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arXiv 2025年

作者： Ma, Chenglong Li, Zilong Li, Yuanlin Han, Jing Zhang, Junping Zhang, Yi Liu, Jiannan Shan, Hongming Institute of Science and Technology for Brain-inspired Intelligence MOE Frontiers Center for Brain Science Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Shanghai200433 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Department of Oral Maxillofacial Head and Neck Oncology Shanghai Ninth People’ s Hospital Shanghai Jiao Tong University School of Medicine College of Stomatology Shanghai Jiao Tong University National Center for Stomatology National Clinical Research Center for Oral Diseases Shanghai Key Laboratory of Stomatology Shanghai Research Institute of Stomatology Shanghai200011 China School of Cyber Science and Engineering Sichuan University Sichuan Chengdu610065 China

Metal artifacts in computed tomography (CT) images can significantly degrade image quality and impede accurate diagnosis. Supervised metal artifact reduction (MAR) methods, trained using simulated datasets, often struggle to perform well on real clinical CT images due to a substantial domain gap. Although state-of-the-art semi-supervised methods use pseudo ground-truths generated by a prior network to mitigate this issue, their reliance on a fixed prior limits both the quality and quantity of these pseudo ground-truths, introducing confirmation bias and reducing clinical applicability. To address these limitations, we propose a novel radiologist-in-the-loop selftraining framework for MAR, termed RISE-MAR, which can integrate radiologists’ feedback into the semi-supervised learning process, progressively improving the quality and quantity of pseudo ground-truths for enhanced generalization on real clinical CT images. For quality assurance, we introduce a clinical quality assessor model that emulates radiologist evaluations, effectively selecting high-quality pseudo ground-truths for semi-supervised training. For quantity assurance, our self-training framework iteratively generates additional high-quality pseudo ground-truths, expanding the clinical dataset and further improving model generalization. Extensive experimental results on multiple clinical datasets demonstrate the superior generalization performance of our RISE-MAR over state-of-the-art methods, advancing the development of MAR models for practical application. Code is available at https:// ***/Masaaki-75/rise-mar . © 2025, CC BY-NC-SA.

关键词： computerized tomography

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Deep Learning-Based Diffusion MRI Tractography: Integrating Spatial and Anatomical Information

arXiv

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arXiv 2025年

作者： Yang, Yiqiong Yuan, Yitian Ren, Baoxing Wu, Ye Feng, Yanqiu Zhang, Xinyuan School of Biomedical Engineering Southern Medical University Guangzhou510515 China Guangdong Provincial Key Laboratory of Medical Image Processing Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou510515 China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health The Ministry of Education Southern Medical University Guangzhou China Department of Radiology Shunde Hospital Southern Medical University The First People's Hospital of Shunde Foshan Foshan China

Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders. However, the accuracy of reconstructed tractograms has been a longstanding challenge. Recently, deep learning methods have been applied to improve tractograms for better white matter coverage, but often comes at the expense of generating excessive false-positive connections. This is largely due to their reliance on local information to predict long-range streamlines. To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the later modeled via a Transformer-decoder. Additionally, we employ a weighted loss function to address fiber class imbalance encountered during training. We evaluate the proposed method on the simulated ISMRM 2015 Tractography Challenge dataset, achieving a valid streamline rate of 66.2%, white matter coverage of 63.8%, and successfully reconstructing 24 out of 25 bundles. Furthermore, on the multi-site Tractoinferno dataset, the proposed method demonstrates its ability to handle various diffusion MRI acquisition schemes, achieving a 5.7% increase in white matter coverage and a 4.1% decrease in overreach compared to RNN-based methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Phosphorus-nitrogen co-doped graphene: Advancements in fast, highly sensitive ammonia sensing at room temperature

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Measurement: Journal of the International Measurement Confederation 2025年 253卷

