Attention Deficit Hyperactivity Disorder (ADHD) is a common neurological disorder that impacts focus, self-control, and other skills important in daily life. Caused by differences in brain anatomy and wiring, it is known to be one of the most common conditions in childhood. As ADHD plays a serious role in how children function in school and in their everyday life, having a deep understanding of this neurological condition is critical. This book explores recent advances in neuroimaging techniques, methods, applications and machine learning algorithms.
Key Features
- Contributions from world-class researchers in neurological disorders imaging
- Introductory section on the fundamentals of various imaging techniques
- A comprehensive overview of imaging related dyslexia, epilepsy, and Parkinson's
- Artificial Intelligence principles incorporated throughout
- Emphasis on deep learning paradigms
Author(s): Ayman El-Baz, Jasjit S. Suri
Publisher: IOP Publishing
Year: 2021
Language: English
Pages: 216
City: Bristol
PRELIMS.pdf
Preface
Acknowledgements
Editor biographies
Ayman El-Baz
Jasjit S Suri
List of contributors
CH001.pdf
Chapter 1 Diagnostic models for attention-deficit hyperactivity disorder based on neuroimaging methods
1.1 Introduction
1.2 Methods
1.2.1 Study population
1.2.2 MRI protocol
1.2.3 Data preprocessing
1.2.4 Feature extraction
1.2.5 Diagnostic model for ADHD
1.3 Results
1.3.1 Diagnostic model based on morphological connectivity
1.3.2 Predictive model based on functional connectivity
1.3.3 Functional connectivity and morphological connectivity
1.4 Discussion
1.5 Conclusion
References
CH002.pdf
Chapter 2 Application of machine learning algorithms to diagnosis attention deficit hyperactivity disorder
2.1 Introduction
2.2 System architecture
2.2.1 Data types and features
2.2.2 Dataset
2.2.3 Feature engineering
2.2.4 Imbalanced data
2.2.5 Learning algorithms
2.3 Criteria
2.4 Recent works
2.4.1 EEG-based
2.4.2 MRI-based (fMRI/SMRI)
2.4.3 Test-based
2.4.4 Others
References
CH003.pdf
Chapter 3 Classification of attention deficit hyperactivity disorder (ADHD) by using statistical features of MR images
3.1 Introduction
3.1.1 Objective
3.2 Methodology
3.2.1 The application domain
3.2.2 Selecting and creating the target dataset
3.2.3 Feature extraction
3.2.4 Statistical texture features
3.2.5 Feature selection using principal component analysis
3.2.6 Classification
3.3 Performance measures
3.3.1 K-fold cross validation
3.3.2 Hold-out cross validation
3.3.3 Confusion matrix
3.4 Experimental results
3.5 Results
References
CH004.pdf
Chapter 4 The new methods of application to attention deficit hyperactivity disorder
4.1 Introduction
4.2 Textual based and brain signal based method
4.2.1 Textual data-based method
4.2.2 Brain signal-based method
4.3 Method based on kinetic data by game with a robot
4.4 Conclusion
References
CH005.pdf
Chapter 5 Resting-state functional magnetic resonance imaging (R-FMRI) as a potential tool for early diagnosis and outcome prediction in attention deficit hyperactivity disorder (ADHD)
5.1 Introduction
5.2 Neurobiology of ADHD and biomarking techniques
5.2.1 Genetics, epigenetics, omics and biochemistry
5.2.2 Neuroimaging
5.3 Resting-state fMRI (R-fMRI)
5.3.1 Functional connectivity and resting-state brain networks
5.3.2 The restless ADHD brain
5.3.3 Conducting an R-fMRI study: a stepwise description
5.4 R-fMRI as a potential biomarking tool in ADHD
5.4.1 Biomarkers and R-fMRI
5.4.2 R-fMRI for early diagnosis, outcome prediction and treatment response biomarker in ADHD
5.5 Conclusion
References
CH006.pdf
Chapter 6 Brain networks related to automatic and controlled processes in ADHD
6.1 Introduction
6.2 Recent neuropsychological models of ADHD
6.2.1 Cognitive theories
6.2.2 Neurobiological theories
6.3 New evidence
6.3.1 Automatic and controlled processes
6.3.2 Automatic processes in ADHD
6.3.3 Neurological basis of automatic and controlled processes and its relationship to ADHD
6.4 A new theoretical framework
6.5 Discussion
6.6 Conclusion
References
CH007.pdf
Chapter 7 Attention deficit and hyperactivity disorder (ADHD) and criminal behavior: a criminological viewpoint
7.1 Introduction
7.2 Definitions, presentation, and co-morbidity
7.2.1 Definitions
7.2.2 Presentation
7.2.3 Co-morbidity
7.3 ADHD neuroscience, reinforcement sensitivity theory, genetics, and environment
7.3.1 ADHD neuroscience
7.3.2 Reinforcement sensitivity theory
7.3.3 Genetics and environment
7.4 ADHD treatment
7.5 ADHD and crime
7.5.1 Co-morbid conditions and crime
7.5.2 ADHD
7.6 Conclusion
References
CH008.pdf
Chapter 8 Supporting academic activities of children with developmental disorders and off-task behavior through technological aids and cognitive-behavioral strategies: a selective overview
8.1 Introduction
8.2 Method
8.3 Literature overview
8.3.1 Executive functions
8.3.2 Neuropsychological features
8.3.3 Emotional control
8.3.4 Time management
8.3.5 Brain–computer interface (BCI)
8.4 Discussion
8.5 Limitations
8.6 Conclusion
References
CH009.pdf
Chapter 9 Neuroimaging in attention deficit hyperactivity disorder
9.1 Introduction
9.2 Gross structural differences in the ADHD brain
9.3 Functional neuroimaging
9.3.1 ADHD and MRI
9.3.2 ADHD and fMRI
9.3.3 ADHD and PET studies
9.3.4 ADHD and DTI
9.4 Clinical application
9.5 Conclusion
References
CH010.pdf
Chapter 10 Application of augmented reality in education of attention deficit hyperactive disorder (ADHD) children
10.1 Introduction
10.2 ADHD and education
10.3 Augmented reality, education and ADHD
10.3.1 Augmented reality for ADHD education
10.4 Challenges
10.5 Conclusion
References
