PURPOSE: Extremely low gestation age newborns (ELGANs) are at high risk for developmental brain abnormalities, which can lead to cognitive, physical, emotional and behavioral deficits. This study is to determine potential brain volumetric abnormalities of ELGAN children at 9 to 10 years of age. METHODS: High-resolution magnetic resonance imaging (MRI) scans were obtained from 82 ELGAN children using a dual-echo turbo spin-echo (DE-TSE) pulse sequence at 3.0T (or 1.5T at only one site). The DICOM MR images were processed with quantitative MRI algorithms programmed in Mathcad. The brain gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were quantified using semi-automated clustering segmentation algorithms. RESULTS: Total brain volumes (GM+WM) of ELGAN children showed a large distribution range from 400 to 1500 mL. About 63% of the children had smaller brain volumes while 5% of them had larger brain volumes compared to the published data from normal children at the same ages1. Smaller brain volumes were observed more often in males (74%) than in females (50%). WM reduction was the major change in ELGANs with over 90% of them (86% of males and 92% of females) having reduced WM volumes. GM volumes were either reduced (15%) or enlarged (32%); GM reduction was observed more often in males (31%) than in females (4.8%), while GM enlargement was more frequently observed in females (35%) than in males (28%). Intracranial CSF volumes range from 25 mL to 600 mL, with 16% of ELGAN children (9% of males and 21% of females) having smaller CSF volume, while 38% of them (53% of males and 27% of females) having larger CSF volume. Correlation analysis revealed a positive correlation between total intracranial matter (ICM) and CSF volumes (male: r = 0.4972, p = 0.0014 and female: r = 0.3233, p = 0.0125), but a negative correlation was found between brain volumes and CSF volumes (male: r = - 0.2998, p = 0.0424 and female: r = - 0.2279, p = 0.0596). Further analysis demonstrated a negative correlation between GM and CSF both in absolute (male: r = - 0.4489, p = 0.0039 and female: r = - 0.3769, p = 0.0041) and in relative (male: r = - 0.8675, p = 0.0000 and female: r = - 0.8350, p = 0.0000) volumes, while WM volumes did not correlate with CSF volumes. CONCLUSION: ELGAN children had mostly smaller brain volumes while some of them displayed larger brain volumes at ages of 9 to 10 years. The reduction of WM was a characteristic change in ELGAN children and contributed to smaller brain volumes. GM volumes either increased or decreased. Larger intracranial CSF volumes were associated with larger intracranial matter (ICM) volume.

The image segmentation task divides an image into regions of similar pixels based on brightness, color, and texture, in which every pixel in the image is as- signed to a label. Segmentation is vital in numerous medical imaging applications, such as quantifying the size of tissues, the localization of diseases, treatment plan- ning, and surgery guidance. This thesis focuses on two medical image segmentation tasks: retinal vessel segmentation in fundus images and brain segmentation in 3D MRI images. Finally, we introduce LEON, a lightweight neural network for edge detection. The first part of this thesis proposes a lightweight neural network for retinal blood vessel segmentation. Our model achieves cutting-edge outcomes with fewer parameters. We obtained the most outstanding performance results on CHASEDB1 and DRIVE datasets with an F1 measure of 0.8351 and 0.8242, respectively. Our model has few parameters (0.34 million) compared to other networks such as ladder net with 1.5 million parameters and DCU-net with 1 million parameters. The second part of this thesis investigates the association between whole and re- gional volumetric alterations with increasing age in a large group of healthy subjects (n=6739, age range: 30–80). We used a deep learning model for brain segmentation for volumetric analysis to extract quantified whole and regional brain volumes in 95 classes. Segmentation methods are called edge or boundary-based methods based on finding abrupt changes and discontinuities in the intensity value. The third part of the thesis introduces a new Lightweight Edge Detection Network (LEON). The proposed approach is designed to integrate the advantages of the deformable unit and DepthWise Separable convolutions architecture to create a lightweight back- bone employed for efficient feature extraction. Our experiments on BSDS500 and NYUDv2 show that LEON, while requiring only 500000 parameters, outperforms the current lightweight edge detectors without using pre-trained weights. / Graduate / 2022-10-12

