List of Contributors xiii
About the Editor xvii
1 Introduction 1
Li Su and Xiong Xiong
1.1 Background and Rationale 1
1.2 Promising Potentials of AI in Neuroscience 6
1.3 Current Advances and Specific Topics Covered in this Book 8
1.4 Major Challenges and Limitations 16
1.5 Future Directions and Prospects 21
1.6 Overview of the Chapters 25
2 Deep Neural Networks in Brain Networks Study 29
Lu Zhang and Dajiang Zhu
2.1 Introduction 29
2.2 Brain Networks 30
2.3 Graph Neural Networks 32
2.4 GCNs in Understanding Brain Structure-Function Relationship 33
2.5 GCNs in Investigating Brain Disease 33
2.6 Future Work 35
3 Cerebrovascular Disease and Cognitive Function 39
Zhongzhao Teng
3.1 The Close Relationship Between the Cerebrovascular System and Cognitive Function 39
3.2 Neurovascular Physiology and the Neurobiological Basis of Cognition 42
3.3 Mechanisms of Cognitive Impairment Caused by Cerebrovascular Pathological Changes 45
3.4 Clinical Spectrum of Cerebrovascular Disease and Cognitive Impairment 56
3.5 Diagnosis and Assessment of VCI and Dementia 65
3.6 Neuropsychological Characteristics and Patterns of Cognitive Impairment 68
3.7 Intervention Strategies: Prevention, Management and Treatment 78
3.8 Challenges, Future Directions and Conclusions 81
4 Multilayer Networks in Neuroimaging 103
Vesna Vuksanovic
4.1 Introduction 103
4.2 Multilayer Networks: The Theoretical Background 104
4.3 Multilayer Networks in Neuroimaging 109
4.4 Multimodal, Multiscale Neuroimaging Data: A Multilayer Network Approach 114
4.5 Application of Multilayer Networks in Modelling Neurodegenerative Disorders 115
4.6 Conclusion and Future Directions 117
5 Artificial Intelligence-inspired Subtype Analysis for Brain Imaging 125
Yong Liu and Kun Zhao
5.1 Introduction 125
5.2 Clustering Methods 126
6 Computer-aided Diagnosis Systems in Neuroscience Based on Responsible Artificial Intelligence 139
Carmen Jimenez-Mesa, David Loìpez, Juan E Arco, Francisco J Martiìnez-Murcia, Javier Ramiìrez, Mariìa Ruz, and Juan M Goìrriz
6.1 Introduction 139
6.2 Neuroimaging Techniques 141
6.3 Machine Learning in Neuroimaging 147
6.4 Small Sample Size in Neuroimaging 151
6.5 Pathologies and Case Studies 156
6.6 Challenges and Trends in Neuroimaging 159
7 Artificial Intelligence in Neurodegenerative Disorders 173
Timothy Rittman
7.1 Introduction 173
8 Applications of Machine Learning in the Genetics of Brain Disorders 205
Matthew Bracher-Smith and Valentina Escott-Price
8.1 Introduction 205
8.2 Genetic Prediction 208
8.3 ML Approaches 211
8.4 Prominent ML Models Applied to Brain Disorders 213
8.5 Challenges and Opportunities in ML Applications 217
8.6 Conclusion 222
9 Multiscale AI for Precision Neuro-oncology 231
Ruodan Yan, Xiaofei Wang, Hao Chen, and Chao Li
9.1 Introduction 231
9.2 AI-based Brain Mapping for Neuro-oncology 232
9.3 AI-based Tissue Mapping for Neuro-oncology 245
9.4 Generalisable AI for Clinical Translation 259
9.5 Conclusion 269
10 Using AI to Detect Alzheimer's Disease from Speech: The Ethical Questions 285
Ulla Petti, Rune Nyrup, Jeffrey M Skopek, and Anna Korhonen
10.1 Introduction 285
10.2 Autonomy 286
10.3 Privacy and Data Protection 289
10.4 Welfare 292
10.5 Transparency 297
10.6 Fairness and Inclusion 299
10.7 Conclusion 300
11 Privacy-preserving AI Methods to Protect Patient Privacy 307
Lewis Hotchkiss
11.1 Introduction 307
11.2 Anonymisation 308
11.3 Privacy Concerns in AI Models 312
11.4 Privacy-preserving Techniques 321
11.5 Conclusion 331
References 331
Index 335
