Abstract
Neuronal activity can be modeled as a nonlinear dynamical system to yield measures of neuronal state and dysfunction. The electrical recordings of stem cell-derived neurons from individuals with autism spectrum disorder (ASD) and controls were analyzed using minimum embedding dimension (MED) analysis to characterize their dynamical complexity. MED analysis revealed a significant reduction in dynamical complexity in ASD neurons during differentiation, which was correlated to bursting and spike interval measures. MED was associated with clinical endpoints, such as nonverbal intelligence, and was correlated with 53 differentially expressed genes, which were overrepresented with ASD risk genes related to neurodevelopment, cell morphology, and cell migration. Spatiotemporal analysis also showed a prenatal temporal enrichment in cortical and deep brain structures. Together, we present dynamical analysis as a paradigm that can be used to distinguish disease-associated cellular electrophysiological and transcriptional signatures, while taking into account patient variability in neuropsychiatric disorders.