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Xiaoping Wu

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9 papers

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YNIMG Journal 2026 Journal Article

Hippocampal subfields volumes as biomarkers for early diagnosis of asymptomatic manganese overexposure

Jiayu Wu
Yuli Gao
Xuying Ru
Sijia Fan
Ming Gao
Mengxue Sun
Yixin Cao
Mingyue Ma

Chronic occupational manganese (Mn) overexposure is associated with motor and cognitive deficits, but its effects on hippocampal subfields remain underexplored. Although neurodegeneration is known to involve the hippocampus, subfield-specific structural abnormalities have received limited attention. This study aimed to assess the diagnostic power of hippocampal subfield volumes in discriminating asymptomatic Mn-exposed welders from healthy controls (HCs). Mn-exposed welders and age-matched HCs were recruited and underwent high-resolution T1-weighted MRI scans. Volumes across 19 hippocampal subfields of each subject were estimated from automated tissue segmentations and surface-based reconstruction using FreeSurfer. The laterality value was defined as: (Right-Left)/(Right+Left)*100. Between-group differences in subfield volumes and laterality were assessed using cross-sectional analysis. Three machine learning classifiers, including logistic regression, K-nearest neighbors and support vector machine (SVM), were applied to differentiate welders from HCs. Compared to HCs, Mn-exposed welders had reduced volumes mainly in the fimbria, subiculum, and presubiculum, while showing higher volumes in the cornu ammonis area 3 (CA3). The welders group demonstrated significant rightward laterality in CA1 and CA4, and leftward laterality in the presubiculum. Among the three classifiers, the SVM classifier achieved the best performance (AUC = 0.96) in distinguishing welders from HCs using subfield volumes. Additionally, the exposure duration was non-linearly associated with left fimbria volume. These results revealed distinct volumetric and asymmetric patterns in hippocampal subfields among Mn-exposed welders, indicating regional vulnerability and potential compensatory responses. Notably, our findings underscored that hippocampal subfield volumes might serve as imaging biomarkers for early diagnosis in individuals with asymptomatic Mn overexposure.

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EAAI Journal 2024 Journal Article

Game-theoretic analytics for privacy preservation in Internet of Things networks: A survey

Yizhou Shen
Carlton Shepherd
Chuadhry Mujeeb Ahmed
Shigen Shen
Xiaoping Wu
Wenlong Ke
Shui Yu

Privacy preservation of the big data generated, deposited, and communicated by smart IoT (Internet of Things) nodes is the major challenge in IoT networks. Anonymization, encryption, and routing protocol constitute the existing prevalent privacy-preserving approaches, most of which have successfully implemented the privacy preservation of data query, data mining and data aggregation. Nevertheless, there has been a gradual switch in the selection of privacy-preserving technology. Predictive game-theoretic analytics for privacy preservation in IoT networks has received significant attention since it can effectively settle the conflicts between attackers and defenders. In this survey, we explain the basics of various games mainly applied for IoT privacy preservation, such as simultaneous game, stochastic game, bargain game, differential game, mean field game, aggregation game, Stackelberg game, signaling game, repeated game, evolutionary game, and cooperative game. We then explore different applications for game theory-based privacy preservation in IoT networks, followed by discussing the differences among the existing solution of privacy-preserving issues using different games under specific IoT scenarios. Moreover, we consider the challenges and outline future research directions. In conclusion, this survey not only presents existing work on applying game theory to preserve privacy in current IoT networks including smart grids, intelligent transportation systems, crowdsensing, edge-based IoT, integrated energy systems, blockchain IoT, Social IoT and Industrial IoT, but it also encourages researches to further dig deeper into rare areas.

