Neural Engineering Techniques for Autism Spectrum Disorder, Volume 2: Diagnosis and Clinical Analysis

Front Cover
Neural Engineering Techniques for Autism Spectrum Disorder, Volume 2
Copyright Page
Dedication
Contents
List of contributors
About the editors
Acknowledgments
1 Autism and clinical analysis: Diagnosis
1 Remote telehealth assessments for autism spectrum disorder
1.1 Introduction
1.1.1 In-person standardized assessments for autism spectrum disorder
1.1.2 Significance of remote assessments for autism spectrum disorder
1.2 Telehealth assessments
1.2.1 Videoconferencing (live/in vivo)
1.2.2 Asynchronous video analysis: current
1.2.3 Asynchronous video analysis: retrospective
1.2.4 Mobile applications
1.2.5 Online websites
1.2.6 Other forms of technology
1.3 Implications
1.3.1 Future directions
References
2 Maternal immune dysregulation and autism spectrum disorder
2.1 Introduction
2.2 Cytokines and chemokines (overview)
2.2.1 Cytokines and chemokines in the central nervous system
2.2.2 Effect of cytokine/chemokine production in brain development
2.2.2.1 Cytokines and chemokines in brain function
2.2.2.2 Immune mediators and brain development
2.2.3 Maternal immune dysregulation and developmental outcomes of offspring
2.2.4 Maternal immune activation and autism spectrum disorder
2.2.5 Maternal stress and autism spectrum disorder
2.2.6 Maternal gut microbiome and autism spectrum disorder
2.2.7 Alterations in cytokine and chemokine profiles during gestation and the neonatal period
2.2.7.1 Other cytokines
2.3 Autoantibodies reactive to brain antigens
2.3.1 Autoantibody overview
2.3.2 Autoantibodies and brain pathologies
2.3.3 Autoantibodies and autism spectrum disorder
2.3.4 Maternal autoantibodies and neurodevelopmental alterations
2.3.5 Maternal autoantibody-related autism spectrum disorder overview
2.3.6 MAR ASD animal models
2.3.7 Maternal autoantibody-related fetal- brain targets and autism spectrum disorder
2.3.8 Maternal autoantibodies as potential autism spectrum disorder-risk biomarkers
2.4 Concluding remarks
References
3 Reading differences in eye-tracking data as a marker of high-functioning autism in adults and comparison to results from ...
3.1 Introduction
3.2 Related work
3.3 Automated detection of high-functioning autism in adults with eye-tracking data from web tasks
3.4 The proposed approach
3.4.1 Data collection
3.4.2 Participants
3.4.3 Stimuli and tasks
3.4.4 Apparatus
3.4.5 Procedure
3.4.6 Data preprocessing
3.5 Experiments
3.6 Results
3.7 Discussion
3.8 Conclusion
3.9 Open data
References
4 Parents of children with autism spectrum disorders: interventions with and for them
4.1 Introduction
4.2 Parent participation in early comprehensive intervention programs
4.2.1 Parental training
4.2.2 Pivotal Response Training Program
4.2.3 Treatment and Education of Autistic related Communication Handicapped Children Program
4.2.4 Early Start Denver Model
4.3 Programs for the development of parent–child interaction
4.3.1 Hanen’s more than words
4.3.2 Preschool autism communication trial
4.3.3 Joint Attention Symbolic Play, Engagement, and Regulation
4.3.4 Improving Parents as Communication Teachers
4.3.5 Parent–child interaction therapy
4.3.6 Stepping Stones Triple P
4.4 Parent–child intervention based on anxiety reduction
4.4.1 Cognitive behavioral therapy for anxiety reduction in children with autism spectrum disorders with parental intervention
4.4.2 Mindfulness-based intervention
4.4.2.1 Mindfulness training for parents mindfulness parenting
4.4.2.2 Mindfulness training for Youngsters with autism spectrum disorder
4.5 Conclusion
References
5 Applications of machine learning methods to assist the diagnosis of autism spectrum disorder
5.1 Introduction
5.2 Background and related work
5.2.1 Analysis of visual attention in autism
5.2.2 Machine learning for autism diagnosis
5.3 Data description
5.3.1 Participants
5.3.2 Experimental protocol
5.3.3 Visualization of eye-tracking scanpaths
5.4 Unsupervised learning: clustering of eye-tracking scanpaths
5.4.1 Image preprocessing
5.4.2 Feature extraction using principal component analysis and t-SNE
5.4.3 Feature extraction using deep autoencoder
5.4.4 K-Means clustering
5.4.5 Quality of clusters
5.4.6 Cluster analysis
5.5 Supervised learning: classification model
5.5.1 Data preprocessing and augmentation
5.5.2 Model design
5.5.3 Classification accuracy
5.6 Demo application
5.7 Limitations
