Using Machine Learning To Differentiate Between Healthy Aging Mild Cognitive Impairment Alzheimer S Disease
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Using Machine Learning To Differentiate Between Healthy Aging Mild Cognitive Impairment Alzheimer S Disease
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Author : Monica Truelove-Hill
language : en
Publisher:
Release Date : 2018
Using Machine Learning To Differentiate Between Healthy Aging Mild Cognitive Impairment Alzheimer S Disease written by Monica Truelove-Hill and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with Alzheimer's disease categories.
Alzheimer's disease (AD) is an insidious disorder in which pathology may develop decades before outward symptoms become apparent. Identification of this disease in its earliest stages would provide the greatest opportunity for successful treatment. Current recommendations place patients in groups based primarily upon CSF -amyloid (A) levels, but the procedure to gather these data is invasive. If less intrusive methods could be identified to successfully predict which individuals are especially prone to develop AD, the benefits would be invaluable. Many studies have attempted to identify these individuals using neuroimaging methods such as MRI or PET, but very few studies have incorporated EEG data, despite research indicating its relationship with AD pathology. In this analysis, multimodal classifiers incorporating EEG, MRI, and PET data were developed and used in an attempt to differentiate between AD patients and a healthy control group, as well as MCI patients with AD A pathology and those without. Additionally, repeated-measures event-related potential (ERP) data were analyzed to directly examine changes related to AD progression.
Anatomical And Functional Neural Networks Changes In Cognitive Impairment And Healthy Aging
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Author : Fermín Segovia
language : en
Publisher: Frontiers Media SA
Release Date : 2023-04-14
Anatomical And Functional Neural Networks Changes In Cognitive Impairment And Healthy Aging written by Fermín Segovia and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-14 with Science categories.
Brainage
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Author : Katja Franke
language : en
Publisher: Sudwestdeutscher Verlag Fur Hochschulschriften AG
Release Date : 2014-09-09
Brainage written by Katja Franke and has been published by Sudwestdeutscher Verlag Fur Hochschulschriften AG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-09 with categories.
Based on the widespread but well-ordered brain tissue loss that occurs with healthy aging into senescence, this work presents a novel magnetic resonance imaging (MRI)-based biomarker, which identifies normal and abnormal aging-related brain atrophy. The novel BrainAGE approach is based on a database of structural MRI data, aggregating the complex, multidimensional aging patterns across the whole brain to one single value, i.e. the estimated brain age. Consequently, subtle deviations in "normal" brain atrophy can be directly quantified in terms of years by analyzing one standard MRI scan per subject. Various neuro-degenerative diseases - especially Alzheimer's disease (AD) - are widely linked to advanced brain aging. The BrainAGE approach is applied to identify advanced brain aging in subjects with mild cognitive impairment and AD, to predict conversion to AD, to relate individual BrainAGE scores with disease severity and prospective worsening of cognitive functions. Furthermore, BrainAGE identifies various risk factors of pathological brain aging that may precede the onset of clinical symptoms (e.g., diabetes mellitus type 2, metabolic syndrome).
Early Indicators Of Cognitive Decline Alzheimer S Disease And Related Dementias Captured By Neurophysiological Tools
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Author : Alexandra Wolf
language : en
Publisher: Frontiers Media SA
Release Date : 2024-04-19
Early Indicators Of Cognitive Decline Alzheimer S Disease And Related Dementias Captured By Neurophysiological Tools written by Alexandra Wolf and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-19 with Science categories.
Major neurocognitive disorders are one of the leading causes of disability and dependency among the elderly worldwide. Notably, their far-reaching impact extends beyond the estimated 50 million people currently living with a major neurocognitive disorder. As the conversion to Alzheimer's disease (AD) progresses, patients’ symptoms (e.g., memory loss, severe impairments in thinking and behavior) place a heavy toll on their caregivers, family, and friends, who face emotional frustration, coupled with great financial stress. Furthermore, in terms of global cost estimation, the World Health Organization predicted that by 2030, the treatment of patients with AD and other forms of acquired cognitive impairment will cost the healthcare system US$1.7 trillion (or US$2.8 trillion, if corrected for the increase in care costs).
