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Approved research

Cognitive outcomes in people with behavioural and brain disorders within UK Biobank

Principal Investigator: Dr Breda Cullen
Approved Research ID: 11332
Approval date: February 1st 2015

Lay summary

The aim of this research is to improve our understanding of variation in cognitive performance in adults with behavioural and brain disorders such as depression, bipolar disorder and multiple sclerosis. We will investigate the nature and extent of cognitive impairment in these groups compared to healthy controls, and we will develop multivariate models to explore the relationship between cognitive performance and medical status, demographic and lifestyle factors, and genetic markers. Cognitive impairment is common and functionally disabling in patients with behavioural and brain disorders, but it remains poorly understood at an individual level. This cross-sectional research will contribute to the refinement of hypotheses regarding risk factors for cognitive impairment, providing a foundation for future longitudinal research focused on understanding, preventing and treating cognitive impairment in these groups. This research will comprise a series of cross-sectional studies of baseline cognitive data from the UK Biobank resource. Complex statistical models will be used to estimate the relationship between key risk factors (e.g. medical status and genetic markers) and cognitive performance, while taking into account the additional influence of other demographic, social and lifestyle factors. This research will make use of the full UK Biobank cohort. Sub-groups with behavioural and brain disorders will be identified, and the remainder of the cohort will serve as a control group for comparison.

The aim of this research is to improve our understanding of variation in cognitive performance in adults with behavioural and brain disorders such as depression, bipolar disorder and multiple sclerosis. We will investigate the nature and extent of cognitive impairment in these groups compared to healthy controls, and we will develop multivariate models to explore the relationship between cognitive performance and medical status, demographic and lifestyle factors, genetic data and other biomarkers.

We will also use machine learning methods to conduct joint analyses of structural and functional brain MRI data, and genotyping data, to investigate co-expression of genetic and brain connectivity patterns associated with cognitive performance in participants with mood disorders and chronic neurological conditions.

We are also interested in exploring the relationship between specific lifestyle, environmental and physical risk factors and cognitive function in the whole UK Biobank cohort (e.g. physical activity, pain, fatigue, inflammation), including whether these factors interact with mental health and neurological disorder status.

We are interested in inflammatory processes as potential mediators or moderators of cognitive function and mental health, and in that context we are also interested in the risk factors for inflammation more generally. We will investigate this by analysing the association between environmental risk factors (e.g. air pollution) and inflammatory diseases (e.g. rheumatic diseases) and biomarkers of inflammation (e.g. CRP), as well as looking at links between inflammatory biomarkers, neuroimaging markers and cognitive, mental and physical health outcomes.