Details

Subject(s)

• Medical informatics [Browse]

Related name

• Bhatt, Ruby [Browse]
• Dhanya, D. [Browse]
• Choudhary, Roshani [Browse]

Summary note

Unlock the future of technology and medicine with this essential book that provides a comprehensive, perceptive study of Brain Informatics, detailing how computational approaches are revolutionizing our understanding of the brain and driving innovations in AI, robotics, and personalized healthcare.

Source of description

Description based on publisher supplied metadata and other sources.

Contents

• Cover
• Series Page
• Title Page
• Copyright Page
• Contents
• Preface
• Part I: Foundations of Brain Informatics
• Chapter 1 Foundations of Brain Informatics: An Overview
• 1.1 Introduction to Brain Informatics
• 1.1.1 Definition and Scope
• 1.1.2 Historical Background
• 1.1.3 Importance in Neuroscience and AI
• 1.2 Theoretical Foundations of Brain Informatics
• 1.2.1 Cognitive Science Principles
• 1.2.2 Computational Neuroscience
• 1.2.3 Information Theory in Brain Science
• 1.3 Investigations of Human Information Processing Systems
• 1.3.1 Human Brain Structure and Function
• 1.3.2 Human Behavior and Cognition
• 1.3.3 Neuroimaging and Neurophysiological Techniques
• 1.4 Technologies and Tools in Brain Informatics
• 1.4.1 Brain Imaging Techniques
• 1.4.2 Computational Models
• 1.4.3 Big Data Analytics
• 1.5 Integration of Technologies in Brain Informatics
• 1.5.1 Emerging Technologies
• 1.5.2 Potential Impact on Society
• 1.5.3 Research and Development Areas
• 1.6 Conclusion
• 1.7 Future Scope
• References
• Chapter 2 Foundation of Cognitive and Computational Brain Science
• 2.1 Introduction
• 2.2 Beginning of Computational Neuroscience
• 2.2.1 Computational Level
• 2.2.2 Algorithmic Level
• 2.2.3 Implementational Level
• 2.3 Key Concepts for Cognitive Brain Science
• 2.3.1 Neuroplasticity
• 2.3.2 Cognitive Load Theory
• 2.3.3 Memory Systems
• 2.3.4 Default Mode Network (DMN)
• 2.3.5 Executive Functions
• 2.3.6 Social Cognition
• 2.3.7 Embodied Cognition
• 2.4 Key Concepts in Computational Brain Science
• 2.4.1 Cognitive Computational Neuroscience
• 2.4.2 Brain-Inspired Learning, Perception, and Cognition
• 2.4.3 Cognitive Computational Neuroscience of Language
• 2.4.4 Computational Neuroscience and Neuroinformatics
• 2.4.5 Deep Learning and Its Applications to Computational Neuroscience.
• 2.5 Application
• 2.5.1 Brain Function and Understanding Human Behavior
• 2.5.2 Neural Circuit Modeling
• 2.5.3 Brain-Computer Interfaces (BCIs)
• 2.5.4 Artificial Intelligence and Machine Learning
• 2.5.5 Neuropharmacology and Drug Discovery
• 2.5.6 Neurorehabilitation and Prosthetics
• 2.6 Future and Challenges in Computational Neurobiology
• 2.6.1 Interdisciplinary Foundations
• 2.7 Challenges and Constraints Today
• 2.7.1 Clinical Translation and Ethical Considerations
• 2.8 Future Directions and Opportunities
• 2.9 Conclusion
• Acknowledgment
• Chapter 3 Future Directions and Challenges in Brain Informatics
• 3.1 Introduction to Brain Informatics
• 3.1.1 Overview of Brain Informatics
• 3.1.2 Importance of Integrating Neuroscience, Data Science, and AI
• 3.1.3 Objectives and Scope of the Chapter
• 3.2 Global Landscape and Future Directions
• 3.2.1 Current Trends in Brain Informatics
