Author name cluster

Ran Shi

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5 papers

2 author rows

YNICL Journal 2025 Journal Article

Mechanisms underlying the spontaneous reorganization of depression network after stroke

Yirong Fang
Xian Chao
Zeyu Lu
Hongmei Huang
Ran Shi
Dawei Yin
Hao Chen
Yanan Lu

Exploring the causal relationship between focal brain lesions and post-stroke depression (PSD) can provide therapeutic insights. However, a gap exists between causal and therapeutic information. Exploring post-stroke brain repair processes post-stroke could bridge this gap. We defined a depression network using the normative connectome and investigated the predictive capacity of lesion-induced network damage on depressive symptoms in discovery cohort of 96 patients, at baseline and six months post-stroke. Stepwise functional connectivity (SFC) was used to examine topological changes in the depression network over time to identify patterns of network reorganization. The predictive value of reorganization information was evaluated for follow-up symptoms in discovery and validation cohort 1 (22 worsening PSD patients) as well as for treatment responsiveness in validation cohort 2 (23 antidepressant-treated patients). We evaluated the consistency of significant reorganization areas with neuromodulation targets. Spatial correlations of network reorganization patterns with gene expression and neurotransmitter maps were analyzed. The predictive power of network damage for symptoms diminished at follow-up compared to baseline (Δadjusted R2 = -0. 070, p < 0. 001). Reorganization information effectively predicted symptoms at follow-up in the discovery cohort (adjust R2 = 0. 217, 95 %CI: 0. 010 to 0. 431), as well as symptom exacerbation (r = 0. 421, p = 0. 033) and treatment responsiveness (r = 0. 587, p = 0. 012) in the validation cohorts. Regions undergoing significant reorganization overlapped with neuromodulatory targets known to be effective in treating depression. The reorganization of the depression network was associated with immune-inflammatory responses gene expressions and gamma-aminobutyric acid. Our findings may yield important insights into the repair mechanisms of PSD and provide a critical context for developing post-stroke treatment strategies.

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EAAI Journal 2025 Journal Article

Multi-modal feature integration network for Visible-Depth-Thermal salient object detection

Fengyv Cui
Xiaofei Zhou
Liuxin Bao
Bin Wan
Ran Shi
Qiang Chen
Jiyong Zhang

In recent years, the task of salient object detection in multi-modal scenarios has attracted more and more attention, where the increase of modalities is beneficial for improving the detection performance of models. However, though the existing saliency models have achieved encouraging performance, they overlook the unbalanced information content between visible modality and other auxiliary modalities (i. e. , depth and thermal modalities), and lack the full utilization of multi-level features. This will lead to insufficient multi-modal fusion and multi-level integration. Therefore, in this paper, we propose a multi-modal feature integration network (MFINet) for Visible-Depth-Thermal (VDT) salient object detection (SOD), which contains three key modules. Firstly, we utilize the three-modal feature fusion (TMFF) module to enhance and fuse the multi-modal features by emphasizing effective feature channels and enlarging the receptive fields of features, where we further emphasize the visible cues. Secondly, we present a neighborhood layer feature enhancement (NLFE) module, which can utilize the complementary information from adjacent TMFF modules to enhance the decoder features by using different spatial attention strategies. Thirdly, a multi-level cascade feature integration (MCFI) module is proposed to aggregate the multi-level decoder features in a cascade way, acquiring the final high-quality saliency maps. Comprehensive experiments conducted on the VDT-2048 dataset demonstrate that our model outperforms the state-of-the-art models in terms of all evaluation metrics. The code is available at https: //github. com/banjamn/MFINet.

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JBHI Journal 2024 Journal Article

Decoding Human Interaction Type From Inter-Brain Synchronization by Using EEG Brain Network

Xiangcun Wang
Ran Shi
Xia Wu
Jiacai Zhang

Cooperation and competition are two common forms of interpersonal interactions and exploring inter-brain synchronization in these two forms can help to further deliberate the underlying neural mechanisms of interpersonal interactions. Recently, studies revealed that electrode-paired inter-brain synchronization plays an important role in human interactions. This study investigated the neural correlates of interpersonal synchronization at the brain network scale and interaction type. Firstly, the network-wise inter-brain synchronization (NIBS) index reflecting cross-brain network synchronization from the global brain perspective was advanced. Secondly, statistical analysis demonstrated that there are differences in NIBS activities between cooperative and competitive interactions. And a row-filtered depthwise separable convolution network was proposed to classify the NIBS features. Results of EEG hyper-scanning data showed significant differences in NIBS between cooperative and competitive tasks, and a comparative study manifested that the cross-brain synchronization in cooperative tasks is more consistent than that of competitive tasks. The neural decoder using a modified convolution network achieved a peak accuracy of 96. 05% under the binary classification(cooperation vs competition).

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IROS Conference 2017 Conference Paper

Contouring error vector and cross-coupled control of multi-axis servo system

Ran Shi
Xiang Zhang 0006
Yunjiang Lou

The contouring error and cross-coupled gains calculation have always been the critical issues in the application of cross-coupled control. Traditionally, the linear approximation and circular approximation are widely used to determine the contouring error and cross-coupled gains. However, for linear approximation and circular approximation, the contouring error and cross-coupled gains are calculated sophisticatedly, especially in three-dimensional applications. In this paper, a contouring error vector is established under task coordinate frame, then the contouring error and cross-coupled gains can be easily obtained based on the magnitude and orientation of the contouring error vector. The experimental results on a three-axis CNC machine indicate the proposed approach simplifies the calculation of contouring error and cross-coupled gains.

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IROS Conference 2016 Conference Paper

A novel contouring error estimation for position-loop cross-coupled control of biaxial servo systems

Ran Shi
Yunjiang Lou
Yongqi Shao 0002
Jiangang Li
Haoyao Chen

How to achieve the required contouring tracking accuracy especially during high-speed and large-curvature contouring tasks, has always been an important problem in manufacturing applications. In this paper, a contouring error estimation method based on natural local approximation is used, and then the position-loop cross-coupled controller is proposed to reduce the estimated contouring error. The effectiveness and superiority of the natural local approximation method using on the position-loop cross-coupled control scheme are demonstrated through experiments on a biaxial linear motor drive servo system.

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