Large-scale neuroimaging studies of major depressive disorder, associated traits and polygenic risk

Major depressive disorder (MDD) is a highly prevalent and disabling condition with a heritability of around 37%. Key symptoms of MDD include low mood and psychological distress, but the mechanisms underlying MDD and its symptoms are unclear. Genetic and neuroimaging techniques are important methods with which to better understand the aetiology and mechanisms of depression. Recently, through the availability of the UK Biobank and ENIGMA datasets, it has been possible to conduct well-powered imaging studies of heterogeneous traits like MDD, with genome-wide genetic data. These genetic data can act as causal instruments and can be utilised to identify differences in neurobiological mechanisms. The current thesis presents neurobiological associations with depressive symptoms and genetic risk for MDD using data from the UK Biobank imaging project (N range from 5,000 to 12,000). My overall aims were to investigate the neurobiological basis of MDD status, depressive symptoms and MDD polygenic risk. First, MDD case-control differences in subcortical volumes and white matter microstructure indexed by fractional anisotropy and mean diffusivity, are presented using the largest structural neuroimaging samples to date. MDD was associated with worse white matter microstructure in the thalamic-radiation subset and forceps major (posterior corpus callosum). No group difference was found for the volume of any subcortical structure. Next, associations between depressive symptom severity (including longitudinal and cross-sectional measures) with white matter microstructure were tested. Over 8,000 participants had repeated measure of depressive symptoms assessed on 2-4 occasions across 5.89 to 10.69 years. I found several novel associations between measures of depressive symptom severity (at the time of imaging, their variance within individuals over time, and with longitudinal increasing depression severity) all associated with lower white matter microstructure in the thalamic radiations. This was the first study of this size looking at imaging associations with longitudinal symptom measures and demonstrates consistent findings implicating thalamocortical connections. The third study presents results of phenotype wide association (‘PheWAS’) analysis of polygenic risk for MDD, including imaging and other available phenotypes. In total, 1,744 phenotypes were tested, covering sociodemographic, physical health, mental health, subcortical volumes, white matter microstructure assessed with FA and MD (mean diffusivity) and resting-state connectivity. I found that MDD polygenic risk was associated with MDD-related phenotypes including severity of depression and neuroticism, sleep, smoking, subjective well-being as well as neurobiological phenotypes including white matter microstructure and resting-state connectivity. In my final data chapter, neurobiological associations with cognition, as an important risk factor of major depressive disorder, were also reported. I found that higher connectivity related to the default mode network was associated with better cognitive performance. These studies suggest two features of neurobiology related to MDD traits and genetic risk. First, they implicate microstructure of thalamic white matter connections as an important biomarker for MDD risk, psychological distress and genetic risk, as reflected by its consistent associations with depressive status, depressive symptoms, within-subject variability of depression and MDD polygenic risk. Secondly, the aberrant connections within the default mode network were related to MDD phenotypes and polygenic risk. These findings, therefore, provide evidence that these features may play a key role in MDD-related neuroarchitecture

Core Point Pixel-Level Localization by Fingerprint Features in Spatial Domain

Singular point detection is a primary step in fingerprint recognition, especially for fingerprint alignment and classification. But in present there are still some problems and challenges such as more false-positive singular points or inaccurate reference point localization. This paper proposes an accurate core point localization method based on spatial domain features of fingerprint images from a completely different viewpoint to improve the fingerprint core point displacement problem of singular point detection. The method first defines new fingerprint features, called furcation and confluence, to represent specific ridge/valley distribution in a core point area, and uses them to extract the innermost Curve of ridges. The summit of this Curve is regarded as the localization result. Furthermore, an approach for removing false Furcation and Confluence based on their correlations is developed to enhance the method robustness. Experimental results show that the proposed method achieves satisfactory core localization accuracy in a large number of samples

A methylome-wide association study of major depression with out-of-sample case-control classification and trans-ancestry comparison

