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Luyao Wang

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13 papers

2 author rows

YNIMG Journal 2026 Journal Article

Individual gray–white matter functional connection predicts tau spread and cognitive decline in Alzheimer’s disease

Luyao Wang
Yiwen Gao
Jiaying Lu
Quanling Jiang
Huanxin Wang
Fan Dong
Qianhua Zhao
Yihui Guan

PURPOSE: and tau deposition and to evaluate its value in predicting longitudinal tau spread. METHODS: and tau deposition and then constructed an individual-level spreading model to predict longitudinal tau accumulation. RESULTS: showed a positive correlation with tau deposition. Model-simulated white-matter tau deposition was associated with clinical scales and predicted cognitive decline. The spreading model, which incorporated baseline tau-PET and the top 10% of gray and white matter, yielded the highest predictive performance for future tau accumulation. CONCLUSION: in understanding tau propagation and support development of network-targeted therapeutic strategies.

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AAAI Conference 2026 Conference Paper

PointChain: Learning Generalizable Point Cloud Representations via Structural Chain Modeling

Luyao Wang
ChuXin Wang
Qiao Li
Tianzhu Zhang

Recent advances in point cloud analysis have increasingly leveraged large-scale unlabeled data through self-supervised representation learning. Autoregressive models based on next-token prediction have shown strong performance, but they usually model point clouds as linear sequences, ignoring their inherent spatial structure. To address this limitation, we propose PointChain, a novel autoregressive paradigm inspired by human perception mechanisms, designed to better align with the structural properties of point cloud. Specifically, we introduce structural chain encoding, which models the understanding process as a global-to-local structural chain inference, preserving spatial relationships throughout the prediction sequence. During pre-training, we design two auxiliary tasks: a next-scale prediction task that encourages cross-scale reasoning, and a scale-level contrastive learning task that promotes semantic consistency across scales. These components guide the model to learn more discriminative and generalizable point cloud representations. Experiments on multiple benchmarks, using both Transformer and Mamba backbones, validate the effectiveness of our approach. PointChain achieves state-of-the-art performance on several downstream tasks, including 93.75% accuracy on the hardest split of ScanObjectNN without voting strategy.

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IROS Conference 2025 Conference Paper

A Dual Tiltrotor UAV with Foldable Wings for Passive Perching and Belly/Back Takeoff

Luyao Wang
Jiangyi Zhang
Liangliang Cheng
Jingrui Yang
Tianchi Ma
Xiang He

This paper presents a novel dual tiltrotor UAV design featuring foldable wings and a strategically positioned center of gravity (CG) to enable passive perching and multi-modal flight. Traditional UAVs rely on additional mechanical components for operations such as takeoff and perching, which increase weight and complexity. Inspired by the mechanics of a balanced bird toy, our design achieves stability in horizontal flight and secure power-off perching on branches or cables. The proposed blade-tip plane-based controller facilitates belly/back takeoff without landing gear, enabling seamless transitions between hovering and horizontal flight within 2 seconds. Wind resistance tests were conducted indoors to assess disturbance rejection capabilities during perching, while the transition performance was evaluated outdoors.

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YNICL Journal 2025 Journal Article

Detection of structural-functional coupling abnormalities using multimodal brain networks in Alzheimer’s disease: A comparison of three computational models

Yinping Lu
Luyao Wang
Toshiya Murai
Jinglong Wu
Dong Liang
Zhilin Zhang

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the disconnection of white matter fibers and disrupted functional connectivity of gray matter; however, the pathological mechanisms linking structural and functional changes remain unclear. This study aimed to explore the interaction between the structural and functional brain network in AD using advanced structural-functional coupling (S-F coupling) models to assess whether these changes correlate with cognitive function, Aβ deposition levels, and gene expression. In this study, we utilized multimodal magnetic resonance imaging data from 41 individuals with AD, 112 individuals with mild cognitive impairment, and 102 healthy controls to explore these mechanisms. We applied different computational models to examine the changes in the S-F coupling associated with AD. Our results showed that the communication and graph harmonic models demonstrated greater heterogeneity and were more sensitive than the statistical models in detecting AD-related pathological changes. In addition, S-F coupling increases with AD progression at the global, subnetwork, and regional node levels, especially in the medial prefrontal and anterior cingulate cortices. The S-F coupling of these regions also partially mediated cognitive decline and Aβ deposition. Furthermore, gene enrichment analysis revealed that changes in S-F coupling were strongly associated with the regulation of cellular catabolic processes. This study advances our understanding of the interaction between structural and functional connectivity and highlights the importance of S-F coupling in elucidating the neural mechanisms underlying cognitive decline in AD.

