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Identification Of Novel Biomarkers For The Diagnosis And Prognosis Of Alzheimers Disease Using Machine Learning Techniques
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Book Synopsis Identification of Novel Biomarkers for the Diagnosis and Prognosis of Alzheimer's Disease Using Machine Learning Techniques by : Antonio Martínez Torteya
Download or read book Identification of Novel Biomarkers for the Diagnosis and Prognosis of Alzheimer's Disease Using Machine Learning Techniques written by Antonio Martínez Torteya and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Biomarkers of Alzheimer's Disease: The Present and the Future by : Sylvain Lehmann
Download or read book Biomarkers of Alzheimer's Disease: The Present and the Future written by Sylvain Lehmann and published by Frontiers Media SA. This book was released on 2016-11-10 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Alzheimer disease (AD) is a neurodegenerative disorder characterized by significant cognitive deficits, behavioral changes, sleep disorders and loss of functional autonomy. AD represents the main cause of dementia and has become a major public health issue. In addition, the number of patients suffering from AD is growing rapidly as the population ages worldwide. Memory impairment is usually the earliest clinical and core symptom of this disease. The diagnosis at a late clinical stage is relatively easy. However, a delay in the diagnosis is damageable for the handling of patients in terms of optimal medical and social care. The actual interest of the scientific head-ways is to optimize the diagnosis in prodromal stage of the disease and to propose personalized therapeutic solutions to individual patients. New revised AD diagnostic criteria include early alteration of cerebrospinal fluid (CSF) biomarkers: decrease of amyloïd peptides (Aβ42), and increase in tau and phosphorylated-tau (p-tau) protein concentration. This recognition of CSF biological biomarkers for the diagnosis of AD is a major step towards the “molecular” diagnosis and follow-up of the disease. Many issues are however still subject of debate. This e-book provides a comprehensive overview of the state of the art of fluid biomarkers for AD, e.g. which novel biomarkers should be implemented in clinical practice for diagnosis or for monitoring treatment or side effects, which ones are new for AD or related dementias or what is the potential of peripheral blood markers. Moreover, the e-Book provides practical guidelines how to optimally and efficiently develop and validate novel biomarker assays, and to document and control pre-analytical variation.
Book Synopsis Identification of Novel Fluid Biomarkers for Alzheimer's Disease by : Rebecca June Craig-Schapiro
Download or read book Identification of Novel Fluid Biomarkers for Alzheimer's Disease written by Rebecca June Craig-Schapiro and published by . This book was released on 2012 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clinicopathological studies suggest that Alzheimer's disease (AD) pathology begins to appear ~10-20 years before the resulting cognitive impairment draws medical attention. Biomarkers that can detect AD pathology in its early stages and predict dementia onset and progression would, therefore, be invaluable for patient care and efficient clinical trial design. To discover such biomarkers, we measured AD-associated changes in the cerebrospinal fluid (CSF) using an unbiased proteomics approach (two-dimensional difference gel electrophoresis with liquid chromatography tandem mass spectrometry). From this, we identified 47 proteins that differed in abundance between cognitively normal (Clinical Dementia Rating [CDR] 0) and mildly demented (CDR 1) subjects. To validate these findings, we measured a subset of the identified candidate biomarkers by enzyme linked immunosorbent assay (ELISA); promising candidates in this discovery cohort (N=47) were further evaluated by ELISA in a larger validation CSF cohort (N=292) that contained an additional very mildly demented (CDR 0.5) group. Levels of four novel biomarkers were significantly altered in AD, and Receiver-operating characteristic (ROC) analyses using a stepwise logistic regression model identified optimal panels containing these markers that distinguished CDR 0 from CDR>0 (tau, YKL-40, NCAM) and CDR 1 from CDR1 (tau, chromogranin-A, carnosinase-I). Plasma levels of the most promising marker, YKL-40, were also found to be increased in CDR 0.5 and 1 groups and to correlate with CSF levels. Importantly, the CSF YKL-40/A[beta]42 ratio predicted risk of developing cognitive impairment (CDR 0 to CDR0 conversion) as well as the best CSF biomarkers identified to date, tau/A[beta]42 and p-tau181/A[beta]42. Additionally, YKL-40 immunoreactivity was observed within astrocytes near a subset of amyloid plaques, implicating YKL-40 in the neuroinflammatory response to A[beta] deposition. Utilizing an alternative, targeted proteomics approach to identify novel biomarkers, 333 CSF samples were evaluated for levels of 190 analytes using a multiplexed Luminex platform. The mean concentrations of 37 analytes were found to differ between CDR 0 and CDR>0 participants. ROC and statistical machine learning algorithms identified novel biomarker panels that improved upon the ability of the current best biomarkers to discriminate very mildly demented from cognitively normal participants, and identified a novel biomarker, Calbindin, with significant prognostic potential.
