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Cheng Zhou

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21 papers

2 author rows

EAAI Journal 2026 Journal Article

Few-shot semantic segmentation for clearance intrusion risk detection in metro tunnel point clouds

Wenbo Qin
Yuxiang Wang
Shangbin Gao
Cheng Zhou

A wide range of mechanical, electrical, and plumbing (MEP) components are mounted along metro tunnel linings, where subtle spatial displacements caused by loosening or deformation may intrude into the train clearance envelope. Detecting such early-stage deviations is challenging due to occlusion, low illumination, and the dense arrangement of facilities in tunnel point clouds acquired using simultaneous localization and mapping (SLAM). To address this problem, this study proposes a training-free few-shot semantic segmentation framework for clearance intrusion detection. The model integrates geometry-based descriptors (linearity, planarity, verticality), a scale factor control mechanism for multi-scale feature enhancement, and a confidence-based filtering strategy to suppress uncertain predictions. Experiments were conducted on metro tunnel point clouds acquired using a backpack-mounted light detection and ranging (LiDAR) SLAM system, with segmentation performed using 1 m under a single-class few-shot setting. The proposed method achieves a mean intersection over union (mIoU) of 78. 4 %, while requiring only a small support set of 15 blocks, and the reconstructed axes of MEP facilities enable deviation detection below 1 cm relative to reference inspection epochs. These results demonstrate that the proposed framework provides a practical and robust solution for early-stage clearance intrusion risk assessment in metro tunnel environments.

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EAAI Journal 2026 Journal Article

Mine hazardous obstacle segmentation for automated bulldozer with segment anything model

Yuxiang Wang
Ke You
Yutian Jiang
Shuai Hu
Zhangang Wu
Cheng Zhou

Ensuring the safety of autonomous bulldozers in open-pit mines requires accurate detection of hazardous obstacles such as sumps, stones, and hollows. However, detecting these hazards is challenging due to low contrast with the background, blurry boundaries, and variations in texture, shape, size, and distribution. To address these challenges, this paper proposes Segment Anything Model based mine hazardous obstacle detection model (Mine-SAM). Mine-SAM enhances segmentation performance for hazardous objects through the Weighted Mixture Adapters structure (WMix adapter), Dual Attention Mechanism (DAT), and Wavelet convolutions-based Receptive Field Blocks (WT_RFB). On a self-constructed dataset spanning multiple scenes, Mine-SAM achieved a mean Intersection over Union (mIoU) of 0. 9158 and maintained an inference speed exceeding 12 FPS (Frames Per Second) for video. Analysis of detection results across adverse operational scenarios and construction stages demonstrates the model's stability and reliable detection performance, highlighting its practical value in ensuring the safety of autonomous bulldozers during operation.

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ICRA Conference 2025 Conference Paper

A Fairness-Oriented Control Framework for Safety-Critical Multi-Robot Systems: Alternative Authority Control

Lei Shi
Qichao Liu
Cheng Zhou
Xiong Li 0001

This paper proposes a fair control framework for multi-robot systems, which integrates the newly introduced Alternative Authority Control (AAC) and Flexible Control Barrier Function (F-CBF). Control authority refers to a single robot which can plan its trajectory while considering others as moving obstacles, meaning the other robots do not have authority to plan their own paths. The AAC method dynamically distributes the control authority, enabling fair and coordinated movement across the system. This approach significantly improves computational efficiency, scalability, and robustness in complex environments. The proposed F-CBF extends traditional CBFs by incorporating obstacle shape, velocity, and orientation. FCBF enhances safety by accurate dynamic obstacle avoidance. The framework is validated through simulations in multi-robot scenarios, demonstrating its safety, robustness and computational efficiency.

