Re-engineering the disordered mind: clinical experimentation, dynamical systems, and AI for personalized psychiatry
Bibliographic record
Abstract
This perspective proposes a neuropsychiatric model of psychological and psychiatric interventions by reframing treatment as a control engineering problem grounded in dynamical systems theory and artificial intelligence (AI). We argue that psychopathology arises from distortions in the geometry of underlying neurobehavioral low-dimensional cognitive-affective manifolds rather than from isolated biological dysfunctions, and we use a formal dynamical framework to show how clinical interventions can be modeled as control inputs that reshape the manifold itself to restore healthy dynamics. To operationalize this approach clinically, we propose a closed-loop, N-of-1 experimental paradigm in which dense longitudinal measurements and strategically designed perturbations are used to train individualized AI surrogate models of a person's manifold. This model supports the simulation of counterfactual interventions and guide the design of optimized, personalized treatments. Active perturbation reduces required sample size dramatically, enabling precise modeling from limited but richly sampled individual data. This engineering-inspired framework reconceptualizes clinical improvement as the restoration of regulatory capacity and resilient trajectories rather than the mere reduction of symptom counts. By integrating dynamical systems theory, AI-based surrogate modeling, and adaptive clinical experimentation, we outline a principled pathway toward personalized neuropsychiatry based on dynamical systems theory and AI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".