Graph Neural Networks for Neurological Disorders
Springer International Publishing (Verlag)
978-3-032-04314-6 (ISBN)
This book represents a unique and comprehensive resource for understanding the intersection of advanced artificial intelligence (AI) and neurology. By focusing on graph neural networks (GNNs), the book addresses a crucial gap in the current literature, providing valuable insights into the analysis and interpretation of complex brain networks and neurological data. Intended for a diverse audience, including clinicians, scientists, researchers, and students, it demystifies the complexities of GNNs and their applications in neurology. For clinicians and healthcare practitioners, the book illustrates how GNNs can enhance diagnostic accuracy, inform personalized treatment plans and predict disease progression. This leads to improved patient outcomes and a deeper understanding of neurological conditions such as Alzheimer's, Parkinson's, multiple sclerosis and epilepsy. Researchers will find the book particularly valuable as it delves into the methodologies and technical aspects of GNNs, showcasing their ability to handle diverse data sources including genetic, imaging and clinical information. By integrating these datasets, GNNs reveal hidden patterns and biomarkers, offering new avenues for research and potential therapeutic targets.
A Guide to Graph Neural Networks for Neurological Disorders addresses the challenge of missing data, a common issue in neurological research, and demonstrates how GNNs can manage and mitigate these gaps. For students, both undergraduate and postgraduate, the book serves as an educational tool, providing clear explanations and practical examples that make complex concepts accessible. It equips the next generation of neuroscientists and data scientists with the knowledge and skills needed to contribute to this rapidly evolving field. The book aims to provide a foundational understanding of GNNs, demonstrate their practical applications in neurology, and inspire further research and innovation. By bridging the gap between AI and medical practice, the book empowers readers to leverage cutting-edge technology in the quest to understand and treat neurological illnesses, ultimately enhancing the quality of care and advancing the field of neuroscience.
Md. Mehedi Hassan is currently a Ph.D. researcher in STEM (Computer and Information Science) at the University of South Australia, working on a fully funded research project. He started his Ph.D. in 2025, building on a strong academic background in computer science and engineering. He holds an M.Sc. in Computer Science and Engineering from Khulna University, Bangladesh, completed in 2024, and a B.Sc. in Computer Science and Engineering from North Western University, completed in 2022. His research spans computer science engineering and data science, with a strong focus on predictive analysis and expert system development. Md. Mehedi Hassan has authored 73 research papers and edited 5 books, actively contributing to the academic community. He serves as a peer reviewer for over 80 prestigious journals and collaborates extensively in interdisciplinary research. As a trainer for the VCourse platform, he has educated over 250 students over the past two years, sharing knowledge in emerging technologies and research methodologies. Beyond publications, he has actively engaged in intellectual property development, with several patents filed and three already granted in his name. His current research interests include computational neuroscience, machine learning for healthcare, and predictive modeling for biometrics.
Anindya Nag obtained an M.Sc. in Computer Science and Engineering from Khulna University in Khulna, Bangladesh, and a B.Tech. in Computer Science and Engineering from Adamas University in Kolkata, India. He is currently a lecturer in the Department of Computer Science and Engineering at the Northern University of Business and Technology in Khulna, Khulna 9100, Bangladesh. His research focuses on health informatics, medical Internet of Things, neuroscience, and machine learning. He serves as a reviewer for numerous prestigious journals and international conferences. He has authored and co-authored about 47 publications, including journal articles, conference papers, and book chapters, and has co-edited nine books.
Herat Joshi is a visionary leader in the field of Healthcare Informatics, healthcare technology and data management, currently serving as the Vice Chair at IEEE Iowa Illinois Section. Holding a Ph.D. in Computer Science & Engineering, Herat has a distinguished track record of pioneering healthcare solutions that integrate AI, IoT, and high-performance computing to enhance clinical outcomes. His notable achievements include leading EHR implementations across multiple health systems, advancing interoperability, and receiving prestigious accolades such as the Outstanding Leadership Award at the Health 2.0 Conference. A recognized thought leader, Herat is a Fellow of the American College of Health Data Management (FACDM), Vice Chair of an American Medical Informatics Association (AMIA) Workgroup, Senior Member of IEEE, Vice Chair of IEEE Iowa-Illinois Section and a Gartner Ambassador. He also contributes as a reviewer and editor for leading scientific journals.
Understanding Graph Neural Networks: Foundations and Applications.- Neurological Disorders: An Overview of Classification and Diagnosis.- Graph Theory Fundamentals for Brain Network Modeling.- Graph Neural Network Architectures: A Comprehensive Review.- Genetic Influences on Brain Connectivity and Neurological Disorders.- Multi-modal Neuroimaging Data Fusion for GNNs.- Predictive Modeling of Neurological Disease Progression.- Diagnostic Applications of Graph Neural Networks.- Personalized Medicine Approaches in Neurology.- Ethical Considerations in GNN Research for Neurological Disorders.- Network Neuroscience: Bridging Gaps in Understanding Brain Connectivity.- GNNs for Studying Cognitive Disorders: Alzheimer's Disease and Dementia.- Parkinson's Disease: Insights from Graph Neural Network Analysis.- GNNs in Epilepsy Research: Seizure Prediction and Classification.- Neurodevelopmental Disorders and GNN Applications.- Brain Tumor Analysis using Graph Neural Networks.- Stroke and GNN-based Rehabilitation Strategies.- GNNs for Understanding Neurodegenerative Disorders.- Neuropsychiatric Disorders: Insights from Graph Neural Network Analysis.- Future Directions and Challenges in GNN Research for Neurology.
| Erscheinungsdatum | 04.11.2025 |
|---|---|
| Zusatzinfo | XV, 242 p. 54 illus., 42 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Angewandte Mathematik |
| Medizin / Pharmazie | |
| Naturwissenschaften ► Biologie ► Humanbiologie | |
| Technik | |
| Schlagworte | AI in Neurology • brain network analysis • Computational Neuroscience • Machine Learning for Neurological Disorders • Neuroinformatics • Personalized Neurological Treatment |
| ISBN-10 | 3-032-04314-X / 303204314X |
| ISBN-13 | 978-3-032-04314-6 / 9783032043146 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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