检索结果-内蒙古大学图书馆

Neuroimaging epicenters as potential sites of onset of the neuroanatomical pathology in schizophrenia

学校读者我要写书评

暂无评论

science Advances 2024年第24期10卷 eadk6063页

作者： Jiang, Yuchao Palaniyappan, Lena Luo, Cheng Chang, Xiao Zhang, Jie Tang, Yingying Zhang, Tianhong Li, Chunbo Zhou, Enpeng Yu, Xin Li, Wei An, Dongmei Zhou, Dong Huang, Chu-Chung Tsai, Shih-Jen Lin, Ching-Po Cheng, Jingliang Wang, Jijun Yao, Dezhong Cheng, Wei Feng, Jianfeng Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Ministry of Education Shanghai China Douglas Mental Health University Institute Department of Psychiatry McGill University MontréalQC Canada Robarts Research Institute University of Western Ontario LondonON Canada Lawson Health Research Institute LondonON Canada Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of life Science and Technology University of Electronic Science and Technology of China Chengdu China High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province Center for Information in Medicine University of Electronic Science and Technology of China Chengdu China Chinese Academy of Medical Sciences Chengdu China Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicine Shanghai200030 China Beijing China Department of Neurology West China Hospital Sichuan University Chengdu610041 China School of Psychology and Cognitive Science East China Normal University Shanghai China Shanghai Changning Mental Health Center Shanghai China Department of Psychiatry Taipei Veterans General Hospital Taipei Taiwan Institute of Neuroscience National Yang Ming Chiao Tung University Taipei Taiwan Department of MRI First Affiliated Hospital of Zhengzhou University Zhengzhou China Department of Neurology Huashan Hospital Fudan University Shanghai China Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence Zhejiang Normal University Jinhua China MOE Frontiers Center for Brain Science Fudan University Shanghai China Zhangjiang Fudan International Innovation Center Shanghai China School of Data Science Fudan University Shanghai China Department of Computer Science University of Warwick CoventryCV 4 7AL United Kingdom

Schizophrenia lacks a clear definition at the neuroanatomical level, capturing the sites of origin and progress of this disorder. Using a network-theory approach called epicenter mapping on cross-sectional magnetic resonance imaging from 1124 individuals with schizophrenia, we identified the most likely "source of origin" of the structural pathology. Our results suggest that the Broca’s area and adjacent frontoinsular cortex may be the epicenters of neuroanatomical pathophysiology in schizophrenia. These epicenters can predict an individual’s response to treatment for psychosis. In addition, cross-diagnostic similarities based on epicenter mapping over of 4000 individuals diagnosed with neurological, neurodevelopmental, or psychiatric disorders appear to be limited. When present, these similarities are restricted to bipolar disorder, major depressive disorder, and obsessive-compulsive disorder. We provide a comprehensive framework linking schizophrenia-specific epicenters to multiple levels of neurobiology, including cognitive processes, neurotransmitter receptors and transporters, and human brain gene expression. Epicenter mapping may be a reliable tool for identifying the potential onset sites of neural pathophysiology in schizophrenia. © 2024 the Authors, some rights reserved

关键词： Mapping

Detecting out-of-distribution samples via variational auto-encoder with reliable uncertainty estimation

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Ran, Xuming Xu, Mingkun Mei, Lingrui Xu, Qi Liu, Quanying Shenzhen Key Laboratory of Smart Healthcare Engineering Department of Biomedical Engineering Southern University of Science and Technology Shenzhen518055 China Center for Brain Inspired Computing Research Department of Precision Instrument Tsinghua University Beijing100084 China China Automotive Engineering Research Institute Chongqing401122 China School of Artifical Intelligence Electronic and Electrical Engineering School of Artifical Intelligence Dalian University of Technology Dalian116024 China College of Mathematics and Statistics Chongqing Jiaotong University Chongqing400074 China College of Computer Science and Technology Zhejiang University Hangzhou310027 China

