250 research outputs found

    Using Image Translation To Synthesize Amyloid Beta From Structural MRI

    Get PDF
    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

    Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

    Get PDF
    Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

    Detecting Alzheimer\u27s Disease using Artificial Neural Networks

    Get PDF
    This project aims to use artificial neural networks (ANN) in order to detect Alzheimer’s disease. More specifically, convolutional neural networks (CNN) will be utilized as this is the most common ANN and has been used in many different image processing applications. The purpose of using artificial neural networks as a detect method is so that an intelligent way for image and signal analysis can be used. A software that implements CNN will be developed so that users in medical settings can utilize this software to detect Alzheimer’s in patients. The input for this software will be the patient’s MRI scans. In addition, this is a project that is relevant with the current trends of an increase in development surrounding artificial intelligence. As technology has become more advanced, there has been an increase in medical developments as well. From the simulation, the hyperbolic tangent activation function provided the best results. The performance resulting from the two classifications when using the hyperbolic tangent function, on average was validation best accuracy of 81.10%, validation stopped tuning accuracy of 81.10%, training best accuracy of 100.00%, testing best accuracy of 68.94%, F-1 score of 70.06%, precision of 71.00%, and recall of 70.06%. This project will open doors to more applications of this detection method. More diseases other than Alzheimer’s disease can utilize artificial neural networks (ANN) to detect diseases early on so that lives can be restored and saved

    Genetic and molecular biomarkers of Alzheimer's disease

    Full text link
    [eng] Alzheimer’s disease (AD) is the leading cause of dementia worldwide. Research in the past decade has led to major progress in understanding the genetic etiology of the disease; since I started my PhD (2019), nearly 20 genetic risk factors have been associated with late onset AD. Among them, the Ԑ4 allele of the APOE gene was the first identified, and remains the major genetic risk factor for AD. Despite extensive genetic research, a large part of the disease heritability remains elusive, the disease mechanisms incomprehensible, and targeted preventive interventions or pharmacological treatments for AD unavailable at the time. In this context, the overarching aim of the studies included in this thesis was to contribute to the knowledge of AD identifying new genetic risk factors and to better understand the role played by the APOE gene in the development of the disease. Such information would allow us to gain new insights into the molecular and biological mechanisms underlaying the disease and ultimately find new targets for treatment. This thesis provides evidence of the possible effectiveness of the use of a polygenic risk scores in a clinical setting for diagnosis of AD and actively improves the knowledge of the genetic factors associated with AD through genome-wide association studies

    Cerebrovascular dysfunction in cerebral small vessel disease

    Get PDF
    INTRODUCTION: Cerebral small vessel disease (SVD) is the cause of a quarter of all ischaemic strokes and is postulated to have a role in up to half of all dementias. SVD pathophysiology remains unclear but cerebrovascular dysfunction may be important. If confirmed many licensed medications have mechanisms of action targeting vascular function, potentially enabling new treatments via drug repurposing. Knowledge is limited however, as most studies assessing cerebrovascular dysfunction are small, single centre, single imaging modality studies due to the complexities in measuring cerebrovascular dysfunctions in humans. This thesis describes the development and application of imaging techniques measuring several cerebrovascular dysfunctions to investigate SVD pathophysiology and trial medications that may improve small blood vessel function in SVD. METHODS: Participants with minor ischaemic strokes were recruited to a series of studies utilising advanced MRI techniques to measure cerebrovascular dysfunction. Specifically MRI scans measured the ability of different tissues in the brain to change blood flow in response to breathing carbon dioxide (cerebrovascular reactivity; CVR) and the flow and pulsatility through the cerebral arteries, venous sinuses and CSF spaces. A single centre observational study optimised and established feasibility of the techniques and tested associations of cerebrovascular dysfunctions with clinical and imaging phenotypes. Then a randomised pilot clinical trial tested two medications’ (cilostazol and isosorbide mononitrate) ability to improve CVR and pulsatility over a period of eight weeks. The techniques were then expanded to include imaging of blood brain barrier permeability and utilised in multi-centre studies investigating cerebrovascular dysfunction in both sporadic and monogenetic SVDs. RESULTS: Imaging protocols were feasible, consistently being completed with usable data in over 85% of participants. After correcting for the effects of age, sex and systolic blood pressure, lower CVR was associated with higher white matter hyperintensity volume, Fazekas score and perivascular space counts. Lower CVR was associated with higher pulsatility of blood flow in the superior sagittal sinus and lower CSF flow stroke volume at the foramen magnum. Cilostazol and isosorbide mononitrate increased CVR in white matter. The CVR, intra-cranial flow and pulsatility techniques, alongside blood brain barrier permeability and microstructural integrity imaging were successfully employed in a multi-centre observational study. A clinical trial assessing the effects of drugs targeting blood pressure variability is nearing completion. DISCUSSION: Cerebrovascular dysfunction in SVD has been confirmed and may play a more direct role in disease pathogenesis than previously established risk factors. Advanced imaging measures assessing cerebrovascular dysfunction are feasible in multi-centre studies and trials. Identifying drugs that improve cerebrovascular dysfunction using these techniques may be useful in selecting candidates for definitive clinical trials which require large sample sizes and long follow up periods to show improvement against outcomes of stroke and dementia incidence and cognitive function