Book Synopsis Novel Biomarkers in Alzheimer’s Disease by : Chiara Villa
Download or read book Novel Biomarkers in Alzheimer’s Disease written by Chiara Villa and published by MDPI. This book was released on 2021-02-05 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt: Alzheimer’s disease (AD) represents the most common form of dementia in the elderly population worldwide. AD is characterized by progressive neurodegeneration that leads to a gradual deterioration of memory and other cognitive functions. Given the global prevalence and impact of AD, there is a critical need to establish biomarkers that can be used to detect AD in individuals before the onset of clinical signs and provide mitigating therapeutics. The aim of this Special Issue is to discuss the current knowledge as well as future perspectives on the role of biomarkers in the screening, diagnosis, treatment and follow-up of AD.
Book Synopsis Alzheimer's Disease by : Harald Hampel
Download or read book Alzheimer's Disease written by Harald Hampel and published by Karger Medical and Scientific Publishers. This book was released on 2012 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: How current biomarkers are modernizing the diagnosis of Alzheimer's disease Expanding knowledge on genetic and epigenetic risk factors is rapidly enhancing our understanding of the complex molecular interactions and systems involved in the pathogenesis of Alzheimer's disease. In this publication, leading experts discuss emerging novel conceptual models of the disease along with advances in the development of surrogate markers that will not only improve the accuracy of diagnostic technologies but also improve the prospects of developing disease-modifying interventions. The novel framework of the disease presented here highlights research on biological markers as well as efforts to validate technologies for early and accurate detection. It also introduces notion of a complex systems dysfunction that extends beyond prevailing ideas derived from the amyloid' or tau' hypotheses. This outstanding publication provides researchers, clinicians, students and other professionals interested in neurodegenerative disorders with a comprehensive update on current trends and future directions in therapy development, with special focus on advances in clinical trial designs.
Book Synopsis Machine Learning for Image-based Classification of Alzheimer's Disease by : Katherine Rachel Gray
Download or read book Machine Learning for Image-based Classification of Alzheimer's Disease written by Katherine Rachel Gray and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Imaging biomarkers for Alzheimer's disease are important for improved diagnosis and monitoring, as well as drug discovery. Automated image-based classification of individual patients could provide valuable support for clinicians. This work investigates machine learning methods aimed at the early identification of Alzheimer's disease, and prediction of progression in mild cognitive impairment. Data are obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL). Multi-region analyses of cross-sectional and longitudinal FDG-PET images from ADNI are performed. Information extracted from FDG-PET images acquired at a single timepoint is used to achieve classification results comparable with those obtained using data from research-quality MRI, or cerebrospinal fluid biomarkers. The incorporation of longitudinal information results in improved classification performance. Changes in multiple biomarkers may provide complementary information for the diagnosis and prognosis of Alzheimer's disease. A multi-modality classification framework based on random forest-derived similarities is applied to imaging and biological data from ADNI. Random forests provide consistent similarities for multiple modalities, facilitating the combination of different types of features. Classification based on the combination of MRI volumes, FDG-PET intensities, cerebrospinal fluid biomarkers, and genetics out-performs classification based on any individual modality. Multi-region analysis of MRI acquired at a single timepoint is used to show volumetric differences in cognitively normal individuals differing in amyloid-based risk status for the development of Alzheimer's disease. Reduced volumes in temporo-parietal and orbito-frontal regions in high-risk individuals from both ADNI and AIBL could be indicative of early signs of neurodegeneration. This suggests that volumetric MRI can reveal structural brain changes preceding the onset of clinical symptoms. Taken together, these results suggest that image-based classification can support diagnosis in Alzheimer's disease and preceding stages. Future work may lead to more finely meshed prognostic data that may be useful clinically and for research.