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YNICL Journal 2025 Journal Article

Arterial spin labeling MRI based perfusion pattern related to motor dysfunction and L-DOPA reactivity in Parkinson’s disease

Qianshi Zheng
Weijin Yuan
Jiaqi Wen
Jianmei Qin
Chenqing Wu
Haoting Wu
Xiaojie Duanmu
Sijia Tan

OBJECTIVE: Identifying intrinsic pattern of Parkinson's disease (PD) helps to better understand of PD and provide insights to disease identification and treatment monitoring. Here we confirmed the PD-related covariance pattern (PDRP) by using arterial spin labelling technology (ASL-PDRP) and explore its potential for predicting motor progression and levodopa (L-DOPA) reactivity reduction. METHODS: Data from an original cohort of 179 PD and 62 normal controls (NC) and a validation cohort including 36 PD and 19 NC to construct and validate the ASL-PDRP. The correlations between the pattern and motor symptoms were analyzed cross-sectionally and longitudinally (71 PD owned longitudinal data) with hierarchical linear regression analysis. Kaplan-Meier analysis was conducted in 54 L-DOPA-managed PD patients to predict the levodopa reactivity reduction. RESULTS: The first principal component was predominantly recognized as the ASL-PDRP, with its expression being higher in PD than NC in both sets (original: P = 0.017, AUC = 0.598; validation: P = 0.024, AUC = 0.661). The pattern expression was associated with UPDRS III (P = 0.006) and sub-symptoms (axial: P < 0.001; rigidity: P = 0.003; bradykinesia: P = 0.015) at baseline. The ASL-PDRP could predict the progression of UPDRS III (P = 0.021, β = 4.930). Higher expression of the pattern had slower rate of levodopa reactivity reduction in PD patients with axial symptom (P = 0.031). CONCLUSION: The identified ASL-PDRP may have potential for characterizing PD with the ability to predict motor progression and L-DOPA reactivity reduction.

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ECAI Conference 2025 Conference Paper

PMR: Physical Model-Driven Multi-Stage Restoration of Turbulent Dynamic Videos

Tao Wu
Jingyuan Ye
Cheng Zhou
Wenlong Chen
Zheng Liu
Huiming Zheng
Wei Liu
Ying Fu

Geometric distortions and blurring caused by atmospheric turbulence degrade the quality of long-range dynamic scene videos. Existing methods struggle with restoring edge details and eliminating mixed distortions, especially under conditions of strong turbulence and complex dynamics. To address these challenges, we introduce a Dynamic Efficiency Index (DEI), which combines turbulence intensity, optical flow, and proportions of dynamic regions to accurately quantify video dynamic intensity under varying turbulence conditions and provide a high-dynamic turbulence training dataset. Additionally, we propose a Physical Model-Driven Multi-Stage Video Restoration (PMR) framework that consists of three stages: de-tilting for geometric stabilization, motion segmentation enhancement for dynamic region refinement, and de-blurring for quality restoration. PMR employs lightweight backbones and stage-wise joint training to ensure both efficiency and high restoration quality. Experimental results demonstrate that the proposed method effectively suppresses motion trailing artifacts, restores edge details and exhibits strong generalization capability, especially in real-world scenarios characterized by high-turbulence and complex dynamics. We will make the code and datasets openly available.

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YNIMG Journal 2025 Journal Article

Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson’s disease diagnosis

Jianmei Qin
Haoting Wu
Chenqing Wu
Tao Guo
Cheng Zhou
Xiaojie Duanmu
Sijia Tan
Jiaqi Wen

Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their accurate delineation challenging on T1WI. In this study, we aimed to introduce and validate a method that incorporates quantitative susceptibility mapping (QSM) into the rs-fMRI analytical pipeline to achieve precise subcortical nuclei segmentation and improve the stability of RSFC measurements in Parkinson's disease. A total of 321 participants (148 patients with Parkinson's Disease and 173 normal controls) were enrolled. We performed cross-modal registration at the individual level for rs-fMRI to QSM (FUNC2QSM) and T1WI (FUNC2T1), respectively.The consistency and accuracy of resting state functional connectivity (RSFC) measurements in two registration approaches were assessed by intraclass correlation coefficient and mutual information. Bootstrap analysis was performed to validate the stability of the RSFC differences between Parkinson's disease and normal controls. RSFC-based machine learning models were constructed for Parkinson's disease classification, using optimized hyperparameters (RandomizedSearchCV with 5-fold cross-validation). The consistency of RSFC measurements between the two registration methods was poor, whereas the QSM-guided approach showed better mutual information values, suggesting higher registration accuracy. The disruptions of RSFC identified with the QSM-guided approach were more stable and reliable, as confirmed by bootstrap analysis. In classification models, the QSM-guided method consistently outperformed the T1WI-guided method, achieving higher test-set ROC-AUC values (FUNC2QSM: 0.87-0.90, FUNC2T1: 0.67-0.70). The QSM-guided approach effectively enhanced the accuracy of subcortical segmentation and the stability of RSFC measurement, thus facilitating future biomarker development in Parkinson's disease.