作者： Li, Quanfu Sun, Manli Li, Fuqian Chen, Zhencheng Luo, Xudong School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips School of Electronic and Information Engineering Guangxi Normal University Guilin541004 China School of Computer Science and Engineering Guangxi Normal University Guilin541004 China

High sensitivity and fast response ammonia gas sensors have wide applications in chemical production, food safety, medical diagnosis, and other fields. This work innovatively utilizes phosphorus and nitrogen as co-doping elements to significantly enhance the ammonia sensing performance of graphene. Ammonia sensing tests have shown that phosphorus-nitrogen co-doped graphene (PNG) exhibits excellent ammonia sensing performance at room temperature: high response (up to 4.34% response to 0.5 ppm ammonia, more than 5 times that of graphene), ultra-low detection limit (33.9 ppb), fast response/recovery (response time (Tres) and recovery time (Trec) are 19 s and 36 s, respectively, which are more than 90% shorter than graphene on average). In addition, the PNG ammonia sensor also has excellent selectivity and good long-term stability. The PNG ammonia sensor studied in this paper has great potential in practical applications, especially in the detection of low concentration ammonia in exhaled breath analysis for medical diagnostics. It is expected to provide a reliable and low-cost solution for various applications of graphene ammonia sensors. © 2025

关键词： Ammonia

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TVEG: Model Selection of the Time-Varying Exponential Family Distributions Graphical Models

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IEEE Transactions on Network science and Engineering 2025年

作者： Liu, Juan Mei, Guofeng Xia, Yuanqing Wu, Xiaoqun Lu, Jinhu Beijing Forestry University School of Technology Beijing100107 China Italy Zhongyuan University of Technology Henan 451191 China Wuhan University School of Mathematics and Statistics Wuhan430072 China Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence School of Automation Science and Electrical Engineering Beijing100191 China

The undirected graphical model, a popular class of statistical model, offers a way to describe and explain the relationships among a set of variables. However, it remains a challenge to choose a certain graphical model to explain the relationships of variables adequately, especially when the relationships of variables are rewiring over time. This paper proposes the Time-Varying Exponential Family Distributions Graphical (TVEG) models, with time-varying structures and exponential family node-wise conditional distributions. TVEG models extend the scope of available graph models and can be applied to time-varying and exponential family distribution observation data in reality. We propose the Temporally Smoothed L1regularized exponential family graphical estimator (TSLEG), an estimator to infer the structure of TVEG from observations. We derive sufficient conditions for the TSLEG to recover the block partition and sparse pattern with high probability. We derive a message-passing optimization method to solve the TSLEG for time-varying Ising, Gaussian, exponential, and Poisson graphs based on the ADMM. The synthetic network simulations corroborate the theoretical analysis. Analysing of real data of stocks and the US Senate by the time-varying exponential model and Poisson model indicates the effectiveness and practicality of TVEG models. © 2025 IEEE. All rights reserved.

关键词： ADMM exponential family distributions Graphical model pseudo maximum likelihood estimation

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Block Sparse Vector Codes for Ultra-Reliable and Low-Latency Short-Packet Transmission

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IEEE Transactions on Communications 2025年

作者： Zhang, Yanfeng Zhu, Xu Liu, Yujie Fan, Xi'an Guan, Yong Liang Wong, M. L. Dennis Lau, Vincent K. N. Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan China Harbin Institute of Technology School of Information Science and Technology Shenzhen518055 China Guangxi Normal University Guangxi Key Laboratory of Brain Inspired Computing and Intelligent Chips Guilin541001 China Shenzhen Municipal Key Laboratory of AIoT Communications Shenzhen518055 China Nanyang Technological University Continental-NTU Corporate Lab Singapore Dongguan University of Technology School of Computer Science and Technology Dongguan China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next-generation communication systems. In this paper, a block SVC (BSVC) based short-packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme is to transmit short-packet data information after block sparse transformation. At the transmitter, the information bits are divided into two parts: one part is mapped into the non-zero indexes of block sparse vectors, and the other part is mapped into non-zero values through phase or amplitude modulation. After pseudo-random spreading, the block sparse vectors are mapped to time-frequency resources for transmission. At the receiver, the decoding problem is transformed into a block sparse signal recovery problem. A cyclic block orthogonal matching pursuit (CBOMP) algorithm is proposed for decoding by leveraging block-structured sparse prior information. The upper bound of block error rate (BLER) performance over Rayleigh channels is derived to verify the decoding performance of the proposed CBOMP algorithm. Extensive simulation results verify that the proposed BSVC scheme outperforms the existing SVC schemes in terms of BLER, transmission latency and spectral efficiency over fading channels. © 1972-2012 IEEE.