Book Synopsis Artificial Intelligence and Alzheimer's Disease by : KHRITISH SWARGIARY
Download or read book Artificial Intelligence and Alzheimer's Disease written by KHRITISH SWARGIARY and published by ERA, US. This book was released on 2024-08-01 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the field of medical research and technological innovation, few challenges are as profound and pressing as the quest to understand and combat Alzheimer’s Disease. This neurodegenerative disorder, characterized by progressive memory loss and cognitive decline, affects millions of individuals worldwide and poses significant challenges for patients, families, and healthcare systems alike. The search for effective diagnostic tools, prognostic indicators, and therapeutic interventions remains a critical area of scientific inquiry. The emergence of Artificial Intelligence (AI) has heralded a new era of possibilities in healthcare, offering transformative potential for the study and treatment of Alzheimer’s Disease. By leveraging advanced computational techniques, machine learning algorithms, and data analytics, AI holds the promise of revolutionizing our approach to understanding the complexities of this disease. From early diagnosis to personalized treatment and patient monitoring, AI's applications in Alzheimer’s research and care are rapidly expanding, presenting both opportunities and challenges that warrant thorough exploration. This book aims to provide a comprehensive overview of the intersection between AI and Alzheimer’s Disease. It is designed to serve as a valuable resource for researchers, clinicians, policymakers, and students who are engaged in the fields of neurology, artificial intelligence, and healthcare technology. Through a detailed examination of current advancements, practical applications, and future directions, this work seeks to illuminate the transformative impact of AI on Alzheimer’s research and patient care.
Book Synopsis Integrative Multi-Omics for Diagnosis, Treatments, and Drug Discovery of Aging-Related Neuronal Diseases by : Min Tang
Download or read book Integrative Multi-Omics for Diagnosis, Treatments, and Drug Discovery of Aging-Related Neuronal Diseases written by Min Tang and published by Frontiers Media SA. This book was released on 2022-11-23 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the cost of high-throughput sequencing goes down, huge volumes of biological and medical data have been produced from various sequencing platforms at multiple molecular levels including genome, transcriptome, proteome, epigenome, metabolome, and so on. For a long time, data analysis on single molecular levels has paved the way to answer many important research questions. However, many Aging-Related Neuronal Diseases (ARNDs) and Central Nervous System (CNS) aging involve interactions of molecules from multiple molecular levels, in which conclusions based on single molecular levels are usually incomplete and sometimes misleading. In these scenarios, multi-omics data analysis has unprecedentedly helped capture much more useful information for the diagnosis, treatment, prognosis, and drug discovery of ARNDs. The first step towards a multi-omics analysis is to establish reliable and robust multi-omics datasets. In the past years, a few important ARNDs-associated multi-omics databases like Allen Brain have been constructed, which raised immediate needs like data curation, normalization, interpretation, and visualization for integrative multi-omics explorations. Though there have been several well-established multi-omics databases for ARNDs like Alzheimer’s disease, similar databases for other ARNDs are still in urgent need. After the databases establish, many computational tools and experiential strategies should be developed specifically for them. First, the multi-omics data are usually extremely noisy, complex, heterogeneous and in high dimension, which