Neurological Disorders and Imaging Physics, Volume 3: Application to Autism Spectrum Disorders and Alzheimer’s

PRELIMS.pdf
	Preface
	Acknowledgments
	Editor biographies
		Ayman El-Baz
		Jasjit S Suri
	List of contributors
CH001.pdf
	Chapter 1 Machine learning applications to recognize autism and Alzheimer’s disease
		1.1 Introduction
		1.2 Brain disorders
			1.2.1 Autism spectrum disorder (ASD)
			1.2.2 Alzheimer’s disease (AD)
			1.2.3 Mild cognitive impairment
		1.3 Deep learning
			1.3.1 ASD and deep learning
			1.3.2 Alzheimer’s and deep learning
		1.4 Conclusion
		References
CH002.pdf
	Chapter 2 Neuropathology and neuroimaging of Alzheimer’s disease
		Abbreviations
		2.1 Alzheimer’s disease: history, concept, clinical picture, and neurobiology
			2.1.1 Brief history and concept
			2.1.2 Clinical presentation
			2.1.3 Neurobiology and physiopathology of Alzheimer’s disease
			2.1.4 Clinical and physiopathological feature assessment
		2.2 Biomarkers
			2.2.1 CSF biomarkers
			2.2.2 Neuroimaging biomarkers
		2.3 Understanding AD progression through structural imaging
			2.3.1 Mapping AD neuropathology through neuronal circuits: what is the point?
			2.3.2 Cortical myelination throughout life
			2.3.3 Models of the neuropathological progression of AD
			2.3.4 Hypothetical models of neurodegeneration
			2.3.5 Understanding the biophysical properties of DTI
		2.4 Conclusions
		References
CH003.pdf
	Chapter 3 Retinal imaging in Alzheimer’s disease
		3.1 Introduction
		3.2 Lipofuscin hypothesis of AD
		3.3 OCT and FAF in retinal diseases
			3.3.1 SD-OCT and FAF in diagnosing AD
		3.4 Misfolded proteins in the retina
		3.5 Cryo-electron microscopy
		3.6 Retinal imaging of misfolded proteins
		3.7 Curcumin
		3.8 AMD and AD
		3.9 Glaucoma and AD
		3.10 Alpha-synuclein in AD
		3.11 Early diagnosis of AD
		3.12 Biomarkers in AD
		3.13 Discussion
		References
CH004.pdf
	Chapter 4 Clinically relevant depression and risk of Alzheimer’s disease in the elderly: meta-analysis of cohort studies
		4.1 Introduction
		4.2 Methods
			4.2.1 Search strategy
			4.2.2 Study selection
			4.2.3 Data extraction
			4.2.4 Quality assessment
			4.2.5 Statistical analysis
		4.3 Results
			4.3.1 Study selection
			4.3.2 Description of included studies
			4.3.3 Effect estimation of AD risk based on depression
			4.3.4 The risk of publication bias
			4.3.5 Influence analysis
			4.3.6 Population attributable fraction
		4.4 Discussion
			4.4.1 Main results
			4.4.2 Comparison with previous studies
			4.4.3 Strengths and limitations
			4.4.4 Pathogenic hypotheses
			4.4.5 Clinical implications
			4.4.6 Public health implications
		4.5 Conclusion
		References
CH005.pdf
	Chapter 5 The implications of genetic factors in autism spectrum disorder and Alzheimer’s disease
		5.1 Autism spectrum disorder
		5.2 Alzheimer’s disease
			5.2.1 Introduction
			5.2.2 Clinical assessment
			5.2.3 Risk and protective factors
			5.2.4 Neuropathological changes
			5.2.5 Genetics of AD
		References
CH006.pdf
	Chapter 6 Nuclear neurology of autism spectrum disorder
		6.1 Introduction
			6.1.1 Imaging modalities
		6.2 Specific neurochemical physiology
			6.2.1 Dopaminergic neurotransmission
			6.2.2 Serotoninergic neurotransmission
			6.2.3 Serotonin synthesis
			6.2.4 Serotonin transporter (SERT)
			6.2.5 Serotonin (5-HT 2A) receptor
			6.2.6 GABA
		6.3 Basal physiology
			6.3.1 Protein synthesis
			6.3.2 Glucose metabolism
		6.4 Regional cerebral blood flow
			6.4.1 PET investigation
			6.4.2 SPECT investigation
		6.5 Conclusion
		References
CH007.pdf
	Chapter 7 Ethylene and ammonia in neurobehavioral disorders
		7.1 Introduction
		7.2 Method
		7.3 Volatile organic compounds in autism and schizophrenia
			7.3.1 Schizophrenia
			7.3.2 Autism
			7.3.3 Ethylene in mental disorders
			7.3.4 Ammonia in mental disorders
		7.4 Results and discussion
			7.4.1 Protocol for breath gas sampling from schizophrenic patients
			7.4.2 Ethylene and ammonia assessment using LPAS from schizophrenic patients
			7.4.3 Protocol for breath gas sampling from autistic young adults
			7.4.4 Ethylene and ammonia assessment using LPAS in autistic young adults
		7.5 Conclusions and future directions
		References
CH008.pdf
	Chapter 8 The impact of stress on parental behavior following a diagnosis of autism
		8.1 Parental stress and the ASD diagnosis
		8.2 The potential effect of stress on parental treatment choices
			8.2.1 The prevalence of fad treatments
		8.3 Parents as the agents of behavioral change
		8.4 Factors affecting parental involvement
