761 research outputs found

    Systemic function impairment and neurodegeneration in the general population

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    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

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

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    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

    Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data

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    This work was supported by the FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto (B-TIC-586-UGR20); the MCIN/AEI/10.13039/501100011033/ and FEDER \Una manerade hacer Europa" under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion,Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18 and P20-00525 projects. Grant by F.J.M.M. RYC2021-030875-I funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR. Work by D.C.B. is supported by the MCIN/AEI/FJC2021-048082-I Juan de la Cierva Formacion'. Work by J.E.A. is supported by Next Generation EU Fund through a Margarita Salas Grant, and work by C.J.M. is supported by Ministerio de Universidades under the FPU18/04902 grant.Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data.As shown by our results, the CAD proposal is able to detect PD with 96.48% of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto B-TIC-586-UGR20MCIN/AEI P20-00525FEDER \Una manerade hacer Europa RYC2021-030875-IJunta de AndaluciaEuropean Union (EU) Spanish Government RTI2018-098913-B100, CV20-45250, A-TIC-080-UGR18European Union (EU)Juan de la Cierva FormacionNext Generation EU Fund through a Margarita Salas GrantMinisterio de Universidades FPU18/0490

    2019 GREAT Day Program

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    SUNY Geneseo’s Thirteenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1013/thumbnail.jp

    Artificial intelligence for dementia drug discovery and trials optimization

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    Drug discovery and clinical trial design for dementia have historically been challenging. In part these challenges have arisen from patient heterogeneity, length of disease course, and the tractability of a target for the brain. Applying big data analytics and machine learning tools for drug discovery and utilizing them to inform successful clinical trial design has the potential to accelerate progress. Opportunities arise at multiple stages in the therapy pipeline and the growing availability of large medical data sets opens possibilities for big data analyses to answer key questions in clinical and therapeutic challenges. However, before this goal is reached, several challenges need to be overcome and only a multi-disciplinary approach can promote data-driven decision-making to its full potential. Herein we review the current state of machine learning applications to clinical trial design and drug discovery, while presenting opportunities and recommendations that can break down the barriers to implementation

    Motoric cognitive risk: epidemiology of a walking speed-based syndrome to predict dementia