presents the need for appropriate denoising and dimension reduction methods. Second, since the multi-omics and non-omics data like pathological and clinical data are usually in different data spaces, a useful algorithm to mapping them into the same data space and integrate them is nontrivial. In the multi-omics era, there are numerous data-centric tools for the integration of multi-omics datasets, which could be generally divided into three categories: unsupervised, supervised, and semi-supervised methods. Commonly used algorithms include but not limited to Bayesian-based methods, Network-based methods, multi-step analysis methods, and multiple kernel learning methods. Third, methods are needed in studying and verifying the association between two or more levels of multi-omics data and non-omics data. For example, expression quantitative trait loci (eQTL) analysis is widely used to infer the association between a single nucleotide polymorphism (SNP) and the expression of a gene. Recently, the association between omics data and more complex data like pathological and clinical imaging data has been a hot research topic. The outcomes may reveal the underlying molecular mechanism and promote de novo drug design as well as drug repurposing for ARNDs. Here, we welcome investigators to share their Original Research, Review, Mini Review, Hypothesis and Theory, Perspective, Conceptual Analysis, Data Report, Brief Research Report, Code related to multi-omics studies of ARNDs, which can be applied for better diagnosis, treatment, prognosis and drug discovery of human diseases in the future era of precision medicine. Potential contents include but are not limited to the following: ▪ Methods for integrating, interpreting, or visualizing two or more omics data. ▪ Methods for identifying interactions between different data modalities. ▪ Methods for disease subtyping, biomarker prediction. ▪ Machine learning or deep learning methods on dimensional reduction and feature selection for big noisy data. ▪ Methods for studying the association among different omics data or between omics and non-omics data like clinical, pathological, and imaging data. ▪ Review of multi-omics resource about ARNDs and/or CNS aging. ▪ Experimental validation of biomarkers identified from multi-omics data analysis. ▪ Disease diagnosis and prognosis prediction from imaging and non-imaging data analysis, or both. ▪ Clinical applications or validations of findings from multi-omics data analysis.
Book Synopsis AI-Driven Alzheimer's Disease Detection and Prediction by : Lilhore, Umesh Kumar
Download or read book AI-Driven Alzheimer's Disease Detection and Prediction written by Lilhore, Umesh Kumar and published by IGI Global. This book was released on 2024-08-09 with total page 477 pages. Available in PDF, EPUB and Kindle. Book excerpt: Alzheimer's disease (AD) poses a significant global health challenge, with an estimated 50 million people affected worldwide and no known cure. Traditional methods of diagnosis and prediction often rely on subjective assessments. They are limited in detecting the disease early, leading to delayed intervention and poorer patient outcomes. Additionally, the complexity of AD, with its multifactorial etiology and diverse clinical manifestations, requires a multidisciplinary approach for effective management. AI-Driven Alzheimer's Disease Detection and Prediction offers a groundbreaking solution by leveraging advanced artificial intelligence (AI) techniques to enhance early diagnosis and prediction of AD. This edited book provides a comprehensive overview of state-of-the-art research, methodologies, and applications at the intersection of AI and AD detection. By bridging the gap between traditional diagnostic methods and cutting-edge technology, this book facilitates knowledge exchange, fosters interdisciplinary collaboration, and contributes to innovative solutions for AD management.
