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Computer science --- Programming --- Artificial intelligence. Robotics. Simulation. Graphics --- artificial intelligence

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Various factors such as age, sex, economic and social background, and DNA define who we are. This thesis explores the complex relationship between genetics, brain structure, and demographic variables, by focusing on the white matter fibres of the brain. Utilizing data from the UK Biobank and additional white matter fibre data from Radwan et al., this research employs advanced diffusion MRI (dMRI) processing and genome-wide association studies (GWAS) to identify genetic variations that influence brain microstructure. The study also investigates how demographic variables such as age, sex, and income correlate with these brain metrics to shape differences in brain anatomy. The study derived Imaging-Derived Phenotypes (IDPs) to quantify different aspects of white matter integrity. IDPs include metrics describing fractional anisotropy (FA), mean diffusivity (MD), apparent fibre density (AFD), dispersion (DISP) and peak amplitude (PEAK_AMP). These metrics provide insights into the microstructural properties of white matter tracts, allowing for a detailed analysis of brain connectivity and health. The results reveal heritability estimates for white matter integrity, ranging from 0.098 to 0.162. For instance, the heritability estimate for FA is 0.162, which is lower than what has been reported in existing literature. However, several SNPs and genes were replicated from other studies, indicating reliable results. Additionally, correlations between demographic factors and brain metrics highlight the impact of age, sex, and brain volume on white matter structure in specific fibres or tracts. For instance, a correlation of 0.58 was found between age and mean diffusivity in the cingulum, and a correlation of -0.45 between sex and dispersion in the optic radiation in the occipital lobe. These findings contribute to our understanding of the genetic architecture of the brain and its association with demographics and health traits. The study underscores the significance of integrating genetic and neuroimaging data to unravel the complexities of human brain development and functioning, offering insights that could possibly inform future research and potential clinical applications.