Schizophrenia is a lifelong disorder that affects 1% of the population worldwide and often begins in late adolescence or early adulthood. It is characterised by positive, negative and cognitive symptoms. Negative and cognitive deficits are more difficult to measure in rodents but may be a better predictor than positive symptoms of the long-term outcome following treatment of patients with schizophrenia. One of the major challenges in schizophrenia research is the development of suitable models for these aspects of the disorder. In this regard, one model we have studied in some detail is the methylazoxymethanol (MAM) neurodevelopmental model. MAM is a neurospecific antimitotic agent that prevents cells from dividing for a short time after injection. It can be administered intraperitoneally to pregnant dams on gestational day 17, theoretically causing neuroanatomical and behavioural alterations in offspring that are akin to core symptoms seen in schizophrenia. Our findings confirm that MAM model offspring exhibit several neurodevelopmental and pathological changes that bear similarities to schizophrenia. These include reductions in cortical thickness and hippocampal size,and enlargement of ventricles. Behavioural consequences of MAM, largely emergent after puberty, include increased locomotor responsiveness to NMDA antagonist administration, and also pre-pulse inhibition and cognitive flexibility deficits. However, the robust neuropathological and neurophysiological findings found in the MAM El7 model do not always translate into behavioural deficits. This is of critical consequence for model validation and use in discovery research. The variability in behaviour effects will be discussed, taking into consideration factors such as litter effects, study design and statistical analysis. Overall, our data suggests that maternal treatment with MAM on embryonic day 17 leads to persistent alterations in the adult offspring of SD-CD rats that are relevant for modelling aspects of schizophrenia - but revised methods for study design and statistical analysis are crucial to avoid misinterpretation of findings.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:602342 |
| Date | January 2013 |
| Creators | Malik, Nadia |
| Publisher | University of Nottingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://eprints.nottingham.ac.uk/28447/ |
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