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