Multimodal Neuroimaging Computing for the Characterization by Sidong Liu

By Sidong Liu

This thesis covers numerous aspects of mind picture computing tools and illustrates the medical realizing of neurodegenerative issues in response to 4 normal elements of multimodal neuroimaging computing: neuroimaging information pre-processing, mind function modeling, pathological trend research, and translational version improvement. It demonstrates how multimodal neuroimaging computing ideas will be built-in and utilized to neurodegenerative illness learn and administration, highlighting correct examples and case reports. Readers also will find a variety of attention-grabbing extension issues in longitudinal neuroimaging reviews, subject-centered research, and the mind connectome. As such, the ebook will gain all well-being informatics postgraduates, neuroscience researchers, neurology and psychiatry practitioners, and policymakers who're drawn to clinical picture computing and computer-assisted interventions.

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Additional info for Multimodal Neuroimaging Computing for the Characterization of Neurodegenerative Disorders

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A series of models and methods were proposed in this study and the main contributions of this study can be summarized using a four-layer architecture, each with a specific goal. Data Computing The data computing layer focuses on neuroimaging data acquisition and pre-processing. In this study, we recruited four groups of subjects from the public ADNI database to ensure sufficient coverage of neurodegeneration progression stages, subject samples, and imaging modalities. These datasets includes the MRI subset (758 subjects), the PET subset (352 subjects), the dMRI subset (233 subjects), and the multimodal MRI-PET subset (331 subjects).

71. , et al. (2015). Multimodal neuroimaging computing: The workflows, methods and platforms. Brain Informatics, 2, 181–195. 72. , et al. (2014). A combined DTI and structural MRI study in medicated-naive chronic schizophrenia. Magnetic Resonance Imaging, 32, 1–8. 73. , & He, B. (2006). Integration of EEG/MEG with MRI and fMRI in functional neuroimaging. IEEE Engineering in Medicine and Biology Magazine, 25, 46–53. 74. , et al. (2014). Brain networks disconnection in early multiple sclerosis cognitive deficits: An anatomofunctional study.

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