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

Raising the visibility of protected data: A pilot data catalog project

Sharing research data that is protected for legal, regulatory, or contractual reasons can be challenging and current mechanisms for doing so may act as barriers to researchers and discourage data sharing. Additionally, the infrastructure commonly used for open data repositories does not easily support responsible sharing of protected data. This chapter presents a case study of an academic university library’s work to configure the existing institutional data repository to function as a data catalog. By engaging in this project, university librarians strive to enhance visibility and access to protected datasets produced at the institution and cultivate a data sharing culture

AUTOMATED INTERPRETATION OF THE BACKGROUND EEG USING FUZZY LOGIC

A new framework is described for managing uncertainty and for deahng with artefact corruption to introduce objectivity in the interpretation of the electroencephalogram (EEG). Conventionally, EEG interpretation is time consuming and subjective, and is known to show significant inter- and intra-personnel variation. A need thus exists to automate the interpretation of the EEG to provide a more consistent and efficient assessment. However, automated analysis of EEGs by computers is complicated by two major factors. The difficulty of adequately capturing in machine form, the skills and subjective expertise of the experienced electroencephalbgrapher, and the lack of a reliable means of dealing with the range of EEG artefacts (signal contamination). In this thesis, a new framework is described which introduces objectivity in two important outcomes of clinical evaluation of the EEG, namely, the clinical factual report and the clinical 'conclusion', by capturing the subjective expertise of the electroencephalographer and dealing with the problem of artefact corruption. The framework is separated into two stages .to assist piecewise optimisation and to cater for different requirements. The first stage, 'quantitative analysis', relies on novel digital signal processing algorithms and cluster analysis techniques to reduce data and identify and describe background activities in the EEG. To deal with artefact corruption, an artefact removal strategy, based on new reUable techniques for artefact identification is used to ensure that artefact-free activities only are used in the analysis. The outcome is a quantitative analysis, which efficiently describes the background activity in the record, and can support future clinical investigations in neurophysiology. In clinical practice, many of the EEG features are described by the clinicians in natural language terms, such as very high, extremely irregular, somewhat abnormal etc. The second stage of the framework, 'qualitative analysis', captures the subjectivity and linguistic uncertainty expressed.by the clinical experts, using novel, intelligent models, based on fuzzy logic, to provide an analysis closely comparable to the clinical interpretation made in practice. The outcome of this stage is an EEG report with qualitative descriptions to complement the quantitative analysis. The system was evaluated using EEG records from 1 patient with Alzheimer's disease and 2 age-matched normal controls for the factual report, and 3 patients with Alzheimer's disease and 7 age-matched nonnal controls for the 'conclusion'. Good agreement was found between factual reports produced by the system and factual reports produced by qualified clinicians. Further, the 'conclusion' produced by the system achieved 100% discrimination between the two subject groups. After a thorough evaluation, the system should significantly aid the process of EEG interpretation and diagnosis

Decision-based data fusion of complementary features for the early diagnosis of Alzheimer\u27s disease

As the average life expectancy increases, particularly in developing countries, the prevalence of Alzheimer\u27s disease (AD), which is the most common form of dementia worldwide, has increased dramatically. As there is no cure to stop or reverse the effects of AD, the early diagnosis and detection is of utmost concern. Recent pharmacological advances have shown the ability to slow the progression of AD; however, the efficacy of these treatments is dependent on the ability to detect the disease at the earliest stage possible. Many patients are limited to small community clinics, by geographic and/or financial constraints. Making diagnosis possible at these clinics through an accurate, inexpensive, and noninvasive tool is of great interest. Many tools have been shown to be effective at the early diagnosis of AD. Three in particular are focused upon in this study: event-related potentials (ERPs) in electroencephalogram (EEG) recordings, magnetic resonance imaging (MRI), as well as positron emission tomography (PET). These biomarkers have been shown to contain diagnostically useful information regarding the development of AD in an individual. The combination of these biomarkers, if they provide complementary information, can boost overall diagnostic accuracy of an automated system. EEG data acquired from an auditory oddball paradigm, along with volumetric T2 weighted MRI data and PET imagery representative of metabolic glucose activity in the brain was collected from a cohort of 447 patients, along with other biomarkers and metrics relating to neurodegenerative disease. This study in particular focuses on AD versus control diagnostic ability from the cohort, in addition to AD severity analysis. An assortment of feature extraction methods were employed to extract diagnostically relevant information from raw data. EEG signals were decomposed into frequency bands of interest hrough the discrete wavelet transform (DWT). MRI images were reprocessed to provide volumetric representations of specific regions of interest in the cranium. The PET imagery was segmented into regions of interest representing glucose metabolic rates within the brain. Multi-layer perceptron neural networks were used as the base classifier for the augmented stacked generalization algorithm, creating three overall biomarker experts for AD diagnosis. The features extracted from each biomarker were used to train classifiers on various subsets of the cohort data; the decisions from these classifiers were then combined to achieve decision-based data fusion. This study found that EEG, MRI and PET data each hold complementary information for the diagnosis of AD. The use of all three in tandem provides greater diagnostic accuracy than using any single biomarker alone. The highest accuracy obtained through the EEG expert was 86.1 ±3.2%, with MRI and PET reaching 91.1 +3.2% and 91.2 ±3.9%, respectively. The maximum diagnostic accuracy of these systems averaged 95.0 ±3.1% when all three biomarkers were combined through the decision fusion algorithm described in this study. The severity analysis for AD showed similar results, with combination performance exceeding that of any biomarker expert alone

