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Xia Zhou

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8 papers

2 author rows

YNICL Journal 2026 Journal Article

Association of MRI indexes of glymphatic system with brain atrophy and cognitive impairment in cerebral small vessel disease

Lulu Ai
Zhiwei Li
Chaojuan Huang
Xia Zhou
Xiaoqun Zhu
Qiaoqiao Xu
Zhongwu Sun

BACKGROUND AND OBJECTIVE: The glymphatic system constitutes a brain-wide perivascular network responsible for brain metabolic waste removal, which may underlie pathogenesis in cerebral small vessel disease (CSVD). This study aimed to explore the associations of glymphatic function, assessed using multi-modal MRI indices, and both brain atrophy and cognitive impairment in CSVD. METHODS: The study included 160 participants comprising 120 patients with CSVD, including 52 without cognitive impairment (CSVD-NCI) and 68 with mild cognitive impairment (CSVD-MCI), alongside 40 healthy controls (HCs). All participants underwent neuropsychological and multi-modal neuroimaging assessments. Glymphatic function was assessed using four complementary MRI indices: choroid plexus (CP) volume, perivascular space (PVS) volume fraction, free water in white matter (FW-WM) fraction, and diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. Gray matter volume (GMV) was evaluated via voxel-based morphology (VBM) analysis. Partial correlation and mediation analyses explored the relationships among glymphatic function, brain structure and cognitive performance. RESULTS: Compared to HCs, CSVD-MCI patients showed increased CP volume, FW-WM fraction, BG/putamen-PVS volume, and reduced DTI-ALPS index, accompanied by multifocal gray matter atrophy involving temporal and frontal regions. Advanced age was associated with increased CP and BG-PVS volume, but decreased DTI-ALPS index. A main effect of sex was observed, where males exhibited larger BG-PVS and FW-WM fraction, with lower DTI-ALPS index compared to females. Impaired glymphatic function was linked to both GMV loss and cognitive deficits, with right superior temporal and left postcentral GMV mediating glymphatic-cognitive associations, particularly in executive function and processing speed. CONCLUSION: Glymphatic dysfunction in CSVD, particularly in cognitive impairment stage, is closely related to brain atrophy and cognitive decline, supporting the potential utility of glymphatic metrics as clinically imaging biomarkers for assessing cognitive impairment risk and monitor disease progression in CSVD.

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AAAI Conference 2026 Conference Paper

CorrectAD: A Self-Correcting Agentic System to Improve End-to-end Planning in Autonomous Driving

Enhui Ma
Lijun Zhou
Tao Tang
Jiahuan Zhang
Junpeng Jiang
Zhan Zhang
Dong Han
Kun Zhan

End-to-end planning methods are the de-facto standard of the current autonomous driving system, while the robustness of the data-driven approaches suffers due to the notorious long-tail problem (i.e., rare but safety-critical failure cases). In this work, we explore whether recent diffusion-based video generation methods (a.k.a. world models), paired with structured 3D layouts, can enable a fully automated pipeline to self-correct such failure cases. We first introduce an agent to simulate the role of product manager, dubbed PM-Agent, which formulates data requirements to collect data similar to the failure cases. Then, we use a generative model that can simulate both data collection and annotation. However, existing generative models struggle to generate high-fidelity data conditioned on 3D layouts. To address this, we propose DriveSora, which can generate spatiotemporally consistent videos aligned with the 3D annotations requested by PM-Agent. We integrate these components into our self-correcting agentic system, CorrectAD. Importantly, our pipeline is end-to-end model agnostic and can be applied to improve any end-to-end planner. Evaluated on both nuScenes and a more challenging in-house dataset across multiple end-to-end planners, CorrectAD corrects 62.5% and 49.8% of failure cases, reducing collision rates by 39% and 27%, respectively.

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AAAI Conference 2026 Conference Paper

DriveLiDAR4D: Sequential and Controllable LiDAR Scene Generation for Autonomous Driving

Kaiwen Cai
Xinze Liu
Xia Zhou
Hengtong Hu
Jie Xiang
Luyao Zhang
Xueyang Zhang
Kun Zhan

The generation of realistic LiDAR point clouds plays a crucial role in the development and evaluation of autonomous driving systems. Although recent methods for 3D LiDAR point cloud generation have shown significant improvements, they still face notable limitations, including the lack of sequential generation capabilities and the inability to produce accurately positioned foreground objects and realistic backgrounds. These shortcomings hinder their practical applicability. In this paper, we introduce DriveLiDAR4D, a novel LiDAR generation pipeline consisting of multimodal conditions and a novel sequential noise prediction model LiDAR4DNet, capable of producing temporally consistent LiDAR scenes with highly controllable foreground objects and realistic backgrounds. To the best of our knowledge, this is the first work to address the sequential generation of LiDAR scenes with full scene manipulation capability in an end-to-end manner. We evaluated DriveLiDAR4D on the nuScenes and KITTI datasets, where we achieved an FRD score of 743.13 and an FVD score of 16.96 on the nuScenes dataset, surpassing the current state-of-the-art (SOTA) method, UniScene, with an performance boost of 37.2% in FRD and 24.1% in FVD, respectively.