关键词： Fading channels

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Overexpression of S100B promotes depressive-like behaviors in stroke-induced rats by modulating the PI3K/AKT/NF-κB pathway

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Behavioural brain Research 2025年 488卷 115597页

作者： Lei, Hao Zhang, Fuping Tao, Mengyang Lang, Jiqing Pei, Yanjiao Liu, Huanhuan Zhang, Zhaohui Song, Jinggui Henan Key Laboratory of Biological Psychiatry the Second Affiliated Hospital of Xinxiang Medical University Henan Xinxiang 453002 China Henan Engineering Research Center of Physical Diagnostics and Treatment Technology for the Mental and Neurological Diseases The Second Affiliated Hospital of Xinxiang Medical University Henan Xinxiang 453002 China Collaborative Innovation Center of Prevention and Treatment of Mental Disorder Henan Xinxiang 453002 China The First Affiliated Hospital of Xinxiang Medical University Henan Key Laboratory of Neurorestoratology Henan Weihui 453100 China Brain Institute Henan Academy of Innovations in Medical Science Henan Zhengzhou 453002 China

Post-stroke depression (PSD) is a common complication following a stroke, primarily characterized by low mood, cognitive sluggishness, and sleep disturbances. Currently, the precise pathogenic mechanisms underlying PSD remain elusive. Research indicates that S100B protein levels may serve as a specific biochemical marker of organic brain injury, with significantly elevated serum S100B levels noted in patients with ischemic stroke, depression, and schizophrenia. S100B facilitates apoptosis through various cellular signaling pathways and is implicated in inflammatory responses, thereby participating in the pathophysiology of numerous diseases. Nonetheless, the role of elevated S100B expression in PSD remains unclear. This study used a PSD rat model created by combining MCAO and CUMS to evaluate depressive behaviors. The expression of S100B and proteins associated with the PI3K/AKT/NF-κB signaling pathway was analyzed, while changes in inflammatory factors such as IL-1, IL-6, and TNF-α were quantified using ELISA. The findings demonstrated that the combination of MCAO and CUMS effectively induced depressive-like behaviors in the rats. In the PSD rat model, overexpression of S100B may inhibit the PI3K/AKT pathway and activate the NF-κB signaling pathway, thereby promoting the expression of inflammatory factors such as IL-1, IL-6, and TNF-α, which exacerbate brain tissue damage. However, the administration of S100B inhibitors improved depressive-like behaviors in PSD rats and reversed the alterations in the aforementioned signaling pathways and inflammatory factors. These findings advance the understanding of PSD pathogenesis and suggest therapeutic strategies. © 2025 Elsevier B.V.