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ICRA Conference 2024 Conference Paper

Learning Highly Dynamic Behaviors for Quadrupedal Robots

Chong Zhang
Jiapeng Sheng
Tingguang Li
He Zhang
Cheng Zhou
Qingxu Zhu 0001
Rui Zhao
Yizheng Zhang

Learning highly dynamic behaviors for robots has been a longstanding challenge. Traditional approaches have demonstrated robust locomotion, but the exhibited behaviors lack diversity and agility. They employ approximate models, which lead to compromises in performance. Data-driven approaches have been shown to reproduce agile behaviors of animals, but typically have not been able to learn highly dynamic behaviors. In this paper, we propose a learning-based approach to enable robots to learn highly dynamic behaviors from animal motion data. The learned controller is deployed on a quadrupedal robot and the results show that the controller is able to reproduce highly dynamic behaviors including sprinting, jumping and sharp turning. Various behaviors can be activated through human interaction using a stick with markers attached to it. Based on the motion pattern of the stick, the robot exhibits walking, running, sitting and jumping, much like the way humans interact with a pet.

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AAAI Conference 2024 Conference Paper

Relative Policy-Transition Optimization for Fast Policy Transfer

Jiawei Xu
Cheng Zhou
Yizheng Zhang
Baoxiang Wang
Lei Han

We consider the problem of policy transfer between two Markov Decision Processes (MDPs). We introduce a lemma based on existing theoretical results in reinforcement learning to measure the relativity gap between two arbitrary MDPs, that is the difference between any two cumulative expected returns defined on different policies and environment dynamics. Based on this lemma, we propose two new algorithms referred to as Relative Policy Optimization (RPO) and Relative Transition Optimization (RTO), which offer fast policy transfer and dynamics modelling, respectively. RPO transfers the policy evaluated in one environment to maximize the return in another, while RTO updates the parameterized dynamics model to reduce the gap between the dynamics of the two environments. Integrating the two algorithms results in the complete Relative Policy-Transition Optimization (RPTO) algorithm, in which the policy interacts with the two environments simultaneously, such that data collections from two environments, policy and transition updates are completed in one closed loop to form a principled learning framework for policy transfer. We demonstrate the effectiveness of RPTO on a set of MuJoCo continuous control tasks by creating policy transfer problems via variant dynamics.

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EAAI Journal 2024 Journal Article

Video surveillance-based multi-task learning with swin transformer for earthwork activity classification

Yanan Lu
Ke You
Cheng Zhou
Jiaxi Chen
Zhangang Wu
Yutian Jiang
Chao Huang

Bulldozers, pivotal in earthworks, traditionally undergo supervision through labor-intensive and potentially unreliable manual methods. This research proposes a vision-based method for automating the monitoring of bulldozer operations. First, this research develops a specialized dataset for deep learning, the bulldozer earthmoving activity dataset. Following this, a novel multi-task video classification network (MTVTNet), the multi-task video transformer network, utilizing a video swin transformer architecture, is proposed. This network is adept at concurrently detecting the shoveling action, state, and soil classification of a bulldozer. The effectiveness of this model is demonstrated through its application in a real-world construction setting, achieving a remarkable 99. 68% mean average precision. This method not only facilitates comprehensive automated supervision of bulldozer earthmoving activities but also serves as a valuable data source for assessing the operational efficiency of these machines.