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

    Get PDF
    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

    Artificial Intelligence for Cognitive Health Assessment: State-of-the-Art, Open Challenges and Future Directions

    Get PDF
    The subjectivity and inaccuracy of in-clinic Cognitive Health Assessments (CHA) have led many researchers to explore ways to automate the process to make it more objective and to facilitate the needs of the healthcare industry. Artificial Intelligence (AI) and machine learning (ML) have emerged as the most promising approaches to automate the CHA process. In this paper, we explore the background of CHA and delve into the extensive research recently undertaken in this domain to provide a comprehensive survey of the state-of-the-art. In particular, a careful selection of significant works published in the literature is reviewed to elaborate a range of enabling technologies and AI/ML techniques used for CHA, including conventional supervised and unsupervised machine learning, deep learning, reinforcement learning, natural language processing, and image processing techniques. Furthermore, we provide an overview of various means of data acquisition and the benchmark datasets. Finally, we discuss open issues and challenges in using AI and ML for CHA along with some possible solutions. In summary, this paper presents CHA tools, lists various data acquisition methods for CHA, provides technological advancements, presents the usage of AI for CHA, and open issues, challenges in the CHA domain. We hope this first-of-its-kind survey paper will significantly contribute to identifying research gaps in the complex and rapidly evolving interdisciplinary mental health field

    Vision transformers for the prediction of mild cognitive impairment to Alzheimer’s disease progression using mid-sagittal sMRI

    Get PDF
    BackgroundAlzheimer’s disease (AD) is one of the most common causes of neurodegenerative disease affecting over 50 million people worldwide. However, most AD diagnosis occurs in the moderate to late stage, which means that the optimal time for treatment has already passed. Mild cognitive impairment (MCI) is an intermediate state between cognitively normal people and AD patients. Therefore, the accurate prediction in the conversion process of MCI to AD may allow patients to start preventive intervention to slow the progression of the disease. Nowadays, neuroimaging techniques have been developed and are used to determine AD-related structural biomarkers. Deep learning approaches have rapidly become a key methodology applied to these techniques to find biomarkers.MethodsIn this study, we aimed to investigate an MCI-to-AD prediction method using Vision Transformers (ViT) to structural magnetic resonance images (sMRI). The Alzheimer’s Disease Neuroimaging Initiative (ADNI) database containing 598 MCI subjects was used to predict MCI subjects’ progression to AD. There are three main objectives in our study: (i) to propose an MRI-based Vision Transformers approach for MCI to AD progression classification, (ii) to evaluate the performance of different ViT architectures to obtain the most advisable one, and (iii) to visualize the brain region mostly affect the prediction of deep learning approach to MCI progression.ResultsOur method achieved state-of-the-art classification performance in terms of accuracy (83.27%), specificity (85.07%), and sensitivity (81.48%) compared with a set of conventional methods. Next, we visualized the brain regions that mostly contribute to the prediction of MCI progression for interpretability of the proposed model. The discriminative pathological locations include the thalamus, medial frontal, and occipital—corroborating the reliability of our model.ConclusionIn conclusion, our methods provide an effective and accurate technique for the prediction of MCI conversion to AD. The results obtained in this study outperform previous reports using the ADNI collection, and it suggests that sMRI-based ViT could be efficiently applied with a considerable potential benefit for AD patient management. The brain regions mostly contributing to prediction, in conjunction with the identified anatomical features, will support the building of a robust solution for other neurodegenerative diseases in future

    An evaluation of the role of biomarkers in Alzheimer’s disease and age-related cognitive decline