关键词： Arundic Acid Neuroinflammation PI3K/AKT/NF-κB Post-stroke depression S100B

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CrossConvPyramid: Deep Multimodal Fusion for Epileptic Magnetoencephalography Spike Detection

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3194-3205页

作者： Liang Zhang Shurong Sheng Xiongfei Wang Jia-Hong Gao Yi Sun Kuntao Xiao Wanli Yang Pengfei Teng Guoming Luan Zhao Lv AHU-IAI AI Joint Laboratory School of Artificial Intelligence Anhui University Hefei China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Neurosurgery Beijing Key Laboratory of Epilepsy Sanbo Brain Hospital Capital Medical University Beijing China McGovern Institute for Brain Research Peking University Beijing China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Peking University Beijing China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

Magnetoencephalography (MEG) is a vital non-invasive tool for epilepsy analysis, as it captures high-resolution signals that reflect changes in brain activity over time. The automated detection of epileptic spikes within these signals can significantly reduce the labor and time required for manual annotation of MEG recording data, thereby aiding clinicians in identifying epileptogenic foci and evaluating treatment prognosis. Research in this domain often utilizes the raw, multi-channel signals from MEG scans for spike detection, commonly neglecting the multi-channel spiking patterns from spatially adjacent channels. Moreover, epileptic spikes share considerable morphological similarities with artifact signals within the recordings, posing a challenge for models to differentiate between the two. In this paper, we introduce a multimodal fusion framework that addresses these two challenges collectively. Instead of relying solely on the signal recordings, our framework also mines knowledge from their corresponding topography-map images, which encapsulate the spatial context and amplitude distribution of the input signals. To facilitate more effective data fusion, we present a novel multimodal feature fusion technique called CrossConvPyramid, built upon a convolutional pyramid architecture augmented by an attention mechanism. It initially employs cross-attention and a convolutional pyramid to encode inter-modal correlations within the intermediate features extracted by individual unimodal networks. Subsequently, it utilizes a self-attention mechanism to refine and select the most salient features from both inter-modal and unimodal features, specifically tailored for the spike classification task. Our method achieved the average F1 scores of 92.88% and 95.23% across two distinct real-world MEG datasets from separate centers, respectively outperforming the current state-of-the-art by 2.31% and 0.88%. We plan to release the code on GitHub later.

关键词： Feature extraction Recording brain modeling Attention mechanisms Convolution Bioinformatics Epilepsy Electroencephalography Computational modeling Physics

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AEP: An adaptive ensemble P300-BCI classifier based on user-feedback and knowledge-transfer

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Applied Intelligence 2025年第8期55卷 1-13页

作者： Huang, Zhihua Chen, Qingzhi Chen, Xuewei Zheng, Wenming Lin, Zhixiong Luo, Tian-jian College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Higher Education Applied Technology Center for Visual Perception and Intelligent Analysis Quanzhou Vocational College of Economics and Business Quanzhou China Key Laboratory of Child Development and Learning Science of Ministry of Education Southeast University Nanjing China Department of Neurosurgery Sanbo Brain Hospital of Capital Medical University Beijing China College of Computer and Cyber Security Fujian Normal University Fuzhou China

As a stable and reliable paradigm, P300-based brain-computer interface (P300-BCI) is expected to play an important role in efforts to replace, restore, enhance, supplement, or improve the natural output of the brain. However, the costly calibration of P300-BCI limits its development. The calibration-free approaches for P300-BCI have become a research focus in the field. In this work, we forwarded our previous study, transferred P300 linear upper confidence bound (TPLUCB), to propose an adaptive ensemble P300-BCI classifier (AEP). This renovation mainly includes a simplified calculation method and a dynamical update strategy. The competitive calculation model in TPLUCB was simplified as a linear calculation model. Based on this, a dynamical update strategy was proposed to facilitate the growth of target domain model and optimize the weights, by which the source domain models and the target domain model are combined as a P300-BCI classifier, i.e. AEP. We conducted the performance evaluation by observing the classifier’s dynamical development and overall performance. The comparison in the two aspects between AEP and TPLUCB demonstrates AEP’s clear advantage over TPLUCB. Without prior calibration, AEP achieved an average ITR exceeding 40 bit/min on electroencephalogram (EEG) data of 20 subjects. This work has provided a better calibration-free approach for P300-BCI and is an important step towards promoting the research on calibration-free BCIs.

