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Visual cognition of birds and its underlying neural mechanism:A review

Avian Research 2022年第2期13卷 216-225页

作者： Xiaoke Niu Zhenyang Jiang Yanyan Peng Shuman Huang Zhizhong Wang Li Shi Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical EngineeringZhengzhou UniversityZhengzhou450001China Department of Automation Tsinghua UniversityBeijing100084China

Birds have acute vision and many remarkable visual cognition abilities,due to their unique living *** underlying neural mechanisms have also attracted interests of researchers in ***,we firstly summarize the visual cognition abilities of birds,and make a comparison with ***,the underlying neural mechanisms are presented,including histological structure of avian brain and visual pathways,typical experimental results and conclusions in electrochemistry and *** latter mainly focuses on several higher brain areas related to visual cognition,including mesopallium ventrolaterale,entopallium,visual Wulst,and nidopallium ***,we make a conclusion and provide a suggestion about future studies on revealing the neural mechanisms of avian visual *** review presents a detailed understanding of avian visual cognition and would be helpful in ornithology studies in the field of cognitive neuroscience.

关键词： Avian Electrophysiology Neural mechanism Visual cognition

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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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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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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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Dynamic and low-dimensional modeling of brain functional connectivity on Riemannian manifolds

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NeuroImage 2025年 314卷 121243-121243页

作者： Mingyu Wang Yueming Wang Yuxiao Yang MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Hangzhou China Nanhu Brain-computer Interface Institute Hangzhou China School of Computer Science and Technology Zhejiang University Hangzhou China State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China Second Affiliated Hospital School of Medicine Zhejiang University Hangzhou China

Modeling brain functional connectivity (FC) is key in investigating brain functions and dysfunctions. FC is typically quantified by symmetric positive definite (SPD) matrices that are located on a Riemannian manifold rather than the regular Euclidean space, whose modeling faces three challenges. First, FC can be time-varying and the temporal dynamics of FC matrix time-series need to be modeled within the constraint of the SPD Riemannian manifold geometry, which remains elusive. Second, the FC matrix time-series exhibits considerable stochasticity, whose probability distribution is difficult to model on the Riemannian manifold. Third, FC matrices are high-dimensional and dimensionality reduction methods for SPD matrix time-series are still lacking. Here, we develop a Riemannian state-space modeling framework to simultaneously address the challenges. First, we construct a new Riemannian state-space model (RSSM) to define a hidden SPD matrix state to achieve dynamic, stochastic, and low-dimensional modeling of FC matrix time-series on the SPD Riemannian manifold. Second, we develop a new Riemannian Particle Filter (RPF) algorithm to estimate the hidden low-dimensional SPD matrix state and predict the FC matrix time-series. Third, we develop a new Riemannian Expectation Maximization (REM) algorithm to fit the RSSM parameters. We evaluate the proposed RSSM, RPF, and REM using simulation and real-world EEG datasets, demonstrating that the RSSM enables accurate prediction of the EEG FC time-series and classification of emotional states, outperforming traditional Euclidean methods. Our results have implications for modeling brain FC on the SPD Riemannian manifold to study various brain functions and dysfunctions.

关键词： brain functional connectivity Riemannian manifold Stochastic matrix dynamics Dimensionality reduction EEG

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Recent Progress of Soft and Bioactive Materials in Flexible Bioelectronics

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Cyborg and Bionic Systems 2025年 6卷 0192页

作者： Wu, Xiaojun Ye, Yuanming Sun, Mubai Mei, Yongfeng Ji, Bowen Wang, Ming Song, Enming Fudan University Shanghai200438 China State Key Laboratory of Medical Neurobiology MOE Frontiers Center for Brain Science State Key Laboratory of Molecular Engineering of Polymer Fudan University Shanghai200438 China Unmanned System Research Institute National Key Laboratory of Unmanned Aerial Vehicle Technology Integrated Research and Development Platform of Unmanned Aerial Vehicle Technology Northwestern Polytechnical University Xi’an710072 China Queen Mary University London Engineering School Northwestern Polytechnical University Xi’an710072 China Institute of Agrofood Technology Jilin Academy of Agricultural Sciences Northeast Agricultural Research Center of China Changchun China International Institute for Intelligent Nanorobots and Nanosystems Neuromodulation and Brain-machine-interface Centre Fudan University Shanghai200438 China Yiwu Research Institute Fudan University Zhejiang Yiwu322000 China Frontier Institute of Chip and System Fudan University Shanghai200433 China

Materials that establish functional, stable interfaces to targeted tissues for long-term monitoring/stimulation equipped with diagnostic/therapeutic capabilities represent breakthroughs in biomedical research and clinical medicine. A fundamental challenge is the mechanical and chemical mismatch between tissues and implants that ultimately results in device failure for corrosion by biofluids and associated foreign body response. Of particular interest is in the development of bioactive materials at the level of chemistry and mechanics for high-performance, minimally invasive function, simultaneously with tissue-like compliance and in vivo biocompatibility. This review summarizes the most recent progress for these purposes, with an emphasis on material properties such as foreign body response, on integration schemes with biological tissues, and on their use as bioelectronic platforms. The article begins with an overview of emerging classes of material platforms for bio-integration with proven utility in live animal models, as high performance and stable interfaces with different form factors. Subsequent sections review various classes of flexible, soft tissue-like materials, ranging from self-healing hydrogel/elastomer to bio-adhesive composites and to bioactive materials. Additional discussions highlight examples of active bioelectronic systems that support electrophysiological mapping, stimulation, and drug delivery as treatments of related diseases, at spatiotemporal resolutions that span from the cellular level to organ-scale dimension. Envisioned applications involve advanced implants for brain, cardiac, and other organ systems, with capabilities of bioactive materials that offer stability for human subjects and live animal models. Results will inspire continuing advancements in functions and benign interfaces to biological systems, thus yielding therapy and diagnostics for human healthcare. Copyright © 2025 Xiaojun Wu et al.

