A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer׳s disease and mild cognitive impairment

AbstractPopulation aging has been occurring as a global phenomenon with heterogeneous consequences in both developed and developing countries. Neurodegenerative diseases, such as Alzheimer׳s Disease (AD), have high prevalence in the elderly population. Early diagnosis of this type of disease allows early treatment and improves patient quality of life. This paper proposes a Bayesian network decision model for supporting diagnosis of dementia, AD and Mild Cognitive Impairment (MCI). Bayesian networks are well-suited for representing uncertainty and causality, which are both present in clinical domains. The proposed Bayesian network was modeled using a combination of expert knowledge and data-oriented modeling. The network structure was built based on current diagnostic criteria and input from physicians who are experts in this domain. The network parameters were estimated using a supervised learning algorithm from a dataset of real clinical cases. The dataset contains data from patients and normal controls from the Duke University Medical Center (Washington, USA) and the Center for Alzheimer׳s Disease and Related Disorders (at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil). The dataset attributes consist of predisposal factors, neuropsychological test results, patient demographic data, symptoms and signs. The decision model was evaluated using quantitative methods and a sensitivity analysis. In conclusion, the proposed Bayesian network showed better results for diagnosis of dementia, AD and MCI when compared to most of the other well-known classifiers. Moreover, it provides additional useful information to physicians, such as the contribution of certain factors to diagnosis

Alzheimer's Disease: A Survey

Alzheimer's Diseases (AD) is one of the type of dementia. This is one of the harmful disease which can lead to death and yet there is no treatment. There is no current technique which is 100% accurate for the treatment of this disease. In recent years, Neuroimaging combined with machine learning techniques have been used for detection of Alzheimer's disease. Based on our survey we came across many methods like Convolution Neural Network (CNN) where in each brain area is been split into small three dimensional patches which acts as input samples for CNN. The other method used was Deep Neural Networks (DNN) where the brain MRI images are segmented to extract the brain chambers and then features are extracted from the segmented area. There are many such methods which can be used for detection of Alzheimer’s Disease

A Novel Assessment and Profiling of Multidimensional Apathy in Alzheimer's Disease

BACKGROUND: Apathy is a complex multidimensional syndrome frequently reported in Alzheimer's disease (AD) and is associated with impaired awareness. Here we present a psychometrically robust method to profile apathy in AD.  OBJECTIVES: To determine the validity and reliability of a multidimensional apathy measure, the Dimensional Apathy Scale (DAS), and explore the apathy subtype profile and its associations in AD.  METHODS: 102 people with AD and 55 healthy controls were recruited. Participants completed the DAS, the Apathy Evaluation Scale (AES), Geriatric Depression Short form (GDS-15), and Lawton Instrumental Activities of Daily Living (LIADL). Psychometric properties of the DAS were determined. AD-Control comparison was performed to explore group differences on the DAS. Latent Class Analysis (LCA) was used to explore the profile of apathy in AD.  RESULTS: The DAS had a good to excellent Cronbach's standardized alpha (self-rated = 0.85, informant/carer-rated = 0.93) and good convergent and divergent validity against standard apathy (AES) and depression (GDS-15) measures. Group comparison showed people with AD were significantly higher for all apathy subtypes than controls (p < 0.001), and lacking in awareness over all apathy subtype deficits. LCA showed three distinct AD subgroups, with 42.2% in the Executive-Initiation apathy, 28.4% in the Global apathy, and 29.4% in the Minimal apathy group.  CONCLUSIONS: The DAS is a psychometrically robust method of assessing multidimensional apathy in AD. The apathy profiles in AD are heterogeneous, with additional specific impairments relating to awareness dependent on apathy subtype

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

Difficulties of Diagnosing Alzheimer's Disease: The Application of Clinical Decision Support Systems