关键词： Reinforcement learning

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Short-Time Variational Mode Decomposition

arXiv

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arXiv 2025年

作者： Jia, Hao Cao, Pengfei Liang, Tong Caiafa, Cesar F. Sun, Zhe Kushihashi, Yasuhiro Grau, Antoni Bolea, Yolanda Duan, Feng Solé-Casals, Jordi School of Medicine Nankai University Tianjin China Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Tianjin China Universitat Politècnica de Catalunya Barcelona Spain Mechanical Systems Engineering Nippon Institute of Technology Saitama Japan Villa Elisa Argentina Computational Bioengineering Laboratory Faculty of Health Data Science Juntendo University Tokyo Japan Data and Signal Processing Research Group University of Vic-Central University of Catalonia Vic Spain

Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies. These methods utilize Fourier transformations to shift signals between time and frequency domains. However, since Fourier transformations span the entire time-domain signal, they are suboptimal for non-stationary time series. We introduce Short-Time Variational Mode Decomposition (STVMD), an innovative extension of the VMD algorithm that incorporates the Short-Time Fourier transform (STFT) to minimize the impact of local disturbances. STVMD segments signals into short time windows, converting these segments into the frequency domain. It then formulates a variational optimization problem to extract band-limited modes representing the windowed data. The optimization aims to minimize the sum of the bandwidths of these modes across the windowed data, extending the cost functions used in VMD and MVMD. Solutions are derived using the alternating direction method of multipliers, ensuring the extraction of modes with narrow bandwidths. STVMD is divided into dynamic and non-dynamic types, depending on whether the central frequencies vary with time. Our experiments show that non-dynamic STVMD is comparable to VMD with properly sized time windows, while dynamic STVMD better accommodates non-stationary signals, evidenced by reduced mode function errors and tracking of dynamic central frequencies. This effectiveness is validated by steady-state visual-evoked potentials in electroencephalogram signals. Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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DISD-Net: A Dynamic Interactive Network with Self-distillation for Cross-subject Multi-modal Emotion Recognition

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IEEE Transactions on Multimedia 2025年

作者： Cheng, Cheng Liu, Wenzhe Wang, Xinying Feng, Lin Jia, Ziyu Liaoning Normal University Research Center of Brain and Cognitive Neuroscience Dalian116029 China Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei230601 China Key Laboratory of Biomedical Engineering of Hainan Province Haikou570228 China Huzhou University School of Information Engineering Huzhou313000 China Dalian University of Technology School of Computer Science and Technology Dalian116081 China Institute of Automation Chinese Academy of Sciences Brainnetome Center Beijing100190 China

Multi-modal Emotion Recognition (MER) has demonstrated competitive performance in affective computing, owing to synthesizing information from diverse modalities. However, many existing approaches still face unresolved challenges, such as: (i) how to learn compact yet representative features from multi-modal data simultaneously, and (ii) how to address differences among subjects and enhance the generalization of the emotion recognition model, given the diverse nature of individual biological signals. To this end, we propose a Dynamic Interactive Network with Self-Distillation (DISD-Net) for cross-subject MER. The DISD-Net incorporates a dynamin interactive module to capture the intra- and inter-modal interactions from multi-modal data. Additionally, to enhance compactness in modal representations, we leverage the soft labels generated by the DISD-Net model as supplemental training guidance. This involves incorporating self-distillation, aiming to transfer the knowledge that the DISD-Net model contains hard and soft labels to each modality. Finally, domain adaptation (DA) is seamlessly integrated into the dynamic interactive and self-distillation components, forming a unified framework to extract subject-invariant multi-modal emotional features. Experimental results indicate that the proposed model achieves a mean accuracy of 75.00% with a standard deviation of 7.68% for the DEAP dataset and a mean accuracy of 65.65% with a standard deviation of 5.08% for the SEED-IV dataset. © 1999-2012 IEEE.

关键词： domain adaptation (DA) dynamic interactive Multi-modal Emotion Recognition (MER) self-distillation

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