Computational Modeling of Genetic Contributions to Excitability and Neural Coding in Layer V Pyramidal Cells: Applications to Schizophrenia Pathology

摘要: Pyramidal cells in layer V of the neocortex are one most widely studied neuron types mammalian brain. Due to their role as integrators feedforward and cortical feedback inputs, they well-positioned contribute symptoms pathology mental disorders-such schizophrenia-that characterized by a mismatch between internal perception external inputs. In this modeling study, we analyze input/output properties pyramidal sensitivity modeled genetic variants schizophrenia-associated genes. We show that excitability way integrate inputs space time altered many ion-channel Ca2+ transporter-encoding genes have been identified risk recent genome-wide association studies. also variability output patterns spiking transients is these model variants. Importantly, predicted effects robust noise qualitatively similar across different computational models cells. Our framework reveals several aspects single-neuron can be linked known schizophrenia-related phenotypes existing hypotheses on disease mechanisms. particular, our predict single-cell steady-state firing rate positively correlated with coding capacity negatively amplitude prepulse-mediated adaptation coincidence stimuli apical dendrite perisomatic region cell. These results help uncover voltage-gated transporter-associated underpinnings schizophrenia biomarkers.