5.8 Conclusions
References
6 Potential approaches and recent advances in biomarker discovery in autism spectrum disorders
6.1 Introduction
6.2 Diagnosis and categories of biomarkers
6.2.1 Human brain connectome: structural, functional, and molecular neuroimaging biomarkers for autism spectrum disorder
6.2.2 Molecular biomarkers
6.2.2.1 Genes involved in autism spectrum disorder
6.2.2.2 Epigenetic regulation
6.2.2.3 Transcriptomic profiling
6.2.2.4 Noncoding RNAs profiling
6.2.2.5 Microparticles and extracellular vesicles
6.2.2.6 Proteomics
6.2.2.6.1 Proteins involved in neurodevelopment and function impairment
6.2.2.6.2 Proteins involved in lipid metabolism
6.2.2.6.3 Proteins involved in immunology, complement system, and inflammation
6.2.2.6.4 Phosphoproteomics
6.2.2.7 Metabolomics
6.2.2.7.1 Neurotransmission-related metabolites
6.2.2.7.2 Abnormal amino acid and fatty acid-related metabolites
6.2.2.7.3 Gut microbiota-related metabolites
6.2.2.8 Mitochondria dysfunction
6.2.3 Maternal and paternal biomarkers: pregnancy and its potential association with ASD
6.2.3.1 Significance of prenatal components in autism spectrum disorder risk development
6.2.3.2 Screening for prenatal hormones as predictive biomarkers of autism spectrum disorder
6.2.3.3 Screening for prenatal inflammatory cytokines as predictive biomarkers of autism spectrum disorder
6.2.3.4 Autoantibodies
6.2.3.5 Screening for metabolites as predictive biomarkers of autism spectrum disorder
6.2.3.6 Sperm DNA methylation
6.2.3.7 Biomarker discovery, drug development, and personalized medicine
6.2.4 Next generation of diagnostic biomarkers
6.2.4.1 Artificial intelligence advancing diagnosis
6.2.4.2 Artificial intelligence and autism spectrum disorder diagnosis: eye-tracking
6.2.4.3 Artificial intelligence-based systems biology approaches in multiomics
6.3 Conclusion
References
7 Detection and identification of warning signs of autism spectrum disorder: instruments and strategies for its application
7.1 Introduction
7.2 Importance of early detection
7.3 Differential diagnosis
7.3.1 A brief history of the relationship between autism and psychosis
7.3.2 Similarities
7.3.3 Distinguishing features
7.4 Detection and screening process
7.5 Symptom detection vs Diagnosis
7.6 Impact on the family of detecting and diagnosing Autism Spectrum Disorder
7.7 Choice of screening instruments according to age of application and cultural environment of implementation
7.8 Discussion
7.9 Conclusions
References
8 Machine learning in autism spectrum disorder diagnosis and treatment: techniques and applications
8.1 Introduction
8.2 Utilizing machine learning algorithms to diagnose autism spectrum disorder
8.2.1 Dataset with behavioral characteristics
8.2.2 Dataset with personal/cognitive characteristics
8.2.3 Recommendations
8.3 Feature analysis
8.3.1 Dimensionality reduction
8.3.2 Feature representation
8.3.3 Recommendations
8.4 Technological applications
8.5 Conclusion
References
9 Inhibition of lysine-specific demethylase 1 enzyme activity by TAK-418 as a novel therapy for autism
9.1 Introduction
9.2 Lysine-specific demethylase 1 as the potential therapeutic target for autism spectrum disorder
9.2.1 Druggability in targeting epigenetic factors
9.2.2 Potential therapeutic functions of lysine-specific demethylase 1 inhibition
9.2.3 Concern regarding the on-target toxicity of general lysine-specific demethylase 1 inhibitors
9.3 Discovery of the “enzyme activity-specific” inhibitors of lysine-specific demethylase 1
9.3.1 Original screening flow
9.3.2 Discovery of T-448 and TAK-418
9.3.3 Unique inhibitory mechanism of T-448 and TAK-418
9.3.4 Low risk of hematological toxicity by T-448 and TAK-418 in rodents
9.3.5 Preclinical efficacy of T-448 and TAK-418
9.3.6 Hypothesis of the mechanism of action of T-448 and TAK-418
9.4 Discussion
9.5 Conclusion
References
10 Behavioral phenotype features of autism
10.1 Introduction
10.2 Eye movement behavior phenotype of autism
10.2.1 Natural stimuli
10.2.1.1 Dataset
10.2.1.2 Analysis
10.2.1.3 Gaze pattern classification and saliency prediction
10.2.1.4 Models submitted to Saliency4ASD
10.2.1.5 Evaluation criteria
10.2.1.6 Results of Saliency4ASD
10.2.2 Face stimuli
10.2.2.1 Dataset
10.2.2.2 Analysis
10.2.2.3 Methods and results
10.2.3 Gaze-following stimuli
10.2.3.1 Dataset
10.2.3.2 Analysis
10.2.3.3 Methods and results
10.3 Action behavior phenotype
10.3.1 Dataset and analysis
