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

Role of Withaferin A as a Neuroprotectant against Beta Amyloid Induced Toxicity and associated mechanism

Neurological disorders are the biggest concern globally and ageing contributes in worsening the disease scenarios. In AD or AD like diseases, there is abnormal accumulation of extracellular amyloid beta produced due to abnormal processing of the transmembrane amyloid precursor protein, by β and γ-secretases. It spreads in the cortical and limbic regions of the brain leading to neuronal toxicity, impairment in memory and neurological functions. Aβ deposition in the CNS is common in aging HIV patients. Neurotoxic protein Tat, results in increased Aβ in combination with drugs of abuse cocaine. We examined the role of Withaferin A, against Aβ induced neurotoxicity. Our in-vitro dose optimization study demonstrates that lower concentrations (0.5–2 μM) of WA significantly reduce the Aβ40, without inducing cytotoxicity in the APP plasmid transfected SH-SY5Y cells (SHAPP). We demonstrate that Aβ secretion is increased in the presence of Tat (50 ng/ml) and coc (0.1 μM), WA reduces the Tat and coc induced increase in Aβ40. Additionally, we studied the role of WA against NF-kB mediated neuroinflammation, and observed that WA inhibits the expression of NFkB2 and RELA transcription factors, which play a major role in the expression of inflammatory chemokines. Further, to address the issue of minimal drug bioavailability in the CNS, we developed the WA loaded liposomal nanoformulation (WA-LNF) and characterized its size (499+/-50nm), toxicity and drug binding efficacy (28%). Our in-vitro 3D BBB transmigration of WA-LNF demonstrated ~40% transmigration efficiency. Furthermore, it was imperative for us to understand the mechanism of action of WA, therefore we studied the molecular mechanism of interaction of WA with Aβ protein by in-silico molecular dynamics simulations. We demonstrated that WA binds to the middle region of Aβ protein and the amino acid motif involved were FAEDVGS highlighting the mid-region Aβ capture by WA. 3 Hydrogen bonds were formed between WA and the amino acids, ASN17, GLY15 and SER16. This study reports WA as a potent neuroprotectant against amyloid induced neurotoxicity. Our study may have an immense therapeutic potential to target Aβ in the CNS, in the ageing patients and/or PLWH and/or ageing drug abusers

Machine Learning Methods for Structural Brain MRIs: Applications for Alzheimer’s Disease and Autism Spectrum Disorder

This thesis deals with the development of novel machine learning applications to automatically detect brain disorders based on magnetic resonance imaging (MRI) data, with a particular focus on Alzheimer’s disease and the autism spectrum disorder. Machine learning approaches are used extensively in neuroimaging studies of brain disorders to investigate abnormalities in various brain regions. However, there are many technical challenges in the analysis of neuroimaging data, for example, high dimensionality, the limited amount of data, and high variance in that data due to many confounding factors. These limitations make the development of appropriate computational approaches more challenging. To deal with these existing challenges, we target multiple machine learning approaches, including supervised and semi-supervised learning, domain adaptation, and dimensionality reduction methods.In the current study, we aim to construct effective biomarkers with sufficient sensitivity and specificity that can help physicians better understand the diseases and make improved diagnoses or treatment choices. The main contributions are 1) development of a novel biomarker for predicting Alzheimer’s disease in mild cognitive impairment patients by integrating structural MRI data and neuropsychological test results and 2) the development of a new computational approach for predicting disease severity in autistic patients in agglomerative data by automatically combining structural information obtained from different brain regions.In addition, we investigate various data-driven feature selection and classification methods for whole brain, voxel-based classification analysis of structural MRI and the use of semi-supervised learning approaches to predict Alzheimer’s disease. We also analyze the relationship between disease-related structural changes and cognitive states of patients with Alzheimer’s disease.The positive results of this effort provide insights into how to construct better biomarkers based on multisource data analysis of patient and healthy cohorts that may enable early diagnosis of brain disorders, detection of brain abnormalities and understanding effective processing in patient and healthy groups. Further, the methodologies and basic principles presented in this thesis are not only suited to the studied cases, but also are applicable to other similar problems