			8.4.1 Making parent training accessible
			8.4.2 Focusing on the contingencies that maintain parents’ behaviors
		8.5 Tying it all together: mitigating stress, selecting evidence-based treatments, and increasing parental involvement
		References
CH009.pdf
	Chapter 9 Visual saliency for medical imaging and computer-aided diagnosis
		9.1 Introduction
		9.2 Visual saliency for medical image analysis
		9.3 Saliency model for Alzheimer’s disease detection from structural MRI
			9.3.1 Visual assessment of brain atrophy for AD diagnosis
			9.3.2 AD saliency map generation
		Algorithm 1. STD map estimation
		9.4 Visual interpretation of visual saliency
			9.4.1 Region of interest detection and quantification
			9.4.2 Comparison with state-of-the-art saliency models
		9.5 AD classification using saliency maps
			9.5.1 Proposed method
			9.5.2 MRI data
			9.5.3 Classification results
		9.6 Conclusion
		Acknowledgement
		References
CH010.pdf
	Chapter 10 The early diagnosis of Alzheimer’s disease using advanced biomedical engineering technology
		10.1 Introduction
		10.2 Literature review
		10.3 Causes and effects of AD
		10.4 Hallmarks of AD
		10.5 The retina and AD
		10.6 Tests for diagnosing AD
		10.7 Early diagnosis of AD
		10.8 Medical imaging techniques
		10.9 Analysis of MRI and OCT images
			10.9.1 Image acquisition
			10.9.2 Pre-processing
			10.9.3 Image segmentation
			10.9.4 Image post-processing
			10.9.5 Feature extraction
			10.9.6 Feature selection
			10.9.7 Classification
		10.10 Discussion
		10.11 Conclusion
		Acknowledgments
		References
CH011.pdf
	Chapter 11 A local/regional computer aided system for the diagnosis of mild cognitive impairment
		11.1 Introduction
		11.2 Material and methods
			11.2.1 Materials
			11.2.2 Methods
		11.3 Results
		11.4 Discussion
		Acknowledgments
		References
CH012.pdf
	Chapter 12 Identifying Alzheimer’s disease using feature reduction of GLCM and supervised classification techniques
		12.1 Introduction
		12.2 Related work
		12.3 The proposed supervised-learning approach for AD identification
			12.3.1 Voxel-based morphometric feature extraction
			12.3.2 Texture feature extraction
			12.3.3 Feature reduction
			12.3.4 Classification
		12.4 Experimental results and discussion
			12.4.1 Dataset
			12.4.2 Experimental work
		12.5 Conclusion and future work
		References
CH013.pdf
	Chapter 13 Current trends and considerations of Alzheimer’s disease
		13.1 Introduction
		13.2 Anatomical background
			13.2.1 Neurological diseases
			13.2.2 Neurodegenerative diseases
			13.2.3 Alzheimer’s disease
		13.3 Medical imaging modalities for AD
			13.3.1 Radiology
			13.3.2 Printed signals/waves
		13.4 AD literature review
			13.4.1 CAD system based studies
		13.5 Discussion
			13.5.1 Databases
			13.5.2 Modalities
			13.5.3 Applied techniques
			13.5.4 Subjects
		13.6 Conclusion
		References
CH014.pdf
	Chapter 14 A noninvasive image-based approach toward an early diagnosis of autism
		14.1 Introduction
		14.2 Methods
			14.2.1 Segmentation
			14.2.2 Feature extraction
		14.3 Experimental results and conclusions
		References
CH015.pdf
	Chapter 15 Towards a robust CAD system for early diagnosis of autism using structural MRI
		15.1 Introduction
		15.2 Methods
			15.2.1 Cx and CWM segmentation from MRI scans
			15.2.2 Extraction of shape features
			15.2.3 Deep fusion classification network (DFCN)
		15.3 Experimental results and conclusions
		References
CH016.pdf
	Chapter 16 Computational analysis techniques: a case study on fMRI for autism spectrum disorder
		16.1 Introduction
		16.2 Task based fMRI analysis
			16.2.1 Building the design matrix
			16.2.2 Multi level general linear model (GLM) in fMRI
			16.2.3 GLM parameter estimates
			16.2.4 Limitations of GLM for parameter estimation in fMRI analysis
		16.3 RfMRI analysis
			16.3.1 Principle components analysis in RfMRI analysis
			16.3.2 Independent components analysis in RfMRI analysis
			16.3.3 Restricted Boltzmann machines in RfMRI analysis
		16.4 A case study of fMRI in autism
			16.4.1 Task based fMRI findings in autism
			16.4.2 Resting state fMRI findings in autism
		16.5 Conclusions and future work
		References
CH017.pdf
	Chapter 17 Autism diagnosis using task-based functional MRI
		17.1 Introduction
		17.2 Materials and methods
			17.2.1 Data preprocessing
			17.2.2 Multi level general linear model (GLM) in fMRI
			17.2.3 Feature selection and classification
		17.3 Experimental results and conclusion
			17.3.1 Higher level analysis
			17.3.2 Classification results
			17.3.3 Conclusion and future work
		References