Introduction: Alzheimer's disease is one of the most common causes of dementia, which gradually causes cognitive impairment. Diagnosis of Alzheimer's disease is a complicated process performed through several tests and examinations. Design and development of Clinical Decision Support System (CDSS) could be an appropriate approach for eliminating the existing difficulties of diagnosing Alzheimer's disease. Materials and Methods: This study reviews the current problems in the diagnosis of Alzheimer's disease with an approach to the application of CDSS. The study reviewed the articles published from 1990 to 2016. The articles were identified by searching electronic databases such as PubMed, Google Scholar, Science Direct. Considering the relevance of articles with the objectives of the study, 29 papers were selected. According to the performed investigations, various reasons cause difficulty in Alzheimer's diagnosis. Results: The complexity of diagnostic process and  the similarity of Alzheimer's disease with other causes of dementia are the most important of them. The results of studies about the application of CDSSs on Alzheimer's disease diagnosis indicated that the implementation of these systems could help to eliminate the existing difficulties in the diagnosis of Alzheimer's disease. Conclusion: Developing CDSSs based on diagnostic guidelines could be regarded as one of the possible approaches towards early and accurate diagnosis of Alzheimer's disease. Applying of computer-interpretable guideline (CIG) models such as GLIF, PROforma, Asbru, and EON can help to design CDSS with the capability of minimizing the burden of diagnostic problems with Alzheimer's disease

Predicting the course of Alzheimer's progression.

Alzheimer's disease is the most common neurodegenerative disease and is characterized by the accumulation of amyloid-beta peptides leading to the formation of plaques and tau protein tangles in brain. These neuropathological features precede cognitive impairment and Alzheimer's dementia by many years. To better understand and predict the course of disease from early-stage asymptomatic to late-stage dementia, it is critical to study the patterns of progression of multiple markers. In particular, we aim to predict the likely future course of progression for individuals given only a single observation of their markers. Improved individual-level prediction may lead to improved clinical care and clinical trials. We propose a two-stage approach to modeling and predicting measures of cognition, function, brain imaging, fluid biomarkers, and diagnosis of individuals using multiple domains simultaneously. In the first stage, joint (or multivariate) mixed-effects models are used to simultaneously model multiple markers over time. In the second stage, random forests are used to predict categorical diagnoses (cognitively normal, mild cognitive impairment, or dementia) from predictions of continuous markers based on the first-stage model. The combination of the two models allows one to leverage their key strengths in order to obtain improved accuracy. We characterize the predictive accuracy of this two-stage approach using data from the Alzheimer's Disease Neuroimaging Initiative. The two-stage approach using a single joint mixed-effects model for all continuous outcomes yields better diagnostic classification accuracy compared to using separate univariate mixed-effects models for each of the continuous outcomes. Overall prediction accuracy above 80% was achieved over a period of 2.5 years. The results further indicate that overall accuracy is improved when markers from multiple assessment domains, such as cognition, function, and brain imaging, are used in the prediction algorithm as compared to the use of markers from a single domain only

Alzheimer Disease Detection Techniques and Methods: A Review

Brain pathological changes linked with Alzheimer's disease (AD) can be measured with Neuroimaging. In the past few years, these measures are rapidly integrated into the signatures of Alzheimer disease (AD) with the help of classification frameworks which are offering tools for diagnosis and prognosis. Here is the review study of Alzheimer's disease based on Neuroimaging and cognitive impairment classification. This work is a systematic review for the published work in the field of AD especially the computer-aided diagnosis. The imaging modalities include 1) Magnetic resonance imaging (MRI) 2) Functional MRI (fMRI) 3) Diffusion tensor imaging 4) Positron emission tomography (PET) and 5) amyloid-PET. The study revealed that the classification criterion based on the features shows promising results to diagnose the disease and helps in clinical progression. The most widely used machine learning classifiers for AD diagnosis include Support Vector Machine, Bayesian Classifiers, Linear Discriminant Analysis, and K-Nearest Neighbor along with Deep learning. The study revealed that the deep learning techniques and support vector machine give higher accuracies in the identification of Alzheimer’s disease. The possible challenges along with future directions are also discussed in the paper

Efficacy of probucol on cognitive function in Alzheimer's disease: study protocol for a double-blind, placebo-controlled, randomised phase II trial (PIA study).

