Genetic determinants of white matter integrity in bipolar disorder

Bipolar disorder is a heritable psychiatric disorder, and several of the genes associated with bipolar disorder and related psychotic disorders are involved in the development and maintenance of white matter in the brain. Patients with bipolar disorder have an increased incidence of white matter hyper-intensities, and quantitative brain imaging studies collectively indicate subtle decreases in white matter density and integrity in bipolar patients. This suggests that genetic vulnerability to psychosis may manifest itself as reduced white matter integrity, and that white matter integrity is an endophenotype of bipolar disorder. This thesis comprises a series of studies designed to test the role of white matter in genetic risk to bipolar disorder by analysis of diffusion tensor imaging (DTI) data in the Bipolar Family Study. Various established analysis methods for DTI, including whole-brain voxel-based statistics, tract-based spatial statistics (TBSS) and probabilistic neighbourhood tractography, were applied with fractional anisotropy (FA) as the outcome measure. Widespread but subtle white matter integrity reductions were found in unaffected relatives of patients with bipolar disorder, whilst more localised reductions were associated with cyclothymic temperament. Next, the relation of white matter to four of the most prominent psychosis candidate genes, NRG1, ErbB4, DISC1 and ZNF804A, was investigated. A core haplotype in NRG1, and three of the four key single nucleotide polymorphisms (SNPs) within it, showed an association with FA in the anterior thalamic radiations and the uncinate fasciculi. For the three SNPs considered in ErbB4, results were inconclusive, but this was consistent with the background literature. Most notable however, was a clear association of a non-synonymous DISC1 SNP, Ser704Cys, with FA extending over most of the white matter in the TBSS and voxel-based analyses. Finally, FA was not associated with a genome-wide supported risk SNP in ZNF804A, a finding which could not be attributed to a lack of statistical power, and which contradicts a strong, but previously untested hypothesis. Whilst the above results need corroboration from independent studies, other studies are needed to address the cellular and molecular basis of these findings. Overall, this work provides strong support for the role of white matter integrity in genetic vulnerability to bipolar disorder and the wider psychosis spectrum and encourages its future use as an endophenotype

Leveraging genomic risk factors for major depressive disorder to provide mechanistic insights and predictive neurobiological markers

Major Depressive Disorder (MDD) is a disabling, common psychiatric disorder and the leading cause of global disability. A complex combination of genetic and environmental factors gives rise to MDD, although the exact aetiology has not been identified. Genome-wide association studies (GWAS) have established that MDD has a moderate heritability of approximately 37%. MDD has in the past also been associated with abnormalities of white matter microstructure, which represents the brain’s connectivity network. This network is also moderately heritable, providing rationale to investigate its relationship to MDD genetic risk. Over recent years, there has been considerable progress in establishing genetic contributions to MDD. These advances can be harnessed, in combination with neuroimaging and epigenomics, to understand the neurobiology of the disorder. This has only recently become possible at sufficient scale with the availability of large publicly available datasets including genomic, epigenomic, and neuroimaging data. In the current thesis, I therefore aimed to leverage genetic, epigenetic, and neuroimaging data in two large datasets, UK Biobank (N range: 6,400 – 14,800) and Generation Scotland: Scottish Family Health Study (N = 625). Specifically, I aimed to uncover links between white matter microstructure, as measured by fractional anisotropy and mean diffusivity, and (i) differential gene expression as indexed by expression quantitative trait loci (eQTLs) scores in chapter 2; here, decreased white matter integrity was found to be associated with 6 scores regulating genes previously reported to be implicated in neurological and neuropsychiatric disorders, while 2 scores regulating neurodevelopment-linked genes were associated with increased white matter integrity; (ii) MDD genetic risk stratified by the NETRIN1 Signalling Pathway, previously implicated in MDD, indexed by polygenic risk scores (PRS) in chapter 3; results indicated novel associations between the pathway-focussed PRS and decreased white matter integrity in thalamic radiations, as well as several association fibres, including superior and inferior longitudinal fasciculus; (iii) a novel wholegenome epigenetic risk score for MDD, which uncovered an association with MDD, but no significant associations with changes in white matter microstructure (chapter 4). The overall aim of the thesis was to use advanced genomic techniques to stratify genetic function and risk and explore epigenetic risk for MDD in order to identify novel links to structural brain connectivity. Overall, the three studies provide a strong rationale for integrating neuroimaging, genomic and epigenomic data. Specifically, findings in chapter 2 indicate the importance of DCAKD, SLC35A4, SEC14L4, SRA1, PLEKHM1, UBE3C, NMT1, and CPNE1, not previously found by conventional GWAS approaches. This suggests that integrating neuroimaging and genetic expression data may uncover novel associations that inform disease- or trait-specific genetic links to brain connectivity. Chapter 3 results provide a rationale for investigating the NETRIN1 Signalling Pathway and emphasise the role of thalamic connections in MDD within this biological pathway, indicating that novel associations with brain connectivity may be uncovered at a more focused level when stratifying MDD risk by biology. Finally, results from chapter 4 indicate that epigenetics play an important role in MDD risk, although further analysis including larger-scale epigenetic and neuroimaging data should be carried out to uncover the role of epigenetics in relation to brain phenotypes

Cognitive and Neurophysiological Models of Brain Asymmetry

Asymmetry is an inherent characteristic of brain organization in both humans and other vertebrate species, and is evident at the behavioral, neurophysiological, and structural levels. Brain asymmetry underlies the organization of several cognitive systems, such as emotion, communication, and spatial processing. Despite this ubiquity of asymmetries in the vertebrate brain, we are only beginning to understand the complex neuronal mechanisms underlying the interaction between hemispheric asymmetries and cognitive systems. Unfortunately, despite the vast number of empirical studies on brain asymmetries, theoretical models that aim to provide mechanistic explanations of hemispheric asymmetries are sparse in the field. Therefore, this Special Issue aims to highlight empirically based mechanistic models of brain asymmetry. Overall, six theoretical and four empirical articles were published in the Special Issue, covering a wide range of topics, from human handedness to auditory laterality in bats. Two key challenges for theoretical models of brain asymmetry are the integration of increasingly complex molecular data into testable models, and the creation of theoretical models that are robust and testable across different species