Data Mining And Machine Learning For Identification Of Risk Factors And Prediction Of Cognitive Changes Among Aging Populations
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Author : Maryam Ahmadzadeh
language : en
Publisher:
Release Date : 2022
Data Mining And Machine Learning For Identification Of Risk Factors And Prediction Of Cognitive Changes Among Aging Populations written by Maryam Ahmadzadeh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Cognitive decline is a common consequence of aging, with dementia at the extreme end of this process. The decline in cognition may decrease the ability and efficiency of performing daily living activities among older adults. Unfortunately, existing pharmacological treatments are not effective at delaying the incidence of dementia and cognitive impairment. As such, many medical recommendations are focused on preventative measures (i.e., lifestyle activities, social engagement, physical activity, and proper diet) to maintain cognitive health. Although the results of the previous studies in this area are promising, there are yet unanswered questions that restrict the practical applications and recommendations of the interventions and their impact on cognition. This thesis research investigates the application of data mining methods to answer some of the yet unanswered questions. Accordingly, this thesis first aims to investigate the impact of engagement in different intensities and frequencies of physical activity on two domains of cognitive function. We seek to test the hypothesis that engaging in a physically active lifestyle leads to relatively preserved cognitive health during aging. The findings of the study assist communities to promote healthy cognitive aging among older populations by implementing new policies and providing recommendations about the details of engaging in optimal physical activity in terms of intensity and frequency. Second, we aim to focus on the impact of cognitive reserve on cross-sectional cognitive function, short-term and long-term rates of cognitive changes over 2 years and 10 years of follow-up. Our objective is to attempt to improve the limitations of previous studies in terms of study design, intervention characteristics, and methodological issues. Our use of data mining approaches and appropriate study design models assist in controlling the impact of confounding factors and moving forward towards investigating the causal relationship rather than correlational association. The results of this study contribute to establishing interventions to be developed during the aging process to delay cognitive decline. Lastly, we aim to investigate the possibility of implementing a model to predict future cognitive changes with the combination of categorical and continuous data from multiple domains such as sociodemographic, health, psychology, and cognition, simultaneously. We also use a machine learning-based framework to identify the most important predictors of future cognitive changes. Incorporation of the findings of the study in public health policies assists in improving the counseling of older adults and caregivers and developing the plan of cognitive care and effective interventions to develop healthy aging.
Differentiation Of Alzheimer S Disease Dementia Mild Cognitive Impairment And Normal Condition Using Pet Fdg And Av 45 Imaging
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Author : Ayesha Anjum
language : en
Publisher:
Release Date : 2013
Differentiation Of Alzheimer S Disease Dementia Mild Cognitive Impairment And Normal Condition Using Pet Fdg And Av 45 Imaging written by Ayesha Anjum and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with categories.
We used PET imaging with tracers F18-FDG and AV45 in conjunction with the classification methods in the field of "Machine Learning". PET images were acquired in dynamic mode, an image every 5 minutes.The images used come from three different sources: the database ADNI (Alzheimer's Disease Neuro-Imaging Initiative, University of California Los Angeles) and two protocols performed in the PET center of the Purpan Hospital. The classification was applied after processing dynamic images by Principal Component Analysis and Independent Component Analysis. The data were separated into training set and test set. To evaluate the performance of the classification we used the method of cross-validation LOOCV (Leave One Out Cross Validation). We give a comparison between the two most widely used classification methods, SVM (Support Vector Machine) and artificial neural networks (ANN) for both tracers. The combination giving the best classification rate seems to be SVM and AV45 tracer. However the most important confusion is found between MCI patients and normal subjects. Alzheimer's patients differ somewhat better since they are often found in more than 90%. We evaluated the generalization of our methods by making learning from set of data and classification on another set . We reached the specifity score of 100% and sensitivity score of more than 81%. SVM method showed a bettrer sensitivity than Artificial Neural Network method. The value of such work is to help the clinicians in diagnosing Alzheimer's disease.
Seven Steps To Managing Your Memory
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Author : Andrew E. Budson MD
language : en
Publisher: Oxford University Press
Release Date : 2017-07-01
Seven Steps To Managing Your Memory written by Andrew E. Budson MD and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-01 with Medical categories.
