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Yin Jiang

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3 papers

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YNIMG Journal 2025 Journal Article

Subthalamic nucleus stimulation at high and low frequencies engages different brain networks to enhance gait performance in Parkinson's disease

Yin Jiang
Hutao Xie
Yutong Bai
Quan Zhang
Yu Diao
Houyou Fan
Xin Zhang
Hua Zhang

BACKGROUND: Subthalamic nucleus (STN) deep brain stimulation (DBS) is used to treat Parkinson's disease (PD), yet neither high-frequency stimulation (HFS) nor low frequency stimulation (LFS) fully resolves gait issues. Previous studies indicate that STN-DBS modulates motor-related brain networks. Given that PD patients with gait disturbances exhibit cognitive deficits-and considering the extensive projections between the STN and cerebral cortex-we hypothesized that varying STN stimulation frequencies may improve gait by modulating distinct brain networks. METHODS: We collected gait data, cortical electrophysiological signals, and resting-state fMRI from 44 PD patients and 32 healthy controls. Multi-network cortical activity and functional connectivity were c ompared under three conditions: DBS OFF, HFS, and LFS. Additionally, the connectivity values were correlated to the gait behaviors and clinical assessment scores. RESULTS: We found that: (1) HFS improved both motor and gait performance, while LFS enhanced gait but may not be optimal for long-term use; (2) STN-DBS induced widespread modulation across sensorimotor, frontoparietal, salience, dorsal attention, and default mode networks. HFS improved motor and gait functions via network modulation related to motor control, whereas LFS may enhance gait by boosting executive-related cortical activities and connections; (3) Relative to healthy controls, PD exhibited widespread reductions in functional connectivity, with DBS modulation trending toward normalization. CONCLUSIONS: These results reveal distinct brain network responses to different STN-DBS frequencies in PD, offering a theoretical basis for optimizing DBS treatment for gait impairments. These findings provide critical insights for tailoring DBS parameters to maximize both motor and cognitive benefits in PD patients.

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YNIMG Journal 2022 Journal Article

Low-frequency oscillations link frontal and parietal cortex with subthalamic nucleus in conflicts

Quan Zhang
Baotian Zhao
Wolf-Julian Neumann
Hutao Xie
Lin Shi
Guanyu Zhu
Zixiao Yin
Guofan Qin

Low-frequency oscillations (LFOs, 28 Hz) in the subthalamic nucleus(STN) are known to reflect cognitive conflict. However, it is unclear if LFOs mediate communication and functional interactions among regions implicated in conflict processing, such as the motor cortex (M1), premotor cortex (PMC), and superior parietal lobule (SPL). To investigate the potential contribution of LFOs to cognitive conflict mediation, we recorded M1, PMC, and SPL activities by right subdural electrocorticography (ECoG) simultaneously with bilateral STN local field potentials (LFPs) by deep brain stimulation electrodes in 13 patients with Parkinson's disease who performed the arrow version of the Eriksen flanker task. Elevated cue-related LFO activity was observed across patients during task trials, with the earliest onset in PMC and SPL. At cue onset, LFO power exhibited a significantly greater increase or a trend of a greater increase in the PMC, M1, and STN, and less increase in the SPL during high-conflict (incongruent) trials than in low-conflict (congruent) trials. The local LFO power increases in PMC, SPL, and right STN were correlated with response time, supporting the notion that these structures are critical hubs for cognitive conflict processing. This power increase was accompanied by increased functional connectivity between the PMC and right STN, which was correlated with response time across subjects. Finally, ipsilateral PMC-STN Granger causality was enhanced during high-conflict trials, with direction from STN to PMC. Our study indicates that LFOs link the frontal and parietal cortex with STN during conflicts, and the ipsilateral PMC-STN connection is specifically involved in this cognitive conflict processing.

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YNICL Journal 2019 Journal Article

A quantitative SVM approach potentially improves the accuracy of magnetic resonance spectroscopy in the preoperative evaluation of the grades of diffuse gliomas

Chong Qi
Yiming Li
Xing Fan
Yin Jiang
Rui Wang
Song Yang
Lanxi Meng
Tao Jiang

OBJECTIVES: H-MRS) metabolic features and the grade of gliomas, and to establish a machine-learning model to predict the glioma grade. METHODS: H-MRS image. The Student's t-test was conducted to screen for differentially expressed features between low- and high-grade gliomas (WHO grades II and III/IV, respectively). Next, the minimum Redundancy Maximum Relevance (mRMR) algorithm was performed to further select features for a support vector machine (SVM) classifier building. Performance of the predictive model was evaluated both in the training and validation sets using ROC curve analysis. RESULTS: H-MRS metabolic features, thirteen features were differentially expressed. Four features were further selected as grade-predictive imaging signatures using the mRMR algorithm. The predictive performance of the machine-learning model measured by the AUC was 0.825 and 0.820 in the training and validation sets, respectively. This was better than the predictive performances of individual metabolic features, the best of which was 0.812. CONCLUSIONS: H-MRS metabolic features could help in predicting the grade of gliomas. The machine-learning model achieved a better prediction performance in grading gliomas than individual features, indicating that it could complement the traditionally used metabolic features.

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