Book Synopsis Early Alzheimer's Detection with Machine Learning by : Muhammed Niyas
Download or read book Early Alzheimer's Detection with Machine Learning written by Muhammed Niyas and published by Meem Publishers. This book was released on 2023-07-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Alzheimer's Disease is a progressive neurodegenerative disorder that affects millions of people worldwide. Detecting this condition in its early stages is critical for timely intervention and better management of the disease. "Early Detection of Alzheimer's Disease using Machine Learning Algorithms" presents a groundbreaking approach to identify early signs of Alzheimer's through advanced machine learning techniques. By harnessing the power of machine learning algorithms, this innovative system analyzes vast amounts of data, including cognitive assessments, brain imaging, and genetic markers. The algorithms can recognize subtle patterns and anomalies indicative of Alzheimer's disease, even before noticeable symptoms manifest. This cutting-edge technology holds great promise in transforming healthcare by enabling early identification of Alzheimer's, potentially leading to more effective treatments and therapies. Moreover, it can provide valuable insights for researchers and medical professionals to better understand the disease's progression and improve patient care. The utilization of machine learning algorithms not only enhances the accuracy and efficiency of Alzheimer's detection but also expedites the diagnostic process, reducing the burden on both patients and healthcare providers. As this field continues to evolve, the application of machine learning in Alzheimer's detection brings hope for a future with improved quality of life for those affected by this challenging condition. Early Alzheimer's Detection with Machine Learning represents a significant leap in the fight against Alzheimer's, offering a beacon of hope for early intervention, better care, and improved outcomes for individuals and their families facing this debilitating disease.
Book Synopsis The Alzheimer's Disease Challenge by : Athanasios Alexiou
Download or read book The Alzheimer's Disease Challenge written by Athanasios Alexiou and published by Frontiers Media SA. This book was released on 2019-12-04 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: Alzheimer’s disease is undoubtedly the major health challenge of our Century with significant social and economic consequences. This Frontiers eBook offers a contribution of 39 innovative papers on the multidimensional and crucial problem of Alzheimer’s disease management and treatment. Several perspectives, research updates, and trials describing methods on potential diagnosis and treatment are presented including biological mechanisms, biomarkers and risk factors for an early and efficient prognosis, diagnosis and prevention. Additionally, while the rapidly increasing Alzheimer’s disease population demands holistic solutions and clinical studies with new therapeutic target approaches, several of the contributive papers present promising drugs targeting Alzheimer’s disease treatment. We give our deepest acknowledgment to all the authors for their important and innovative contributions, to the reviewers for their valuable recommendations on improving the submitting studies and all the Frontiers Editorial team for continuous support.
Book Synopsis Risk Factors and Outcome Predicating Biomarker of Neurodegenerative Diseases by : Chaur-Jong Hu
Download or read book Risk Factors and Outcome Predicating Biomarker of Neurodegenerative Diseases written by Chaur-Jong Hu and published by Frontiers Media SA. This book was released on 2019-04-03 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomarkers for risk detection and outcome prediction of neurodegenerative diseases become more and more important for the clinical practise and neuroscience research. This eBook presents novel findings in this field, including amyotrophic lateral sclerosis, Alzheimer's disease, Parkinson's disease, Creutzfeldt-Jakob disease and application of neuroimaging, underlying mechanism of oxidative stress. The readers can catch new information about this emerging frontier of neurology with this eBook.
Book Synopsis Biological, Diagnostic and Therapeutic Advances in Alzheimer's Disease by : Ghulam Md Ashraf
Download or read book Biological, Diagnostic and Therapeutic Advances in Alzheimer's Disease written by Ghulam Md Ashraf and published by Springer Nature. This book was released on 2019-10-11 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the latest research into the highly prevalent neurodevelopmental disease most commonly associated with aging: Alzheimer’s disease (AD). Even after years of research, Alzheimer’s disease is still far from being cured. It presents a range of common symptoms in the form of behavioral and cognitive impairments. This book describes the symptoms and the biology behind them. The contents covers latest findings on the genetics involved and various factors and pathways influencing disease development. It also covers various non-pharmacological therapies like immunotherapy, use of natural products, and employing nanotechnology in both the detection and treatment of AD. This book also highlights the role of diet and nutrition in healthy aging. Given its scope, it offers a valuable asset for researchers and clinicians alike.
