LEADER 00737nam0-22002771i-450- 001 990001736560403321 005 20070529154731.0 035 $a000173656 035 $aFED01000173656 035 $a(Aleph)000173656FED01 035 $a000173656 100 $a20030910d1941----km-y0itay50------ba 101 0 $aita 200 1 $a<>Egitto e il suo cotone$fItalo Paviolo 210 $aBologna$cEdiz. Agricole$d1941 215 $a103 p.$cill.$d16 cm 610 0 $aCotone 676 $a677.21 700 1$aPaviolo,$bItalo$075225 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990001736560403321 952 $a60 677.21 C 3$b33429$fFAGBC 959 $aFAGBC 996 $aEgitto e il suo cotone$9364715 997 $aUNINA LEADER 05418nam 22005655 450 001 9911039321203321 005 20260605203525.0 010 $a9783032043153$b(electronic bk.) 024 7 $a10.1007/978-3-032-04315-3 035 $a(MiAaPQ)EBC32384782 035 $a(Au-PeEL)EBL32384782 035 $a(CKB)41996879700041 035 $a(DE-He213)978-3-032-04315-3 035 $a(OCoLC)1549523937 035 $a(EXLCZ)9941996879700041 100 $a20251101d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGraph Neural Networks for Neurological Disorders $eFundamentals, Applications and Benefits in Research and Diagnostics /$fedited by Md. Mehedi Hassan, Anindya Nag, Shariful Islam, Herat Joshi 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (0 pages) 225 1 $aMedicine Series 311 08$aPrint version: Hassan, Mehedi Graph Neural Networks for Neurological Disorders Cham : Springer,c2025 9783032043146 327 $aUnderstanding 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. 330 $aThis 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. 410 0$aMedicine Series 606 $aMedical informatics 606 $aNeurosciences 606 $aNeural networks (Computer science) 606 $aHealth Informatics 606 $aNeuroscience 606 $aMathematical Models of Cognitive Processes and Neural Networks 615 0$aMedical informatics. 615 0$aNeurosciences. 615 0$aNeural networks (Computer science) 615 14$aHealth Informatics. 615 24$aNeuroscience. 615 24$aMathematical Models of Cognitive Processes and Neural Networks. 676 $a610.285 700 $aHassan$b Mehedi$01833381 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9911039321203321 996 $aGraph Neural Networks for Neurological Disorders$94454531 997 $aUNINA