检索结果-内蒙古大学图书馆

Subject inefficiency phenomenon of motor imagery brain-computer interface: Influence factors and potential solutions

引用

brain science Advances 2020年第3期6卷 224-241页

作者： Rui Zhang Fali Li Tao Zhang Dezhong Yao Peng Xu Henan Key Laboratory of Brain Science and Brain‐Computer Interface Technology School of Electrical EngineeringZhengzhou UniversityZhengzhou 450001HenanChina MOE Key Lab for NeuroInformation University of Electronic Science and Technology of ChinaChengdu 611731SichuanChina Science of School Xihua UniversityChengdu 610039SichuanChina

Motor imagery brain–computer interfaces(MI-BCIs)have great potential value in prosthetics control,neurorehabilitation,and gaming;however,currently,most such systems only operate in controlled laboratory *** of the most important obstacles is the MI-BCI inefficiency *** accuracy of MI-BCI control varies significantly(from chance level to 100%accuracy)across subjects due to the not easily induced and unstable MI-related EEG *** MI-BCI inefficient subject is defined as a subject who cannot achieve greater than 70%accuracy after sufficient training time,and multiple survey results indicate that inefficient subjects account for 10%–50%of the experimental *** widespread use of MI-BCI has been seriously limited due to these large percentages of inefficient *** this review,we summarize recent findings of the cause of MI-BCI inefficiency from resting-state brain function,task-related brain activity,brain structure,and psychological *** factors help understand the reasons for inter-subject MI-BCI control performance variability,and it can be concluded that the lower resting-state sensorimotor rhythm(SMR)is the key factor in MI-BCI inefficiency,which has been confirmed by multiple independent *** then propose to divide MI-BCI inefficient subjects into three categories according to the resting-state SMR and offline/online accuracy to apply more accurate approaches to solve the inefficiency *** potential solutions include developing transfer learning algorithms,new experimental paradigms,mindfulness meditation practice,novel training strategies,and identifying new motor imagery-related EEG *** date,few studies have focused on improving the control accuracy of MI-BCI inefficient subjects;thus,we appeal to the BCI community to focus more on this research *** by reducing the percentage of inefficient subjects can we create the opportunity to expand the value and influence of MI-BCI.

关键词： motor imagery brain-computer interface(MI-BCI) inefficient BCI user EEG indicator brain structure transfer learning

来源：评论

学校读者我要写书评

暂无评论

Trust toward humans and trust toward artificial intelligence are not associated: Initial insights from self-report and neurostructural brain imaging

引用

Personality Neuroscience 2023年第1期6卷 e3-e3页

作者： Montag, Christian Klugah-Brown, Benjamin Zhou, Xinqi Wernicke, Jennifer Liu, Congcong Kou, Juan Chen, Yuanshu Haas, Brian W. Becker, Benjamin Department of Molecular Psychology Institute of Psychology and Education Ulm University Ulm Germany Clinical Hospital of Chengdu Brain Science Institute MOE Key Laboratory for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology Chengdu China Institute of Brain and Psychological Sciences Sichuan Normal University Chengdu China Department of Psychology Xinxiang Medical University Henan China Department of Psychology University of Georgia Athens GA United States

The present study examines whether self-reported trust in humans and self-reported trust in [(different) products with built-in] artificial intelligence (AI) are associated with one another and with brain structure. We sampled 90 healthy participants who provided self-reported trust in humans and AI and underwent brain structural magnetic resonance imaging assessment. We found that trust in humans, as measured by the trust facet of the personality inventory NEO-PI-R, and trust in AI products, as measured by items assessing attitudes toward AI and by a composite score based on items assessing trust toward products with in-built AI, were not significantly correlated. We also used a concomitant dimensional neuroimaging approach employing a data-driven source-based morphometry (SBM) analysis of gray-matter-density to investigate neurostructural associations with each trust domain. We found that trust in humans was negatively (and significantly) correlated with an SBM component encompassing striato-Thalamic and prefrontal regions. We did not observe significant brain structural association with trust in AI. The present findings provide evidence that trust in humans and trust in AI seem to be dissociable constructs. While the personal disposition to trust in humans might be hardwired to the brain's neurostructural architecture (at least from an individual differences perspective), a corresponding significant link for the disposition to trust AI was not observed. These findings represent an initial step toward elucidating how different forms of trust might be processed on the behavioral and brain level. © The Author(s), 2023. Published by Cambridge University Press.