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    Dementia is a huge global health challenge without a cure. Identifying the early stages enables the implementation of risk-modifying interventions when they may be most effective. Slow gait speed and self-reported cognitive complaints are among the earliest findings reported in the preclinical stage of dementia. The Motoric Cognitive Risk (MCR) syndrome is a high-risk predementia state combining objective slow gait speed and subjective cognitive complaint in independent, dementia-free individuals. This thesis investigates the association between MCR and dementia using meta-analysis and several epidemiological approaches in a Scottish cohort of community-dwelling older adults. The first study presents a systematic review and meta-analysis of the prognostic ability of MCR. This review also outlined hypotheses regarding the underlying mechanisms of MCR, areas that are explored further in the final chapter of the thesis. It examined longitudinal cohort studies that compared an MCR group to a non-MCR group for any health outcome. A thorough search returned 705 records with 15 cohorts eligible for meta-analysis. The meta-analysis included only health outcomes reported from at least three cohorts and judged satisfactory by our clinical content experts. When a study reported an incompatible effect measure, I contacted authors to request data to allow for our own calculation, or I converted the effect measure where possible and appropriate. The meta-analysis found that participants with MCR were at an increased risk of cognitive impairment (adjusted Hazard Ratio [aHR] 1.76, 95% CI 1.49–2.08; I2 = 24.9%), dementia (aHR 2.12, 1.85–2.42; 33.1%), falls (adjusted relative risk 1.38, 1.15–1.66; 62.1%), and mortality (aHR 1.49, 1.16–1.91; 79.2%). There was considerable heterogeneity in how studies diagnosed MCR, cognitive impairment, and dementia. Our review of the underlying mechanisms of MCR suggested that interactions between MCR, poor brain health, falls, and increased mortality are likely due to a range of biological, psychological, and social mechanisms. A major strength of this systematic review and meta-analysis is the thoroughness of its methodology. The second study of the thesis described the prevalence of MCR and associated factors in the Lothian Birth Cohort 1936 (LBC1936). It was the first time MCR had been derived in a Scottish cohort, so it detailed how MCR was coded and implemented. This study also reported slow gait speed cut-offs for the first time in an older Scottish population. It also assessed the overlap of MCR with three other high-risk states of ageing - Mild Cognitive Impairment (MCI), Prefrailty, and Frailty, thus clarifying the degree of cross-over between these related states. MCR was derived in three waves of the cohort at mean ages of 76.3 years (n = 690), 79.3 years (n = 543) and 82 years (n = 425). MCR prevalence rate ranged from 5.3% to 5.7% across the three waves, a little lower than the global average. Factors associated with MCR in this cohort included age, socioeconomic status, and tests of executive function. There was partial overlap between individuals with MCR and MCI, indicating that these concepts, although derived using similar criteria, capture different cohorts of people. This supports the conceptualisation of MCR as complementary to MCI rather than an alternative. The study highlights the need to explore further the strong association between lower socioeconomic status in early and mid-life with MCR later in life. Building on a key finding from the second study of the thesis, the third study focused on socioeconomic status as a risk factor for MCR. This longitudinal observational study used logistic regression analysis adjusting for important demographic, lifestyle, and health covariates to explore the association between MCR at age 76 years, and years of education and occupational social class, categorised into manual versus non-manual occupations. The final model included 671 participants. Results show that lower socioeconomic status as defined by non-manual versus manual occupation (and not years of education) is associated with a greater than three-fold risk of having MCR later in life (adjusted odds ratio 3.55, 95% CI 1.46–8.74; p = 0.005). Putting this study in context with the literature is difficult as there is a paucity of work focussing on socioeconomic status as a risk factor for MCR. However, having a low socioeconomic status is a widely accepted predictor of ill health generally, and dementia more specifically. Therefore, it is no surprise that it was strongly associated with MCR, which is a high-risk state for dementia. This study highlights a novel risk factor for MCR and offers a hypothesis on underlying mechanisms but concludes by recommending further work to unravel the relationship between lower socioeconomic status and MCR. The fourth study shifted temporarily to focus on identifying dementia in LBC1936, an essential piece of work to allow for the later study of MCR as a predictor of dementia. Previously, the LBC1936 cohort lacked a clinically diagnosed dementia outcome. Our study introduced a novel approach to identifying dementia in cohort studies and reported for the first time the incidence and prevalence of all-cause dementia and its subtypes in the LBC1936. We comprehensively evaluated all participants' electronic health records to identify any indications of cognitive impairment. In addition, we performed in-person clinician assessments whenever a participant's cognition was in doubt. Clinical dementia specialists from Old Age Psychiatry, Neurology, and Geriatrics agreed on a diagnosis of probable dementia, possible dementia, or the absence of dementia, and determined the subtype whenever possible. Of the 865 LBC1936 participants included, 118 (13.6%) had dementia by an average age of approximately 86 years. Dementia was more common with increasing age and in women, and the most common type of dementia was due to Alzheimer disease (49.2%). Self-reported dementia diagnoses were positive in only 17.8% of clinically identified dementia diagnoses. This illustrates the importance of a robust clinical dementia diagnosis instead of relying on self-reported diagnoses. Our work will enable researchers to explore the extensive LBC936 data accumulated over a 16-year period for signals that differentiate participants currently living with dementia from those who are not. This includes my newly derived MCR measure, which brings us to the final study of the thesis. The fifth and final study provided a time-to-event analysis with MCR as the predictor variable and dementia as the outcome of interest. It also explored the various trajectories of participants diagnosed with MCR. It classified a total of 680 community-dwelling participants (mean [SD] age 76.3 [0.8] years) free from dementia into non-MCR or MCR groups. It used Cox proportional hazards methods and competing risk regression to evaluate the risk of developing all-cause dementia in the years following MCR diagnosis. The final model adjusted for potential confounders. Results show that, after 10 years of follow-up, 79 of 680 (11.6%) participants developed dementia. The presence of MCR increased the risk of dementia (aHR 2.34 [1.14 to 4.78, p=0.020]) in this Scottish cohort to a similar extent as in other populations. Individuals with MCR follow similar trajectories to the related predementia syndrome, MCI. This study reinforces that MCR could potentially identify a target group for early interventions of modifiable risk factors for dementia. However, it illustrates the heterogeneous nature of MCR progression and highlights that not all older adults with MCR will follow a similar path. This thesis explores the predementia syndrome MCR through meta-analysis and several epidemiological approaches in the Lothian Birth Cohort 1936. The findings represent a significant advancement in our understanding of MCR prevalence, risk factors, predictive ability, and trajectories. Since there are no effective treatments for dementia, prevention is paramount. By improving our understanding of this high-risk predementia state, this thesis brings us closer to the ultimate goal of intervening early in the lifecourse to reduce the number of people living with dementia