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YNIMG Journal 2025 Journal Article

LUMEN–A deep learning pipeline for analysis of the 3D morphology of the cerebral lenticulostriate arteries from time-of-flight 7T MRI

Rui Li
Soumick Chatterjee
Yeerfan Jiaerken
Xia Zhou
Chethan Radhakrishna
Philip Benjamin
Stefania Nannoni
Daniel J. Tozer

The lenticulostriate arteries (LSAs) supply critical subcortical brain structures and are affected in cerebral small vessel disease (CSVD). Changes in their morphology are linked to cardiovascular risk factors and may indicate early pathology. 7T Time-of-Flight MR angiography (TOF-MRA) enables clear LSA visualisation. We aimed to develop a semi-automated pipeline for quantifying 3D LSA morphology from 7T TOF-MRA in CSVD patients. We used data from a local 7T CSVD study to create a pipeline, LUMEN, comprising two stages: vessel segmentation and LSA quantification. For segmentation, we fine-tuned a deep learning model, DS6, and compared it against nnU-Net and a Frangi-filter pipeline, MSFDF. For quantification, centrelines of LSAs within basal ganglia were extracted to compute branch counts, length, tortuosity, and maximum curvature. This pipeline was applied to 69 subjects, with results compared to traditional analysis measuring LSA morphology on 2D coronal maximum intensity projection (MIP) images. For vessel segmentation, fine-tuned DS6 achieved the highest test Dice score (0.814±0.029) and sensitivity, whereas nnU-Net achieved the best balanced average Hausdorff distance and precision. Visual inspection confirmed that DS6 was most sensitive in detecting LSAs with weak signals. Across 69 subjects, the pipeline with DS6 identified 23.5 ± 8.5 LSA branches. Branch length inside the basal ganglia was 26.4 ± 3.5 mm, and tortuosity was 1.5 ± 0.1. Extracted LSA metrics from 2D MIP analysis and our 3D analysis showed fair-to-moderate correlations. Outliers highlighted the added value of 3D analysis. This open-source deep-learning-based pipeline offers a validated tool quantifying 3D LSA morphology in CSVD patients from 7T-TOF-MRA for clinical research.

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NeurIPS Conference 2025 Conference Paper

RLGF: Reinforcement Learning with Geometric Feedback for Autonomous Driving Video Generation

Tianyi Yan
Wencheng Han
Xia Zhou
Xueyang Zhang
Kun Zhan
Cheng-Zhong Xu
Jianbing Shen

Synthetic data is crucial for advancing autonomous driving (AD) systems, yet current state-of-the-art video generation models, despite their visual realism, suffer from subtle geometric distortions that limit their utility for downstream perception tasks. We identify and quantify this critical issue, demonstrating a significant performance gap in 3D object detection when using synthetic versus real data. To address this, we introduce Reinforcement Learning with Geometric Feedback (RLGF), RLGF uniquely refines video diffusion models by incorporating rewards from specialized latent-space AD perception models. Its core components include an efficient Latent-Space Windowing Optimization technique for targeted feedback during diffusion, and a Hierarchical Geometric Reward (HGR) system providing multi-level rewards for point-line-plane alignment, and scene occupancy coherence. To quantify these distortions, we propose GeoScores. Applied to models like DiVE on nuScenes, RLGF substantially reduces geometric errors (e. g. , VP error by 21\%, Depth error by 57\%) and dramatically improves 3D object detection mAP by 12. 7\%, narrowing the gap to real-data performance. RLGF offers a plug-and-play solution for generating geometrically sound and reliable synthetic videos for AD development.

