Deep Learning Applications in Neuroinformatics

Advances, Methods, and Perspectives

Karthik Ramamurthy

• Publisher: Academic Press
• Year: 2026
• eBook ISBN: 9780443414602
• Topic: Biological Sciences
• Subtopic: Biotechnology
• Index: Biological Sciences

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

• Front Cover
• Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives
• Copyright Page
• Dedication
• Contents
• List of contributors
• About the editor
• Preface
• 1 Fundamentals of deep learning in neuroinformatics
• 2 Data preprocessing and augmentation techniques for neuroinformatics
• 3 Deep learning in stroke care
• 4 Deep learning for Alzheimer's disease and mild cognitive impairment detection
• 5 Ensemble deep learning with generative augmentation for Alzheimer's stage classification
• 6 Deep learning applications in Parkinson's disease and movement disorders
• 7 Advancing early diagnosis of Parkinson's disease: the role of deep learning and innovative diagnostic tools
• 8 Deep learning networks for multiple sclerosis lesion classification and segmentation—a review
• 9 Deep learning for autism spectrum disorder
• 10 Deep learning for neurodevelopmental and psychiatric disorders
• 11 Deep learning techniques for brain tumor detection and classification: trends, challenges, and future directions
• 12 Transfer learning and domain adaptation in neuroinformatics
• 13 Explainable artificial intelligence in neuroinformatics: enhancing transparency and understanding in computational neuroscience
• Index
• Back Cover