Deep Learning In Aging Neuroscience
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Deep Learning In Aging Neuroscience
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Author : Javier Ramírez
language : en
Publisher: Frontiers Media SA
Release Date : 2020-12-28
Deep Learning In Aging Neuroscience written by Javier Ramírez 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 2020-12-28 with Science categories.
This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact.
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).
Methods And Applications In Aging Neuroscience
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Author : Yang Jiang
language : en
Publisher: Frontiers Media SA
Release Date : 2023-07-10
Methods And Applications In Aging Neuroscience written by Yang Jiang 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 2023-07-10 with Science categories.
Deep Learning Approaches For Early Diagnosis Of Neurodegenerative Diseases
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Author : Rodriguez, Raul Villamarin
language : en
Publisher: IGI Global
Release Date : 2024-02-14
Deep Learning Approaches For Early Diagnosis Of Neurodegenerative Diseases written by Rodriguez, Raul Villamarin and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-14 with Medical categories.
Within the context of global health challenges posed by intractable neurodegenerative diseases like Alzheimer's and Parkinson's, the significance of early diagnosis is critical for effective intervention, and scientists continue to discover new methods of detection. However, actual diagnosis goes beyond detection to include a significant analysis of combined data for many cases, which presents a challenge of several complicated calculations. Deep Learning Approaches for Early Diagnosis of Neurodegenerative Diseases stands as a groundbreaking work at the intersection of artificial intelligence and neuroscience. The book orchestrates a symphony of cutting-edge techniques and progressions in early detection by assembling eminent experts from the domains of deep learning and neurology. Through a harmonious blend of research areas and pragmatic applications, this monumental work charts the transformative course to revolutionize the landscape of early diagnosis and management of neurodegenerative disorders. Within the pages, readers will embark through the intricate landscape of neurodegenerative diseases, the fundamental underpinnings of deep learning, the nuances of neuroimaging data acquisition and preprocessing, the alchemy of feature extraction and representation learning, and the symphony of deep learning models tailored for neurodegenerative disease diagnosis. The book also delves into integrating multimodal data to augment diagnosis, the imperative of rigorously evaluating and validating deep learning models, and the ethical considerations and challenges entwined with deep learning for neurodegenerative diseases.
Machine Learning And Deep Learning In Neuroimaging Data Analysis
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Author : Anitha S. Pillai
language : en
Publisher: CRC Press
Release Date : 2024-02-15
Machine Learning And Deep Learning In Neuroimaging Data Analysis written by Anitha S. Pillai and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-15 with Computers categories.
Machine learning (ML) and deep learning (DL) have become essential tools in healthcare. They are capable of processing enormous amounts of data to find patterns and are also adopted into methods that manage and make sense of healthcare data, either electronic healthcare records or medical imagery. This book explores how ML/DL can assist neurologists in identifying, classifying or predicting neurological problems that require neuroimaging. With the ability to model high-dimensional datasets, supervised learning algorithms can help in relating brain images to behavioral or clinical observations and unsupervised learning can uncover hidden structures/patterns in images. Bringing together artificial intelligence (AI) experts as well as medical practitioners, these chapters cover the majority of neuro problems that use neuroimaging for diagnosis, along with case studies and directions for future research.
Deep Learning Techniques And Their Applications To The Healthy And Disordered Brain During Development Through Adulthood And Beyond
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Author : Amir Shmuel
language : en
Publisher: Frontiers Media SA
Release Date : 2023-02-07
Deep Learning Techniques And Their Applications To The Healthy And Disordered Brain During Development Through Adulthood And Beyond written by Amir Shmuel 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 2023-02-07 with Science categories.
Machine Learning In Neuroscience Volume Ii
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Author : Reza Lashgari
language : en
Publisher: Frontiers Media SA
Release Date : 2022-11-14
Machine Learning In Neuroscience Volume Ii written by Reza Lashgari 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 2022-11-14 with Science categories.
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.
Neuroscience In The 21st Century New Tools And Techniques Driving Exciting Discoveries
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Author : Aliasghar Tabatabaei Mohammadi
language : en
Publisher: Nobel Sciences
Release Date :
Neuroscience In The 21st Century New Tools And Techniques Driving Exciting Discoveries written by Aliasghar Tabatabaei Mohammadi and has been published by Nobel Sciences this book supported file pdf, txt, epub, kindle and other format this book has been release on with Medical categories.
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.