关键词： Artificial intelligence MRI Trust

来源：评论

学校读者我要写书评

暂无评论

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

引用

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)

来源：评论

学校读者我要写书评

暂无评论

Manipulating Piezoelectric and Electro-Strain Properties of BiFeO3-BaTiO3-Based Ceramics Through Chemical Doping-Controlled Domain-Size Engineering

SSRN

引用

SSRN 2025年

作者： Lin, Jiamin Liu, Bing Liu, Mengxiang Yang, Shan Zhou, Linming Hong, Zijian Zhu, Xiaoli Wu, Yongjun Li, Juan Huang, Yuhui School of Materials Science and Engineering State Key Laboratory of Silicon and Advanced Semiconductor Materials Cyrus Tang Center for Sensor Materials and Applications Institute of Fundamental and Transdisciplinary Research Zhejiang University Hangzhou310027 China Nanhu Brain-computer Interface Institute Zhejiang Hangzhou311100 China College of Electronic Information and Engineering Hangzhou Dianzi University Zhejiang Hangzhou310018 China Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Taizhou Institute of Zhejiang University Zhejiang Taizhou318000 China School of Engineering Hangzhou City University Zhejiang Hangzhou310015 China College of Materials and Science Engineering Zhejiang University of Technology Zhejiang Hangzhou310006 China

Ferroelectric domains are crucial for the performance of piezoelectric ceramics, as the size and switching dynamics affect polarization response directly, manipulating both ferroelectric and piezoelectric properties. In this study, we achieved BiFeO3-BaTiO3 ceramics with domain sizes from macroscopic to nanoscale by incorporating a small amount of antiferroelectric phase. Contrary to the long-standing belief that smaller ferroelectric domains own lower polarization switching barriers and yield higher piezoelectricity, we found that in chemical doping-controlled domain size engineering, the smaller the domain size, the more difficult it is for polarization switching. Ceramics with the largest domain exhibited superior positive piezoelectric performance due to the sufficient polarization switching and reduced domain wall pinning, achieving a piezoelectric coefficient (d33) of 460 pC/N at 350°C. Middle-sized domains achieved optimal electro-strain performance determined by the trade-off between the domain walls pinning and the increased local tetragonal or orthorhombic symmetries, with a strain (0.29%) and an excellent piezoelectric strain coefficient (d33*) of 726.4 pm/V at 160°C. This work provides novel insights into how domain-size engineering affects domain switching and local distortion, offering guidance for performance optimization of piezoelectric ceramics. © 2025, The Authors. All rights reserved.

关键词： Piezoelectricity

来源：评论

学校读者我要写书评

暂无评论

CEBRA Method: Decoding brain Activity for Advanced brain-computer interface technology

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jingcheng Yang Frank Kulwa Xuanwei Liu Yiqing Lu Yufa Fu Guanglin Li Yaping Huai Xin Zhang Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Shenzhen Dapeng New District Nanao People’s Hospital Shenzhen China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen China Brain Cognition and Brain-Computer Intelligence Integration Group Kunming University of Science and Technology Kunming China Shandong Zhongke Advanced Technology Co. Ltd. Jinan China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The emerging neurorehabilitation technology, brain-computer interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients’ brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.

关键词： Support vector machines brain Accuracy Stroke (medical condition) Feature extraction Motors brain-computer interfaces Neurorehabilitation Decoding Paralysis

来源：评论

学校读者我要写书评

暂无评论

Deep Fusion of Shifted MLP and CNN for Medical Image Segmentation

Deep Fusion of Shifted MLP and CNN for Medical Image Segment...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chengyu Yuan Hao Xiong Guoqing Shangguan Hualei Shen Dong Liu Haojie Zhang Zhonghua Liu Kun Qian Bin Hu Björn W. Schuller Yoshiharu Yamamoto Shlomo Berkovsky College of Computer and Information Engineering Henan Normal University China Key Laboratory of Artificial Intelligence and Personalized Learning in Education Henan China Big Data Engineering Lab of Teaching Resources & Assessment of Education Quality Henan China Centre for Health Informatics Australian Institute of Health Innovation Macquarie University Australia Key Laboratory of Brain Health Intelligent Evaluation and Intervention (BIT) Ministry of Education China School of Medical Technology Beijing Institute of Technology China School of Information Engineering Zhejiang Ocean University China GLAM – Group on Language Audio & Music Imperial College London U.K. Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan

Medical image segmentation is an important task in modern analysis of medical images. Current methods tend to extract either local features with convolutions or global features with Transformers. However, few of them are able to effectively fuse global and local features to facilitate segmentation. In this work, we propose a novel hybrid network that involves three main branches: the Multi-Layer Perception (MLP) branch, the Convolutional Neural Network (CNN) branch, and a Fusion branch. The MLP and CNN branches aim to learn global and local features, respectively. To fuse these, the fusion branch introduces a novel hierarchical fusion that performs multi-layered fusions that generate high-level representations to enhance segmentation. Our evaluation with two datasets shows strong performance of the proposed method compared to state-of-the-art baselines.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Single-cell spatial transcriptomic atlas of the whole mouse brain

引用

Neuron 2025年

作者： Han, Lei Liu, Zhen Jing, Zehua Liu, Yuxuan Peng, Yujie Chang, Huizhong Lei, Junjie Wang, Kexin Xu, Yuanfang Liu, Wei Wu, Zihan Li, Qian Shi, Xiaoxue Zheng, Mingyuan Wang, He Deng, Juan Zhong, Yanqing Pan, Hailin Lin, Junkai Zhang, Ruiyi Chen, Yu Wu, Jinhua Xu, Mingrui Ren, Biyu Cheng, Mengnan Yu, Qian Song, Xinxiang Lu, Yanbing Tang, Yuanchun Yuan, Nini Sun, Suhong An, Yingjie Ding, Wenqun Sun, Xing Wei, Yanrong Zhang, Shuzhen Dou, Yannong Zhao, Yun Han, Luyao Zhu, Qianhua Xu, Junfeng Wang, Shiwen Wang, Dan Bai, Yinqi Liang, Yikai Liu, Yuan Chen, Mengni Xie, Chun Bo, Binshi Li, Mei Zhang, Xinyan Ting, Wang Chen, Zhenhua Fang, Jiao Li, Shuting Jiang, Yujia Tan, Xing Zuo, Guolong Xie, Yue Li, Huanhuan Tao, Quyuan Li, Yan Liu, Jianfeng Liu, Yuyang Hao, Mingkun Wang, Jingjing Wen, Huiying Liu, Jiabing Yan, Yizhen Zhang, Hui Sheng, Yifan Yu, Shui Liao, Xiaoyan Jiang, Xuyin Wang, Guangling Liu, Huanlin Wang, Congcong Feng, Ning Liu, Xin Ma, Kailong Xu, Xiangjie Han, Tianyue Cao, Huateng Zheng, Huiwen Chen, Yadong Lu, Haorong Yu, Zixian Zhang, Jinsong Wang, Bo Wang, Zhifeng Xie, Qing Pan, Shanshan Liu, Chuanyu Xu, Chan Cui, Luman Li, Yuxiang Liu, Shiping Liao, Sha Chen, Ao Wu, Qing-Feng Wang, Jian Liu, Zhiyong Sun, Yidi Mulder, Jan Yang, Huanming Wang, Xiaofei Li, Chao Yao, Jianhua Xu, Xun Liu, Longqi Shen, Zhiming Wei, Wu Sun, Yan-Gang BGI Research Hangzhou 310030 China Lingang Laboratory Shanghai 200031 China CAS Key Laboratory of Computational Biology Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai 200031 China College of Life Sciences University of Chinese Academy of Sciences Beijing 100049 China Institute of Neuroscience State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology CAS Center for Excellence in Brain Science and Intelligence Technology University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai 200031 China Tencent AI Lab Shenzhen 518057 China BGI Research Shenzhen 518083 China Department of Anesthesiology Huashan Hospital State Key Laboratory of Medical Neurobiology Institute for Translational Brain Research MOE Frontiers Center for Brain Science Fudan University Shanghai 200032 China State Key Laboratory of Molecular Development Biology Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing 100101 China BGI College & Henan Institute of Medical and Pharmaceutical Sciences Zhengzhou University Zhengzhou 450000 China School of Biology and Biological Engineering South China University of Technology Guangzhou 510006 China BGI Research Qingdao 266555 China China National GeneBank BGI Research Shenzhen 518120 China Shenzhen Proof-of-Concept Center of Digital Cytopathology BGI Research Shenzhen 518083 China Shanxi Medical University-BGI Collaborative Center for Future Medicine Shanxi Medical University Taiyuan 030001 China BGI Research Wuhan 430074 China BGI Research Chongqing 401329 China JFL-BGI STOmics Center Jinfeng Laboratory Chongqing 401329 China Department of Biology University of Copenhagen Copenhagen 2200 Denmark Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 201602 China Department of Protein Science Science for Life Laboratory KTH-Royal Institute of Technology Stockholm