Advancing translational research with the Semantic Web

<p>Abstract</p> <p>Background</p> <p>A fundamental goal of the U.S. National Institute of Health (NIH) "Roadmap" is to strengthen <it>Translational Research</it>, defined as the movement of discoveries in basic research to application at the clinical level. A significant barrier to translational research is the lack of uniformly structured data across related biomedical domains. The Semantic Web is an extension of the current Web that enables navigation and meaningful use of digital resources by automatic processes. It is based on common formats that support aggregation and integration of data drawn from diverse sources. A variety of technologies have been built on this foundation that, together, support identifying, representing, and reasoning across a wide range of biomedical data. The Semantic Web Health Care and Life Sciences Interest Group (HCLSIG), set up within the framework of the World Wide Web Consortium, was launched to explore the application of these technologies in a variety of areas. Subgroups focus on making biomedical data available in RDF, working with biomedical ontologies, prototyping clinical decision support systems, working on drug safety and efficacy communication, and supporting disease researchers navigating and annotating the large amount of potentially relevant literature.</p> <p>Results</p> <p>We present a scenario that shows the value of the information environment the Semantic Web can support for aiding neuroscience researchers. We then report on several projects by members of the HCLSIG, in the process illustrating the range of Semantic Web technologies that have applications in areas of biomedicine.</p> <p>Conclusion</p> <p>Semantic Web technologies present both promise and challenges. Current tools and standards are already adequate to implement components of the bench-to-bedside vision. On the other hand, these technologies are young. Gaps in standards and implementations still exist and adoption is limited by typical problems with early technology, such as the need for a critical mass of practitioners and installed base, and growing pains as the technology is scaled up. Still, the potential of interoperable knowledge sources for biomedicine, at the scale of the World Wide Web, merits continued work.</p

Cognitive and neural mechanisms of sense of self in neurodegenerative disorders

The ability to maintain a coherent and continuous ‘sense of self’ is a fundamental component of being human, enabling us to interact and function successfully in everyday life. While a sense of self is commonly accepted to involve both ‘extended’ (i.e., memories) and ‘interpersonal’ (i.e., social) elements, the precise cognitive and neural mechanisms underlying these aspects of the self remain poorly understood. This thesis draws upon theory and methods from contemporary cognitive neuroscience to examine the neurocognitive underpinnings of the extended and interpersonal self in Alzheimer’s disease (AD), semantic dementia (SD), and the behavioural variant of frontotemporal dementia (bvFTD): neurodegenerative disorders involving progressive cognitive and behavioural change as the result of degeneration to distinct brain networks. Employing a novel experimental method (the ‘NExt’ taxonomy), Part 1 of the thesis (Chapters 3 and 4) reveals how a full spectrum of episodic and semantic memory representations may be drawn upon to support one’s past and future life stories, giving rise to a sense of continuity of the extended self. Part 2 (Chapters 5 and 6) illustrates how the complex social interactions that comprise the interpersonal self may be deconstructed into several distinct, yet interacting, psychological components. Furthermore, neuroimaging analyses uncover widespread neural regions to be associated with both the extended and interpersonal aspects of the self, incorporating brain networks beyond those typically implicated in self-related processing. The improved neurocognitive characterisation of the self provided by this thesis highlights the complex, multifaceted nature of this construct. Moreover, from a clinical perspective, distinct profiles of the self unveiled across AD, SD, and bvFTD reveal how ultimately, ‘all is not lost’ in neurodegeneration

