High-Frequency Neuronal Oscillatory Abnormalities in the Phospholipase C-β1 Knockout Mouse Model of Schizophrenia

Abstract

Background: Schizophrenia is a complex neuropsychiatric disorder characterized by psychoses, socioaffective disturbances, and cognitive deficits. The phosphodiesterase enzyme phospholipase C-β1 has been reported to be reduced in postmortem tissue of schizophrenia patients. Dysregulation of neuronal oscillations, particularly those in the higher frequency range such as beta (12-30 Hz) and gamma (30-80 Hz), are also associated with this disorder. We investigated the influence of phospholipase C-β1 gene deletion on cortical oscillatory activity and sensorimotor gating behavior. Methods: Adult phospholipase C-β1 knockout and wild-type C57Bl/6J control mice (total n = 26) underwent surgical implantation of extradural electrodes to allow electrocorticography recordings. Electrocorticography was recorded during prepulse inhibition behavior sessions to measure ongoing and auditory-evoked electrophysiological responses. Mice were also pretreated with antipsychotic drugs haloperidol (0.25 mg/kg), clozapine (2.5 mg/kg), and olanzapine (5 mg/kg). Results: Phospholipase C-β1 knockout mice exhibited reduced prepulse inhibition and diminished power and phase synchrony of beta and gamma oscillatory responses to auditory stimuli as well as elevated ongoing beta oscillations. Reductions in prepulse inhibition were highly correlated with the power and phase synchrony of evoked oscillations. Clozapine and olanzapine ameliorated the prepulse inhibition deficit in phospholipase C-β1 knockout mice, but not the electrophysiology abnormalities. Conclusions: Phospholipase C-β1 reduction leads to disturbances to beta and gamma oscillatory dynamics and prepulse inhibition behavior. The strong relationships between these measures demonstrate the importance of event-related oscillatory activity to sensorimotor gating behavior. However, dissociation of these measures observed in the drug studies suggests that abnormalities in neuronal networks may not necessarily need to be corrected for behavioral improvement.