Deep Learning Applications in Neuroinformatics
Advances, Methods, and Perspectives
- 1st Edition - May 1, 2026
- Latest edition
- Editor: Karthik Ramamurthy
- Language: English
Deep Learning Applications in Neuroinformatics: Advances, Methods, and Perspectives explores how deep learning revolutionizes neuroinformatics, covering the latest methods and ap… Read more
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
Researchers, professionals, and graduate students in the fields of computer science, biomedical engineering, medical imaging, and neuroscience
1. Introduction to Deep Learning in Neuroinformatics
1.1. Introduction
1.2. Background
1.3. Overview of Neuroinformatics
1.4. Role of Deep Learning in Neuroscience
1.5. Current Trends and Future Directions
1.6. Conclusion
2. Fundamentals of Deep Learning in Neuroinformatics
2.1. Introduction
2.2. Background
2.3. Basics of Neural Networks
2.4. Convolutional Neural Networks (CNNs) in Neuroimaging and Signal Processing
2.5. Recurrent Neural Networks (RNNs) for Time-Series Neurodata
2.6. Advanced Architectures (GANs, Transformers) in Neuroinformatics
2.7. Conclusion
3. Data Preprocessing and Augmentation Techniques for Neuroinformatics
3.1. Introduction
3.2. Background
3.4. Data Cleaning and Normalization for Neuroimaging and Signal Data
3.5. Augmentation Strategies for Neuroimaging and Signal Datasets
3.6. Handling Imbalanced Neuroimaging and Signal Data
3.7. Data Privacy and Ethical Considerations in Neuroinformatics
3.8. Conclusion Applications of Deep Learning in Neuroinformatics
4. Deep Learning for Alzheimer’s Disease and Mild Cognitive Impairment
4.1. Introduction
4.2. Background
4.3. Modalities: EEG, MRI, PET
4.4. Early Detection and Diagnosis using CNNs and RNNs
4.5. Disease Progression Monitoring with Autoencoders
4.6. Treatment Response Prediction using GANs
4.7. Personalized Intervention Strategies with Deep Learning
4.8 Conclusion
5. Deep Learning in Stroke Detection and Rehabilitation
5.1. Introduction
5.2. Background
5.3. Modalities: MRI, CT, EEG
5.4. Acute Stroke Detection with CNNs
5.5. Post-Stroke Recovery Analysis using RNNs
5.6. Personalized Rehabilitation Strategies with Reinforcement Learning
5.7. Predictive Modeling for Stroke Recurrence using Deep Learning
5.8. User Role and Trustworthiness of AI in Stroke Management
5.9. Conclusion
6. Deep Learning for Autism Spectrum Disorder
6.1. Introduction
6.2. Background
6.3. Modalities: MRI, fMRI, EEG
6.4. Diagnostic Tools using CNNs and RNNs
6.5. Behavioral and Neuroimaging Analysis with Autoencoders
6.6. Early Intervention and Therapy Monitoring using GANs
6.7. Predictive Analysis for Therapy Outcomes with Deep Learning
6.8. User Role and Trustworthiness of AI in ASD
6.9. Conclusion
7. Deep Learning in Epilepsy Detection and Management
7.1. Introduction
7.2. Background
7.3. Modalities: EEG, MRI
7.4. Seizure Detection Algorithms using CNNs and LSTMs
7.5. Pre-Surgical Planning with Deep Neural Networks
7.6. Long-Term Monitoring Solutions with RNNs
7.7. Personalized Treatment Plans using Deep Learning
7.8. User Role and Trustworthiness of AI in Epilepsy Management
7.9. Conclusion
8. Deep Learning Applications in Parkinson’s Disease and Movement Disorders
8.1. Introduction
8.2. Background
8.3. Modalities: MRI, PET, SPECT
8.4. Early Diagnosis and Monitoring using CNNs
8.5. Motor Function Analysis with LSTMs and RNNs
8.6. Actigraphy and Its Role in Data-Intensive Monitoring
8.7. Treatment Optimization Strategies with GANs
8.8. Predictive Modeling for Disease Progression with Deep Learning
8.9. User Role and Trustworthiness of AI in Parkinson’s Disease Management
8.10. Conclusion
9. Deep Learning for Multiple Sclerosis
9.1. Introduction
9.2. Background
9.3. Modalities: MRI, OCT
9.4. Lesion Detection and Segmentation with CNNs
9.5. Disease Progression Tracking using RNNs
9.6. Treatment Response Assessment with Autoencoders
9.7. Predictive Modeling for Relapse using Deep Learning
9.8. Conclusion