Major Depression (MD) is a leading cause of global disease burden, and both experimental and population-based studies suggest that differences in DNA methylation (DNAm) may be associated with the condition. However, previous DNAm studies have not so far been widely replicated, suggesting a need for larger meta-analysis studies. In the present study, the Psychiatric Genomics Consortium Major Depressive Disorder working group conducted a meta-analysis of methylome-wide association analysis (MWAS) for life-time MD across 18 studies of 24,754 European-ancestry participants (5,443 MD cases) and an East Asian sample (243 cases, 1846 controls). We identified fifteen CpG sites associated with lifetime MD with methylome-wide significance (p < 6.42e-8). Top CpG effect sizes in European ancestries were positively correlated with those from an independent East Asian MWAS (r = 0.482 and p = 0.068 for significant CpG sites, r = 0.261 and p = 0.009 for the top 100 CpG sites). Methylation score (MS) created using the MWAS summary statistics was significantly associated with MD status in an out-of-sample classification analysis (beta = 0.122, p = 0.005, AUC = 0.53). MS was also associated with five inflammatory markers, with the strongest association found with Tumor Necrosis Factor Beta (beta=-0.154, p=1.5e-5). Mendelian randomisation (MR) analysis demonstrated that 23 CpG sites were potentially causally associated with MD and six of those were replicated in an independent mQTL dataset (Wald's ratio test, absolute β ranged from 0.056 to 0.932, p ranged from 7e-3 to 4.58e-6). CpG sites located in the Major Histocompatibility complex (MHC) region showed the strongest evidence from MR analysis of being associated with MD. Our study provides evidence that variations in DNA methylation are associated with MD, and further evidence supporting involvement of the immune system. Larger sample sizes in diverse ancestries are likely to reveal replicable associations to improve mechanistic inferences with the potential to inform molecular target identification

Comprehensive Assessment of Sleep Duration, Insomnia and Brain Structure within the UK Biobank Cohort

STUDY OBJECTIVES: To assess for associations between sleeping more than or less than recommended by the National Sleep Foundation (NSF), and self-reported insomnia, with brain structure. METHODS: Data from the UK Biobank cohort were analysed (N between 9K and 32K, dependent on availability, aged 44 to 82 years). Sleep measures included self-reported adherence to NSF guidelines on sleep duration (sleeping between 7 and 9 hours per night), and self-reported difficulty falling or staying asleep (insomnia). Brain structural measures included global and regional cortical or subcortical morphometry (thickness, surface area, volume), global and tract-related white matter microstructure, brain age gap (difference between chronological age and age estimated from brain scan), and total volume of white matter lesions. RESULTS: Longer-than-recommended sleep duration was associated with lower overall grey and white matter volumes, lower global and regional cortical thickness and volume measures, higher brain age gap, higher volume of white matter lesions, higher mean diffusivity globally and in thalamic and association fibers, and lower volume of the hippocampus. Shorter-than-recommended sleep duration was related to higher global and cerebellar white matter volumes, lower global and regional cortical surface areas, and lower fractional anisotropy in projection fibers. Self-reported insomnia was associated with higher global grey and white matter volumes, and with higher volumes of the amygdala, hippocampus and putamen. CONCLUSIONS: Sleeping longer than recommended by the NSF is associated with a wide range of differences in brain structure, potentially indicative of poorer brain health. Sleeping less than recommended is distinctly associated with lower cortical surface areas. Future studies should assess the potential mechanisms of these differences and investigate long sleep duration as a putative marker of brain health

Structural neuroimaging measures and lifetime depression across levels of phenotyping in UK biobank

Depression is assessed in various ways in research, with large population studies often relying on minimal phenotyping. Genetic results suggest clinical diagnoses and self-report measures of depression show some core similarities, but also important differences. It is not yet clear how neuroimaging associations depend on levels of phenotyping. We studied 39,300 UK Biobank imaging participants (20,701 female; aged 44.6 to 82.3 years, M = 64.1, SD = 7.5) with structural neuroimaging and lifetime depression data. Past depression phenotypes included a single-item self-report measure, an intermediate measure of ‘probable’ lifetime depression, derived from multiple questionnaire items relevant to a history of depression, and a retrospective clinical diagnosis according to DSM-IV criteria. We tested (i) associations between brain structural measures and each depression phenotype, and (ii) effects of phenotype on these associations. Depression-brain structure associations were small (β < 0.1) for all phenotypes, but still significant after FDR correction for many regional metrics. Lifetime depression was consistently associated with reduced white matter integrity across phenotypes. Cortical thickness showed negative associations with Self-reported Depression in particular. Phenotype effects were small across most metrics, but significant for cortical thickness in most regions. We report consistent effects of lifetime depression in brain structural measures, including reduced integrity of thalamic radiations and association fibres. We also observed significant differences in associations with cortical thickness across depression phenotypes. Although these results did not relate to level of phenotyping as expected, effects of phenotype definition are still an important consideration for future depression research