Book Synopsis Neuroimaging biomarkers in Alzheimer’s disease by : Samuel Barrack
Download or read book Neuroimaging biomarkers in Alzheimer’s disease written by Samuel Barrack and published by iMedPub. This book was released on 2013-10-20 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: In view of the growing prevalence of AD worldwide, there is an urgent need for the development of better diagnostic tools and more effective therapeutic interventions. Indeed, much work in this field has been done during last decades. As such, a major goal of current clinical research in AD is to improve early detection of disease and presymptomatic detection of neuronal dysfunction, concurrently with the development of better tools to assess disease progression in this group of disorders. All these putative correlates are commonly referred to as AD-related biomarkers. The ideal biomarker should be easy to quantify and measure, reproducible, not subject to wide variation in the general population and unaffected by co- morbid factors. For evaluation of therapies, a biomarker needs to change linearly with disease progression and closely correlate with established clinico-pathological parameters of the disease. There is growing evidence that the use of biomarkers will increase our ability to better indentify the underlying biology of AD, especially in its early stages. These biomarkers will improve the detection of the patients suitable for research studies and drug trials, and they will contribute to a better management of the disease in the clinical practice. Indeed, much work in this field has been done during last decades. The vast number of important applications, combined with the untamed diversity of already identified biomarkers, show that there is a pressing need to structure the research made on AD biomarkers into a solid, comprehensive and easy to use tool to de deployed in clinical settings. To date there are few publications compiling results on this topic. That is why when I was asked to address this task I accepted inmediately. I am happy to present you a bundle of the best articles published about biomarkers for Alzheimer’s disease in recent times.
Book Synopsis Biomarkers in Alzheimer's Disease by : Tapan Khan
Download or read book Biomarkers in Alzheimer's Disease written by Tapan Khan and published by Academic Press. This book was released on 2016-08-02 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Biomarkers in Alzheimer’s Disease provides a comprehensive overview of all modalities of Alzheimer’s disease biomarkers, including neuroimaging, cerebrospinal fluid, genomic, and peripheral systems. Each chapter integrates molecular/cellular abnormality due to Alzheimer’s disease and technological advancement of biomarkers techniques. The book is ideal for clinical neuroscience and molecular/cellular neuroscience researchers, psychiatrists, and allied healthcare practitioners involved in the diagnosis and management of patients with cognitive impairment and Alzheimer’s disease, and for differential diagnosis of Alzheimer’s disease with other non-Alzheimer’s dementia. Presents a comprehensive overview detailing all modalities of Alzheimer’s disease biomarkers Written for neuroscience researchers and clinicians studying or treating patients with Alzheimer’s Disease Integrates, in each chapter, the molecular/cellular abnormality due to Alzheimer’s disease and the technological advancement of biomarkers techniques
Book Synopsis Early Detection in Alzheimer's Disease by : Dennis Chan
Download or read book Early Detection in Alzheimer's Disease written by Dennis Chan and published by Academic Press. This book was released on 2024-12-01 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Early Detection of Alzheimer’s Disease: Biological and Technological Advances aims to introduce to a wide audience the high global priority problem of detecting AD prior to dementia onset. According to the Alzheimer’s Association, 5.8 million Americans are living with Alzheimer’s and care costs will cost the nation approximately $290 billion (2019 Alzheimer’s Disease Facts and Figures). With the failure of recent AD drug trials, many hypothesize that by the time symptoms appear, it is too late to be treated. Early detection can offer benefits such as more choice of medications, ability to participate in clinical trials, more time for family and for care planning. This book outlines potential solutions to the above problem using opportunities arising from the technology revolution, advances in neuroscience, and molecular biology. Most importantly, it discusses a paradigm shift from a reactive to a proactive diagnostic approach, aiming to detect disease before occurrence of symptoms. Topics covered include the use of sensing technologies (e.g. smartphones, smartwatches, Internet of Things) to detect early disease-related changes, the application of data science (machine learning/AI) to extract otherwise invisible disease features from these datasets and the potential to personalize diagnosis based on tracking changes in individual behaviours. Advances in blood-based biomarkers, brain imaging, and the potential for early diagnosis to aid interventions (lifestyle, dietary, pharmacological) to delay future development of dementia are also discussed. Outlines the importance of early diagnosis of Alzheimer’s Disease Helps readers understand the limitations of current clinical approaches and the need for a paradigmatic shift in diagnostic practice Discusses the potential role of technology in clinical practice using machine learning and artificial intelligence and the potential to personize diagnosis and treatment