Spatial heterogeneity of functional Magnetic Resonance Imaging indices of dorsolateral prefrontal cortex activation evoked by a working memory task: A comparison of patients with schizophrenia and healthy controls

Background: This study is based on the hypothesis that functional Magnetic Resonance Imaging (fMRI) indices in the dorsolateral prefrontal cortex (DLPFC) of schizophrenia patients are spatially more heterogeneous than healthy controls for activation evoked by a working memory (WM) task. Patients have shown to have greater activation than controls in the DLPFC, but this seems to cancel out when performing group averages, which could be explained by patients having more spatially distributed activation. This may one of the causes for discrepant findings concerning hypo- or hyperactivation in the DLPFC of patients when performing a WM task. Methods: The cohort consisted of demographically matched schizophrenia patients and healthy controls. fMRI data was acquired to study the activation evoked by a modified Sternberg Item Recognition Paradigm (SIRP) known to induce robust activation of the main brain areas subserving WM both in schizophrenia patients and healthy controls. Those areas are the DLPFC, the intraparietal sulcus, the insula and the primary motor cortex. The fMRI data was analyzed with the FMRIB Software Library (FSL). We limited the analysis to the DLPFC by filtering the data with a region of interest (ROI) individually defined for each subject based on its own brain anatomy and conservative Talairach coordinates. For the study of fMRI indices, we used the centers of gravity (COG) of activation clusters. The COG is a 3 dimensional coordinate (x, y, z) computed based on the z-values of all voxels constituting a cluster. Results: The paradigm induced activation in the brain areas known to be involved in WM. In response to the WM paradigm, the COGs of the activation clusters in the DLPFC had a significantly greater spatial heterogeneity in patients compared to controls in the left hemisphere. The right hemisphere did not show any significant difference between the two groups. Conclusion: Our hypothesis is supported by our findings in the left hemisphere, but not the right. The methods that were developed for this study are a first attempt to study the spatial heterogeneity of the activation in the DLPFC. The power of the results would benefit from improvement of those methods. In particular, the definition of the DLPFC ROI is to be improved in order to better target the activation patterns of interes

Micro-, Meso- and Macro-Connectomics of the Brain

Neurosciences, Neurolog

MRI for gray matter: statistical modelling for in-vivo application and histological validation of dMRI

Gray matter (GM) forms the ‘computational engine’ of our brain and plays the key role in brain function. Measures derived from MRI (e.g., structural MRI (sMRI) and diffusion MRI (dMRI)) provide a unique opportunity to non-invasively study GM structure in-vivo and thus can be used to probe GM pathology in development, aging and neuropsychiatric disorders. Investigation of the influence of various factors on MRI measures in GM is critical to facilitate their use for future non-invasive studies in healthy and diseased populations. In this dissertation, GM structure was studied with MRI to understand how it is influenced by genetic and environmental factors. Validation of dMRI- derived measures was conducted by comparing them with histological data from monkeys to better understand the cytoarchitectural features that influence GM measures. First, the influence of genetic and environmental factors was quantified on gray matter macrostructure and microstructure measures using phenotypic modelling of structural and diffusion MRI data obtained from a large twin and sibling population (N = 840). Results of this study showed that in GM, while sMRI measures like cortical thickness and GM volume are mainly affected by genetic factors, advanced dMRI measures of mean squared displacement (MSD) and return to origin probability (RTOP) derived from advanced biexponential model can tap into regionally specific patterns of both genetic and environmental influence in cortical and subcortical GM. Our results thus highlight the potential of these advanced dMRI measures for use in future studies that aim to investigate and follow in healthy and clinical population changes in GM microstructure linked with both genes and environment. Second, using data from a large healthy population (n=550), we investigated changes in sMRI tissue contrast at the gray-white matter boundary with biological development during adolescence to assess how this affects estimation of the developmental trajectory of cortical thickness. Results of this study suggest that increased myelination during brain development contributes to age-related changes in gray-white boundary contrast in sMRI scans causing an apparent shift of the estimated gray-white boundary towards the cortical surface, in turn reducing estimations of cortical thickness and its developmental trajectory. Based on these results, we emphasize the importance of accounting for the effects of myelination on T1 gray-white matter boundary contrast to enable more precise estimation of cortical thickness during neurodevelopment. Finally, we conducted histological validation of dMRI measures in gray matter by comparing dMRI measures derived from two models, conventional Diffusion Tensor Imaging (DTI) model and an advanced biexponential model with histology acquired from the same 4 rhesus monkeys. Results demonstrate differences in the ability of distinct dMRI measures including DTI-derived measures of fractional anisotropy (FA), Trace and advanced Biexponential model-derived measures of MSD and RTOP to capture the biological features of underlying cytoarchitecture and identify the dMRI measures that best reflect underlying gray matter cytoarchitectural properties. Investigation of the contribution of underlying cytoarchitecture (cellular organization) to dMRI measures in gray matter provides validation of dMRI measures of average and regional heterogeneity in MSD & Trace as markers of cytoarchitecture as measured by regional average and heterogeneity in cell area density. This postmortem validation of these dMRI measures makes their use possible for treatment monitoring of various GM pathologies. These studies and their results together demonstrate the utility of imaging measures to investigate the complex relationships between GM cellular organization, brain development, environment and genes