Abstract:
BACKGROUND Pharmacological intervention is the most effective means for controlling the symptoms of schizophrenia but is ineffective in a proportion of individuals. This thesis attempted to identifying biomarkers of treatment resistance in people with schizophrenia, using structural and functional magnetic resonance imaging (MRI). METHODS Two cross-sectional studies were included in this thesis: the TRS study (including first-line responders (FLR), individuals with treatment-resistant schizophrenia (TRS), individuals with ultra-treatment-resistant schizophrenia (UTRS) and healthy controls) and the CloRes study (including FLR and those who were eligible for clozapine). A prospective arm of the CloRes study included clozapine-eligible participants who were later diagnosed with TRS or UTRS following a three-month trial of clozapine. Functional connectivity patterns were compared across groups in both studies using independent components analysis. Structural connectivity in FLR and those who were clozapine-eligible was assessed using diffusion tensor imaging (DTI) and probabilistic tractography. Graph theory was used to examine network organisation and communication in participants from the TRS study. As a final experiment, we investigated whether structural and functional connectivity could predict response to clozapine using the NeuCube spiking neural network algorithm. RESULTS ICA revealed increased functional connectivity within the language network of individuals with UTRS compared with healthy controls, specifically in the left paracingulate gyrus. In addition, graph theoretical analysis revealed large disruptions in modularity, connection strength and network organisation in people with schizophrenia, particularly those with UTRS. Those who were clozapine-eligible showed increased connectivity between areas of the sensorimotor network and the precuneus compared with FLR. A structural connectivity analysis identified white matter abnormalities in the corpus callosum and respective branching tracts in those who were eligible for clozapine compared with FLR. The NeuCube algorithm achieved 74.9% accuracy for classification of TRS study data. CONCLUSIONS These data provide evidence to support a role for both structural and functional dysconnection in the resistance to antipsychotics. Dysconnectivity was greatest in those with UTRS but also present in FLR and those who were eligible for clozapine. The nature of dysconnection varied between response subtypes, suggesting that the mechanisms responsible for dysconnectivity may dictate an individual’s susceptibility to the effects of antipsychotics.