    Automatic identification of ischemia using lightweight attention network in PET cardiac perfusion imaging

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    Ischemic disease, caused by inadequate blood supply to organs or tissues, poses a significant global health challenge. Early detection of ischemia is crucial for timely intervention and improved patient outcomes. Myocardial perfusion imaging with positron-emission tomography (PET) is a non-invasive technique used to identify ischemia. However, accurately interpreting PET images can be challenging, necessitating the development of reliable classification methods. In this study, we propose a novel approach using MS-DenseNet, a lightweight attention network, for the detection and classification of ischemia from myocardial polar maps. Our model incorporates the squeeze and excitation modules to emphasize relevant feature channels and suppress unnecessary ones. By effectively utilizing channel interdependencies, we achieve optimum reuse of interchannel interactions, enhancing the model's performance. To evaluate the efficacy and accuracy of our proposed model, we compare it with transfer learning models commonly used in medical image analysis. We conducted experiments using a dataset of 138 polar maps (JPEG) obtained from 15O_H2O stress perfusion studies, comprising patients with ischemic and non-ischemic condition. Our results demonstrate that MS-DenseNet outperforms the transfer learning models, highlighting its potential for accurate ischemia detection and classification. This research contributes to the field of ischemia diagnosis by introducing a lightweight attention network that effectively captures the relevant features from myocardial polar maps. The integration of the squeeze and excitation modules further enhances the model's discriminative capabilities. The proposed MS-DenseNet offers a promising solution for accurate and efficient ischemia detection, potentially improving the speed and accuracy of diagnosis and leading to better patient outcomes

    Generative Adversarial Network (GAN) for Medical Image Synthesis and Augmentation

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    Medical image processing aided by artificial intelligence (AI) and machine learning (ML) significantly improves medical diagnosis and decision making. However, the difficulty to access well-annotated medical images becomes one of the main constraints on further improving this technology. Generative adversarial network (GAN) is a DNN framework for data synthetization, which provides a practical solution for medical image augmentation and translation. In this study, we first perform a quantitative survey on the published studies on GAN for medical image processing since 2017. Then a novel adaptive cycle-consistent adversarial network (Ad CycleGAN) is proposed. We respectively use a malaria blood cell dataset (19,578 images) and a COVID-19 chest X-ray dataset (2,347 images) to test the new Ad CycleGAN. The quantitative metrics include mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), spatial correlation coefficient (SCC), spectral angle mapper (SAM), visual information fidelity (VIF), Frechet inception distance (FID), and the classification accuracy of the synthetic images. The CycleGAN and variant autoencoder (VAE) are also implemented and evaluated as comparison. The experiment results on malaria blood cell images indicate that the Ad CycleGAN generates more valid images compared to CycleGAN or VAE. The synthetic images by Ad CycleGAN or CycleGAN have better quality than those by VAE. The synthetic images by Ad CycleGAN have the highest accuracy of 99.61%. In the experiment on COVID-19 chest X-ray, the synthetic images by Ad CycleGAN or CycleGAN have higher quality than those generated by variant autoencoder (VAE). However, the synthetic images generated through the homogenous image augmentation process have better quality than those synthesized through the image translation process. The synthetic images by Ad CycleGAN have higher accuracy of 95.31% compared to the accuracy of the images by CycleGAN of 93.75%. In conclusion, the proposed Ad CycleGAN provides a new path to synthesize medical images with desired diagnostic or pathological patterns. It is considered a new approach of conditional GAN with effective control power upon the synthetic image domain. The findings offer a new path to improve the deep neural network performance in medical image processing
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