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YNIMG Journal 2025 Journal Article

Development of a multichannel hand-adaptive tactile stimulation device for somatotopic map of human hand in somatosensory cortex with fMRI

Yutong Wang
Di Luo
Lihua Ma
Luyao Wang
Jinglong Wu
Jian Zhang
Tianyi Yan

The 7T functional magnetic resonance imaging (fMRI) can provide a detailed somatotopic map. However, due to the constraints of MR-compatible applications, current tactile stimulation devices for the human hand are insufficient for precise somatotopic mapping experiments. In this study, we developed a novel 23-channel, hand-adaptive tactile stimulation device with high temporal and spatial resolution. The device consisted of an execution module and a control module. The device's output performance was measured using a laser displacement sensor. We investigated the somatotopic map of the non-dominant hand in the primary somatosensory cortex (S1) using the Bayesian population receptive field (pRF) model. The activation patterns, relative volumes, and activation center locations on S1 were assessed in somatotopic mapping experiments involving traveling wave stimulus paradigms with three stimulus orders (forward, backward, and random) in two dimensions (between-digit and within-digit). The percussive stimulation provided by the tactile stimulation device exhibited a stable displacement (2.58 mm) and a minimal output delay (4.45 milliseconds) across a wide range of vibration frequencies (0-30 Hz). The representation of digits and the palm in the between-digit dimension showed consistent somatotopic organization (D1-D2-D3-D4-D5-palm along the postcentral gyrus (poCG) from ventral to dorsal) across all three stimulation orders. Additionally, the relative volume of D1 in the random paradigm was significantly larger than in the forward and backward paradigms. The relative volume of the palm in the random paradigm was significantly larger than in the backward paradigm. The representation of the phalanges and palm in the within-digit dimension exhibited different activation patterns across different stimulation orders. These results provide new insights into the neural mechanisms in S1 and validate that the developed stimulation device can contribute to exploring the somatotopic map of the human hand.

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YNIMG Journal 2024 Journal Article

Detection of individual brain tau deposition in Alzheimer's disease based on latent feature-enhanced generative adversarial network

Jiehui Jiang
Rong Shi
Jiaying Lu
Min Wang
Qi Zhang
Shuoyan Zhang
Luyao Wang
Ian Alberts

OBJECTIVE: The conventional methods for interpreting tau PET imaging in Alzheimer's disease (AD), including visual assessment and semi-quantitative analysis of fixed hallmark regions, are insensitive to detect individual small lesions because of the spatiotemporal neuropathology's heterogeneity. In this study, we proposed a latent feature-enhanced generative adversarial network model for the automatic extraction of individual brain tau deposition regions. METHODS: The latent feature-enhanced generative adversarial network we propose can learn the distribution characteristics of tau PET images of cognitively normal individuals and output the abnormal distribution regions of patients. This model was trained and validated using 1131 tau PET images from multiple centres (with distinct races, i.e., Caucasian and Mongoloid) with different tau PET ligands. The overall quality of synthetic imaging was evaluated using structural similarity (SSIM), peak signal to noise ratio (PSNR), and mean square error (MSE). The model was compared to the fixed templates method for diagnosing and predicting AD. RESULTS: The reconstructed images archived good quality, with SSIM = 0.967 ± 0.008, PSNR = 31.377 ± 3.633, and MSE = 0.0011 ± 0.0007 in the independent test set. The model showed higher classification accuracy (AUC = 0.843, 95 % CI = 0.796-0.890) and stronger correlation with clinical scales (r = 0.508, P < 0.0001). The model also achieved superior predictive performance in the survival analysis of cognitive decline, with a higher hazard ratio: 3.662, P < 0.001. INTERPRETATION: The LFGAN4Tau model presents a promising new approach for more accurate detection of individualized tau deposition. Its robustness across tracers and races makes it a potentially reliable diagnostic tool for AD in practice.

