289 research outputs found

    Ovarian hormones shape brain structure, function, and chemistry: A neuropsychiatric framework for female brain health

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    There are robust sex differences in brain anatomy, function, as well as neuropsychiatric and neurodegenerative disease risk (1-6), with women approximately twice as likely to suffer from a depressive illness as well as Alzheimer’s Disease. Disruptions in ovarian hormones likely play a role in such disproportionate disease prevalence, given that ovarian hormones serve as key regulators of brain functional and structural plasticity and undergo major fluctuations across the female lifespan (7-9). From a clinical perspective, there is a wellreported increase in depression susceptibility and initial evidence for cognitive impairment or decline during hormonal transition states, such as the postpartum period and perimenopause (9-14). What remains unknown, however, is the underlying mechanism of how fluctuations in ovarian hormones interact with other biological factors to influence brain structure, function, and chemistry. While this line of research has translational relevance for over half the population, neuroscience is notably guilty of female participant exclusion in research studies, with the male brain implicitly treated as the default model and only a minority of basic and clinical neuroscience studies including a female sample (15-18). Female underrepresentation in neuroscience directly limits opportunities for basic scientific discovery; and without basic knowledge of the biological underpinnings of sex differences, we cannot address critical sexdriven differences in pathology. Thus, my doctoral thesis aims to deliberately investigate the influence of sex and ovarian hormones on brain states in health as well as in vulnerability to depression and cognitive impairment:Table of Contents List of Abbreviations ..................................................................................................................... i List of Figures .............................................................................................................................. ii Acknowledgements .....................................................................................................................iii 1 INTRODUCTION .....................................................................................................................1 1.1 Lifespan approach: Sex, hormones, and metabolic risk factors for cognitive health .......3 1.2 Reproductive years: Healthy models of ovarian hormones, serotonin, and the brain ......4 1.2.1 Ovarian hormones and brain structure across the menstrual cycle ........................4 1.2.2 Serotonergic modulation and brain function in oral contraceptive users .................6 1.3 Neuropsychiatric risk models: Reproductive subtypes of depression ...............................8 1.3.1 Hormonal transition states and brain chemistry measured by PET imaging ...........8 1.3.2 Serotonin transporter binding across the menstrual cycle in PMDD patients .......10 2 PUBLICATIONS ....................................................................................................................12 2.1 Publication 1: Association of estradiol and visceral fat with structural brain networks and memory performance in adults .................................................................................13 2.2 Publication 2: Longitudinal 7T MRI reveals volumetric changes in subregions of human medial temporal lobe to sex hormone fluctuations ..............................................28 2.3 Publication 3: One-week escitalopram intake alters the excitation-inhibition balance in the healthy female brain ...............................................................................................51 2.4 Publication 4: Using positron emission tomography to investigate hormone-mediated neurochemical changes across the female lifespan: implications for depression ..........65 2.5 Publication 5: Increase in serotonin transporter binding across the menstrual cycle in patients with premenstrual dysphoric disorder: a case-control longitudinal neuro- receptor ligand PET imaging study ..................................................................................82 3 SUMMARY ...........................................................................................................................100 References ..............................................................................................................................107 Supplementary Publications ...................................................................................................114 Author Contributions to Publication 1 .....................................................................................184 Author Contributions to Publication 2 .....................................................................................186 Author Contributions to Publication 3 .....................................................................................188 Author Contributions to Publication 4 .....................................................................................190 Author Contributions to Publication 5 .....................................................................................191 Declaration of Authenticity ......................................................................................................193 Curriculum Vitae ......................................................................................................................194 List of Publications ................................................................................................................195 List of Talks and Posters ......................................................................................................19

    Neonatal ECMO: be ready!:Navigating pharmacotherapy and vulnerability through training and monitoring

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    Neonatal ECMO: be ready!:Navigating pharmacotherapy and vulnerability through training and monitoring

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    Using Statistics, Computational Modelling and Artificial Intelligence Methods to Study and Strengthen the Link between Kinematic Impacts and mTBIs

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    Mild traumatic brain injuries (mTBIs) are frequently occurring, yet poorly understood, injuries in sports (e.g., ice hockey) and other physical recreation activities where head impacts occur. Helmets are essential pieces of equipment used to protect participants’ heads from mTBIs. Evaluating the performance of helmets to prevent mTBIs using simulations on anatomically accurate computational head finite element models is critically important for advancing the development of safer helmets. Advancing the level of detail in, and access to, such models, and their continued validation through state-of-the-art brain imaging methods and traditional head injury assessment procedures, is also essential to improve safety. The significant research contributions in this thesis involve evaluating the decrease in blunt impact-induced brain axon fiber tract strains that various helmets provide by studying outputs of existing finite element brain models and implementing open-source artificial intelligence technology to create a novel pipeline for predicting such strains