INTRODUCTION: Preclinical, clinical and epidemiological studies support the hypothesis that aberrant systemic metabolism of amyloid beta (Aβ) in the peripheral circulation is causally related to the development of Alzheimer's disease (AD). Specifically, recent studies suggest that increased plasma concentrations of lipoprotein-Aβ compromise the brain microvasculature, resulting in extravasation and retention of the lipoprotein-Aβ moiety. The latter results in an inflammatory response and neurodegeneration ensues. Probucol, a historic cholesterol-lowering drug, has been shown in murine models to suppress lipoprotein-Aβ secretion, concomitant with maintaining blood-brain-barrier function, suppressing neurovascular inflammation and supporting cognitive function. This protocol details the probucol in Alzheimer's study, a drug intervention trial investigating if probucol has potential to attenuate cognitive decline, delay brain atrophy and reduce cerebral amyloid burden in patients with mild-to-moderate AD. METHODS AND ANALYSIS: The study is a phase II, randomised, placebo-controlled, double-blind single-site clinical trial held in Perth, Australia. The target sample is 314 participants with mild-to-moderate AD. Participants will be recruited and randomised (1:1) to a 104-week intervention consisting of placebo induction for 2 weeks followed by 102 weeks of probucol (Lorelco) or placebo. The primary outcome is changed in cognitive performance determined via the Alzheimer's Disease Assessment Scales-Cognitive Subscale test between baseline and 104 weeks. Secondary outcomes measures will be the change in brain structure and function, cerebral amyloid load, quality of life, and the safety and tolerability of Lorelco, after a 104week intervention. ETHICS AND DISSEMINATION: The study has been approved by the Bellberry Limited Human Research Ethics Committee (approval number: HREC2019-11-1063; Version 4, 6 October 2021). Informed consent will be obtained from participants prior to any study procedures being performed. The investigator group will disseminate study findings through peer-reviewed publications, key conferences and local stakeholder events. TRIAL REGISTRATION NUMBER: Australian New Zealand Clinical Trials Registry (ACTRN12621000726853)

Determinants of Theory of Mind performance in Alzheimer’s disease: A data-mining study

Whether theory of mind (ToM) is preserved in Alzheimer’s disease (AD) remains a controversial subject. Recent studies have showed that performance on some ToM tests might be altered in AD, though to a lesser extent than in behavioural-variant Frontotemporal Dementia (bvFTD). It is however, unclear if this reflects a genuine impairment of ToM or a deficit secondary to the general cognitive decline observed in AD. Aiming to investigate the cognitive determinants of ToM performance in AD, a data-mining study was conducted in 29 AD patients then replicated in an independent age-matched group of 19 AD patients to perform an independent replication of the results. 44 bvFTD patients were included as a comparison group. All patients had an extensive neuropsychological examination. Hierarchical clustering analyses showed that ToM performance clustered with measures of executive functioning in AD. ToM performance was also specifically correlated with the executive component extracted from a principal component analysis. In a final step, automated linear modelling conducted to determine the predictors of ToM performance showed that 48.8% of ToM performance was significantly predicted by executive measures. Similar findings across analyses were observed in the independent group of AD patients, thereby replicating our results. Conversely, ToM impairments in bvFTD appeared independent of other cognitive impairments. These results suggest that difficulties of AD patients on ToM tests do not reflect a genuine ToM deficit, rather mediated by general (and particularly executive) cognitive decline. They also suggest that executive functioning has a key role in mental state attribution, which support interacting models of ToM functioning. Finally, our study highlights the relevancy of data-mining statistical approaches in clinical and cognitive neurosciences