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IROS Conference 2023 Conference Paper

A Unified Trajectory Generation Algorithm for Dynamic Dexterous Manipulation

Cheng Zhou
Wentao Gao
Weifeng Lu
Yanbo Long
Sicheng Yang
Longfei Zhao
Bidan Huang
Yu Zheng 0001

This paper proposes a novel efficient multi-phase trajectory generation algorithm for dynamic dexterous manipulation tasks, such as throwing, catching, dynamic regrasping, and dynamic handover, which can be decomposed into multiple manipulation primitives, including sticking, rolling, approaching, separating, colliding, and grasping. Each manipulation primitive is formulate as a free-terminal optimal control problem (OCP), aimed at computing the optimal pose (position and orientation) trajectories of the object and the robot subject to the pose and force linkage constraints between them and the expected force maintenance at contact. A single-arm regrasping task and a dual-arm dynamic handover task are conducted to demonstrate the effectiveness of the proposed algorithm.

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AAMAS Conference 2023 Conference Paper

CraftEnv: A Flexible Collective Robotic Construction Environment for Multi-Agent Reinforcement Learning

Rui Zhao
Xu Liu
Yizheng Zhang
Minghao Li
Cheng Zhou
Shuai Li
Lei Han

CraftEnv is a flexible Collective Robotic Construction (CRC) environment for Multi-Agent Reinforcement Learning (MARL) research. CraftEnv can be used to study how artificial intelligent agents may learn to cooperate and solve complex real world tasks, such as collective construction and intelligent warehousing. The environment contains a set of collective construction tasks, which require a group of robotic vehicles to cooperate and learn to build different constructions efficiently. There are different elements in the CraftEnv, such as smartcars, blocks, and slopes. The smartcars can use the blocks and slopes to build different structures. The CraftEnv is highly flexible and simple to use, which enables creative and quick task-designs. The environment is written in python and can be rendered using PyBullet. The simulation is built based on real world robotic systems, designed with real-world constraints in mind. The learned policy can be transferred to the real world robotic system. CraftEnv is tailored for effective use by the research community and pushing forward collective intelligence and swarm technology.

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ICRA Conference 2023 Conference Paper

Differential Dynamic Programming based Hybrid Manipulation Strategy for Dynamic Grasping

Cheng Zhou
Yanbo Long
Lei Shi
Longfei Zhao
Yu Zheng 0001

To fully explore the potential of robots for dexterous manipulation, this paper presents a whole dynamic grasping process to achieve fluent grasping of a target object by the robot end-effector. The process starts from the phase of approaching the object over the phases of colliding with the object and letting it roll about the colliding point to the final phase of catching it by the palm or grasping it by the fingers of the end-effector. We derive a unified model for this hybrid dynamic manipulation process embodied as approaching-colliding-rolling-catching/grasping from the spatial vector based articulated body dynamics. Then, the whole process is formulated as a free-terminal constrained multi-phase optimal control problem (OCP). We extend the traditional differential dynamic programming (DDP) to solving this free-terminal OCP, where the backward pass of DDP involves constrained quadratic programming (QP) problems and we solve them by the primal-dual Augmented Lagrangian (PDAL) method. Simulations and real experiments are conducted to show the effectiveness of the proposed method for robotic dynamic grasping.

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IROS Conference 2023 Conference Paper

Learning Terrain-Adaptive Locomotion with Agile Behaviors by Imitating Animals

Tingguang Li
Yizheng Zhang
Chong Zhang
Qingxu Zhu 0001
Jiapeng Sheng
Wanchao Chi
Cheng Zhou
Lei Han 0001

In this paper, we present a general learning framework for controlling a quadruped robot that can mimic the behavior of real animals and traverse challenging terrains. Our method consists of two steps: an imitation learning step to learn from motions of real animals, and a terrain adaptation step to enable generalization to unseen terrains. We capture motions from a Labrador on various terrains to facilitate terrain adaptive locomotion. Our experiments demonstrate that our policy can traverse various terrains and produce a natural-looking behavior. We deployed our method on the real quadruped robot $\boldsymbol{Max}$ [1] via zero-shot simulation-to-reality transfer, achieving a speed of 1. 1 m/s on stairs climbing.

