The Phenotypic Equation of Autism: A Sigmoid Model of Autism Expression Through Neurotransmitter Dynamics and Neuroplastic Adaptation

Autism Spectrum Disorder (ASD) presents a complex interaction between a stable genetic architecture and dynamic neurochemical modulation, resulting in substantial variability in phenotypic expression throughout life. This study proposes a mathematically elegant sigmoid model that quantifies the observable behavioral expression of autism (AS) as a function of genetic predisposition, neuroplasticity, neurotransmitter systems, and residual phenotypic variance. The model integrates eight major neurotransmitters: dopamine, serotonin, melatonin, oxytocin, norepinephrine, endorphins, GABA, and epinephrine, organizing them into two functional macro-systems: modulatory and activation-driven. By weighing each neurotransmitter according to established neurobiological evidence, the framework captures how inhibitory, excitatory, affective, and social pathways converge to shape observable autistic characteristics. A computational demonstration based on a fictitious case, including an adult diagnosed at age 40, illustrates how lifelong masking, sensory dysregulation, and neurochemical imbalances interact in a non-linear fashion. The results highlight that, while the genetic substrate remains unchanged, phenotypic visibility can shift as a function of neurochemical optimization, reduced autonomic activation, and behavioral authenticity. This model provides a conceptual and quantitative tool for understanding how neurobiological mechanisms contribute to heterogeneity in ASD, offering a foundation for future empirical validations and computational extensions.