Neurological disorders and imaging physics. Volume 3, Application to autism spectrum disorders and Alzheimer's /

Autism spectrum disorder (ASD) and Alzheimer's disease (AD) are two significant neurological disorders, which represent the scope of this book. Both ASD and AD effect a significant number of the population and present themselves in numerous ways. This volume covers the state-of-the-art topics t...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: El-Baz, Ayman S. (Editor ), Suri, Jasjit S. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK) : IOP Publishing, [2020]
Colección:IOP ebooks. 2020 collection.
Temas:
Central nervous system > Magnetic resonance imaging.
Central nervous system > Diseases > Diagnosis.
Alzheimer's disease > Magnetic resonance imaging.
Autism spectrum disorders > Magnetic resonance imaging.
Biomedical engineering.
TECHNOLOGY & ENGINEERING / Biomedical.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 1. Machine learning applications to recognize autism and Alzheimer's disease
  • 1.1. Introduction
  • 1.2. Brain disorders
  • 1.3. Deep learning
  • 1.4. Conclusion
  • 2. Neuropathology and neuroimaging of Alzheimer's disease
  • 2.1. Alzheimer's disease : history, concept, clinical picture, and neurobiology
  • 2.2. Biomarkers
  • 2.3. Understanding AD progression through structural imaging
  • 2.4. Conclusions
  • 3. Retinal imaging in Alzheimer's disease
  • 3.1. Introduction
  • 3.2. Lipofuscin hypothesis of AD
  • 3.3. OCT and FAF in retinal diseases
  • 3.4. 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
  • 4. Clinically relevant depression and risk of Alzheimer's disease in the elderly : meta-analysis of cohort studies
  • 4.1. Introduction
  • 4.2. Methods
  • 4.3. Results
  • 4.4. Discussion
  • 4.5. Conclusion
  • 5. The implications of genetic factors in autism spectrum disorder and Alzheimer's disease
  • 5.1. Autism spectrum disorder
  • 5.2. Alzheimer's disease
  • 6. Nuclear neurology of autism spectrum disorder
  • 6.1. Introduction
  • 6.2. Specific neurochemical physiology
  • 6.3. Basal physiology
  • 6.4. Regional cerebral blood flow
  • 6.5. Conclusion
  • 7. Ethylene and ammonia in neurobehavioral disorders
  • 7.1. Introduction
  • 7.2. Method
  • 7.3. Volatile organic compounds in autism and schizophrenia
  • 7.4. Results and discussion
  • 7.5. Conclusions and future directions
  • 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
  • 8.3. Parents as the agents of behavioral change
  • 8.4. Factors affecting parental involvement
  • 8.5. Tying it all together : mitigating stress, selecting evidence-based treatments, and increasing parental involvement
  • 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
  • 9.4. Visual interpretation of visual saliency
  • 9.5. AD classification using saliency maps
  • 9.6. Conclusion
  • 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
  • 10.10. Discussion
  • 10.11. Conclusion
  • 11. A local/regional computer aided system for the diagnosis of mild cognitive impairment
  • 11.1. Introduction
  • 11.2. Material and methods
  • 11.3. Results
  • 11.4. Discussion
  • 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
  • 12.4. Experimental results and discussion
  • 12.5. Conclusion and future work
  • 13. Current trends and considerations of Alzheimer's disease
  • 13.1. Introduction
  • 13.2. Anatomical background
  • 13.3. Medical imaging modalities for AD
  • 13.4. AD literature review
  • 13.5. Discussion
  • 13.6. Conclusion
  • 14. A noninvasive image-based approach toward an early diagnosis of autism
  • 14.1. Introduction
  • 14.2. Methods
  • 14.3. Experimental results and conclusions
  • 15. Towards a robust CAD system for early diagnosis of autism using structural MRI
  • 15.1. Introduction
  • 15.2. Methods
  • 15.3. Experimental results and conclusions
  • 16. Computational analysis techniques : a case study on fMRI for autism spectrum disorder
  • 16.1. Introduction
  • 16.2. Task based fMRI analysis
  • 16.3. RfMRI analysis
  • 16.4. A case study of fMRI in autism
  • 16.5. Conclusions and future work
  • 17. Autism diagnosis using task-based functional MRI
  • 17.1. Introduction
  • 17.2. Materials and methods
  • 17.3. Experimental results and conclusion.

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