579 research outputs found

    Cerebrovascular dysfunction in cerebral small vessel disease

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    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

    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

    Graphonomics and your Brain on Art, Creativity and Innovation : Proceedings of the 19th International Graphonomics Conference (IGS 2019 – Your Brain on Art)

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    [Italiano]: “Grafonomia e cervello su arte, creatività e innovazione”. Un forum internazionale per discutere sui recenti progressi nell'interazione tra arti creative, neuroscienze, ingegneria, comunicazione, tecnologia, industria, istruzione, design, applicazioni forensi e mediche. I contributi hanno esaminato lo stato dell'arte, identificando sfide e opportunità, e hanno delineato le possibili linee di sviluppo di questo settore di ricerca. I temi affrontati includono: strategie integrate per la comprensione dei sistemi neurali, affettivi e cognitivi in ambienti realistici e complessi; individualità e differenziazione dal punto di vista neurale e comportamentale; neuroaesthetics (uso delle neuroscienze per spiegare e comprendere le esperienze estetiche a livello neurologico); creatività e innovazione; neuro-ingegneria e arte ispirata dal cervello, creatività e uso di dispositivi di mobile brain-body imaging (MoBI) indossabili; terapia basata su arte creativa; apprendimento informale; formazione; applicazioni forensi. / [English]: “Graphonomics and your brain on art, creativity and innovation”. A single track, international forum for discussion on recent advances at the intersection of the creative arts, neuroscience, engineering, media, technology, industry, education, design, forensics, and medicine. The contributions reviewed the state of the art, identified challenges and opportunities and created a roadmap for the field of graphonomics and your brain on art. The topics addressed include: integrative strategies for understanding neural, affective and cognitive systems in realistic, complex environments; neural and behavioral individuality and variation; neuroaesthetics (the use of neuroscience to explain and understand the aesthetic experiences at the neurological level); creativity and innovation; neuroengineering and brain-inspired art, creative concepts and wearable mobile brain-body imaging (MoBI) designs; creative art therapy; informal learning; education; forensics

    Multimodal Assessment of Cognitive Decline: Applications in Alzheimer’s Disease and Depression

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    The initial diagnosis and assessment of cognitive decline are generally based around the judgement of clinicians, and commonly used semi-structured interviews, guided by pre-determined sets of topics, in a clinical set-up. Publicly available multimodal datasets have provided an opportunity to explore a range of experiments in the automatic detecting of cognitive decline. Drawing on the latest developments in representation learning, machine learning, and natural language processing, we seek to develop models capable of identifying cognitive decline with an eye to discovering the differences and commonalities that should be considered in computational treatment of mental health disorders. We present models that learn the indicators of cognitive decline from audio and visual modalities as well as lexical, syntactic, disfluency and pause information. Our study is carried out in two parts: moderation analysis and predictive modelling. We do some experiments with different fusion techniques. Our approaches are motivated by some of the recent efforts in multimodal fusion for classifying cognitive states to capture the interaction between modalities and maximise the use and combination of each modality. We create tools for detecting cognitive decline and use them to analyze three major datasets containing speech produced by people with and without cognitive decline. These findings are being used to develop multimodal models for the detection of depression and Alzheimer’s dementia

    Diagnosing Alzheimer's Disease using Early-Late Multimodal Data Fusion with Jacobian Maps

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    Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disorder impacting a large aging population. Detecting AD in all its presymptomatic and symptomatic stages is crucial for early intervention and treatment. An active research direction is to explore machine learning methods that harness multimodal data fusion to outperform human inspection of medical scans. However, existing multimodal fusion models have limitations, including redundant computation, complex architecture, and simplistic handling of missing data. Moreover, the preprocessing pipelines of medical scans remain inadequately detailed and are seldom optimized for individual subjects. In this paper, we propose an efficient early-late fusion (ELF) approach, which leverages a convolutional neural network for automated feature extraction and random forests for their competitive performance on small datasets. Additionally, we introduce a robust preprocessing pipeline that adapts to the unique characteristics of individual subjects and makes use of whole brain images rather than slices or patches. Moreover, to tackle the challenge of detecting subtle changes in brain volume, we transform images into the Jacobian domain (JD) to enhance both accuracy and robustness in our classification. Using MRI and CT images from the OASIS-3 dataset, our experiments demonstrate the effectiveness of the ELF approach in classifying AD into four stages with an accuracy of 97.19%.Comment: To be published in Proceedings of 2023 IEEE Healthcom, December 202

    Mixture-Based Clustering and Hidden Markov Models for Energy Management and Human Activity Recognition: Novel Approaches and Explainable Applications

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    In recent times, the rapid growth of data in various fields of life has created an immense need for powerful tools to extract useful information from data. This has motivated researchers to explore and devise new ideas and methods in the field of machine learning. Mixture models have gained substantial attention due to their ability to handle high-dimensional data efficiently and effectively. However, when adopting mixture models in such spaces, four crucial issues must be addressed, including the selection of probability density functions, estimation of mixture parameters, automatic determination of the number of components, identification of features that best discriminate the different components, and taking into account the temporal information. The primary objective of this thesis is to propose a unified model that addresses these interrelated problems. Moreover, this thesis proposes a novel approach that incorporates explainability. This thesis presents innovative mixture-based modelling approaches tailored for diverse applications, such as household energy consumption characterization, energy demand management, fault detection and diagnosis and human activity recognition. The primary contributions of this thesis encompass the following aspects: Initially, we propose an unsupervised feature selection approach embedded within a finite bounded asymmetric generalized Gaussian mixture model. This model is adept at handling synthetic and real-life smart meter data, utilizing three distinct feature extraction methods. By employing the expectation-maximization algorithm in conjunction with the minimum message length criterion, we are able to concurrently estimate the model parameters, perform model selection, and execute feature selection. This unified optimization process facilitates the identification of household electricity consumption profiles along with the optimal subset of attributes defining each profile. Furthermore, we investigate the impact of household characteristics on electricity usage patterns to pinpoint households that are ideal candidates for demand reduction initiatives. Subsequently, we introduce a semi-supervised learning approach for the mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. The integration of the uniform distribution within the inner mixture bolsters the model's resilience to outliers. In the unsupervised learning approach, the minimum message length criterion is utilized to ascertain the optimal number of mixture components. The proposed models are validated through a range of applications, including chiller fault detection and diagnosis, occupancy estimation, and energy consumption characterization. Additionally, we incorporate explainability into our models and establish a moderate trade-off between prediction accuracy and interpretability. Finally, we devise four novel models for human activity recognition (HAR): bounded asymmetric generalized Gaussian mixture-based hidden Markov model with feature selection~(BAGGM-FSHMM), bounded asymmetric generalized Gaussian mixture-based hidden Markov model~(BAGGM-HMM), asymmetric generalized Gaussian mixture-based hidden Markov model with feature selection~(AGGM-FSHMM), and asymmetric generalized Gaussian mixture-based hidden Markov model~(AGGM-HMM). We develop an innovative method for simultaneous estimation of feature saliencies and model parameters in BAGGM-FSHMM and AGGM-FSHMM while integrating the bounded support asymmetric generalized Gaussian distribution~(BAGGD), the asymmetric generalized Gaussian distribution~(AGGD) in the BAGGM-HMM and AGGM-HMM respectively. The aforementioned proposed models are validated using video-based and sensor-based HAR applications, showcasing their superiority over several mixture-based hidden Markov models~(HMMs) across various performance metrics. We demonstrate that the independent incorporation of feature selection and bounded support distribution in a HAR system yields benefits; Simultaneously, combining both concepts results in the most effective model among the proposed models

    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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