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YNIMG Journal 2023 Journal Article

Predictability of inter-regional cerebral perfusion similarity on dopamine responsiveness and the moderation role of cognition in PD patients

Zhengye Cao
Chenqing Wu
Hui Hong
Peiyu Huang
Cheng Zhou
Xiaojun Guan
Haoting Wu
Xiaojie Duanmu

BACKGROUND: Large heterogeneity can be found in dopamine responsiveness of patients with Parkinson's disease (PD). Instantly and objectively understanding dopamine responsiveness of patients may help clinical practice. PURPOSE: This PD study explored the predictability of off-state inter-regional cerebral blood flow (CBF) perfusion similarity on patient's dopamine responsiveness and tested whether the predictive power could be moderated by patient's cognitive status. MATERIALS AND METHOD: The PD cohort with 192 patients (containing off state and on state (PD-off and PD-on)) and the normal control (NC) cohort with 92 subjects were included. The intra-individual CBF relative variation networks were constructed and compared between PD-off and PD-on, PD-off and NC to identify the alterations caused by dopamine depletion. Based on that, regression analysis of off-state inter-regional CBF perfusion similarity on patient's dopamine responsiveness was performed. Finally, moderation analysis was conducted to test the moderation role of cognition on the regression model. RESULTS: In the PD-off cohort, a total of 82 edges in the network were identified that affected by dopamine depletion. Off-state inter-regional CBF perfusion similarity was found that had a significant influence on patient's dopamine responsiveness. Cognitive status was validated that positively moderated the relationship between off-state inter-regional CBF perfusion similarity and dopamine responsiveness. CONCLUSION: Dopamine responsiveness of PD patient could be predicted by off-state inter-regional CBF perfusion similarity. Patient's cognitive status might have a positive moderation effect on his/her dopamine responsiveness.

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ECAI Conference 2023 Conference Paper

Turn on the Right Track: Weakly Supervised Video Moment Retrieval with Self-Improving Query Reconstruction

Yiming Zhong
Haifeng Sun 0001
Jiachang Hao
Jing Wang 0039
Cheng Zhou
Qi Qi 0001
Jingyu Wang 0001
Jianxin Liao

Existing weakly-supervised temporal sentence grounding methods typically regard query reconstruction as the pretext task in place of the absent temporal supervision. However, their approaches suffer from two flaws, i. e. insignificant reconstruction and discrepancy in alignment. Insignificant reconstruction indicates the randomly masked words may not be discriminative enough to distinguish the target event from unrelated events in the video. Discrepancy in alignment indicates the incorrect partial alignment built by query reconstruction task. The flaws undermine the reliability of current reconstruction-based methods. To this end, we propose a novel Self-improving Query ReconstrucTion (SQRT) framework for weakly-supervised temporal sentence grounding. To deal with insignificant reconstruction, we devise a key words mining strategy to determine the important words for language grounding. To attain better moment-query alignment, we introduce inter-sample contrast to tackle the partial alignment built by query reconstruction. The self-improving framework utilizes query reconstruction for language grounding and alleviates the discrepancy in alignment, thus turning on the right track. Experiments on two popular datasets show that SQRT achieves state-of-the-art performance on Charades-STA and comparable performance to the state-of-the-art on ActivityNet Captions.

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YNIMG Journal 2022 Journal Article

Altered brain iron depositions from aging to Parkinson's disease and Alzheimer's disease: A quantitative susceptibility mapping study

Xiaojun Guan
Tao Guo
Cheng Zhou
Jingjing Wu
Qingze Zeng
Kaicheng Li
Xiao Luo
Xueqin Bai

