Neurological Disorders and Imaging Physics: Applications in dyslexia, epilepsy and Parkinson’s

PRELIMS.pdf
Preface
Acknowledgements
Editor biographies
Ayman El-Baz
Jasjit S Suri
List of contributors
CH001.pdf
Chapter 1 Majority vote of machine learning methods for real-time epileptic seizure prediction applied on EEG pediatric data
1.1 Introduction
1.1.1 Epilepsy and epileptic seizure
1.1.2 EEG signals related to epileptic seizures
1.2 Literature review
1.3 Methodology
1.3.1 Data and data collection
1.3.2 EEG signal processing techniques
1.3.3 Meta heuristic methods
1.4 Experimental setup
1.4.1 Data setup
1.4.2 Data preparation with de-noising and feature extraction
1.4.3 Training the data—Matlab GUI
1.4.4 Data testing, processing and seizure prediction
1.5 Results
1.6 Discussion
1.7 Conclusion
References
CH002.pdf
Chapter 2 Delineation of epileptogenic zone
2.1 Introduction
2.2 Seizure semiology
2.3 Scalp electroencephalography (EEG) and long-term video-EEG (VEEG) monitoring
2.3.1 Introduction
2.3.2 Terminology
2.3.3 Applications to epilepsy
2.3.4 Long-term video-EEG monitoring
2.4 PET-CT
2.4.1 Introduction
2.4.2 Applications to epilepsy
2.4.3 Conclusion
2.5 Magneto-electroencephalography (MEG)
2.5.1 Introduction
2.5.2 Applications and limitations
2.6 Single photon emission computed tomography (SPECT)
2.6.1 Introduction
2.6.2 Applications to epilepsy
2.7 Functional MRI (fMRI)
2.7.1 Introduction
2.7.2 Application to epilepsy
2.8 Subdural grids (SBG)
2.8.1 Introduction
2.8.2 Applications to epilepsy
2.8.3 Complications
2.8.4 Conclusions
2.9 Stereoelectroencephalography (SEEG)
2.9.1 Introduction
2.9.2 SEEG versus SBG
2.9.3 Patterns of explorations
2.9.4 Technique description
2.9.5 Surgical outcomes
2.9.6 Conclusions
2.10 Illustrative case
2.11 Conclusion
References
CH003.pdf
Chapter 3 Dyslexia: behavioral and biological correlates and treatment
3.1 Introduction
3.2 Definitional issues
3.3 Phenotypes (behavioral markers) and genetic bases of dyslexia
3.3.1 Phenotypes
3.3.2 Genetic findings
3.4 Assessment of dyslexia
3.4.1 Family history and neurodevelopmental history
3.4.2 Educational history
3.4.3 Language
3.4.4 Motor
3.5 Phenotypic markers
3.5.1 Verbal comprehension
3.5.2 Assessing word level reading and spelling skills
3.5.3 Assessing verbal working memory components most often impaired in dyslexia [5]
3.6 Brain imaging behavioral and brain correlates
3.6.1 Brain imaging methods
3.6.2 Example brain imaging tasks linked to behavioral phenotypes outside scanner
3.6.3 Brain imaging before and after treatment
3.7 Future research directions
3.8 Assessment–instruction links for treatment
3.8.1 Educational and clinical applications
3.8.2 Biological and environmental risk factors
References
CH004.pdf
Chapter 4 Cholesterol and oxidized cholesterol derivatives: potential biomarkers of Parkinson’s disease?
