8,149 research outputs found

    Systemic function impairment and neurodegeneration in the general population

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    The glymphatic system and multiple sclerosis: An evolving connection

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    Multiple sclerosis (MS) is a complex autoimmune disorder that affects the central nervous system, resulting in demyelination and an array of neurological manifestations. Recently, there has been significant scientific interest in the glymphatic system, which operates as a waste-clearance system for the brain. This article reviews the existing literature, and explores potential links between the glymphatic system and MS, shedding light on its evolving significance in the context of MS pathogenesis. The authors consider the pathophysiological implications of glymphatic dysfunction in MS, the impact of disrupted sleep on glymphatic function, and the bidirectional relationship between MS and sleep disturbances. By offering an understanding of the intricate interplay between the glymphatic system and MS, this review provides valuable insights which may lead to improved diagnostic techniques and more effective therapeutic interventions

    Using Image Translation To Synthesize Amyloid Beta From Structural MRI

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    Amyloid-beta and brain atrophy are known hallmarks of Alzheimer’s Disease (AD) and can be quantified with positron emission tomography (PET) and structural magnetic resonance imaging (MRI), respectively. PET uses radiotracers that bind to amyloid-beta, whereas MRI can measure brain morphology. PET scans have limitations including cost, invasiveness (involve injections and ionizing radiation exposure), and have limited accessibility, making PET not practical for screening early-onset AD. Conversely, MRI is a cheaper, less-invasive (free from ionizing radiation), and is more widely available, however, it cannot provide the necessary molecular information. There is a known relationship between amyloid-beta and brain atrophy. This thesis aims to synthesize amyloid-beta PET images from structural MRI using image translation, an advanced form of machine learning. The developed models have reported high-similarity metrics between the real and synthetic PET images and high-degree of accuracy in radiotracer quantification. The results are highly impactful as it enables amyloid-beta measurements form every MRI, for free

    The medical applications of hyperpolarized Xe and nonproton magnetic resonance imaging

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    Hyperpolarized 129Xe (HP 129Xe) magnetic resonance imaging (MRI) is a relatively young field which is experiencing significant advancements each year. Conventional proton MRI is widely used in clinical practice as an anatomical medical imaging due to its superb soft tissue contrast. HP 129Xe MRI, on the other hand, may provide valuable information about internal organs functions and structure. HP 129Xe MRI has been recently clinically approved for lung imaging in the United Kingdom and the United States. It allows quantitative assessment of the lung function in addition to structural imaging. HP 129Xe has unique properties of anaesthetic, and may transfer to the blood stream and be further carried to the highly perfused organs. This gives the opportunity to assess brain perfusion with HP 129Xe and perform molecular imaging. However, the further progression of the HP 129Xe utilization for brain perfusion quantification and molecular imaging implementation is limited by the absence of certain crucial milestones. This thesis focused on providing important stepping stones for the further development of HP 129Xe molecular imaging and brain imaging. The effect of glycation on the spectroscopic characteristics of HP 129Xe was studied in whole sheep blood with magnetic resonance spectroscopy. An additional peak of HP 129Xe bound to glycated hemoglobin was observed. This finding should be implemented in the spectroscopic HP 129Xe studies in patients with diabetes. [...

    Small vessel disease burden and functional brain connectivity in mild cognitive impairment

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    Background: The role of small vessel disease in the development of dementia is not yet completely understood. Functional brain connectivity has been shown to differ between individuals with and without cerebral small vessel disease. However, a comprehensive measure of small vessel disease quantifying the overall damage on the brain is not consistently used and studies using such measure in mild cognitive impairment individuals are missing.Method: Functional brain connectivity differences were analyzed between mild cognitive impairment individuals with absent or low (n = 34) and high (n = 34) small vessel disease burden using data from the Parelsnoer Institute, a Dutch multicenter study. Small vessel disease was characterized using an ordinal scale considering: lacunes, microbleeds, perivascular spaces in the basal ganglia, and white matter hyperintensities. Resting state functional MRI data using 3 Tesla scanners was analyzed with group-independent component analysis using the CONN toolbox.Results: Functional connectivity between areas of the cerebellum and between the cerebellum and the thalamus and caudate nucleus was higher in the absent or low small vessel disease group compared to the high small vessel disease group.Conclusion: These findings might suggest that functional connectivity of mild cognitive impairment individuals with low or absent small vessel disease burden is more intact than in mild cognitive impairment individuals with high small vessel disease. These brain areas are mainly responsible for motor, attentional and executive functions, domains which in previous studies were found to be mostly associated with small vessel disease markers. Our results support findings on the involvement of the cerebellum in cognitive functioning

    A comparative study of GNN and MLP based machine learning for the diagnosis of Alzheimer’s Disease involving data synthesis

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    Alzheimer’s Disease (AD) is a neurodegenerative disease that commonly occurs in older people. It is characterized by both cognitive and functional impairment. However, as AD has an unclear pathological cause, it can be hard to diagnose with confidence. This is even more so in the early stage of Mild Cognitive Impairment (MCI). This paper proposes a U-Net based Generative Adversarial Network (GAN) to synthesize fluorodeoxyglucose -positron emission tomography (FDG-PET) from magnetic resonance imaging - T1 weighted imaging (MRI-T1WI) for further usage in AD diagnosis including its early-stage MCI. The experiments have displayed promising results with Structural Similarity Index Measure (SSIM) reaching 0.9714. Furthermore, three types of classifiers are developed, i.e., one Multi-Layer Perceptron (MLP) based classifier, two Graph Neural Network (GNN) based classifiers where one is for graph classification and the other is for node classification. 10-fold cross-validation has been conducted on all trials of experiments for classifier comparison. The performance of these three types of classifiers has been compared with the different input modalities setting and data fusion strategies. The results have shown that GNN based node classifier surpasses the other two types of classifiers, and has achieved the state-of-the-art (SOTA) performance with the best accuracy at 90.18% for 3-class classification, namely AD, MCI and normal control (NC) with the synthesized fluorodeoxyglucose - positron emission tomography (FDG-PET) features fused at the input level. Moreover, involving synthesized FDG-PET as part of the input with proper data fusion strategies has also proved to enhance all three types of classifiers’ performance. This work provides support for the notion that machine learning-derived image analysis may be a useful approach to improving the diagnosis of AD

    Impact of Head Injury on Cognitive Functioning and Social Cognition in UK-based Female Rugby Players

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    Introduction: Increasing attention is focused on the potential long-term impact of sports-related mild traumatic brain injuries (SRmTBI). Evidence suggests poorer cognitive and psychosocial outcomes in SRmTBI, including increased risk of developing certain neurodegenerative conditions. Research to date has focused on males neglecting female athletes, despite evidence suggesting sex-specific differences in frequency and recovery of SRmTBI. Aims: To explore the association between SRmTBI and cognitive functioning with a specific focus on social cognition in female rugby players. Method: A quantitative cross-sectional design was employed allowing for thirteen female rugby players with a history of SRmTBI to complete a neuropsychological battery of general cognitive functioning and social cognition. Results: Weaknesses relative to normative data, were found for domains of social cognition including theory of mind and cognitive empathy, despite typical scores on domains of general cognitive functioning relative to normative data. Group level analysis confirmed poorer performance for theory of mind and cognitive empathy measures in contrast to overall performance on domains of general cognitive functioning. Discussion: Findings from this preliminary study indicate that measures of social cognition should be incorporated into routine assessment and management of SRmTBI. Further research is needed to investigate the association between social cognition and SRmTBI
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