Brain iron deposition is a promising marker for human brain health, providing insightful information for understanding aging as well as neurodegenerations, e. g. , Parkinson's disease (PD) and Alzheimer's disease (AD). To comprehensively evaluate brain iron deposition along with aging, PD-related neurodegeneration, from prodromal PD (pPD) to clinical PD (cPD), and AD-related neurodegeneration, from mild cognitive impairment (MCI) to AD, a total of 726 participants from July 2013 to December 2020, including 100 young adults, 189 old adults, 184 pPD, 171 cPD, 31 MCI and 51 AD patients, were included. Quantitative susceptibility mapping data were acquired and used to quantify regional magnetic susceptibility, and the resulting spatial standard deviations were recorded. A general linear model was applied to perform the inter-group comparison. As a result, relative to young adults, old adults showed significantly higher iron deposition with higher spatial variation in all of the subcortical nuclei (p < 0. 01). pPD showed a high spatial variation of iron distribution in the subcortical nuclei except for substantia nigra (SN); and iron deposition in SN and red nucleus (RN) were progressively increased from pPD to cPD (p < 0. 01). AD showed significantly higher iron deposition in caudate and putamen with higher spatial variation compared with old adults, pPD and cPD (p < 0. 01), and significant iron deposition in SN compared with old adults (p < 0. 01). Also, linear regression models had significances in predicting motor score in pPD and cPD (Rmean = 0. 443, Ppermutation = 0. 001) and cognition score in MCI and AD (Rmean = 0. 243, Ppermutation = 0. 037). In conclusion, progressive iron deposition in the SN and RN may characterize PD-related neurodegeneration, namely aging to cPD through pPD. On the other hand, extreme iron deposition in the caudate and putamen may characterize AD-related neurodegeneration.

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IROS Conference 2022 Conference Paper

Optimal Nonprehensile Interception Strategy for Objects in Flight

Cheng Zhou
Yanbo Long
Ying Cao
Longfei Zhao
Bidan Huang
Yu Zheng 0001

Intercepting an object in flight through nonpre-hensile manipulation is a challenging problem, which is aimed at catching and stopping a flying object using little contacts without completely restraining its relative motion to the robot. This paper presents a two-stage optimal trajectory generation method to tackle this problem. At the pre-catching stage, optimal position and attitude trajectories of the robot's end-effector to approach the object are generated by a variational method. At the post-catching stage, the end-effector's trajectories are generated to optimally eliminate the translational and rotational motion of the object and a convex-MPC algorithm combined with admittance control is used to realize the trajectory tracking. A series of simulations and experiments have been conducted to verify the effectiveness of the proposed method.

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IROS Conference 2022 Conference Paper

RECCraft System: Towards Reliable and Efficient Collective Robotic Construction

Qiwei Xu
Yizheng Zhang
Shenghao Zhang 0001
Rui Zhao
Zhuoxing Wu
Dongsheng Zhang
Cheng Zhou
Xiong Li 0001

This research presents a novel Collective Robotic Construction (CRC) system named RECCraft. The RECCraft hardware system is composed of the mobile manipulation vehicles, the cubic blocks, and the folding ramp blocks. Solid connection and easy removal of the blocks are achieved by an electropermanent magnet and silicon steel sheets. With one degree of freedom (DOF) lifting manipulator, the robot can carry a block 3. 7 times its volume. An active folding ramp block can provide a robust passage to the upper level for the robot. Our study focuses on systemic improvement of the construction speed and reliability of the robotic construction system. Visual perception system realized by Apritag is adopted, featured by convenient deployment and high precision, to provide a reliable guarantee for robotic construction. RL-based planner provides end-to-end solution for planning tasks of building multi-layer constructions, which is validated by simulation platform and real prototype. Compared with construction speed of existing robotic construction systems, our proposed RECCraft system achieves state-of-the-art level. The robot builds a 2-layer construction by RL-based planner in 4 minutes and 16 seconds, which achieves construction volumetric throughput of 6. 7×10 5 mm 3 /s.

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ICRA Conference 2022 Conference Paper

TOPP-MPC-Based Dual-Arm Dynamic Collaborative Manipulation for Multi-Object Nonprehensile Transportation

Cheng Zhou
Maolin Lei
Longfei Zhao
Zunran Wang
Yu Zheng 0001

This paper presents a unified controller for dual-arm robot dynamic multi-object nonprehensile transportation. The controller is composed of time-optimal path parameteri-zation (TOPP) and model predictive control (MPC) and aimed at efficiently and dynamically transporting objects using the dual-arm robot under physical constraints while avoiding the slippage of the objects. A force tracking controller without using the force sensor is also proposed to achieve accurate contact force control between the arms and objects. Experiments on the real robot show the effectiveness of the proposed TOPP-MPC-based controller.