A comprehensive atlas of genes, cell types, and their spatial distribution across a whole mammalian brain is fundamental for understanding the function of the brain. Here, using single-nucleus RNA sequencing (snRNA-seq) and Stereo-seq techniques, we generated a mouse brain atlas with spatial information for 308 cell clusters at single-cell resolution, involving over 4 million cells, as well as for 29,655 genes. We have identified cell clusters exhibiting preference for cortical subregions and explored their associations with brain-related diseases. Additionally, we pinpointed 155 genes with distinct regional expression patterns within the brainstem and unveiled 513 long non-coding RNAs showing region-enriched expression in the adult brain. Parcellation of brain regions based on spatial transcriptomic information revealed fine structure for several brain areas. Furthermore, we have uncovered 411 transcription factor regulons showing distinct spatiotemporal dynamics during neurodevelopment. Thus, we have constructed a single-cell-resolution spatial transcriptomic atlas of the mouse brain with genome-wide coverage. © 2025 Elsevier Inc.

关键词： development mouse brain non-coding gene parcellation Stereo-seq transcriptome

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Spike Sorting Using Local Adaptive Projection and Gaussian Mixture Model

Unsupervised Spike Sorting Using Local Adaptive Projection a...

引用

Chinese Automation Congress (CAC)

作者： Yuli Huang Mengmeng Li Xinyu Liu Lifang Yang Hong Wan School of Electrical Engineering Zhengzhou University Zhengzhou China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou China School of Intelligent Manufacturing Huanghuai University Zhumadian China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Many researches in neuroscience rely on the analysis of neuronal spike activities recorded under different behavioral conditions, due to the fact that different types of spikes recorded by multi-channel microelectrode arrays may show specific firing in different cognitive tasks. Therefore, accurate and reliable sorting of spikes plays an important role in the related researches in neuroscience. Based on the nonstationarity and local amplitude jump characteristics of spikes, a novel spikes sorting algorithm based on unsupervised local adaptive projection (LAP) feature selection algorithm and Gaussian mixture model (GMM) clustering algorithm (LAGM) was proposed in this work. Firstly, the adaptive similarity matrix and projection matrix were learned by iterative method to select the features of the original spike waveforms using LAP, and the low-dimensional feature set of the spikes was obtained. Then, these low-dimensional features were clustered for spikes sorting based on GMM. Finally, the feasibility and effectiveness of the LAGM was assessed by simulation data from the publish database, and the practicability was validated by the real signals recorded from the hippocamp region of pigeons under the free-foraging with the GMM clustering based on principal component analysis (PCA) dimension reduction and K-means clustering based on LAP feature selection as the algorithms for comparison. Experimental results indicated that the proposed LAGM in this paper shown high accuracy for spike sorting, which provided an effective means for the reliable analysis of spike signals.