10.3.2 Methods and results
10.4 Drawing behavior phenotype
10.4.1 Dataset
10.4.2 Analysis
10.4.3 Results and discussion
10.5 Discussion and conclusion
References
11 Development of an animated infographic about autistic spectrum disorder
11.1 Introduction
11.2 Infographics
11.2.1 Study population
11.2.2 Development
11.2.3 Validation and testing
11.3 Results
11.4 Discussion
11.5 Conclusion
References
12 Fundamentals of machine-learning modeling for behavioral screening and diagnosis of autism spectrum disorder
12.1 Introduction
12.2 Current autism spectrum disorder screening and diagnostic practices
12.2.1 Commonly used autism spectrum disorder screening instruments
12.2.2 Common problems with current autism spectrum disorder screening and diagnostic practices
12.3 Machine learning-based assessment of autism spectrum disorder
12.3.1 Commonly used datasets for machine learning-based behavioral assessment of autism spectrum disorder
12.3.2 Dimensionality reduction
12.3.3 Commonly used dimensionality reduction techniques
12.3.3.1 Trial-and-error technique
12.3.3.2 Feature selection techniques
12.3.3.3 Feature transformation techniques
12.3.4 Classification algorithms
12.3.5 Model selection
12.3.5.1 Decision trees
12.3.5.2 K-nearest neighbor
12.3.5.3 Naïve Bayes
12.3.5.4 Logistic regression
12.3.5.5 Support vector machine
12.3.6 Confusion matrix
12.3.6.1 Sensitivity and specificity
12.4 Conclusion
References
13 A comprehensive study on atlas-based classification of autism spectrum disorder using functional connectivity features f...
13.1 Introduction
13.2 Overview of functional magnetic resonance imaging
13.2.1 Clinical application
13.3 Literature review
13.3.1 Structural magnetic resonance imaging-based autism detection
13.3.2 Functional magnetic resonance imaging-based autism detection
13.3.3 Structural and functional magnetic resonance imaging-based autism detection
13.4 Materials and methods
13.4.1 Preprocessing
13.4.2 Blood oxygen level dependent time-series signal extraction from four dimensional functional magnetic resonance imagi...
13.4.2.1 Automated anatomical labeling
13.4.2.2 Bootstrap analysis of stable clusters
13.4.2.3 Craddock 200
13.4.2.4 Craddock 400
13.4.2.5 Power
13.4.2.6 Eickhoff–Zilles
13.4.2.7 Harvard–Oxford
13.4.2.8 Talaraich and tournoux
13.4.2.9 Dosenbach 160
13.4.2.10 Massive online dictionary learning
13.4.3 Building functional connectivity matrix
13.4.3.1 Full correlation/Pearson’s correlation
13.4.3.2 Partial correlation
13.4.3.3 Tangent space embedding
13.4.4 Feature vector
13.4.5 Classification
13.4.5.1 Hidden layers and neurons per layer
13.4.5.2 Activation function
13.4.5.3 Optimizer
13.4.5.4 Loss function
13.4.5.5 Regularizer
13.4.5.5.1 L2 regularizer
13.4.5.5.2 Dropout
13.4.5.6 Batch size
13.4.5.7 Callback functions
13.4.5.7.1 Early stopping
13.4.5.7.2 Model checkpoint
13.5 Experimental results and analysis
13.5.1 Dataset description
13.5.2 Evaluation of autism spectrum disorder detection framework
13.5.3 Performance evaluation using model-2
13.6 Conclusion
13.7 Future work
References
14 Event-related potentials and gamma oscillations in EEG as functional diagnostic biomarkers and outcomes in autism spectr...
14.1 Introduction
14.2 Neurophysiological biomarkers
14.2.1 Introduction to event-related potentials and evoked brain waves oscillations
14.2.2 Rationale for approach using EEG/ERP measures in studying attention in ASD
14.2.3 Visual oddball task with illusory figures
14.2.4 ERP data acquisition and signal processing
14.2.5 Event-related potentials in autism and ADHD
14.2.6 ERP measures in illusory figure (Kanizsa) categorization task
14.2.7 Motor preparation deficits in ASD
14.2.8 ERP in Posner cued spatial attention task
14.2.9 Lateralized Readiness Potential (LRP) as an index of motor preparation in ASD and ADHD
14.3 Gamma oscillations as potential neuromarkers in neurodevelopmental disorders
14.3.1 Gamma oscillations
14.3.2 Cortical excitation/inhibition (E/I) bias and brainwave oscillations
14.3.3 Gamma oscillations in ASD
14.3.4 Hemispheric asymmetry of gamma
14.4 ERP and induced gamma oscillations in facial categorization task in ASD, ADHD, and TD groups
14.4.1 ERP results in ToM task
14.4.2 Induced gamma analysis and results in ToM task
14.5 Evoked and induced EEG data acquisition and processing in Kanizsa oddball task
14.6 Conclusions
References
Index
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