Dynamical Electrical Complexity Is Reduced during Neuronal Differentiation in Autism Spectrum Disorder

Abstract

Summary: 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. : Marchetto, Kim, and colleagues describe the application of dynamical analysis to stem cell-derived neuronal recordings from patients with ASD. They find that dynamical complexity is reduced in ASD electrical activity, as measured by minimum embedding dimension (MED). They go on to describe the gene expression, biological pathway, and neurodevelopmental correlates of the MED signature, supporting findings in the literature. Keywords: autism spectrum disorder, multielectrode array, minimum embedding dimension, dynamical complexity, neurodevelopmental disorder models