Previous magnetic resonance imaging (MRI) studies have demonstrated brain atrophy in major depressive disorder (MDD) that is progressive with continuing illness and may be reversible with antidepressant treatment. What remains unclear is whether brain structure can be positively affected by pharmacological intervention even if patients fail to remit on the treatment. The primary aim of this thesis was to prospectively track changes in brain structure in patients with treatment-resistant depression while they underwent pharmacotherapy with the goal of attaining remission. There is evidence that gene variants associated with poorer antidepressant response also confer greater risk of volume reduction in the hippocampus. A secondary aim of the thesis was to investigate the effects of monoaminergic-related gene variants on hippocampal volume in patients and controls at baseline imaging. Outpatients with treatment-resistant MDD underwent structural MRI scans at baseline and after either 6-months of sustained remission or 12-months of failure to remit. Matched controls were scanned once to provide comparison data for patients’ baseline scans. Participants also provided blood samples for genetic analyses. Imaging outcome measures included longitudinal changes in whole-brain volume, and gray matter volume and mean cortical thickness within specific cortico-limbic regions of interest (ROIs). Over follow-up, remitted patients had an increase in whole-brain volume, while nonremitted patients lost brain volume despite receiving more treatment strategies. Remitters and nonremitters also showed subtle changes in volume and thickness over time in several ROIs in opposing directions, with increasing hippocampal volume and cortical thickness in the rostral middle frontal gyrus and orbitofrontal cortex in remitters, and decreasing volume or thickness in these regions in nonremitters. Genetic imaging analyses revealed that polymorphisms in certain norepinephrine- and serotonin-related genes have similar effects on hippocampal volume in patients and controls, while the serotonin transporter polymorphism differentially affects hippocampal volume in the presence of depression. Given the observations of volume increase in remitted patients and continuing atrophy in nonremitters, pharmacotherapy in the absence of sustained remission is likely insufficient to elicit structural recovery in depression. This finding is important since the restoration of brain structure in patients with treatment-resistant depression may have positive implications for their future prognosis.

Physical activity is positively related to cognitive functioning and brain volume in older adults. Interestingly, different types of physical activity vary in their effects on cognition and on the brain. For example, dancing has become an interesting topic in aging research, as it is a popular leisure activity among older adults, involving cardiovascular and motor fitness dimensions that can be positively related to cognition. However, studies on brain structure are missing. In this study, we tested the association of long-term senior dance experience with cognitive performance and gray matter brain volume in older women aged 65 to 82 years. We compared nonprofessional senior dancers (n=28) with nonsedentary control group participants without any dancing experience (n=29), who were similar in age, education, IQ score, lifestyle and health factors, and fitness level. Differences neither in the four tested cognitive domains (executive control, perceptual speed, episodic memory, and long-term memory) nor in brain volume (VBM whole-brain analysis, region-of-interest analysis of the hippocampus) were observed. Results indicate that moderate dancing activity (1-2 times per week, on average) has no additional effects on gray matter volume and cognitive functioning when a certain lifestyle or physical activity and fitness level are reached.