• 3.2.2 Opportunities and Challenges in the Field
• 3.2.3 Future Research and Application Scenarios
• 3.3 Multi-Modal Brain Data Integration
• 3.3.1 Limitations of Single-Data-Source Approaches (e.g., fMRI, EEG)
• 3.3.2 Need for Integrating Diverse Data Types (Imaging, Electrophysiological, Genetic, Behavioral)
• 3.3.3 Traditional Methods vs. Multi-Modal Approaches
• 3.3.4 Advantages of Integrating Imaging, Electrophysiological, Genetic, and Behavioral Data
• 3.3.4.1 Enhanced Representation of Neural Activity
• 3.3.4.2 Genetic Insights into Brain Disorders
• 3.3.4.3 Contextual Understanding through Behavioral Data
• 3.3.4.4 Applications in Cognitive and Psychiatric Research
• 3.3.4.5 Precision Medicine and Personalized Treatments
• 3.4 Data Fusion Techniques and Machine Learning
• 3.4.1 Advances in Data Fusion Techniques
• 3.4.2 Machine Learning Methods for Enhanced Brain Data Analysis.
• 3.4.3 Increased Precision and Scope of Brain Data Analysis
• 3.5 Computational Neuroscience and Brain Modeling
• 3.5.1 Brain Functions Modeling and Simulation
• 3.5.2 The Role of AI and Advanced Computing in Understanding Neural Activities
• 3.5.3 Machine Learning for Predicting Brain Functions
• 3.5.3.1 Supervised Learning Models
• 3.5.3.2 Unsupervised Learning Methods
• 3.5.3.3 Reinforcement Learning (RL) Models
• 3.5.4 Targeted Therapy for Brain Diseases
• 3.6 Brain-Computer Interfaces (BCIs)
• 3.6.1 Recent Advances in Non-Invasive BCIs
• 3.6.2 Real-Time Data Processing
• 3.6.3 Applications in Medical and Assistive Technologies
• 3.6.4 Future Impact of BCIs on Human-Computer Interaction
• 3.7 Data Privacy and Security in Brain Informatics
• 3.7.1 Sensitivity of Brain Data and Privacy Concerns
• 3.7.2 Current Strategies and Future Directions for Data Privacy and Security
• 3.7.3 Ethical Management of Brain Data
• 3.8 Interdisciplinary Collaboration in Brain Informatics
• 3.8.1 The Need for Cross-Departmental Collaboration
• 3.8.2 Challenges in Facilitating Interdisciplinary Communication
• 3.8.3 The Role of Collaboration in Advancing the Field
• 3.9 Ethical and Societal Implications
• 3.9.1 Ethical Controversies in Brain Informatics
• 3.9.2 Issues of Consent and Potential Misuse of Brain Data
• 3.9.3 Impact on Personal Identity and Societal Norms
• 3.9.4 Proposals for Ethical Guidelines and Research Supervision
• 3.10 Conclusion
• 3.10.1 Summary of Key Points
• 3.10.2 Navigating the Challenges and Opportunities in Brain Informatics
• 3.10.3 Future Directions for Brain Informatics
• 3.10.4 The Informed Brain: A Vision for the Future
• Part II: Data Acquisition and Ethical Considerations and Techniques
• Chapter 4 Data Acquisition Technologies in Brain Informatics: Tools and Techniques.
• 4.1 Introduction
• 4.2 Brain Informatics
• 4.2.1 Biochemical Signals in Brain Informatics
• 4.2.2 Data Needs for Brain Research
• 4.3 Electroencephalography (EEG)
• 4.3.1 Categories of EEG
• 4.3.2 Recording of EEG Data
• 4.3.3 EEG Data Processing Techniques
• 4.3.4 Scope of EEG
• 4.3.5 Limitations and Challenges
• 4.4 Magnetoencephalography (MEG)
• 4.4.1 MEG Sensors and Equipment
• 4.4.2 MEG Data Acquisition Protocols
• 4.4.3 Data Processing and Analysis in MEG
• 4.4.4 Applications of MEG in Brain Studies
• 4.4.5 Challenges and Future Directions in MEG
