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Title: White matter connectivity, cognition, symptoms and genetic risk factors in Schizophrenia
Author: Alloza Romero, Clara
ISNI:       0000 0004 7969 1181
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2019
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Schizophrenia is a highly heritable complex neuropsychiatric disorder with a lifetime prevalence of around 1%. It is often characterised by impaired white matter structural dysconnectivity. In vivo and post-mortem alterations in white matter microstructure have been reported, along with differences in the topology of the structural connectome; overall these suggest a reduced communication between distal brain regions. Schizophrenia is characterised by persistent cognitive impairments that predate the occurrence of symptoms and have been shown to have a neural foundation reflecting aberrant brain connectivity. So far, 179 independent genome-wide significant single nucleotide polymorphisms (SNPs) have been associated with a diagnosis of schizophrenia. The high heritability and polygenicity of schizophrenia, white matter parameters and cognitive functions provides a great opportunity to investigate the potential relationships between them due to the genetic overlap shared among these factors. This work investigates the psychopathology of schizophrenia from a neurobiological, psychological and genetic perspective. The datasets used here include data from the Scottish Family Mental Health (SFMH) study, the Lothian Birth Cohort 1936 (LBC1936) and UK Biobank. The main goal of this thesis was to study white matter microstructure in schizophrenia using diffusion MRI (dMRI) data. Our first aim was to examine whether processing speed mediated the association between white matter structure and general intelligence in patients diagnosed with schizophrenia in the SFMH study. Secondly, we investigated specific networks from the structural connectome and their topological properties in both healthy controls and patients diagnosed with schizophrenia in the SFMH study. These networks were studied alongside cognition, clinical symptoms and polygenic risk factor for schizophrenia (szPGRS). The third aim of this thesis was to study the effects of szPGRS on the longitudinal trajectories of white matter connectivity (measured using tractography and graph theory metrics) in the LBC1936 over a period of three-years. Finally, we derived the salience network which has been previously associated with schizophrenia and examined the effect of szPGRS on the grey matter nodes associated with this network and their connecting white matter tracts in UK Biobank. With regards to the first aim, we found that processing speed significantly mediates the association between a general factor of white matter structure and general intelligence in schizophrenia. These results suggest that, as in healthy controls, processing speed acts as a key cognitive resource facilitating higher order cognition by allowing multiple cognitive processes to be simultaneously available. Secondly, we found that several graph theory metrics were significantly impaired in patients diagnosed with schizophrenia compared with healthy controls. Moreover, these metrics were significantly associated with intelligence. There was a strong tendency towards significance for a correlation between intelligence and szPGRS that was significantly mediated by graph theory metrics in both healthy controls and schizophrenia patients of the SFMH study. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. In the LBC1936 we found that higher szPGRS showed significant associations with longitudinal increases in MD in several white matter tracts. Significant declines over time were observed in graph theory metrics. Overall these findings suggest that szPGRS confer risk for ageing-related degradation of some aspects of structural connectivity. Moreover, we found significant associations between higher szPGRS and decreases in cortical thickness, in particular, in a latent factor for cortical thickness of the salience network. Taken together, our findings suggest that white matter connectivity plays a significant role in the disorder and its psychopathology. The computation of the structural connectome has improved our understanding of the topological characteristics of the brain's networks in schizophrenia and how it relates to the microstructural level. In particular, the data suggests that white matter structure provides a neuroanatomical substrate for cognition and that structural connectivity mediates the relationship between szPGRS and intelligence. Additionally, these results suggest that szPGRS may have a role in age-related changes in brain structural connectivity, even among individuals who are not diagnosed with schizophrenia. Further work will be required to validate these results and will hopefully examine additional risk factors and biomarkers, with the ultimate aims of improving scientific knowledge about schizophrenia and conceivably of improving clinical practice.
Supervisor: Lawrie, Stephen ; Sibley, Heather ; Bastin, Mark ; Cox, Simon Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: schizophrenia ; brain connectivity ; genetic risk factors ; cognitive functions