关键词： Tissue

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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 Fuzhou350108 China Fujian Provincial Higher Education Applied Technology Center for Visual Perception and Intelligent Analysis Quanzhou Vocational College of Economics and Business Quanzhou362000 China Key Laboratory of Child Development and Learning Science of Ministry of Education Southeast University Nanjing210096 China Department of Neurosurgery Sanbo Brain Hospital of Capital Medical University Beijing100093 China College of Computer and Cyber Security Fujian Normal University Fuzhou350117 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. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Reinforcement learning

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Relationship Between the Long-Term Practice of Tai Chi Stance with Neural Patterns of the brain: An Exploratory Study

Relationship Between the Long-Term Practice of Tai Chi Stanc...

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2024 China Automation Congress, CAC 2024

作者： Li, Mengmeng Zhang, Jianbo Wan, Hong School of Electrical and Information Engineering Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350368604

Research has shown that practicing Tai Chi can alleviate brain tension and enhance the activity of the autonomic nervous system in the human body. However, there is limited research on the effects of Tai Chi Stance on brain function. This study aims to analyze the electroencephalogram (EEG) characteristics of long-term Tai Chi Zhuang practitioners and investigate the changes in brain dynamic neural patterns during different stages of Tai Chi Stance, together with the differences between long-term practitioners and a control group. In this study, we first analyzed the changes in EEG spectral characteristics during different stages of Tai Chi Stance practice of long-term practitioners. We found that the alpha (8-13 Hz) power significantly increased during the stance, indicating a state of deep relaxation during the Tai Chi practice. The experimental group also exhibited significant changes in brain functional network parameters in the alpha band during the stance, including increased clustering coefficient and global efficiency. Finally, inter-group differences in EEG characteristic parameters were analyzed between the experimental group and control group. The results showed significant differences in relative power and functional connectivity in the alpha band between the two groups, indicating that long-term Tai Chi Stance practice can effectively enhance brain network efficiency and improve information integration capabilities. This study provides scientific evidence for Tai Chi Stance training to enhance brain health. © 2024 IEEE.

关键词： Electroencephalography

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Concurrent TMS-EEG to characterize cortical responses in the motor and prefrontal cortices in Parkinson's disease

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Neurotherapeutics 2025年 e00577页

作者： Zhu, Lin Cai, Min Pei, Zian Shi, Xue Dang, Ge Lan, Xiaoyong Luo, Xiaoguang Che, Xianwei Guo, Yi Department of Neurology Shenzhen People's Hospital (The Second Clinical Medical College Jinan University The First Affiliated Hospital Southern University of Science and Technology) Guangdong Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Shenzhen Bay Laboratory Guangdong Shenzhen China Centre for Cognition and Brain Disorders The Affiliated Hospital of Hangzhou Normal University Hangzhou China Henan Key Laboratory of Neurorestoratology Henan Joint International Research Laboratory of Neurorestoratology for Senile Dementia Department of Neurology The First Affiliated Hospital of Xinxiang Medical University Xinxiang China

Patients with Parkinson's disease (PD) experience both motor and non-motor symptoms. However, it remains unclear the full spectrum of PD, which requires a comprehensive assessment of both motor and non-motor cortical regions. The use of combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) allows the examination of neural circuit beyond motor cortex. In this study, TMS-EEG data were collected over the primary motor cortex (M1) and dorsolateral prefrontal cortex (DLPFC) in 46 patients with PD and 27 healthy controls (HC). We analyzed TMS-evoked potentials and oscillatory powers to identify differences in cortical excitability and neural connectivity dynamics between the two cohorts. Patients with PD exhibited higher P30 amplitude following M1 stimulation compared to HCs, and there was a positive correlation between P30 amplitude and the severity of motor symptoms. DLPFC stimulation revealed an increased global mean field amplitude area under the curve (GMFA-AUC) at P30 and P60 in the PD group compared to HCs. A significant correlation was also observed within the PD group between P30 and P60 with depression scores. In addition, PD showed a significant power reduction in the alpha and beta bands during stimulation and distinct patterns emerged for each stimulation site. These findings provide novel insights into cortical network abnormalities and contribute to a better understanding of the mechanisms underlying PD. © 2025 The Authors

关键词： Electroencephalography (EEG) Motor and prefrontal cortex Oscillatory responses Parkinson's disease (PD) Transcranial magnetic stimulation (TMS)

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The Bird Flight Movement Information Acquisition Equipment Design and Implementation Based on The STM32

The Bird Flight Movement Information Acquisition Equipment D...

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2022 International Conference on computer, Artificial Intelligence, and Control Engineering, CAICE 2022

作者： Guo, Zhixin Yang, Long Ma, Erteng Li, Yang Shang, Zhigang School of Electrical Engineering Zhengzhou University Zhengzhou450001 China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou450001 China

ISBN: (纸本)9781510655942

Birds have excellent three-dimensional space movement ability. The research and design of small and lightweight bird flight movement behavior information collection equipment has important application value in the research of bird robots and other aspects. Based on STM32F103 series chips, this paper designs a low cost small lightweight bird flight movement information acquisition device, and uses FREERTOS multi-task real-time operating system to realize the acquisition and storage of movement behavior information on this device. © 2022 SPIE.

关键词： Birds

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