophysiology and treatment. Lancet Psychiatry. 2015;2(3):258–270.
  36. Benedetti F, Poletti S, Vai B, et al. Higher baseline interleukin-1β and TNF-α hamper antidepressant response in major depressive disorder. Eur Neuropsychopharmacol. 2021;42:35–44.
  37. Clarke SL, et al. The progress in the field of clinical staging for mental disorders: recent advances and future directions. Front Psychiatry. 2025;15:1473051.
  38. Lilienfeld SO. The Research Domain Criteria (RDoC): an analysis of methodological and conceptual challenges and proposed remedies. Behav Res Ther. 2014;62:1–15.
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