    What Drives Apathy after Moderate-to-Severe Traumatic Brain Injury? A biopsychosocial approach

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    Goal-directed behaviour forms the basis of human day-to-day activity and represents a central pathway to personal development. Apathy, the reduction of goal-directed behaviour, is a common and debilitating syndrome after moderate-to-severe traumatic brain injury (TBI). Despite its prevalence and impact, little is known about mechanisms and contributors of apathy. This thesis aims to investigate socio-environmental, individual and reward processing factors associated with apathy, based on a biopsychosocial framework and a specific cultural context in Vietnam – a country with high prevalence of moderate-to-severe TBI. Study 1 (Chapter 2) is the first rigorous systematic review synthesising neurobiological, socio-environmental and personal correlates of apathy after moderate-to-severe TBI. Findings indicate that TBI-related (such as TBI severity or time since injury) and patient demographic factors do not contribute to apathy. However, complex neurocognitive alterations, socio-environmental and cultural factors as well as patients’ self-related factors, although not excessively studied, may be pivotal components. Study 2 (Chapter 3) adapted and validated the Vietnamese Dimensional Apathy Scale (V-DAS). The study provided the first well-established assessment tool to investigate apathy for the Vietnamese. Study 3 (Chapter 4) applied the V-DAS to explore the socio-environmental and individual factors of apathy based on the Vietnamese cultural context. Novel evidence for the impact of carers’ overprotective behaviour, family functioning changes and patients’ self-efficacy is reported. Studies 4 and 5 (Chapters 5 and 6) examine neurobiological disruptions relating to apathy based on two novel approaches to reward processing. Study 4 used an ecological framework of decision making in which participants must compare current (foreground) reward information to the background reward context to guide adaptive behaviour. Apathy was demonstrated to be associated with reduced background, but not foreground reward processing. Study 5 examined apathy from an intrinsic and extrinsic reward processing perspective. The world-first study revealed that patients with apathy show difficulties processing both intrinsic and extrinsic rewards, underscoring the importance of considering both intrinsic and extrinsic components of reward processing deficits associated with apathy. The research in this thesis is the first to comprehensively uncover the biopsychosocial contributors of apathy after moderate-to-severe TBI. The findings provide critical evidence for using a multifaceted approach to developing individually tailored assessment and interventions of this devastating motivational disorder

    Artificial intelligence for dementia research methods optimization

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    Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care

    Environmental Effects of Stratospheric Ozone Depletion, UV Radiation, and interactions with Climate Change: 2022 Assessment Report

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    The Montreal Protocol on Substances that Deplete the Ozone Layer was established 35 years ago following the 1985 Vienna Convention for protection of the environment and human health against excessive amounts of harmful ultraviolet-B (UV-B, 280-315 nm) radiation reaching the Earth’s surface due to a reduced UV-B-absorbing ozone layer. The Montreal Protocol, ratified globally by all 198 Parties (countries), controls ca 100 ozone-depleting substances (ODS). These substances have been used in many applications, such as in refrigerants, air conditioners, aerosol propellants, fumigants against pests, fire extinguishers, and foam materials. The Montreal Protocol has phased out nearly 99% of ODS, including ODS with high global warming potentials such as chlorofluorocarbons (CFC), thus serving a dual purpose. However, some of the replacements for ODS also have high global warming potentials, for example, the hydrofluorocarbons (HFCs). Several of these replacements have been added to the substances controlled by the Montreal Protocol. The HFCs are now being phased down under the Kigali Amendment. As of December 2022, 145 countries have signed the Kigali Amendment, exemplifying key additional outcomes of the Montreal Protocol, namely, that of also curbing climate warming and stimulating innovations to increase energy efficiency of cooling equipment used industrially as well as domestically. As the concentrations of ODS decline in the upper atmosphere, the stratospheric ozone layer is projected to recover to pre-1980 levels by the middle of the 21st century, assuming full compliance with the control measures of the Montreal Protocol. However, in the coming decades, the ozone layer will be increasingly influenced by emissions of greenhouse gases and ensuing global warming. These trends are highly likely to modify the amount of UV radiation reaching the Earth\u27s surface with implications for the effects on ecosystems and human health. Against this background, four Panels of experts were established in 1988 to support and advise the Parties to the Montreal Protocol with up-to-date information to facilitate decisions for protecting the stratospheric ozone layer. In 1990 the four Panels were consolidated into three, the Scientific Assessment Panel, the Environmental Effects Assessment Panel, and the Technology and Economic Assessment Panel. Every four years, each of the Panels provides their Quadrennial Assessments as well as a Synthesis Report that summarises the key findings of all the Panels. In the in-between years leading up to the quadrennial, the Panels continue to inform the Parties to the Montreal Protocol of new scientific information

    Artificial intelligence for dementia research methods optimization

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    Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation
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