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IROS Conference 2025 Conference Paper

Set Phasers to Stun: Beaming Power and Control to Mobile Robots with Laser Light

Charles J. Carver
Hadleigh Schwartz
Toma Itagaki
Zachary Englhardt
Kechen Liu
Megan Graciela Nauli Manik
Chun-Cheng Chang
Vikram Iyer

We present Phaser, a flexible system that directs narrow-beam laser light to moving robots for concurrent wireless power delivery and communication. We design a semiautomatic calibration procedure to enable fusion of stereo-vision-based 3D robot tracking with high-power beam steering, and a low-power optical communication scheme that reuses the laser light as a data channel. We fabricate a Phaser prototype using off-the-shelf hardware and evaluate its performance with battery-free autonomous robots. Phaser delivers optical power densities of over 110 mW/cm 2 and error-free data to mobile robots at multi-meter ranges, with on-board decoding drawing 0. 3 mA (97% less current than Bluetooth Low Energy). We demonstrate Phaser fully powering gram-scale battery-free robots to nearly 2x higher speeds than prior work while simultaneously controlling them to navigate around obstacles and along paths. Code, an open-source design guide, and a demonstration video of Phaser is available at: mobilex. cs. columbia.edu/phaser

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YNIMG Journal 2024 Journal Article

Characterizing the role of the microbiota-gut-brain axis in cerebral small vessel disease: An integrative multi‑omics study

Yu Song
Xia Zhou
Han Zhao
Wenming Zhao
Zhongwu Sun
Jiajia Zhu
Yongqiang Yu

BACKGROUND: Prior efforts have revealed changes in gut microbiome, circulating metabolome, and multimodal neuroimaging features in cerebral small vessel disease (CSVD). However, there is a paucity of research integrating the multi-omic information to characterize the role of the microbiota-gut-brain axis in CSVD. METHODS: We collected gut microbiome, fecal and blood metabolome, multimodal magnetic resonance imaging data from 37 CSVD patients with white matter hyperintensities and 46 healthy controls. Between-group comparison was performed to identify the differential gut microbial taxa, followed by performance of multi-stage microbiome-metabolome-neuroimaging-neuropsychology correlation analyses in CSVD patients. RESULTS: Our data showed both depleted and enriched gut microbes in CSVD patients. Among the differential microbes, Haemophilus and Akkermansia were associated with a range of metabolites enriched for Aminoacyl-tRNA biosynthesis pathway. Furthermore, the affected metabolites were associated with neuroimaging measures involving gray matter morphology, spontaneous intrinsic brain activity, white matter integrity, and global structural network topology, which were in turn related to cognition and emotion in CSVD patients. CONCLUSION: Our findings provide an integrative framework to understand the pathophysiological mechanisms underlying the interplay between gut microbiota dysbiosis and CSVD, highlighting the potential of targeting the microbiota-gut-brain axis as a therapeutic strategy in CSVD patients.

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YNICL Journal 2020 Journal Article

Cerebral functional activity and connectivity changes in anti-N-methyl-D-aspartate receptor encephalitis: A resting-state fMRI study

Luhui Cai
Yanli Liang
Huanjian Huang
Xia Zhou
Jinou Zheng

BACKGROUND: Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis showing severe neuropsychiatric symptoms is the most common type of autoimmune encephalitis. However, the corresponding standard clinical magnetic resonance imaging (MRI) presents normal or atypical in the majority of patients with anti-NMDAR encephalitis. Here, this study aimed to investigate the alterations in brain functional activity in patients with anti-NMDAR encephalitis and whether these alterations contributed to cognition and mood disorders. METHODS: )) and resting-state functional MRI. MRI data was firstly analyzed by amplitude of low-frequency fluctuation (ALFF), and brain regions with altered ALFF between groups were selected as regions of interest for the further functional connectivity (FC) analysis. Correlation analyses were performed to investigate the associations between brain dysfunction and neuropsychological performance. RESULTS: scores (all p < 0.05). In the brain functional activity analysis, the patients showed decreased ALFF values in the bilateral posterior cingulate gyrus, left precuneus, and bilateral cerebellum (false- discovery- rate corrected, p < 0.05). Furthermore, relative to the control group, the patients showed significantly increased FC between the left posterior cingulate cortex (PCC) and the bilateral lingual gyrus, right calcarine, right cuneus, also between the right PCC and the right fusiform gyrus, bilateral lingual gyrus, left calcarine, left cuneus, and right posterior central gyrus (false- discovery- rate corrected, p < 0.05). FC strength between the left posterior cingulate gyrus and right cuneus, and between the right posterior cingulate gyrus and left cuneus were both positively correlated with MoCA memory scores (r = 0.485, p = 0.048; r = 0.550, p = 0.022). CONCLUSION: The present study highlight that decreased spontaneous neural activities and abnormal FC exhibited in the patients with anti-NMDAR encephalitis, which may participate in the process of cognition and emotion deficits. These results may help to elucidate the clinical radiological contradictions in anti-NMDAR encephalitis and contribute to deeper understanding of the pathophysiological mechanism of the disease.

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