Predictive analytics applied to Alzheimer’s disease : a data visualisation framework for understanding current research and future challenges

Dissertation as a partial requirement for obtaining a master’s degree in information management, with a specialisation in Business Intelligence and Knowledge Management.Big Data is, nowadays, regarded as a tool for improving the healthcare sector in many areas, such as in its economic side, by trying to search for operational efficiency gaps, and in personalised treatment, by selecting the best drug for the patient, for instance. Data science can play a key role in identifying diseases in an early stage, or even when there are no signs of it, track its progress, quickly identify the efficacy of treatments and suggest alternative ones. Therefore, the prevention side of healthcare can be enhanced with the usage of state-of-the-art predictive big data analytics and machine learning methods, integrating the available, complex, heterogeneous, yet sparse, data from multiple sources, towards a better disease and pathology patterns identification. It can be applied for the diagnostic challenging neurodegenerative disorders; the identification of the patterns that trigger those disorders can make possible to identify more risk factors, biomarkers, in every human being. With that, we can improve the effectiveness of the medical interventions, helping people to stay healthy and active for a longer period. In this work, a review of the state of science about predictive big data analytics is done, concerning its application to Alzheimer’s Disease early diagnosis. It is done by searching and summarising the scientific articles published in respectable online sources, putting together all the information that is spread out in the world wide web, with the goal of enhancing knowledge management and collaboration practices about the topic. Furthermore, an interactive data visualisation tool to better manage and identify the scientific articles is develop, delivering, in this way, a holistic visual overview of the developments done in the important field of Alzheimer’s Disease diagnosis.Big Data é hoje considerada uma ferramenta para melhorar o sector da saúde em muitas áreas, tais como na sua vertente mais económica, tentando encontrar lacunas de eficiência operacional, e no tratamento personalizado, selecionando o melhor medicamento para o paciente, por exemplo. A ciência de dados pode desempenhar um papel fundamental na identificação de doenças em um estágio inicial, ou mesmo quando não há sinais dela, acompanhar o seu progresso, identificar rapidamente a eficácia dos tratamentos indicados ao paciente e sugerir alternativas. Portanto, o lado preventivo dos cuidados de saúde pode ser bastante melhorado com o uso de métodos avançados de análise preditiva com big data e de machine learning, integrando os dados disponíveis, geralmente complexos, heterogéneos e esparsos provenientes de múltiplas fontes, para uma melhor identificação de padrões patológicos e da doença. Estes métodos podem ser aplicados nas doenças neurodegenerativas que ainda são um grande desafio no seu diagnóstico; a identificação dos padrões que desencadeiam esses distúrbios pode possibilitar a identificação de mais fatores de risco, biomarcadores, em todo e qualquer ser humano. Com isso, podemos melhorar a eficácia das intervenções médicas, ajudando as pessoas a permanecerem saudáveis e ativas por um período mais longo. Neste trabalho, é feita uma revisão do estado da arte sobre a análise preditiva com big data, no que diz respeito à sua aplicação ao diagnóstico precoce da Doença de Alzheimer. Isto foi realizado através da pesquisa exaustiva e resumo de um grande número de artigos científicos publicados em fontes online de referência na área, reunindo a informação que está amplamente espalhada na world wide web, com o objetivo de aprimorar a gestão do conhecimento e as práticas de colaboração sobre o tema. Além disso, uma ferramenta interativa de visualização de dados para melhor gerir e identificar os artigos científicos foi desenvolvida, fornecendo, desta forma, uma visão holística dos avanços científico feitos no importante campo do diagnóstico da Doença de Alzheimer

AI and Non AI Assessments for Dementia

Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.Comment: 49 page

A comparison of visual problems in the parkinsonian syndromes

Five disorders currently comprise the ‘Parkinsonian syndromes’, viz. Parkinson’s disease (PD), progressive supranuclear palsy (PSP), dementia with Lewy bodies (DLB), multiple system atrophy (MSA), and corticobasal degeneration (CBD). Differential diagnosis of these disorders can be challenging but if ocular signs and symptoms are present they may aid clinical diagnosis. Visual problems in the Parkinsonism syndromes may involve visual acuity (VA), contrast sensitivity (CS), color vision, pupil reactivity, and eye movements; more complex aspects of vision such as reading ability, visuo-spatial orientation, the identification and naming of objects, and visual hallucinations. No single visual feature can definitively diagnose a specific Parkinsonism syndrome. Nevertheless, the presence of visual hallucinations and color vision problems may be more characteristic of DLB and PD than CBD or PSP and vertical supranuclear gaze palsy may be a significant feature of PSP. In addition, variation in saccadic eye movement (SEM) problems may help to distinguish PD and CBD from PSP. A multidisciplinary approach is often necessary to manage the visual problems of patients with a Parkinsonism syndrome

The clinical utility of multidisciplinary rehabilitation in individuals with Huntington’s Disease