10. Deep Learning in Traumatic Brain Injury (TBI)
10.1. Introduction
10.2. Background
10.3. Modalities: MRI, CT, EEG
10.4. Detection and Classification using CNNs and RNNs
10.5. Prognosis and Recovery Monitoring with LSTMs
10.6. Rehabilitation Strategies using Reinforcement Learning
10.7. Predictive Analysis for Recovery Outcomes with Deep Learning
10.8. Conclusion
11. Deep Learning for Neurodevelopmental and Psychiatric Disorders
11.1. Introduction
11.2. Background
11.3. Modalities: MRI, fMRI, EEG
11.4. DL Techniques for ADHD, Schizophrenia, and Depression
11.5. Diagnostic and Monitoring Tools using CNNs and RNNs
11.6. Personalized Treatment and Intervention Analysis with GANs
11.7. Predictive Modeling for Therapeutic Outcomes with Deep Learning
11.8. Conclusion
12. Explainable AI in Neuroinformatics
12.1. Introduction
12.2. Background
12.3. Importance of Explainability in Deep Learning
12.4. Techniques for Interpretability in Neuroimaging and Signal Processing
12.5. Applications and Case Studies
12.6. Conclusion
13. Transfer Learning and Domain Adaptation in Neuroinformatics
13.1. Introduction
13.2. Background
13.4. Fundamentals of Transfer Learning for Neuroimaging and Signal Data
13.5. Domain Adaptation Techniques in Neuroinformatics
13.6. Applications in Multi-Modal Data Integration
13.7. Conclusion
14. Integrating Multi-Modal Neuroimaging and Signal Data with Deep Learning
14.1. Introduction
14.2. Background
14.3. Challenges in Multi-Modal Data Integration
14.4. Techniques for Combining EEG, MRI, PET, and Other Data
14.5. Deep Learning Approaches for Integration
14.6. Applications and Case Studies
14.7. Conclusion
15. Conclusion and Future Perspectives
15.1. Summary of Key Insights in DL and Neuroinformatics
15.2. Impact on Neuroscience and Healthcare
15.3. Emerging Deep Learning Technologies
15.4. Future Research Directions in Neuroinformatics
15.5. Potential Challenges and Solutions
15.6. Final Thoughts and Future Outlook
1.1. Introduction
1.2. Background
1.3. Overview of Neuroinformatics
1.4. Role of Deep Learning in Neuroscience
1.5. Current Trends and Future Directions
1.6. Conclusion
2. Fundamentals of Deep Learning in Neuroinformatics
2.1. Introduction
2.2. Background
2.3. Basics of Neural Networks
2.4. Convolutional Neural Networks (CNNs) in Neuroimaging and Signal Processing
2.5. Recurrent Neural Networks (RNNs) for Time-Series Neurodata
2.6. Advanced Architectures (GANs, Transformers) in Neuroinformatics
2.7. Conclusion
3. Data Preprocessing and Augmentation Techniques for Neuroinformatics
3.1. Introduction
3.2. Background
3.4. Data Cleaning and Normalization for Neuroimaging and Signal Data
3.5. Augmentation Strategies for Neuroimaging and Signal Datasets
3.6. Handling Imbalanced Neuroimaging and Signal Data
3.7. Data Privacy and Ethical Considerations in Neuroinformatics
3.8. Conclusion Applications of Deep Learning in Neuroinformatics
4. Deep Learning for Alzheimer’s Disease and Mild Cognitive Impairment
4.1. Introduction
4.2. Background
4.3. Modalities: EEG, MRI, PET
4.4. Early Detection and Diagnosis using CNNs and RNNs
4.5. Disease Progression Monitoring with Autoencoders
4.6. Treatment Response Prediction using GANs
4.7. Personalized Intervention Strategies with Deep Learning
4.8 Conclusion
5. Deep Learning in Stroke Detection and Rehabilitation
5.1. Introduction
5.2. Background
5.3. Modalities: MRI, CT, EEG
5.4. Acute Stroke Detection with CNNs
5.5. Post-Stroke Recovery Analysis using RNNs
5.6. Personalized Rehabilitation Strategies with Reinforcement Learning
5.7. Predictive Modeling for Stroke Recurrence using Deep Learning
5.8. User Role and Trustworthiness of AI in Stroke Management
5.9. Conclusion
6. Deep Learning for Autism Spectrum Disorder
6.1. Introduction
6.2. Background
6.3. Modalities: MRI, fMRI, EEG
6.4. Diagnostic Tools using CNNs and RNNs
6.5. Behavioral and Neuroimaging Analysis with Autoencoders
6.6. Early Intervention and Therapy Monitoring using GANs
6.7. Predictive Analysis for Therapy Outcomes with Deep Learning