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YNIMG Journal 2021 Journal Article

NOise reduction with DIstribution Corrected (NORDIC) PCA in dMRI with complex-valued parameter-free locally low-rank processing

Steen Moeller
Pramod Kumar Pisharady
Sudhir Ramanna
Christophe Lenglet
Xiaoping Wu
Logan Dowdle
Essa Yacoub
Kamil Uğurbil

Diffusion-weighted magnetic resonance imaging (dMRI) has found great utility for a wide range of neuroscientific and clinical applications. However, high-resolution dMRI, which is required for improved delineation of fine brain structures and connectomics, is hampered by its low signal-to-noise ratio (SNR). Since dMRI relies on the acquisition of multiple different diffusion weighted images of the same anatomy, it is well-suited for denoising methods that utilize correlations across the image series to improve the apparent SNR and the subsequent data analysis. In this work, we introduce and quantitatively evaluate a comprehensive framework, NOise Reduction with DIstribution Corrected (NORDIC) PCA method for processing dMRI. NORDIC uses low-rank modeling of g-factor-corrected complex dMRI reconstruction and non-asymptotic random matrix distributions to remove signal components which cannot be distinguished from thermal noise. The utility of the proposed framework for denoising dMRI is demonstrated on both simulations and experimental data obtained at 3 Tesla with different resolutions using human connectome project style acquisitions. The proposed framework leads to substantially enhanced quantitative performance for estimating diffusion tractography related measures and for resolving crossing fibers as compared to a conventional/state-of-the-art dMRI denoising method.

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YNICL Journal 2020 Journal Article

Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium

Yicheng Long
Hengyi Cao
Chaogan Yan
Xiao Chen
Le Li
Francisco Xavier Castellanos
Tongjian Bai
Qijing Bo

BACKGROUND: Major depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach. METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels. RESULTS: ). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients. CONCLUSIONS: Our findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.

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YNIMG Journal 2020 Journal Article

Denoise magnitude diffusion magnetic resonance images via variance-stabilizing transformation and optimal singular-value manipulation

Xiaodong Ma
Kâmil Uğurbil
Xiaoping Wu

Although shown to have a great utility for a wide range of neuroscientific and clinical applications, diffusion-weighted magnetic resonance imaging (dMRI) faces a major challenge of low signal-to-noise ratio (SNR), especially when pushing the spatial resolution for improved delineation of brain’s fine structure or increasing the diffusion weighting for increased angular contrast or both. Here, we introduce a comprehensive denoising framework for denoising magnitude dMRI. The framework synergistically combines the variance stabilizing transform (VST) with optimal singular value manipulation. The purpose of VST is to transform the Rician data to Gaussian-like data so that an asymptotically optimal singular value manipulation strategy tailored for Gaussian data can be used. The output of the framework is the estimated underlying diffusion signal for each voxel in the image domain. The usefulness of the proposed framework for denoising magnitude dMRI is demonstrated using both simulation and real-data experiments. Our results show that the proposed denoising framework can significantly improve SNR across the entire brain, leading to substantially enhanced performances for estimating diffusion tensor related indices and for resolving crossing fibers when compared to another competing method. More encouragingly, the proposed method when used to denoise a single average of 7 Tesla Human Connectome Project-style diffusion acquisition provided comparable performances relative to those achievable with ten averages for resolving multiple fiber populations across the brain. As such, the proposed denoising method is expected to have a great utility for high-quality, high-resolution whole-brain dMRI, desirable for many neuroscientific and clinical applications.

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AAAI Conference 2020 Conference Paper

Re-Attention for Visual Question Answering

Wenya Guo
Ying Zhang
Xiaoping Wu
Jufeng Yang
Xiangrui Cai
Xiaojie Yuan

Visual Question Answering (VQA) requires a simultaneous understanding of images and questions. Existing methods achieve well performance by focusing on both key objects in images and key words in questions. However, the answer also contains rich information which can help to better describe the image and generate more accurate attention maps. In this paper, to utilize the information in answer, we propose a reattention framework for the VQA task. We ﬁrst associate image and question by calculating the similarity of each objectword pairs in the feature space. Then, based on the answer, the learned model re-attends the corresponding visual objects in images and reconstructs the initial attention map to produce consistent results. Beneﬁting from the re-attention procedure, the question can be better understood, and the satisfactory answer is generated. Extensive experiments on the benchmark dataset demonstrate the proposed method performs favorably against the state-of-the-art approaches.

