Re-engineering the disordered mind: clinical experimentation, dynamical systems, and AI for personalized psychiatry

Area of Science:

• Neuroscience
• Psychiatry
• Control Engineering
• Artificial Intelligence (AI)

Background:

• Psychopathology is viewed as distortions in neurobehavioral manifolds, not isolated dysfunctions.
• Dynamical systems theory provides a framework for understanding complex neurobehavioral dynamics.
• Current psychiatric treatments often lack personalization and precise modeling.

Purpose of the Study:

• To propose a novel neuropsychiatric model integrating dynamical systems theory and AI.
• To reframe clinical interventions as control engineering problems.
• To outline a pathway toward personalized neuropsychiatry.

Main Methods:

• Utilizing a formal dynamical framework to model interventions as control inputs.
• Employing a closed-loop, N-of-1 experimental paradigm with dense longitudinal measurements.
• Training individualized AI surrogate models of neurobehavioral manifolds using perturbations.

Main Results:

• Clinical interventions can be modeled to reshape neurobehavioral manifolds and restore healthy dynamics.
• AI surrogate models enable simulation of counterfactual interventions for personalized treatment design.
• Active perturbation significantly reduces sample size requirements for precise individual modeling.

Conclusions:

• This engineering-inspired framework reconceptualizes clinical improvement as restored regulatory capacity.
• Integrating dynamical systems theory, AI, and adaptive experimentation offers a principled approach to personalized neuropsychiatry.
• The model emphasizes resilient trajectories over mere symptom reduction.