Background Huntington’s disease (HD) is a chronic neurodegenerative disorder characterised by a progressive loss of cognitive function, motor control and psychiatric features. Individuals also display a variety of systemic features. Progressive neuronal dysfunction and neuronal cell death are thought to underlie the onset and progression of many clinical features of HD. Despite scientific progress, there is still no cure or disease modifying therapy for HD, and available pharmaceutical agents only provide partial relief of motor and psychiatric features. An emerging body of evidence indicates that lifestyle enrichment may delay the onset and progression of clinical features, and exert favourable effects on neuropathological aspects of HD. Few studies have evaluated the effects of lifestyle enrichment strategies like multidisciplinary rehabilitation on the clinical features of HD. Moreover, no study has evaluated the effects of multidisciplinary rehabilitation on neuropathological aspects of HD. Aims The initial aim of this thesis was to determine factors that contribute to features of the disease that negatively impact on activities of daily living such as mobility and balance (Chapter 2), and to identify, using a literature review, a rehabilitation strategy that could positively impact on these features of HD (Chapter 3). These studies informed our ultimate aim which was to investigate the clinical utility of multidisciplinary rehabilitation on clinical and neuropathological features of HD (Chapters 4, 5 and 6) Methods In study 1 (Chapter 2), 22 participants were assessed using a battery of balance, mobility, cognitive tests, assessments of muscle strength and body composition measures. Data was . then statistically examined using stepwise linear regression to identify factors that contribute to balance and mobility impairments in individuals with manifest HD. In study 2 (Chapter 3), a systematic search of journal databases was made from inception to July 2014 for studies reporting on resistance exercise in patients with neurodegenerative disorders. Selected studies were abstracted and critically appraised using a quality control checklist. For the intervention studies, (3 and 4 Chapters 4 and 5), 20 participants with manifest HD were randomly assigned to either a control or training group. Individuals randomised to the intervention group were provided with a nine month multidisciplinary intervention comprising once weekly supervised clinical exercise, thrice weekly home based exercise and fortnightly occupational therapy, while those randomised to the control group were asked to continue with their standard care and daily activities. Participants were assessed using motor, cognitive, psychological, body composition and quality of life measures at baseline and at the completion of the intervention. In study 5 (Chapter 6), 15 participants with manifest HD were assessed using magnetic resonance imaging and a battery of cognitive assessments after nine months of multidisciplinary rehabilitation to see whether such a therapy is capable of inducing favourable changes in brain structure and cognitive function. Results The main factors that contribute to mobility and balance impairments in patients with manifest HD were found to be lower limb muscle weakness and a loss of cognitive function (Study 1). Systematic evaluation of the effects of resistance exercise for neurodegenerative disorders showed that it is beneficial for multiple sclerosis and Parkinson’s disease. In particular, improvements in muscle strength, mobility, balance, clinical disease progression, fatigue, functional capacity, quality of life, disease biology, electromyography activity, mood, skeletal muscle volume and architecture were reported in individuals with multiple sclerosis or Parkinson’s disease (PD) after resistance exercise. The most robust effects of resistance exercise were found for muscle strength outcomes, and were more pronounced in individuals with PD (Study 2). The multidisciplinary rehabilitation intervention studies conducted as part of this thesis significantly improved isometric and isokinetic muscle strength, self-perceived balance, body mass, lean tissue mass and fat mass in patients with HD (Studies 3 and 4). Moreover, multidisciplinary rehabilitation also increased grey matter (GM) volume in the caudate nucleus and dorsolateral prefrontal cortex of patients. The significant increases in GM volume were accompanied by, and correlated to, a significant improvement in performance in verbal learning and memory. Conclusions The work presented here shows that lower extremity muscle weakness and a loss of cognitive function significantly contribute to impairments in mobility and balance. This work also shows that strength training has favourable effects on motor function, including strength, mobility and balance, as well as other clinical features in similar neurodegenerative disorders, and thus should be integrated into multidisciplinary rehabilitation interventions for HD. In addition, this study provides evidence that multidisciplinary rehabilitation can significantly improve aspects of motor control, cognitive function and body composition. Finally we show, for the first time, that multidisciplinary rehabilitation can increase GM volume in structures known to degenerate in HD, and that such increases are functionally related to changes in verbal learning and memory. Future work is urgently required to confirm and expand on these exciting findings, particularly with respect to the neurorestorative properties of multidisciplinary rehabilitation

Hippocampus

The hippocampus is a bicortical structure with extensive fiber connections with multiple brain regions. It is involved in several functions, such as learning, memory, attention, emotion, and more. This book covers various aspects of the hippocampus including cytoarchitecture, functions, diseases, and treatment. It highlights the most advanced findings in research on the hippocampus. It discusses circuits, pattern formation process of grid cells, and zinc dynamics of the hippocampus. The book also addresses the tau pathology and circRNAs related to Alzheimer’s disease and potential treatment strategies. It is a useful resource for general readers, students, and researchers

Cerebral Small Vessel Disease and Cerebral Amyloid Angiopathy

Sporadic cerebral small vessel disease (SVD) is considered to be among the most commonly known neuropathological processes in the brain, hosting a crucial role in stroke, cognitive impairment, and functional loss in elderly subjects. We investigated clinical (neuroimaging and cognitive) biomarkers in the SVD, through a series of analyses from our five studies. Sporadic cerebral SVD is a complex ‘micro-world’ to be globally considered. All the relevant lesion types and SVD neuroimaging burden should be taken into account. The cumulative effects of microangiopathy burden in the brain of patients affected by SVD are crucial. Cognitive rehabilitation could represent a promising approach to prevent vascular dementia or to improve cognitive performances in patients with cerebral SVD. Longitudinal studies may provide more robust information about the progression and prognostic significance of our findings

Neurorehabilitation in dementia on the move : Influences of physical activity on cognition, mood, and the rest-activity rhythm

Scherder, E.J.A. [Promotor]Swaab, D.F. [Promotor