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YNIMG Journal 2019 Journal Article

Human Connectome Project-style resting-state functional MRI at 7 Tesla using radiofrequency parallel transmission

Xiaoping Wu
Edward J. Auerbach
An T. Vu
Steen Moeller
Pierre-François Van de Moortele
Essa Yacoub
Kâmil Uğurbil

We investigate the utility of radiofrequency (RF) parallel transmission (pTx) for whole-brain resting-state functional MRI (rfMRI) acquisition at 7 Tesla (7T). To this end, Human Connectome Project (HCP)-style data acquisitions were chosen as a showcase example. Five healthy subjects were scanned in pTx and single-channel transmit (1Tx) modes. The pTx data were acquired using a prototype 16-channel transmit system and a commercially available Nova 8-channel transmit 32-channel receive RF head coil. Additionally, pTx single-spoke multiband (MB) pulses were designed to image sagittal slices. HCP-style 7T rfMRI data (1. 6-mm isotropic resolution, 5-fold slice and 2-fold in-plane acceleration, 3600 image volumes and ∼ 1-h scan) were acquired with pTx and the results were compared to those acquired with the original 7T HCP rfMRI protocol. The use of pTx significantly improved flip-angle uniformity across the brain, with coefficient of variation (i. e. , std/mean) of whole-brain flip-angle distribution reduced on average by ∼39%. This in turn yielded ∼17% increase in group temporal SNR (tSNR) as averaged across the entire brain and ∼10% increase in group functional contrast-to-noise ratio (fCNR) as averaged across the grayordinate space (including cortical surfaces and subcortical voxels). Furthermore, when placing a seed in either the posterior parietal lobe or putamen to estimate seed-based dense connectome, the increase in fCNR was observed to translate into stronger correlation of the seed with the rest of the grayordinate space. We have demonstrated the utility of pTx for slice-accelerated high-resolution whole-brain rfMRI at 7T; as compared to current state-of-the-art, the use of pTx improves flip-angle uniformity, increases tSNR, enhances fCNR and strengthens functional connectivity estimation.

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YNIMG Journal 2019 Journal Article

Optimizing BOLD sensitivity in the 7T Human Connectome Project resting-state fMRI protocol using plug-and-play parallel transmission

Vincent Gras
Benedikt A. Poser
Xiaoping Wu
Raphaël Tomi-Tricot
Nicolas Boulant

The Human Connectome Project (HCP) has a 7T component that aims to study the human brain's organization and function with high spatial and temporal resolution fMRI and diffusion-weighted acquisitions. For whole brain applications at 7T, a major weakness however remains the heterogeneity of the radiofrequency transmission field ( B 1 + ), which prevents from achieving an optimal signal and contrast homogeneously throughout the brain. In this work, we use parallel transmission (pTX) Universal Pulses (UP) to improve the flip angle homogeneity and demonstrate their application to highly accelerated multi-band EPI (MB5 and GRAPPA2, as prescribed in the 7T HCP protocol) sequence, but also to acquire at 7T B 1 + -artefact-free T 1 - and T 2 -weighted anatomical scans used in the pre-processing pipeline of the HCP protocol. As compared to typical implementations of pTX, the proposed solution is fully operator-independent and allows ”plug and play” exploitation of the benefits offered by multi-channel transmission. Validation in five healthy adults shows that the proposed technique achieves a flip angle homogeneity comparable to that of a clinical 3 T system. Compared to standard single-channel transmission, the use of UPs at 7T yielded up to a two-fold increase of the temporal signal-to-noise ratio in the temporal lobes as well as improved detection of functional connectivity in the brain regions most strongly affected by B 1 + inhomogeneity.

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YNIMG Journal 2013 Journal Article

Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project

Kamil Uğurbil
Junqian Xu
Edward J. Auerbach
Steen Moeller
An T. Vu
Julio M. Duarte-Carvajalino
Christophe Lenglet
Xiaoping Wu

The Human Connectome Project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce ‘functional connectivity’; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3T, leading to whole brain coverage with 2mm isotropic resolution in 0. 7s for fMRI, and 1. 25mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total dMRI data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7T magnetic field are also presented, targeting higher spatial resolution, enhanced specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields, and reduced power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure.

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