6.8. User Role and Trustworthiness of AI in ASD
6.9. Conclusion
7. Deep Learning in Epilepsy Detection and Management
7.1. Introduction
7.2. Background
7.3. Modalities: EEG, MRI
7.4. Seizure Detection Algorithms using CNNs and LSTMs
7.5. Pre-Surgical Planning with Deep Neural Networks
7.6. Long-Term Monitoring Solutions with RNNs
7.7. Personalized Treatment Plans using Deep Learning
7.8. User Role and Trustworthiness of AI in Epilepsy Management
7.9. Conclusion
8. Deep Learning Applications in Parkinson’s Disease and Movement Disorders
8.1. Introduction
8.2. Background
8.3. Modalities: MRI, PET, SPECT
8.4. Early Diagnosis and Monitoring using CNNs
8.5. Motor Function Analysis with LSTMs and RNNs
8.6. Actigraphy and Its Role in Data-Intensive Monitoring
8.7. Treatment Optimization Strategies with GANs
8.8. Predictive Modeling for Disease Progression with Deep Learning
8.9. User Role and Trustworthiness of AI in Parkinson’s Disease Management
8.10. Conclusion
9. Deep Learning for Multiple Sclerosis
9.1. Introduction
9.2. Background
9.3. Modalities: MRI, OCT
9.4. Lesion Detection and Segmentation with CNNs
9.5. Disease Progression Tracking using RNNs
9.6. Treatment Response Assessment with Autoencoders
9.7. Predictive Modeling for Relapse using Deep Learning
9.8. Conclusion
10. Deep Learning in Traumatic Brain Injury (TBI)
10.1. Introduction
10.2. Background
10.3. Modalities: MRI, CT, EEG
10.4. Detection and Classification using CNNs and RNNs
10.5. Prognosis and Recovery Monitoring with LSTMs
10.6. Rehabilitation Strategies using Reinforcement Learning
10.7. Predictive Analysis for Recovery Outcomes with Deep Learning
10.8. Conclusion
11. Deep Learning for Neurodevelopmental and Psychiatric Disorders
11.1. Introduction
11.2. Background
11.3. Modalities: MRI, fMRI, EEG
11.4. DL Techniques for ADHD, Schizophrenia, and Depression
11.5. Diagnostic and Monitoring Tools using CNNs and RNNs
11.6. Personalized Treatment and Intervention Analysis with GANs
11.7. Predictive Modeling for Therapeutic Outcomes with Deep Learning
11.8. Conclusion
12. Explainable AI in Neuroinformatics
12.1. Introduction
12.2. Background
12.3. Importance of Explainability in Deep Learning
12.4. Techniques for Interpretability in Neuroimaging and Signal Processing
12.5. Applications and Case Studies
12.6. Conclusion
13. Transfer Learning and Domain Adaptation in Neuroinformatics
13.1. Introduction
13.2. Background
13.4. Fundamentals of Transfer Learning for Neuroimaging and Signal Data
13.5. Domain Adaptation Techniques in Neuroinformatics
13.6. Applications in Multi-Modal Data Integration
13.7. Conclusion
14. Integrating Multi-Modal Neuroimaging and Signal Data with Deep Learning
14.1. Introduction
14.2. Background
14.3. Challenges in Multi-Modal Data Integration
14.4. Techniques for Combining EEG, MRI, PET, and Other Data
14.5. Deep Learning Approaches for Integration
14.6. Applications and Case Studies
14.7. Conclusion
15. Conclusion and Future Perspectives
15.1. Summary of Key Insights in DL and Neuroinformatics
15.2. Impact on Neuroscience and Healthcare
15.3. Emerging Deep Learning Technologies
15.4. Future Research Directions in Neuroinformatics
15.5. Potential Challenges and Solutions
15.6. Final Thoughts and Future Outlook
- Edition: 1
- Latest edition
- Published: May 1, 2026
- Language: English
KR
Karthik Ramamurthy
Dr. Karthik Ramamurthy obtained his Doctoral degree from Vellore Institute of Technology, India and Master’s degree from Anna University, India. Currently, He serves as Associate Professor in the Research Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai. His research interest includes Artificial Intelligence, Deep Learning, Computer Vision, Digital Image Processing, and Medical Image Analysis. He has published around 80 papers in peer reviewed journals and conferences. He is an active reviewer for journals published by Elsevier, IEEE Springer and Nature.
Affiliations and expertise
Vellore Institute of Technology, Chennai, India