Fang Yang

EAAI Journal 2024 Journal Article

Electric bikes charging anomaly detection from alternating current side based on big data

• Fang Yang
• Yang Yang
• Xu Wang
• Xin Ouyang
• Chunyan Shuai

YNIMG Journal 2021 Journal Article

Recycling diagnostic MRI for empowering brain morphometric research – Critical & practical assessment on learning-based image super-resolution

• Gaoping Liu
• Zehong Cao
• Qiang Xu
• Qirui Zhang
• Fang Yang
• Xinyu Xie
• Jingru Hao
• Yinghuan Shi

Preliminary studies have shown the feasibility of deep learning (DL)-based super-resolution (SR) technique for reconstructing thick-slice/gap diagnostic MR images into high-resolution isotropic data, which would be of great significance for brain research field if the vast amount of diagnostic MRI data could be successively put into brain morphometric study. However, less evidence has addressed the practicability of the strategy, because lack of a large-sample available real data for constructing DL model. In this work, we employed a large cohort (n = 2052) of peculiar data with both low through-plane resolution diagnostic and high-resolution isotropic brain MR images from identical subjects. By leveraging a series of SR approaches, including a proposed novel DL algorithm of Structure Constrained Super Resolution Network (SCSRN), the diagnostic images were transformed to high-resolution isotropic data to meet the criteria of brain research in voxel-based and surface-based morphometric analyses. We comprehensively assessed image quality and the practicability of the reconstructed data in a variety of morphometric analysis scenarios. We further compared the performance of SR approaches to the ground truth high-resolution isotropic data. The results showed (i) DL-based SR algorithms generally improve the quality of diagnostic images and render morphometric analysis more accurate, especially, with the most superior performance of the novel approach of SCSRN. (ii) Accuracies vary across brain structures and methods, and (iii) performance increases were higher for voxel than for surface based approaches. This study supports that DL-based image super-resolution potentially recycle huge amount of routine diagnostic brain MRI deposited in sleeping state, and turning them into useful data for neurometric research.

YNICL Journal 2019 Journal Article

Effects of high- and low-frequency repetitive transcranial magnetic stimulation on motor recovery in early stroke patients: Evidence from a randomized controlled trial with clinical, neurophysiological and functional imaging assessments

• Juan Du
• Fang Yang
• Jianping Hu
• Jingze Hu
• Qiang Xu
• Nathan Cong
• Qirui Zhang
• Ling Liu

BACKGROUND: Repetitive transcranial magnetic stimulation (rTMS) can modulate cortical excitability, and may be beneficial for motor recovery after stroke. However, the neuroplasticity effects of rTMS have not been thoroughly investigated in the early stage after stroke. OBJECTIVE: To comprehensively assess the effects of high- and low-frequency repetitive transcranial magnetic stimulations on motor recovery in early stroke patients, using a randomized controlled trial based on clinical, neurophysiological and functional imaging assessments. METHODS: Sixty hospitalized, first-ever ischemic stroke patients (within 2 weeks after stroke) with motor deficits were randomly allocated to receive, in addition to standard physical therapy, five consecutive sessions of either: (1) High-frequency (HF) rTMS at 10 Hz over the ipsilesional primary motor cortex (M1); (2) Low-frequency (LF) rTMS at 1 Hz over the contralesional M1; (3) sham rTMS. The primary outcome measure was a motor impairment score (Upper Extremity Fugl-Meyer) evaluated at baseline, after rTMS intervention, and at 3-month follow-up. Cortical excitability and functional magnetic resonance imaging (fMRI) data were obtained within 24 h before and after rTMS intervention. Analyses of variance were conducted to compare the recovery effects among the three rTMS groups, assessed using clinical, neurophysiological and fMRI tests. RESULTS: Motor improvement was significantly larger in the two rTMS groups than in the control group. The HF-rTMS group showed significantly increased cortical excitability and motor-evoked fMRI activation in ipsilesional motor areas, whereas the LF-rTMS group had significantly decreased cortical excitability and motor-evoked fMRI activation in contralesional motor areas. Activity in ipsilesional motor cortex significantly correlated with motor function, after intervention as well as at 3-month follow-up. CONCLUSION: HF- and LF-rTMS can both improve motor function by modulating motor cortical activation in the early phase of stroke.