Objective:

To develop interpretable mathematical models representing the dynamic, context-dependent nature of core symptoms of Autism Spectrum Disorder (ASD), explicitly grounded in neuropsychological theories such as Predictive Coding, Information Theory, and Network Dynamics.

Key Findings:

• Theoretical equations quantify temporal dynamics and contextual modulation for core ASD symptoms, linking them to specific neurobiological mechanisms.
• Models provide formalized representations of how symptoms fluctuate based on neurobiological mechanisms, enhancing understanding of symptom interactions.
• Generated models yield testable predictions about symptom patterns and intervention effects, paving the way for future empirical studies.

Interpretation:

The mathematical models offer a novel framework for understanding ASD symptoms, integrating mechanistic theories with dynamic formulations, potentially enhancing clinical assessment and informing personalized treatment strategies.

Limitations:

• No empirical data were collected to validate the models, which limits their applicability.
• Parameter ranges were derived from theoretical considerations rather than clinical populations, raising questions about generalizability.

Conclusion:

The proposed models advance the understanding of ASD by framing symptoms as adaptive responses to neurobiological differences, necessitating empirical validation for clinical utility.