关键词： Feature extraction Clustering algorithms Sorting Neurons Reliability Neuroscience Microelectrodes

来源：评论

学校读者我要写书评

暂无评论

Family history, inflammation, and cerebellum in major depression: a combined VBM and dynamic functional connectivity study

引用

Translational Psychiatry 2025年第1期15卷 1-7页

作者： Jianyue Pang Huimin Teng Chenxiao Su Jin He Hengfen Li Jiaying Xu Zhiyuan Zhang Lingyun Gao Rui Zhang Gang Liu Yajing Pang Long Chen Yuan Chen Department of Psychiatry The First Affiliated Hospital of Zhengzhou University Zhengzhou China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Department of Neurosurgery Zhengzhou Central Hospital Affiliated to Zhengzhou University Zhengzhou China Department of Magnetic Resonance Imaging The First Affiliated Hospital of Zhengzhou University Zhengzhou China

A family history (FH) of depression significantly influences the progress of major depressive disorder (MDD). However, the underlying neural mechanism of FH remains unclear. This study examined the association between brain structural and connectivity alterations, inflammation, and FH in MDD. A total of 134 MDD patients with (FH group,n= 43) and without (NFH group,n= 91) first-degree FH and 96 demographic-matched healthy controls (HCs) were recruited. Voxel-based morphometry (VBM) and sliding-window dynamic functional connectivity (dFC) analyses were performed, and inflammatory biomarkers (C-reactive protein (CRP) and interleukin-6 (IL-6)) were detected. Compared with HCs, FH and NFH groups showed decreased gray matter volume (GMV) in the left cerebellum posterior lobe and increased dFC between this region and the left inferior parietal lobule. The FH group showed increased dFC between the cerebellum region and medial prefrontal cortex (mPFC) compared to NFH and HCs. The combination of these brain measurements further differentiated between FH and NFH. Moreover, the GMV of the cerebellum was positively correlated with CRP in the NFH group, while the dFC between the cerebellum and mPFC was positively correlated with IL-6 in the FH group. The present findings indicate that cerebellar structure and dynamic function vary according to FH of MDD and are related to inflammatory factors, potentially offering novel insights into the underlying pathogenic mechanisms of MDD.

关键词：

来源：评论

学校读者我要写书评

暂无评论

多肽靶向嵌合体通过促进tau蛋白特异性去磷酸化治疗阿尔茨海默病和其他tau蛋白病

引用

science Bulletin 2024年第8期69卷 1137-1152页

作者：苏静芬肖越魏林郁雷慧杨孙飞王围霞尹君熊瑞李师宏张配周颖王小川郑杰王建枝 Department of Pathophysiology School of Basic MedicineKey Laboratory of Education Ministry of China/Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430030China Sino-UK Joint Laboratory of Brain Function and Injury of Henan Province Department of Physiology and PathophysiologySchool of Basic Medical SciencesXinxiang Medical UniversityXinxiang 453003China Department of Pathophysiology School of Basic Medical SciencesWuhan UniversityWuhan 430030China Department of Anesthesiology The First Affiliated Hospital of Gannan Medical UniversityGanzhou 341000China The Core Facility and Technical Support Wuhan Institute of VirologyChinese Academy of SciencesWuhan 430030China Research Center for Medicine and Structural Biology Wuhan UniversityWuhan 430030China Neuroscience Research Institute and Department of Neurobiology School of Basic Medical SciencesPeking UniversityKey Laboratory for NeuroscienceMinistry of Education/National Health CommissionBeijing 100083China Co-innovation Center of Neuroregeneration Nantong UniversityNantong 226000China

Abnormal hyperphosphorylation and accumulation of tau protein play a pivotal role in neurodegeneration in Alzheimer’s disease(AD)and many other *** elimination of hyperphosphorylated tau is promising for the therapy of these *** have conceptualized a strategy,named dephosphorylation-targeting chimeras(DEPTACs),for specifically hijacking phosphatases to tau to debilitate its ***,we conducted the step-by-step optimization of each constituent motif to generate DEPTACs with reasonable effectiveness in facilitating the dephosphorylation and subsequent clearance of pathological ***,for one of the selected chimeras,D16,we demonstrated its significant efficiency in rescuing the neurodegeneration caused by neurotoxic K18-tau seeds in ***,intravenous administration of D16 also alleviated tau pathologies in the brain and improved memory deficits in AD *** results suggested DEPTACs as targeted modulators of tau phosphorylation,which hold therapeutic potential for AD and other tauopathies.

关键词： tau Dephosphorylation-targeting chimeras Therapeutic potential Tauopathy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：