Thermodynamic Scaling Laws in Neurodevelopment: Clinical Phenotypes as Metric Phase Transitions
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| Format: | Recurso digital |
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2025
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| _version_ | 1866901097999761408 |
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| author | Calloway, Erik |
| author_facet | Calloway, Erik |
| contents | <p dir="ltr">Current psychiatric nosology categorizes clinical phenotypes as discrete "disorders," yet high comorbidity rates suggest a general factor of psychopathology (p-factor). We propose a biophysical framework where these phenotypes represent distinct topological attractors within a developing information-geometric landscape. Building on the Free Energy Principle, we introduce Geodesic Information Dynamics (GID): a model where the brain is treated as a Riemannian manifold whose curvature is determined by information density. We demonstrate that the predicted risk of cognitive network divergence scales with the complexity of the configuration landscape. </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18015758 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Thermodynamic Scaling Laws in Neurodevelopment: Clinical Phenotypes as Metric Phase Transitions Calloway, Erik Computational Psychiatry Information Geometry Thermodynamic Scaling Laws Neurodevelopment p-factor (General Psychopathology) Phase Transitions Free Energy Principle Metric Stiffness Geodesic Information Dynamics Entropy <p dir="ltr">Current psychiatric nosology categorizes clinical phenotypes as discrete "disorders," yet high comorbidity rates suggest a general factor of psychopathology (p-factor). We propose a biophysical framework where these phenotypes represent distinct topological attractors within a developing information-geometric landscape. Building on the Free Energy Principle, we introduce Geodesic Information Dynamics (GID): a model where the brain is treated as a Riemannian manifold whose curvature is determined by information density. We demonstrate that the predicted risk of cognitive network divergence scales with the complexity of the configuration landscape. </p> |
| title | Thermodynamic Scaling Laws in Neurodevelopment: Clinical Phenotypes as Metric Phase Transitions |
| topic | Computational Psychiatry Information Geometry Thermodynamic Scaling Laws Neurodevelopment p-factor (General Psychopathology) Phase Transitions Free Energy Principle Metric Stiffness Geodesic Information Dynamics Entropy |
| url | https://doi.org/10.5281/zenodo.18015758 |