As you age, you may find yourself worrying about your memory. Where did I put those car keys? What time was my appointment? What was her name again? With more than 41 million Americans over the age of 65 in the United States, the question becomes how much (or, perhaps, what type) of memory loss is to be expected as one gets older and what should trigger a visit to the doctor. Seven Steps to Managing Your Memory addresses these key concerns and more, such as... · What are the signs that suggest your memory problems are more than just part of normal aging? · Is it normal to have concerns about your memory? · What are the markers of mild cognitive impairment, dementia, Alzheimer's, and other neurodegenerative diseases? · How should you convey your memory concerns to your doctor? · What can your doctor do to evaluate your memory? · Which healthcare professional(s) should you see? · What medicines, alternative therapies, diets, and exercises are available to improve your memory? · Can crossword puzzles, computer brain-training games, memory aids, and strategies help strengthen your memory? · What other resources are available when dealing with memory loss? Seven Steps to Managing Your Memory is written in an easy-to-read yet comprehensive style, featuring clinical vignettes and character-based stories that provide real-life examples of how to successfully manage age-related memory loss.
Machine Learning In Healthcare Informatics
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Author : Sumeet Dua
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-12-09
Machine Learning In Healthcare Informatics written by Sumeet Dua and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-09 with Technology & Engineering categories.
The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.
Use Of Machine Learning Technology In The Diagnosis Of Alzheimer S Disease
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Author : Noel O'Kelly
language : en
Publisher:
Release Date : 2016
Use Of Machine Learning Technology In The Diagnosis Of Alzheimer S Disease written by Noel O'Kelly and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Alzheimer's disease (AD) is thought to be the most common cause of dementia and it is estimated that only 1-in-4 people with Alzheimer's are correctly diagnosed in a timely fashion. While no definitive cure is available, when the impairment is still mild the symptoms can be managed and treatment is most effective when it is started before significant downstream damage occurs, i.e., at the stage of mild cognitive impairment (MCI) or even earlier. AD is clinically diagnosed by physical and neurological examination, and through neuropsychological and cognitive tests. There is a need to develop better diagnostic tools, which is what this thesis addresses. Dublin City University School of Nursing and Human Sciences runs a memory clinic, Memory Works where subjects concerned about possible dementia come to seek clarity. Data collected at interview is recorded and one aim of the work in this thesis is to explore the use of machine learning techniques to generate a classifier that can assist in screening new individuals for different stages of AD. However, initial analysis of the features stored in the Memory Works database indicated that there is an insufficient number of instances available (about 120 at this time) to train a machine learning model to accurately predict AD stage on new test cases. The National Azheimers Cordinating Center (NACC) in the U.S collects data from National Institute for Aging (NIA)-funded Alzheimer's Disease Centers (ADCs) and maintains a large database of standardized clinical and neuropathological research data from these ADCs. NACC data are freely available to researchers and we have been given access to 105,000 records from the NACC. We propose to use this dataset to test the hypothesis that a machine learning classifier can be generated to predict the dementia status for new, previously unseen subjects. We will also, by experiment, establish both the minimum number of instances required and the most important features from assessment interviews, to use for this prediction.
Separating Symptomatic Alzheimer S Disease From Depression Based On Structural Mri
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Author : Stefan Klöppel
language : en
Publisher:
Release Date : 2018
Separating Symptomatic Alzheimer S Disease From Depression Based On Structural Mri written by Stefan Klöppel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
Abstract: Older patients with depression or Alzheimer's disease (AD) at the stage of early dementia or mild cognitive impairment may present with objective cognitive impairment, although the pathology and thus therapy and prognosis differ substantially. In this study, we assessed the potential of an automated algorithm to categorize a test set of 65 T1-weighted structural magnetic resonance images (MRI). A convenience sample of elderly individuals fulfilling clinical criteria of either AD (n = 28) or moderate and severe depression (n = 37) was recruited from different settings to assess the potential of the pattern recognition method to assist in the differential diagnosis of AD versus depression. We found that our algorithm learned discriminative patterns in the subject's grey matter distribution reflected by an area under the receiver operator characteristics curve of up to 0.83 (confidence interval ranged from 0.67 to 0.92) and a balanced accuracy of 0.79 for the separation of depression from AD, evaluated by leave-one-out cross validation. The algorithm also identified consistent structural differences in a clinically more relevant scenario where the data used during training were independent from the data used for evaluation and, critically, which included five possible diagnoses (specifically AD, frontotemporal dementia, Lewy body dementia, depression, and healthy aging). While the output was insufficiently accurate to use it directly as a means for classification when multiple classes are possible, the continuous output computed by the machine learning algorithm differed between the two groups that were investigated. The automated analysis thus could complement, but not replace clinical assessments