Introduction: Bipolar disorder is associated with cortical abnormalities, including deficits in intracortical myelination. Intracortical myelin follows an inverted-U trajectory over the lifetime, but this trajectory is blunted in individuals with bipolar disorder. Little is understood about which genetic factors contribute to these deficits. Neuregulin-1, a cell-signalling protein, has been shown to contribute to cortical abnormalities and increase susceptibility to related disorders. Assessing the prevalence of neuregulin-1 polymorphisms, notably rs6994992, in bipolar disorder may elucidate the genetic contributors of intracortical myelin deficits and increase our understanding of factors causing susceptibility to bipolar disorder. Methods: 67 participants with bipolar disorder type I and 75 healthy control participants were included. T1-weighted MRI images were collected and processed to create R1 cortical maps, a proxy measure of intracortical myelin. Participant blood samples were genotyped at the rs6994992 locus. Linear models were used to test whether intracortical myelin can be predicted by age, bipolar diagnosis and NRG1 genotype. Results: Intracortical myelin is significantly predicted by age, diagnosis and genotype together in the motor cortex (left: R2 = 0.09, p < 0.01, right: R2 = 0.06, p < 0.05), the right premotor cortex (R2 = 0.095, p < 0.001), and the right inferior frontal cortex (R2 = 0.098, p < 0.001). Age is a significant individual predictor of intracortical myelin in the right dorsal anterior cingulate cortex, the bilateral motor cortex, the right premotor cortex, and the right inferior frontal cortex. Conclusions and Future Directions: Our study suggests that the right premotor, bilateral primary motor, and right inferior frontal cortices are regions of interest for understanding how intracortical myelin changes throughout the lifetime, especially in bipolar disorder. Future work should examine the impact of polygenic risk scores of bipolar disorder on intracortical myelin. / Thesis / Master of Science (MSc) / Bipolar disorder is associated with neurobiological changes, including cortical abnormalities, contributing to a greater disorder burden. Cortical myelination changes throughout the lifetime and larger deficits are found in individuals with bipolar disorder. However, the role of genetics in these intracortical myelin deficits is largely unknown. This thesis investigates how intracortical myelin content in various regions of the cortex is impacted by age, bipolar disorder diagnosis, and neuregulin gene variants. The goal of this research is to contribute to a better understanding of how genetics and age impact intracortical myelin in bipolar disorder to better understand the neurobiological changes of the disorder.

Multi-site MRI studies collect large amounts of data in a short time frame. Large sample sizes are desirable to address power and replicability issues that have been problematic for scientists in the past. Although multi-site MRI solves the sample size problem, it brings with it a new set of challenges. Scanning the same person at different sites might result in differences in MRI derived measurements. In this thesis we compared three approaches to facilitate the analysis of multi-site MRI data: quantitative R1 mapping, adding site as a covariate in a linear model, and using the ComBat method. We also investigated the relationship between two common MRI measurements: signal and volume. We collected data from 64 healthy participants across 3 GE scanners and 1 Siemens scanner at 3T. We found that signal intensity was different between vendors whereas volume was not. Our R1 method resulted in values that were different across vendor and significantly lower than those reported in the literature. B1+ maps used to calculate R1 were different across sites. Using a scale factor, we were able to compensate for mistakes in R1 mapping. We also found that adding site as a covariate corrected mean differences in signal intensity across sites, but not differences in variance. The ComBat method gave best similarity between sites. However, since different people were scanned at each site, we couldn’t evaluate the effectiveness of each method as variation in the data could have been due to site effects or heterogeneity in participants. White matter volume and signal intensity in the white matter were correlated in males but not in females. We found that this low correlation was caused by outliers in our female sample. The correlation between white matter volume and signal in males suggests that both metrics are measuring myelin and can be used as converging evidence to detect changes in brain myelination. / Thesis / Master of Science (MSc)