Assessing Cognitive Function in Chronic Sport-Related Head Impacts and Aging

Healthy normal aging and cumulative head trauma (concussion and subconcussion), can influence cognition independently and concomitantly leading to substantial late-life cognitive impairments (e.g., as seen in increased rates of dementia). With this as motivation, this dissertation explores three aspects of aging, head injury and cognition using the Cambridge Brain Sciences (CBS) cognitive battery (www.cambridgebrainsciences.com). Study 1 (Chapter 2): Concussion-specific testing combines assessments from multiple domains to evaluate a variety of functions. While clinically relevant, their succinct nature limits the amount of cognitive information available. Eighteen male football athletes were examined at baseline using the Sport Concussion Assessment Tool (SCAT) 3, and CBS battery. SCAT3 cognition test (Standardized Assessment of Concussion) scores significantly correlated with just the verbal cognitive domain assessed by CBS. This suggests a narrow scope which may miss other aspects of cognition that could be equally vulnerable in concussion. Study 2 (Chapter 3): It is likely that both subconcussive and concussive impacts contribute to the cognitive changes seen in retired athletes. What remains unclear is when these changes first appear and how they can be detected. This study compared 81 male football athletes (high cumulative impact burden) and matched controls (low cumulative impact burden) on cognitive test performance and response time. Results demonstrated response time deficits (slowed and more variable) without score impairments in football athletes in comparison to controls, which may represent pre-clinical compensatory mechanisms mitigating an increased cognitive demand. To address limitations in repeating Study 2 in contact sport retirees, Study 3 (Chapter 4) employed discriminant function analysis (DFA) to reduce the CBS battery for better application in aging populations. 118 younger and 118 older participants were included. Five of the 12 CBS tests were necessary to retain 98% of the variance accounted for between groups in the full model. Additionally, CBS tests were divided into 3 categories based on significant differences in the full and reduced models: no significant differences (n = 2), ii significant differences only on full model (n = 5), and significant differences on both models (n = 5). Results support the use of a modified CBS battery in age-related studies

Down Syndrome

Down syndrome, the most cutting-edge book in the field congenital disorders. This book features up-to-date, well referenced research and review articles on Down syndrome. Research workers, scientists, medical graduates and pediatricians will find it to be an excellent source for references and review. It is hoped that such individuals will view this book as a resource that can be consulted during all stages of their research and clinical investigations. Key features of this book are: Common diseases in Down syndrome Molecular Genetics Neurological Disorders Prenatal Diagnosis and Genetic Counselling Whilst aimed primarily at research workers on Down syndrome, we hope that the appeal of this book will extend beyond the narrow confines of academic interest and be of interest to a wider audience, especially parents, relatives and health-care providers who work with infants and children with Down syndrome

Cognition in first-episode psychosis: Characterisation, reserve and relationship to functioning

Schizophrenia is marked by deficits in cognition and social functioning that present early in the course of illness. A relationship between neurocognitive impairment (e.g. memory and processing speed), and social functioning is reported in the literature, with poorer neurocognition associated with worse social outcomes. There is emerging evidence for the existence of neurocognitive subtypes with different cognitive trajectories, hypothesised to reflect separate etiological processes and risk factors for clinical and social outcomes. Despite neurocognition being considered one of the best predictors of social outcomes, there is still a large amount of variance in outcomes left unexplained. In addition to neurocognitive deficits, processes required for successful social interactions, collectively known as ‘social cognition’ i.e. theory of mind, attribution bias, social perception and emotion perception, have been shown to be impaired in those with established schizophrenia. There is some evidence that social cognition mediates the relationship between neurocognition and functional outcomes. Studying individuals with a first-episode psychosis (FEP) allows the examination of the fundamental features of schizophrenia, without the confounding effects of prolonged medication, hospitalisation and social isolation. Using two clinical-trial FEP groups, the studies presented in this thesis examined: the existence and magnitude of global and domain-specific neuro- and social cognitive impairment; the existence of neurocognitive-trajectory based subtypes and their brain volumetric and inflammatory profiles; the relationship between neurocognition and social functioning; and whether social cognition mediates the relationship between neurocognition and social functioning. Three IQ-trajectory based subtypes that were stable over time and distinguished by biological underpinnings were found. Social cognition deficits were present early in the course of illness and significantly overlapped with neurocognitive impairments, but it could not be concluded that social cognition mediates the relationship between neurocognition and social functioning. The results of the studies enabled recommendations for remedial strategies to improve social functioning and quality of life of individuals early in the course of illness