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YNIMG Journal 2024 Journal Article

fMRI signals in white matter rewire gray matter community organization

Luyao Wang
Huanyu Xu
Ziyan Song
Huanxin Wang
Wenjing Hu
Yiwen Gao
Zhilin Zhang
Jiehui Jiang

Human brain gray matter (GM) has usually been clustered into multiple functional networks. The white matter (WM) fiber bundles are known to interconnect these networks simultaneously, engaging in numerous cognitive functions. However, the exact interconnections between GM and WM are still unclear, whether functional signals in WM rewires GM community organization remains to be explored. In this study, we divided brain functional connections into three types by using edge-centric method, including intra-GM, intra-WM and GM-WM connections, and calculated the edge community evaluation indexes for quantifying GM community engagement. The results showed that the involvement of WM significantly enhanced community entropy in the heteromodal system, while the sensory-attention system remained barely changed. In addition, delta community entropy showed a significant correlation with clinical cognitive scale. Our results suggested that WM rewired GM community organization, enhancing the community engagement of brain regions in the heteromodal system. This involvement was observed to be disrupted in disease groups. Our study revealed that considering the functional signals of GM and WM simultaneously could better understand the brain's functional organization.

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EAAI Journal 2024 Journal Article

Intermittent fault diagnosis of analog circuit based on enhanced one-dimensional vision transformer and transfer learning strategy

Shengdong Wang
Zhenbao Liu
Zhen Jia
Wen Zhao
Zihao Li
Luyao Wang

As the major cause of false alarms in built-in test (BIT) system, intermittent faults of analog circuits may trigger abnormal equipment shutdown and lead to catastrophic accidents. With complete randomness and great non-repeatability, intermittent faults are arduous to be detected. To enhance the reliability and safety of electronic systems, an end-to-end approach based on enhanced one-dimensional Vision Transformer (1DViT) is proposed to realize intelligent diagnosis for intermittent faults of analog circuits. The signal anomaly caused by intermittent faults can be regarded as a kind of random anomaly from global perspective, and there are also rich local feature information in the fault interval. Completely composed of self-attention mechanism, Vision Transformer possesses prominent performance on extracting global features and modelling global representations, thus can be applied to identify intermittent faults. Meanwhile, to further enrich the feature representation, one multi-scale convolution fusion module (MSC) incorporating a series of convolution operations is designed and combined with 1DViT to extract and fuse the valuable local information. However, in practical test, due to the complex operation process, it is cumbersome to collect sufficient fault data to guarantee the effective training of the proposed model. To cope with this problem, transfer learning strategy is introduced. The model will be first pre-trained with adequate simulation data which is easily accessible, and then fine-tuned with a relatively small amount of actual fault data to help match the practical feature distribution. Experiments on two typical circuits demonstrate that the proposed method could achieve excellent diagnostic result in practical test.

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AAAI Conference 2024 Conference Paper

Pseudo-Label Calibration Semi-supervised Multi-Modal Entity Alignment

Luyao Wang
Pengnian Qi
Xigang Bao
Chunlai Zhou
Biao Qin

Multi-modal entity alignment (MMEA) aims to identify equivalent entities between two multi-modal knowledge graphs for integration. Unfortunately, prior arts have attempted to improve the interaction and fusion of multi-modal information, which have overlooked the influence of modal-specific noise and the usage of labeled and unlabeled data in semi-supervised settings. In this work, we introduce a Pseudo-label Calibration Multi-modal Entity Alignment (PCMEA) in a semi-supervised way. Specifically, in order to generate holistic entity representations, we first devise various embedding modules and attention mechanisms to extract visual, structural, relational, and attribute features. Different from the prior direct fusion methods, we next propose to exploit mutual information maximization to filter the modal-specific noise and to augment modal-invariant commonality. Then, we combine pseudo-label calibration with momentum-based contrastive learning to make full use of the labeled and unlabeled data, which improves the quality of pseudo-label and pulls aligned entities closer. Finally, extensive experiments on two MMEA datasets demonstrate the effectiveness of our PCMEA, which yields state-of-the-art performance.

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IROS Conference 2023 Conference Paper

Can Quadruped Guide Robots be Used as Guide Dogs?

Luyao Wang
Qihe Chen
Yan Zhang 0122
Ziang Li
Tingmin Yan
Fan Wang
Guyue Zhou
Jiangtao Gong

Quadruped robots have the potential to guide blind and low vision (BLV) people due to their highly flexible locomotion and emotional value provided by their bionic forms. However, the development of quadruped guide robots rarely involves BLV users' participatory designs and evaluations. In this paper, we conducted two empirical experiments both in indoor controlled and outdoor field scenarios, exploring the benefits and drawbacks of quadruped guide robots. The results show that the nowadays commercial quadruped robots exposed significant disadvantages in usability and trust compared with wheeled robots. It is concluded that the moving gait and walking noise of quadruped robots would limit the guiding effectiveness to a certain extent, and the empathetic effect of its bionic form for BLV users could not be fully reflected. Based on the findings of wheeled robots and quadruped robots' advantages, we discuss the design implications for the future guide robot design for BLV users. This paper reports the first empirical experiment about quadruped guide robots with BLV users and preliminary explores their potential improvement space in substituting guide dogs, which can inspire the further specialized design of quadruped guide robots.

