Yongxia Zhou

University of Southern California

Yongxia Zhou obtained a Ph.D. in Biomedical Imaging from the University of Southern California. Her research interest is radiology and neuroscience technology and application. She had been trained as an imaging scientist at several prestigious institutes including Columbia University, the University of Pennsylvania, and the National Institutes of Health (NIH). Her research focuses on multi-modal neuroimaging integration such as MRI/PET and EEG/MEG instrumentation to make the best use of multiple modalities for better interpretation of underlying disease mechanisms. She is the author and editor of more than twelve books for well-known publishers including IntechOpen and Nova Science. She has published more than 100 papers and abstracts in many reputed international journals and conferences and served as reviewer and editor for several academic associations.

5books edited

2chapters authored

Latest work with IntechOpen by Yongxia Zhou

This book examines the principles and applications of biomedical imaging and signals processing as well as the advances of multimodal imaging and multi-feature quantification for disease diagnosis and treatments in ophthalmology, stroke, chemotherapy, and neurology. Chapters cover such topics as image segmentation and registration, feature selection for classification, micro-texture characterization, simulation of tissue deformation, and high-level statistical analyses. The chapters also discuss different imaging modalities including MRI and EEG, confocal microscopy, and molecular imaging for improving the accuracy of disease detection via higher spatiotemporal resolution and better illustration. Overall, the book provides a comprehensive review of biomedical imaging and signal processing, informing readers with current and insightful knowledge in these fields.

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Biomedical Signal and Image Processing

Edited by Yongxia Zhou

Books edited

By Yongxia Zhou

The emphasis of this work is on developing novel data-processing techniques to achieve a higher spatiotemporal resolution in dynamic functional magnetic resonance imaging (fMRI). Due to partial volume effects, a pixel in fMRI may contain signals from a mixture of micro- and macrovasculature, with very different temporal characteristics. This mixture effect provides a way to separate microvasculature from macrovasculature in fMRI. A multi-component model representing a mixture of many reference functions is used to fit the time course of pixels in fMRI. The results suggest that it may be possible to separate the micro- and macrovasculature fractional contributions to pixels by this approach. Compared to the classical single-component model, the multi-component model fits the measured fMRI time course with a higher correlation coefficient and also detects voxels with low latencies more efficiently. Spatial independent component analysis (ICA) as a preprocessing step is implemented to remove major physiological noise and artifacts. The results of mixture model fitting after ICA cleaning show better results for microvasculature detection.

Part of the book: Neuroimaging

Longitudinal Changes of Structural and Functional Connectivity and Correlations with Neurocognitive Metrics

By Yongxia Zhou

Revealing brain functional and micro-structural changes over a relatively short period at individual levels are especially important given that many risks associated with age including vascular and neuroinflammation increases and could confound the baseline fMRI parametric images. Cellular-level axonal injury and/or demyelination as well as dispersed mesoscopic level substance abnormal aggregation and structural/functional abnormality could occur in short subacute/acute phases, while literatures related to longitudinal changes with age are limited with only our previous fMRI findings. Longitudinal data were used to characterize these multi-parameters including random intercept and interval per individual. No significant age by gender interactions have been found to either DTI fractional anisotropy (FA) or diffusivity metrics. The interval effective regions showed longitudinal change of FA and radial diffusivity (RD)/axial diffusivity (AX) values remained similar to the aging results found with cross-sectional data. Significant correlations between DTI and fMRI metrics as well as between imaging and neurocognitive data including speed and memory were found. Our results indicate significant and consistent age, gender and apolipoprotein E (APOE) genotypic effects on structural and functional connectivity at both short-interval and cross-sectional ranges, together with correlational neurocognitive functions.

Part of the book: Medical Imaging