A5047202385- Medical Image Segmentation Techniques
- Medical Imaging Techniques and Applications
- Advanced MRI Techniques and Applications
- Urinary Bladder and Prostate Research
- Neural dynamics and brain function
- Face Recognition and Perception
- Advanced Neuroimaging Techniques and Applications
- Pelvic floor disorders treatments
- Functional Brain Connectivity Studies
- Cell Image Analysis Techniques
- Connexins and lens biology
- Intraocular Surgery and Lenses
- Image Processing Techniques and Applications
- Attention Deficit Hyperactivity Disorder
- Visual Attention and Saliency Detection
- EEG and Brain-Computer Interfaces
- Pediatric Urology and Nephrology Studies
- Visual perception and processing mechanisms
- CCD and CMOS Imaging Sensors
Advanced Telecommunications Research Institute International
2017-2019
East China Normal University
2011-2014
The mental contents of perception and imagery are thought to be encoded in hierarchical representations the brain, but previous attempts visualize perceptual have failed capitalize on multiple levels hierarchy, leaving it challenging reconstruct internal imagery. Recent work showed that visual cortical activity measured by functional magnetic resonance imaging (fMRI) can decoded (translated) into features a pre-trained deep neural network (DNN) for same input image, providing way make use...
Deep neural networks (DNNs) have recently been applied successfully to brain decoding and image reconstruction from functional magnetic resonance imaging (fMRI) activity. However, direct training of a DNN with fMRI data is often avoided because the size available thought be insufficient for complex network numerous parameters. Instead, pre-trained usually serves as proxy hierarchical visual representations, are used decode individual features stimulus using simple linear model, which then...
Previous research has demonstrated that there are specific white matter abnormalities in patients with attention deficit/hyperactivity disorder (ADHD). However, the results of these studies not consistent and one most important factors affects inconsistency previous maybe ADHD subtype. Different subtypes may have some overlapping microstructural damage, but they also unique abnormalities. The objective this study was to investigate associated two ADHD: combined (ADHD-C) inattentive (ADHD-I)....
Abstract Primary monosymptomatic nocturnal enuresis (PMNE) is a common disorder in school‐aged children. However, little known about resting‐state neural function individuals with PMNE. In this work, functional magnetic resonance imaging (fMRI) was used to investigate changes spontaneous brain activity children We analyzed fMRI data using statistical parametric mapping (SPM) and analysis toolkit (REST). Regional homogeneity (ReHo) amplitude of low‐frequency fluctuation (ALFF) values were...
Background Primary monosymptomatic nocturnal enuresis (PMNE) is a common disorder in school-aged children. Previous studies have suggested that developmental delay might play role the pathology of children with PMNE. However, microstructural abnormalities brains these not been thoroughly investigated. Methodology/Principal Findings In this work, we evaluated structural changes PMNE using diffusion tensor imaging (DTI). Two groups consisting 26 and healthy controls were scanned magnetic...
Abstract Nocturnal enuresis is a common developmental disorder in children, and primary nocturnal (PNE) the dominant subtype. The main purpose of this study was to investigate brain functional abnormalities specifically related motor response inhibition children with PNE using fMRI combination Go/NoGo task. Twenty‐two 22 healthy group‐matched for age sex, took part experiment. Although no significant between‐group differences task performance accuracy were observed, patients showed...
Abstract Machine learning-based analysis of human functional magnetic resonance imaging (fMRI) patterns has enabled the visualization perceptual content. However, it been limited to reconstruction with low-level image bases (Miyawaki et al., 2008; Wen 2016) or matching exemplars (Naselaris 2009; Nishimoto 2011). Recent work showed that visual cortical activity can be decoded (translated) into hierarchical features a deep neural network (DNN) for same input image, providing way make use...
Abstract Multivariate pattern classification analysis ( MVPA ) has been applied to functional magnetic resonance imaging (f MRI data decode brain states from spatially distributed activation patterns. Decoding upper limb movements non‐invasively recorded human is crucial for implementing a brain–machine interface that directly harnesses an individual's thoughts control external devices or computers. The aim of this study was the individual finger f single‐trial data. Thirteen healthy...
Abstract Deep neural networks (DNNs) have recently been applied successfully to brain decoding and image reconstruction from functional magnetic resonance imaging (fMRI) activity. However, direct training of a DNN with fMRI data is often avoided because the size available thought be insufficient train complex network numerous parameters. Instead, pre-trained has served as proxy for hierarchical visual representations, were used decode individual features stimulus using simple linear model,...