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

• DOI: https://doi.org/10.22533/at.ed.01596626110515

• Palavras-chave: Autism Spectrum Disorder; Neurotransmission; Mathematical Modeling; Sigmoid Function;

• Keywords: Autism Spectrum Disorder; Neurotransmission; Mathematical Modeling; Sigmoid Function;

• Abstract:

  Autism Spectrum Disorder (ASD) involves 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 (EA ) as a function of genetic predisposition (Γ ), neuroplasticity (k ), neurotransmitter systems, and residual phenotypic variance (ε ). The model integrates eight major neurotransmitters: dopamine, serotonin, melatonin, oxytocin, norepinephrine, endorphins, GABA, and epinephrine, organizing them into two functional macrosystems: modulatory (M ) and activation-driven (S ). By weighting each neurotransmitter according to established neurobiological evidence, the structure captures how inhibitory, excitatory, affective, and social pathways converge to shape observable autistic characteristics. A computational demonstration based on a fictional case , including an adult diagnosed at age 40, illustrates how lifelong masking, sensory dysregulation, and neurochemical imbalances interact in a nonlinear manner. The results highlight that, although the genetic substrate remains unchanged, phenotypic visibility can shift depending on 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 basis for future empirical validations and computational extensions.