Schizophrenia remains an enigmatic disorder with unclear neuropathology. Recent advances in neuroimaging and genetic research suggest alterations in glutamate-dopamine interactions adversely affecting synaptic plasticity both intracortically and subcortically. Relating these changes to the manifestation of symptoms presents a great challenge, requiring a constrained framework to capture the most salient elements. Here, a biologically-grounded computational model of basal ganglia-mediated action selection was used to explore two pathological processes that hypothetically underpin schizophrenia. These were a drop in the efficiency of cortical transmission, reducing both the signal-to-noise ratio (SNR) and overall activity levels; and an excessive compensatory upregulation of subcortical dopamine release. It was proposed that reduced cortical efficiency was the primary process, which led to a secondary disinhibition of subcortical dopamine release within the striatum. This compensation was believed to partly recover lost function, but could then induce disorganised-type symptoms - summarised as selection ”Instability” - if it became too pronounced. This overcompensation was argued to be countered by antipsychotic medication. The model’s validity was tested during an fMRI (functional magnetic resonance imaging) study of 16 healthy volunteers, using a novel perceptual decision-making task, and was found to provide a good account for pallidal activation. Its account for striatum was developed and improved with a small number of principled model modifications: the inclusion of fast spiking interneurons within striatum, and their inhibition by the basal ganglia’s key regulatory nucleus, external globus pallidus. A key final addition was the explicit modelling of dopaminergic midbrain, which is dynamically regulated by both cortex and the basal ganglia. This enabled hypotheses concerning the effects of cortical inefficiency, compensatory dopamine release and medication to be directly tested. The new model was verified with a second set of 12 healthy controls. Its pathological predictions were compared to data from 12 patients with schizophrenia. Model simulations suggested that Instability went hand-in-hand with cortical inefficiency and secondary dopamine upregulation. Patients with high Instability scores showed a loss of SNR within decision-related cortex (consistent with cortical inefficiency); an exaggerated response to task demands within substantia nigra (consistent with dopaminergic upregulation); and had an improved fit to simulated data derived from increasingly cortically-inefficient models. Simulations representing the healthy state provided a good account for patients’ motor putamen, but only cortically-inefficient simulations representing the ill state provided a fit for ventral-anterior striatum. This fit improved as the simulated model became more medicated (increased D2 receptor blockade). The relative improvement of this account correlated with patients’ medication dosage. In summary, by distilling the hypothetical neuropathology of schizophrenia into two simplified umbrella processes, and using a computational model to consider their effects within action selection, this work has successfully related patients’ fMRI activation to particular symptomatology and antipsychotic medication. This approach has the potential to improve patient care by enabling a neurobiological appreciation of their current illness state, and tailoring their medication level appropriately.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:563593 |
| Date | January 2011 |
| Creators | Romaniuk, Liana |
| Contributors | Willshaw, David. ; Lawrie, Stephen |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/5758 |
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