Book Synopsis Development and Validation of Structural Magnetic Resonance Imaging (MRI)-based Biomarkers for Diagnosis and Prognosis of Prodromal Alzheimer's Disease by : Azar Zandifar
Download or read book Development and Validation of Structural Magnetic Resonance Imaging (MRI)-based Biomarkers for Diagnosis and Prognosis of Prodromal Alzheimer's Disease written by Azar Zandifar and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Alzheimer's disease (AD) is a neurodegenerative disease and the most common cause of dementia. Currently in Canada, there are about 750,000 people living with dementia with more than 60,000 new cases diagnosed each year. Considering the relative failures in recent clinical trials, combined with the promising prospect of life-style changes, early prediction of the future onset of dementia in the early stages of Alzheimer's disease continuum can be highly beneficial. Advances in better identifying future progressors may both enhance population design for clinical trials and better identify the at-risk populations for life-style interventions. This thesis focuses on enhancement of dementia onset in patients with Mild Cognitive Impairment (MCI) using structural Magnetic Resonance Images (MRI) driven markers. The volume of the hippocampus is one of the best-known MRI-based biomarkers for Alzheimer's disease. Here, it is investigated using four different automatic hippocampus segmentation methods, each enhanced with bias error correction. Results show that multi-atlas-based hippocampus segmentation methods are accurate and show high conformity with manual delineation, and they can be further enhanced using a bias correction technique. The methods compared are not significantly different in their ability to capture AD related pathology. The Cohen's d group difference in hippocampal volume between patients with Alzheimer's dementia and healthy controls is high for all the methods and is of medium size between patients with MCI who convert to dementia in the near future and to those who remain stable for all the methods.Due to the wide-spread use of hippocampal volume a recent effort has been made by researchers in the field to harmonize the hippocampus segmentation protocol. The resulting new protocol, known as the EADC-ADNI harmonized protocol or the HarP, needs more validation. I validate the protocol using a large multi-center database designed for AD biomarker research. The results show that the HarP-based automatic segmentation is an accurate hippocampal segmentation which can be used in AD-related research to capture the Alzheimer's pathology. The methods described above are used in a novel model to predict the onset of dementia in an MCI population during different follow-up periods. The model combines information from MRI-driven markers from the hippocampus and entorhinal cortex with cognitive scores at baseline and tries to predict the diagnostic stage of each subject in different time intervals following the baseline visit. The model produces promising results using information from both cognitive scores and MRI markers to reach higher performance. MRI markers are more sensitive, while cognitive scores bring specificity to the prediction. Furthermore, for a follow-up period of 5 years, the model reaches an area under the receiver operating curve of 0.92 and an accuracy of 87%. Therefore, the prognostic model demonstrates promise as a candidate to be used in the clinic to identify subjects with prodromal Alzheimer's dementia at the MCI stage. Finally, I show that the AD-related atrophy pattern is even present in cognitively normal subjects and can be captured through hippocampal-based markers. I show that there is a small effect size (as measured by Cohen's d) between hippocampal volume in subjects who progress to MCI and/or dementia or subjects who maintain their normal cognition, while the effect is of medium size for the hippocampal grade. This thesis evaluates different automatic and manual hippocampus segmentation techniques and validates hippocampus volume as the most widely used AD marker. Moreover, I present a model to predict the onset of dementia in subjects MCI. I further validate hippocampal driven markers as an Alzheimer's biomarker in very early stages of the disease. Therefore, the results of this work will improve early detection of Alzheimer's leading to decrease the prevalence of the disease." --