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IROS Conference 2023 Conference Paper

Magnetically Controlled Cell Robots with Immune-Enhancing Potential

Hongyan Sun
Yuguo Dai
Jiaying Zhang
Junjie Xu
Lina Jia
Chutian Wang
Luyao Wang
Chan Li

Magnetic microrobots exhibit enormous potential in targeted drug delivery owing to the remote wireless manipulation and minimum invasion for medical treatment. High degree of freedom offers the magnetic propelled robots extraordinary application prospect since they can be controlled precisely when different magnetic fields sources working cooperatively. However, the biocompatibility of microrobots have attracted sustained and general concern. Therefore, it is highly necessary to develop a promising carrier with high biocompatibility and investigate the mechanism of drug loading-release triggered by special microenvironment in the targeted region. In this paper, we proposed a magnetically controlled cell robots (MCRs) based on macrophages propelled by a rotating magnetic field. The innovative MCRs exhibit good biocompatibility and low toxicity by optimizing the concentration of polylysine-coated Fe nanoparticles (PLL@FeNPs) to 40 µg/mL. These MCRs loaded with murine interleukin-12 (IL-12), murine chemokine (C-C motif) ligand 5 (CCL-5), and murine C-X-C motif chemokine ligand 10 (CXCL-10) which can stimulate T cell differentiation and recruitment of monocytes, respectively. The macrophages showed an obvious M1-polarization tendency of macrophages to phagocytose intracellular pathogens and resist the growth of tumor cells. Under the control of a magnetic propelling system composed of 3 pairs of Helmholtz coil, the cell robot can be propelled wirelessly and moved along a predefined path with high accuracy. Moreover, the MCRs could approach to cancer cells and stop at places of interest in vitro. In conclusion, we have accomplished the preliminary construction of a targeted drug delivery system which displays great immune-enhancing potential for targeted drug delivery.

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IROS Conference 2023 Conference Paper

Microrobot Control Method Based on Movement of Field Free Point in Gradient Magnetic Field

Chutian Wang
Yiming Ji
Xinyun Luo
Chunyuan Gan
Jiapeng Yang
Jiawei Zhao
Luyao Wang
Lin Feng 0002

The untethered microrobots driven by multiple external physics fields have promising ability in minimally invasive disease treatments. One common type of the driving fields is gradient magnetic field, which can provide microrobots with adequate driving force in complicated environment. In this study, a control method of microrobot through gradient magnetic field system is presented, which is realized by moving the field free point (FFP) to produce an alterable magnetic driving force. A confirmatory experiment of the robot reciprocating motion control is undertaken in a 1D gradient magnetic robot system. The control method could be applied to further studies on in vivo applications of targeted microrobot drug delivery system.

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IROS Conference 2021 Conference Paper

Precise Control of Magnetized Macrophage Cell Robot for Targeted Drug Delivery

Luyao Wang
Yuguo Dai
Hongyan Sun
Li Song
Lina Jia
Chiju Jiang
Fumihito Arai
Lin Feng 0002

Micro-nano-robots are considered to be a promising platform for drug delivery in biological organisms, but there are still urgent technical problems in biocompatibility and degradability of 3D-printed-based micro-robots that need to be solved. Therefore, in this paper, we design a magnetized bio-hybrid robot, which uses mouse macrophages as carriers, and allowed it to swallow Fe 2 O 3 particles with a diameter of 10 nm. The robot takes advantage of macrophage’s natural biocompatibility and targeting characteristics to reach and function in complex environments such as: eye, knee, tumor, etc. , and finally being able to be actively metabolized by the organism. More importantly, the cell robot can move precisely along a preplanned path under the control of a three-dimensional magnetic control system built in this study, and be delivered accurately to the vicinity of cancer cells in vitro environment. In future work, cellular robots could be allowed to carry anti-cancer drugs and release them in a targeted manner at the lesion. These microrobots have shown great potential for tumor reginal targeted drug delivery.

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