Pharmacopsychiatry 2011; 21 - A47
DOI: 10.1055/s-0031-1292488

Deep brain stimulation on schizophrenic symptoms in the WIN rat model of schizophrenia

R Hadar 1, T Götz 1, J Klein 2, J Rummel 1, M Schneider 3, R Morgenstern 4, C Winter 1
  • 1Department of Psychiatry, Technical University Dresden, Dresden, Germany
  • 2Department of Psychiatry, Charité University Medicine Berlin, Berlin, Germany
  • 3Developmental Neuropsychopharmacology, Central Institute of Mental Health Mannheim, Mannheim, Germany
  • 4Institute of Pharmacology and Toxicology, Charité University Medicine Berlin, Berlin, Germany

Schizophrenia is a severe psychiatric affliction with a significant proportion of treatment-resistant patients that challenge existing therapeutic methods. In this context, deep brain stimulation (DBS) may appear to be a promising therapeutic alternative. In compliance with that, we previously demonstrated that DBS affects behavioural deficits phenotypic of schizophrenia, i.e. PPI, in the Polyl:C model of this disorder. In order to further validate our previous findings, we here utilized an additional approach for modelling schizophrenic symptomatology in the rat – the repeated application of cannabinoids during puberty. Juvenile rats were chronically injected with either the cannabinoid receptor 1 agonist WIN 55,21-2 or saline. At early adulthood, all rats were implanted bilaterally with electrodes in either the mPFC, the dorsomedial thalamus (DM) or the nucleus accumbens (Nacc). DBS effects were then assessed in two behavioural paradigms, PPI and object recognition. DBS was found to differentially affect behavioral performance of WIN treated rats in both tested paradigms depending on the stimulation target. These results allow drawing implications regarding the relevance of the investigated brain sites and functional circuits in the manifestation and therapy of schizophrenia symptoms. In addition, effects partially resembled our previous results highlighting the necessity of complimentary approaches for a comprehensive modeling of schizophrenia symptomatology.