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YNICL Journal 2021 Journal Article

Locus coeruleus degeneration is associated with disorganized functional topology in Parkinson’s disease

Cheng Zhou
Tao Guo
Xueqin Bai
Jingjing Wu
Ting Gao
Xiaojun Guan
Xiaocao Liu
Luyan Gu

Degeneration of the locus coeruleus (LC) is recognized as a critical hallmark of Parkinson's disease (PD). Recent studies have reported that noradrenaline produced from the LC has critical effects on brain functional organization. However, it is unknown if LC degeneration in PD contributes to cognitive/motor manifestations through modulating brain functional organization. This study enrolled 94 PD patients and 68 healthy controls, and LC integrity was measured using the contrast-to-noise ratio of the LC (CNRLC) calculated from T1-weighted magnetic resonance imaging. We used graph-theory-based network analysis to characterize brain functional organization. The relationships among LC degeneration, network disruption, and cognitive/motor manifestations in PD were assessed. Whether network disruption was a mediator between LC degeneration and cognitive/motor impairments was assessed further. In addition, an independent PD subgroup (n = 35) having functional magnetic resonance scanning before and after levodopa administration was enrolled to evaluate whether LC degeneration-related network deficiencies were independent of dopamine deficiency. We demonstrated that PD patients have significant LC degeneration compared to healthy controls. CNRLC was positively correlated with Montreal Cognitive Assessment score and the nodal efficiency (NE) of several cognitive-related regions. Lower NE of the superior temporal gyrus was a mediator between LC degeneration and cognitive impairment in PD. However, levodopa treatment could not normalize the reduced NE of the superior temporal gyrus (mediator). In conclusion, we provided evidence for the relationship between LC degeneration and extensive network disruption in PD, and highlight the role of network disorganization in LC degeneration-related cognitive impairment.

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YNICL Journal 2020 Journal Article

Fixel-based analysis reveals fiber-specific alterations during the progression of Parkinson’s disease

Yanxuan Li
Tao Guo
Xiaojun Guan
Ting Gao
Wenshuang Sheng
Cheng Zhou
Jingjing Wu
Min Xuan

Disruption of brain circuits is one of the core mechanisms of Parkinson's disease (PD). Understanding structural connection alterations in PD is important for effective treatment. However, due to methodological limitations, most studies were unable to account for confounding factors such as crossing fibers and were unable to identify damages to specific fiber tracts. In the present study, we aimed to demonstrate tract-specific white matter structural changes in PD patients and their relationship with clinical symptoms. Ninety-eight PD patients, divided into early (ES) and middle stage (MS) groups, and 76 healthy controls (HCs) underwent brain magnetic resonance imaging scans and clinical assessments. Fixel-based analysis was used to investigate fiber tract alterations in PD patients. Compared to HCs, the PD patients showed decreased fiber density (FD) in the corpus callosum (CC), increased FD in the cortical spinal tract (CST), and increased fiber-bundle cross-section (FC, log-transformed: log-FC) in the superior cerebellar peduncle (SCP). Analysis of variance (ANOVA) revealed significant differences in FD in the CST and log-FC in the SCP among the three groups. Post-hoc analysis revealed that the mean FD values of the CST were higher in ES and MS patient groups compared to HCs, and the mean log-FC values of the SCP were higher in ES and MS patient groups compared to HCs. Additionally, the FD values of the CC in PD patients were negatively correlated with the Unified Parkinson's Disease Rating Scale part-III (UPDRS-III) scores (r = -0.257, p = 0.032), Hamilton Depression Rating Scale 17 Items (HAMD-17) scores (r = -0.230, p = 0.033), and Hamilton Anxiety Scale (HAMA) scores (r = -0.248, p = 0.032). Moreover, log-FC values of the SCP (r = 0.274, p = 0.028) and FD values of the CST (r = 0.384, p < 0.001) were positively correlated with the UPDRS-III scores. We concluded that PD patients had both decreased and increased white matter integrity within specific fiber bundles. Additionally, these white matter alterations were different across disease stages, suggesting the occurrence of complex pathological and compensatory changes during the development of PD.

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