• 4.5 Functional Magnetic Resonance Imaging (fMRI)
• 4.5.1 fMRI Data Acquisition Techniques
• 4.5.2 Processing and Analysis of fMRI Data
• 4.6 Positron Emission Tomography (PET)
• 4.6.1 PET Data Acquisition
• 4.6.2 PET Data Processing and Analysis
• 4.6.3 Applications of PET in Brain Studies
• 4.6.4 Limitations and Considerations of PET Data Acquisition
• 4.7 Near-Infrared Spectroscopy (NIRS)
• 4.7.1 NIRS Data Acquisition Techniques
• 4.7.2 NIRS Data Processing and Analysis
• 4.7.3 Applications of NIRS in Brain Informatics
• 4.7.4 Challenges and Emerging Trends in NIRS
• 4.8 Invasive Techniques in Brain Data Acquisition
• 4.8.1 ECoG (Electrocorticography)
• 4.8.2 Deep Brain Stimulation (DBS)
• 4.8.3 Ethical and Practical Issues for Invasive Methods
• 4.9 Multimodal Data Acquisition Approaches
• 4.9.1 Multimodal Integration Techniques
• 4.9.2 Data Fusion and Co-Registration Methods
• 4.9.3 Applications and Case Studies in Multimodal Acquisition
• 4.9.4 Multi-Modal Data Acquisition Difficulties
• 4.10 Emerging Technologies and Trends in Brain Data Acquisition
• 4.10.1 Real-Time Acquisition Technology of Brain Data
• 4.10.2 Miniaturization and Wearable Brain Monitoring Devices
• 4.10.3 Wireless Brain Data Acquisition Tools.
• 4.10.4 Advances in Artificial Intelligence for Brain Signal Processing
• 4.10.5 Future Directions and Breakthroughs
• 4.11 Data Quality, Storage, and Management in Brain Informatics
• 4.11.1 Data Quality and Reliability
• 4.11.2 Standardization of Procedures
• 4.11.3 Big Data Issues in Brain Informatics
• 4.11.4 Cloud Storage for Brain Data
• 4.12 Ethical Issues Involved with the Acquisition of Brain Data
• 4.12.1 Privacy and Data Protection in Brain Informatics
• 4.12.2 Educated Consent and Human Rights
• 4.12.3 Balancing Technological Advancements and Ethical Standards
• 4.13 Case Studies and Applications in Brain Data Acquisition
• 4.13.1 Clinical Case Studies
• 4.13.2 Non-Clinical Applications
• 4.13.3 Emerging Applications in Mental Health and Cognitive Science
• 4.14 Conclusion
• Future Directions
• Chapter 5 Ethical Consideration in Brain Informatics Color Blindness Research and Applications
• 5.1 Introduction
• 5.2 Literature Review
• 5.3 Research Methodology
• 5.3.1 Data Collection
• 5.3.2 System Development
• 5.3.3 Testing and Evaluation
• 5.3.4 Convolutional Neural Network (CNN)
• 5.4 Implications/Conclusion of the Study
• 5.4.1 Data Analysis
• 5.4.2 Funding of Infrastructure and Various Models
• 5.5 Discussion
• 5.6 Conclusion
• 5.7 Challenges and Future Scope
• Part III: Neural Networks and Machine Learning
• Chapter 6 Neural Networks: The Core Foundations, Challenges, and Applications in Brain Informatics
• 6.1 Introduction
• 6.1.1 Role of Neural Networks in Cognitive Studies
• 6.1.2 Importance of Neural Networks in Brain Informatics Research
• 6.2 Fundamentals of Neural Networks in Brain Informatics
• 6.2.1 Biological Inspiration and Artificial Implementation
• 6.2.2 Types of Neural Networks Used in Brain Informatics (e.g., CNNs, RNNs, GANs).
• 6.3 Architectures and Models of Neural Networks.

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ISBN

• 1-394-34562-3
• 1-394-34561-5
• 9781394345618

OCLC

• 1547906261
• 1547113681

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