    Get PDF
    An ageing population will lead to an increase in age-related cognitive decline and dementia syndromes such as Alzheimer’s disease (AD), which can seriously limit an individual’s independence and quality of life. Identifying biomarkers associated with cognitive impairment in both ageing and AD are needed as they will improve our understanding of underlying pathophysiology and may eventually improve prognoses via the identification of at-risk individuals and the development of novel therapeutics. Several pathological changes in the brain which are typically seen in AD can be detected in the cerebrospinal fluid (CSF) and plasma of middle- and late-life adults without dementia. Previous work has identified associations between CSF markers and cognitive functions, although a synthesis of the large number of studies is needed. Furthermore CSF marker levels may also differ with AD risk factors, however evidence is mixed. Increasingly, research has shifted to focus on blood-based biomarkers which provide the benefit of being less invasive and more accessible. Several plasma biomarkers have been associated with cognitive functions in ageing, although few studies use appropriate cognitive tests, and even fewer have examined these proteins in the brain. There remains no gold-standard biomarkers associated with cognitive functions in either AD or age-related cognitive decline, therefore additional approaches are needed to fully understand their relationship. The aims of the current thesis are to: investigate CSF biomarkers associated with cognition in dementia and ageing; assess the relationship between CSF biomarkers and AD risk factors; examine whether plasma biomarkers are associated with age-related cognitive decline; and lastly, to examine the level of proteins (which have previously been investigated as biomarkers) in post-mortem brain tissue. Cerebrospinal fluid biomarkers associated with cognition have been investigated across a range of dementia syndromes and age-related cognitive decline. While much of the work has focused on tau and amyloid-beta (Aβ), there is burgeoning research around markers such as neurogranin and neurofilament-light. Due to a wide range of markers investigated across several dementia syndromes and ageing, the roles of each marker are less clear. Therefore, a systematic review was conducted examining the association between CSF synaptic/axonal markers, and cognitive functions across dementia syndromes and typical ageing. Sixty-seven studies were included in the review in Chapter 3. Despite substantial heterogeneity in the field, there was evidence for an association between CSF neurofilament-light and cognition in AD, frontotemporal dementia, and typical cognitive ageing. Cerebrospinal fluid neurogranin tended to be associated with cognition in those with CSF tau and CSF Aβ profiles indicative of AD. Chapter 4 focuses on the interaction between Apolipoprotein E (APOE) and sex on CSF tau levels in a middle-life cohort without dementia. Females account for an estimated 60% of those diagnosed with AD and the APOE4 allele is widely recognised to be the strongest genetic risk factor for late-onset AD. However, evidence for the interaction between these two risk factors is mixed. In this chapter, a significant interaction between APOE, sex, and CSF AD biomarkers was found, suggesting that tau accumulation may be independent of Ab in females, but not males. This has potential implications for the implementation of CSF AD biomarkers in clinical practice and pharmacological interventions which target cortical Ab. Chapter 5 focuses on the relationship between plasma biomarkers and cognitive functions in typical ageing. Previous studies have focused on this relationship, however, few use appropriate cognitive tests for a sample without dementia. In this chapter, the association between cognitive ability and plasma phosphor-tau 181 (ptau181), Ab, neurofilament-light (NfL), and glial fibrillary acidic protein (GFAP) were investigated in the Lothian Birth Cohort 1936. A significant relationship was observed between baseline p-tau181, NfL, GFAP and cognitive decline up to ~ 10-years later. Further, increasing levels of p-tau181 over time were associated with steeper cognitive decline. The results of this chapter suggest that plasma p-tau181, NfL, and GFAP may be useful biomarkers of age-related cognitive decline. In Chapter 6, several of the aforementioned markers that were previously investigated in the CSF and plasma are examined in post-mortem brain tissue. While previous work has focused on these markers in the CSF and plasma, few studies have investigated them in post-mortem tissue and how levels differ between AD and typically ageing participants. Relative differences in neurogranin, p-tau181, p-tau231, total tau, and SNAP-25 were examined by western blot in AD cases, healthy ageing cases, and mid-life cases. The results of this chapter provide evidence of a reduction of neurogranin and SNAP-25 at the synapse in AD, as well as an increase of p-tau231. This suggests that the elevations of CSF neurogranin, SNAP-25, and p-tau231 seen in AD may reflect both the loss of neurogranin/SNAP-25 and the accumulation of ptau231 in synapses. The final chapter of the thesis summarise the findings of the previous chapters, their limitations, and the impact of this work on the field

    Neuropsychological Studies in older adults: A comprehensive MMSE-MoCA conversion table – Prevention of postoperative delirium – Cognitive sequelae of atrial fibrillation

    Get PDF
    As life expectancy increases, so does the proportion of elderly individuals in most industrialized countries. As people age, they undergo both physical and cognitive changes. Thus, cognitive difficulties and other agerelated pathologies such as cardiovascular and neurological diseases increase with age. In this context, atrial fibrillation (AF) and delirium are of great clinical relevance not only because of their epidemiological data but also, in particular, because of their major role in the development of cognitive dysfunction. Hence, sufficient knowledge and identification of potential risk factors of AF and delirium as well as early recognition are essential to take preventive measures. The present doctoral thesis aims to define corresponding scores for two widely used cognitive screening tools and provide insights into cognitive changes in elderly adults with atrial fibrillation and the validity of a preexisting preoperative delirium prediction model after cardiac surgery. In study I, a comprehensive conversion table of two commonly used cognitive screening tests was created. We could define corresponding scores for the Mini-Mental Status Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) in 803 German-speaking Memory Clinic outpatients. Further, a systematic review of existing MMSE-MoCA conversions was conducted to create a comprehensive conversion table. This enables a direct comparison of cognitive test scores at screening examinations and over the course of disease in patients with predominantly neurocognitive disorders. Study II investigated the associations between AF and cognition in aging. A small, constant increase in cognitive functioning over a median duration of 3.97 years in AF patients was found, presumably explained by learning effects that were less pronounced in non-paroxysmal AF patients, specifically in processing speed and executive functions. Some evidence suggests diabetes, history of stroke/transient ischemic attack (TIA) and depression being associated with faster cognitive decline in AF patients. In study III, an independent external validation of an existing preoperative risk prediction model for delirium was provided in 348 patients who had undergone cardiac surgery. The evaluated predictive model showed poor discriminative capacity but fair calibration. As an outlook, reflections on future directions concerning the role of cognitive performance in AF and delirium are given as well as discussed
    corecore