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

Performance of people with mild cognitive impairment or early Alzheimer's disease on the behavioural assessment of the dysexecutive syndrome test battery

Aim: Decline in executive functioning in Mild Cognitive Impairment has only been investigated with single tests to date. A battery of executive function tasks (BADS: Behavioural Assessment of the Dysexecutive Syndrome) was used to investigate and compare the extent of executive function difficulties in people with Mild Cognitive Impairment and early Alzheimer Disease. Degree and prevalence of decline were examined for each of the groups, and performance patterns compared between the two groups. Participants: 37 Participants (19 MCI, 18 early AD) were recruited from one urban, one suburban and one rural centre. Participants were selected on the basis of clinical judgments made by local psychiatrists, and for the MCI group checked against Petersen criteria (1999) as far as information was accessible to the main researcher. Probable Alzheimer's disease had been diagnosed either according to ICD- 10 criteria (centres I and 3) or NINCDS-ADRDA criteria (centre 2). Groups did not differ significantly on socio-demographic variables. Design: A mixed cross-sectional exploratory design was employed, examining performance on executive function tasks within each of two clinical groups separately, and comparing performance between the two clinical groups. Effects of confounding variables were examined, and subsequently effects of 'age' were controlled for. Main results: Both groups showed decline on executive functioning tasks, but this was mild in the MCI group compared to normative data, whereas significantly poorer performance was observed in the early AD group. Impairment was not ubiquitous in either group. Whereas patterns of performance across subtests were similar for both groups, performance levels for different subtests differed. Hence different tasks might be differentially suited to assess executive function deficits in each group

Reinforcement Learning, Error-Related Negativity, and Genetic Risk for Alzheimer\u27s Disease

Reinforcement learning (RL) has been widely used as a model of animal and human learning and decision-making. The neural networks underlying RL involve many of the same structures primarily affected by Alzheimer’s disease (AD) such as the hippocampus. Yet, RL and non-invasive evaluation of its neural underpinnings have been underutilized as a framework for understanding disease pathology and its pre-clinical states. This study aimed to provide a novel approach for assessing subtle changes in asymptomatic apolipoprotein-E (APOE) carriers and non-carriers. Electroencephalography was collected from forty APOE genotyped older adults (Male n = 11; Mage = 79.30; Meducation = 14.88 years) during an RL task comprised of distinct phases (RL, implicit). Neural components associated with the error detection system involved in RL, the response error-related negativity (ERN) and the feedback error-related negativity (FRN), were examined for individuals at low (APOE ε4-; n=20) and high risk (APOE ε4+; n=20). RL task performance did not differ between risk groups. However, the high-risk group consistently elicited greater peak amplitudes than the low-risk group. The pattern of results indicated that the high-risk group deviated from typical RL processes such that peak amplitudes did not differ between early and late learning. Additionally, despite intact learning, latent hippocampal atrophy is believed to have disrupted the transfer and use of learned information to novel situations thus altering the hippocampal-frontostriatal circuit responsible for adaptive behavior and the corresponding neural signal. The results indicate that disease related changes can be identified prior to clinical diagnosis or functional decline using RL and a non-invasive assessment of neural function, which may prove to inform clinical conceptualization, assessment, and treatment

Psychometric Extension of the Memory for Names Test

The purpose of this study was to re-evaluate the psychometric properties of the Memory for Names (Mem4Names) test among a sample of older adults without cognitive impairment. Mem4Names is a test of famous face recognition that was shown to be a reliable and valid measure of semantic memory in older adults both with and without cognitive impairment (Brouillette et al., 2011). The current study re-examined the psychometric properties of the Mem4Names test among 133 volunteers at Pennington Biomedical Research Center’s Institute for Dementia Research and Prevention. The study confirmed previously reported calculations of the test’s reliability by calculating Cronbach’s alpha and Guttman’s split-half coefficient. Convergent validity for the Mem4Names test was established through its correlation with a theoretically similar measure of memory, the Wechsler Memory Scale Logical Memory Delayed subtest. Confirmatory factor analysis identified a one-factor solution for the Mem4Names test. The results concluded that the Mem4Names test is a reliable and valid measure of semantic memory for cognitively intact older adults