4.1 Introduction
4.2 Parkinson’s disease
4.3 Cholesterol and Parkinson’s disease
4.4 Oxysterols and the brain
4.5 Oxysterols in Parkinson’s disease
4.6 Potential involvement of oxysterols in mechanisms implicated in Parkinson’s disease
4.7 Conclusion
References
CH005.pdf
Chapter 5 Computer assisted diagnosis of gait dynamics neurodegenerative diseases using a machine learning approach
5.1 Introduction
5.2 Methodology
5.2.1 Gait dataset
5.2.2 Feature extraction
5.2.3 Artificial neural network (ANN)
5.3 Results and discussion
5.4 Conclusion
References
CH006.pdf
Chapter 6 Rett syndrome
6.1 Background
6.2 Clinical features
6.2.1 Congenital Rett syndrome
6.2.2 Early onset seizure variant (Hanefeld variant)
6.2.3 Preserved speech variants
6.2.4 Male variants
6.3 Important clinical symptoms and signs in Rett syndrome
6.3.1 Seizures
6.3.2 Respiratory and cardial disturbances
6.3.3 Sleep
6.3.4 Gastrointestinal disturbances
6.3.5 Communication
6.4 Genetic basis of Rett syndrome
6.5 Conclusions
References
CH007.pdf
Chapter 7 Knowledge about epilepsy in university health students
7.1 Introduction
7.2 Cultural differences in knowledge of epilepsy
7.3 Assessment of knowledge
7.4 Future perspectives
References
CH008.pdf
Chapter 8 Current methods and new trends in signal processing and pattern recognition for the automatic assessment of motor impairments: the case of Parkinson’s disease
8.1 Introduction
8.2 Clinical assessment of the disease
8.2.1 General symptoms and common tools for neurological evaluation of PD
8.2.2 Clinical evaluation of dysarthria
8.2.3 The modified Frenchay dysarthria assessment (m-FDA)
8.3 Automated analysis of the disease
8.3.1 Motor signals for the study of PD
8.4 Revealing features for the automatic diagnosis and monitoring of Parkinson’s disease
8.4.1 Speech
8.4.2 Gait
8.4.3 Handwriting
8.5 Existing data
8.5.1 Speech
8.5.2 Gait
8.5.3 Handwriting
8.5.4 The extended multimodal PC-GITA corpus
8.6 Automatic analysis of the extracted features
8.6.1 Performance metrics
8.7 Experiments and results
8.7.1 Experiments with speech signals
8.7.2 Experiments with gait signals
8.7.3 Experiments with handwriting signals
8.7.4 Fusion of modalities
8.8 Discussion
8.9 Future trends in PD analysis
Acknowledgments
References
CH009.pdf
Chapter 9 ‘They labelled me ignorant’: the role of neuroscience to support students with a profile of dyslexia
9.1 Introduction
9.2 Method
9.2.1 The participants
9.2.2 Data collection and analysis
9.3 Bridging the gap: linking neuroscience and educational research
9.3.1 The dyslexia debate: is dyslexia a real learning difficulty or is it a myth?
9.3.2 Paying attention to early diagnosis, support and intervention
9.3.3 ‘They labelled me ignorant’—Examinations and dyslexia
9.4 Final reflections
References
CH010.pdf
Chapter 10 Advances in epilepsy: from gender to genetics
10.1 Introduction
10.2 Minimally invasive techniques used to achieve seizure control
10.3 Rhythms in seizure frequency
10.3.1 Hormonal regulation of seizures
10.3.2 Catamenial epilepsy
10.3.3 Progesterone
10.3.4 Estrogen
10.4 Genetic epilepsies
10.4.1 GABAAR associated epilepsies
10.4.2 Glutamate receptors associated epilepsies
10.4.3 Voltage-gated sodium channels associated epilepsies
10.4.4 Voltage-gated potassium channels associated epilepsies
10.4.5 Voltage-gated calcium channels associated epilepsies
10.4.6 Nicotinic acetyl-choline receptor associated epilepsies
10.5 Conclusions
References
CH011.pdf
Chapter 11 Neuroimaging in Parkinson’s disease
11.1 Introduction
11.2 Neuroimaging biomarkers in Parkinson’s disease
11.3 Molecular imaging of Parkinson’s disease
11.3.1 Dopamine
11.3.2 Serotonin
11.3.3 Cholinergic dysfunction
11.3.4 Noradrenergic function
11.4 Imaging midbrain structural changes in PD
11.4.1 Magnetic resonance imaging techniques
11.4.2 Transcranial sonography (TCS)
11.5 Positron emission tomography (PET)
11.5.1 Presynaptic and postsynaptic dopaminergic imaging
11.5.2 Assessment of cerebral glucose metabolism
11.6 Single photon emission computed tomography in Parkinsonian disorders (SPECT)
11.7 Diffusion tensor magnetic resonance imaging (DTI)
11.8 Proton magnetic resonance spectroscopy (1H-MRS)
References