Description
Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives explores how deep learning revolutionizes neuroinformatics, covering the latest methods and applications of deep learning in analyzing neuroimaging data from EEG, MRI, PET, and more. The book addresses critical neurological disorders like Alzheimer's disease, Mild Cognitive Impairment, Stroke, and Autism Spectrum Disorder, bridging the gap between neuroscience and artificial intelligence. It is an ideal resource for researchers, practitioners, and students with insights from leading experts.- Consolidates scattered information on deep learning techniques in neuroimaging data analysis, facilitating access for researchers, practitioners, and students- Explores deep learning algorithms applied to various neuroimaging data types, including EEG, MRI, and PET scans- Highlights methodologies like CNNs and RNNs- Includes real-world case studies that demonstrate how deep learning enhances research and clinical applications, such as identifying biomarkers for Alzheimer's disease and stroke
Table of Contents
1. Introduction to Deep Learning in Neuroinformatics2. Fundamentals of Deep Learning in Neuroinformatics3. Data Preprocessing and Augmentation Techniques for Neuroinformatics4. Deep Learning for Alzheimer's Disease and Mild Cognitive Impairment5. Deep Learning in Stroke Detection and Rehabilitation6. Deep Learning for Autism Spectrum Disorder7. Deep Learning in Epilepsy Detection and Management8. Deep Learning Applications in Parkinson's Disease and Movement Disorders9. Deep Learning for Multiple Sclerosis10. Deep Learning in Traumatic Brain Injury (TBI)11. Deep Learning for Neurodevelopmental and Psychiatric Disorders12. Explainable AI in Neuroinformatics13. Transfer Learning and Domain Adaptation in Neuroinformatics14. Integrating Multi-Modal Neuroimaging and Signal Data with Deep Learning15. Conclusion and Future Perspectives