Major depressive disorder (MDD) is a heritable and highly debilitating condition with antidepressants, first-line treatment, demonstrating low to modest response rates. No current biological mechanism substantially explains MDD but both neurostructural and neurochemical pathways have been suggested. Further explication of these may aid in identifying subgroups of MDD that are better defined by their aetiology. Specifically, genetic stratification provides an array of tools to do this, including the intermediate phenotype approach which was applied in this thesis. This thesis explores genetic overlap with regional brain volume and MDD and the genetic and non-genetic components of antidepressant response. The first study utilised the most recent published data from ENIGMA (Enhancing Neuroimaging Genetics through Meta-analysis) Consortium's genome-wide association study (GWAS) of regional brain volume to examine shared genetic architecture between seven subcortical brain volumes and intracranial volume (ICV) and MDD. This was explored using linkage disequilibrium score regression (LDSC), polygenic risk scoring (PRS) techniques, Mendelian randomisation (MR) analysis and BUHMBOX (Breaking Up Heterogeneous Mixture Based On Cross-locus correlations). Results indicated that hippocampal volume was positively genetically correlated with MDD (rg= 0.46, P= 0.02), although this did not survive multiple comparison testing. Additionally, there was evidence for genetic subgrouping in Generation Scotland: Scottish Family Health Study (GS:SFHS) MDD cases (P=0.00281), however, this was not replicated in two other independent samples. This study does not support a shared architecture for regional brain volumes and MDD, however, provided some evidence that hippocampal volume and MDD may share genetic architecture in a subgroup of individuals, albeit the genetic correlation did not survive multiple testing correction and genetic subgroup heterogeneity was not replicated. To explore antidepressant treatment resistance, the second study utilised prescription data in (GS:SFHS) to define a measure of (a) treatment resistance (TR) and (b) stages of resistance (SR) by inferring antidepressant switching as non-response. GWAS were conducted separately for TR in GS:SFHS and the GENDEP (Genome-based Therapeutic Drugs for Depression) study and then meta-analysed (meta-analysis n=4,213, cases=358). For SR, a GWAS on GS:SFHS only was performed (n=3,452). Additionally, gene-set enrichment, polygenic risk scoring (PRS) and genetic correlation analysis were conducted. No significant locus, gene or gene-set was associated with TR or SR, however power analysis indicated that this analysis was underpowered. Pedigree-based correlations identified genetic overlap with psychological distress, schizotypy and mood disorder traits. Finally, the role of neuroticism, psychological resilience and coping styles in antidepressant resistance was investigated. Univariate, moderation and mediation models were applied using logistic regression and structural equation modelling techniques. In univariate models, neuroticism and emotion-orientated coping demonstrated significant negative association with antidepressant resistance, whereas resilience, task-orientated and avoidance-orientated coping demonstrated significant positive association. No moderation of the association between neuroticism and TR was detected and no mediating effect of coping styles was found. However, resilience was found to partially mediate the association between neuroticism and TR. Whilst the first study does not indicate a genetic overlap between regional brain volumes and MDD, it demonstrates the utility of the intermediate approach in complex disease. Antidepressant resistance was associated with neuroticism both genetically and phenotypically, indicating its role as an intermediate phenotype. Nonetheless, larger sample sizes are needed to adequately address the components of antidepressant resistance. Further work in antidepressant non-response may help to identify biological mechanisms responsible in MDD pathology and help stratify individuals into more tractable groups.

Physical activity is positively related to cognitive functioning and brain volume in older adults. Interestingly, different types of physical activity vary in their effects on cognition and on the brain. For example, dancing has become an interesting topic in aging research, as it is a popular leisure activity among older adults, involving cardiovascular and motor fitness dimensions that can be positively related to cognition. However, studies on brain structure are missing. In this study, we tested the association of long-term senior dance experience with cognitive performance and gray matter brain volume in older women aged 65 to 82 years. We compared nonprofessional senior dancers (n=28) with nonsedentary control group participants without any dancing experience (n=29), who were similar in age, education, IQ score, lifestyle and health factors, and fitness level. Differences neither in the four tested cognitive domains (executive control, perceptual speed, episodic memory, and long-term memory) nor in brain volume (VBM whole-brain analysis, region-of-interest analysis of the hippocampus) were observed. Results indicate that moderate dancing activity (1-2 times per week, on average) has no additional effects on gray matter volume and cognitive functioning when a certain lifestyle or physical activity and fitness level are reached.

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The glymphatic system (GS) is primarily a neural waste clearance system that relies on cerebrospinal fluid (CSF) to transport neuronal byproducts and nutrients. Studies demonstrate that sleep facilitates movement within the GS to clear metabolites and maintain cerebral homeostasis. However, functions of the GS during sleep and its implications have predominantly been examined in animals, clinical/at-risk, and ageing populations. Our understanding of the neural mechanisms underlying GS during sleep in typically developing human infants is limited. The objective of this study was to investigate the relationship between GS imbalance (characterized by extra-axial CSF [EA-CSF] from MRI structural images) and sleep problems in early development. Data from 75 infants were obtained from the Baby Connectome Project. Sleep was indexed with the Brief Infant Sleep Questionnaire. Multilevel models were utilized to explore the associations of EA-CSF volumes and EA-CSF/total cerebral volume (TCV) ratios with age and sleep. We replicated previous findings on lower TCV and overall CSF volumes in infants with dysregulated sleep compared to infants with regulated sleep. Results also demonstrated a decline in EA-CSF/TCV ratios from 9 to 34 months of age (b = -0.0005, <i>t</i> = -2.19, <i>p</i> = .032). Sleep problems were not associated with differential developmental trajectories of EA-CSF volumes or EA-CSF/TCV ratios. Findings from the present study do not support sleep problems as a mechanism through which CSF disbursement within the GS is altered. Although elevated EA-CSF is associated with developmental and neurodegenerative pathology, in early typical development, its links with sleep dysregulation are not robust.