Variational autoencoders (VAEs) are influential generative models with rich representation capabilities from the deep neural network architecture and Bayesian method. However, VAE models have a weakness that assign a higher likelihood to out-of-distribution (OOD) inputs than in-distribution (ID) inputs. To address this problem, a reliable uncertainty estimation is considered to be critical for in-depth understanding of OOD inputs. In this study, we propose an improved noise contrastive prior (INCP) to be able to integrate into the encoder of VAEs, called INCPVAE. INCP is scalable, trainable and compatible with VAEs, and it also adopts the merits from the INCP for uncertainty estimation. Experiments on various datasets demonstrate that compared to the standard VAEs, our model is superior in uncertainty estimation for the OOD data and is robust in anomaly detection tasks. The INCPVAE model obtains reliable uncertainty estimation for OOD inputs and solves the OOD problem in VAE models. © 2020, CC BY.

关键词： Bayesian networks

Networked Integrated Sensing and Communications for 6G Wireless Systems

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Li, Jiapeng Shao, Xiaodan Chen, Feng Wan, Shaohua Liu, Chang Wei, Zhiqiang Ng, Derrick Wing Kwan The College of Artificial Intelligence Southwest University Chongqing400715 China The Brain-Inspired Computing and Intelligent Control Key Laboratory Southwest University Chongqing400715 China The Key Laboratory of Luminescence Analysis and Molecular Sensing Southwest University Chongqing400715 China The Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China The School of Mathematics and Statistics Xi'an Jiaotong University Xi'An710049 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The Department of Computer Science and Information Technology La Trobe University MelbourneVIC3150 Australia The School of Electrical Engineering and Telecommunications University of New South Wales SydneyNSW2052 Australia

Integrated sensing and communication (ISAC) is envisioned as a key pillar for enabling the upcoming sixth generation (6G) communication systems, requiring not only reliable communication functionalities but also highly accurate environmental sensing capabilities. In this paper, we design a novel networked ISAC framework to explore the collaboration among multiple users for environmental sensing. Specifically, multiple users can serve as powerful sensors, capturing back scattered signals from a target at various angles to facilitate reliable computational imaging. Centralized sensing approaches are extremely sensitive to the capability of the leader node because it requires the leader node to process the signals sent by all the users. To this end, we propose a two-step distributed cooperative sensing algorithm that allows low-dimensional intermediate estimate exchange among neighboring users, thus eliminating the reliance on the centralized leader node and improving the robustness of sensing. This way, multiple users can cooperatively sense a target by exploiting the block-wise environment sparsity and the interference cancellation technique. Furthermore, we analyze the mean square error of the proposed distributed algorithm as a networked sensing performance metric and propose a beamforming design for the proposed network ISAC scheme to maximize the networked sensing accuracy and communication performance subject to a transmit power constraint. Simulation results validate the effectiveness of the proposed algorithm compared with the state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Beamforming

ECG-DPM: Electrocardiogram Generation via a Spectrogram-based Diffusion Probabilistic Model

学校读者我要写书评

暂无评论

ECG-DPM: Electrocardiogram Generation via a Spectrogram-base...

Smart World Congress (SWC), IEEE

作者： Lujundong Li Tong Xia Haojie Zhang Dongchen He Kun Qian Bin Hu Yoshiharu Yamamoto Björn W. Schuller Cecilia Mascolo Key Laboratory of Brain Health Intelligent Evaluation and Intervention Ministry of Education Beijing Institute of Technology Beijing China School of Medical Technology Beijing Institute of Technology Beijing China Info Hub AI Thrust the Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Technology University of Cambridge UK School of Life Sciences Peking University Beijing China Guaduate School of Frontier Sciences Computational Biology and Medical Sciences The University of Tokyo Japan Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan GLAM – the Group on Language Audio & Music Imperial College London UK CHI – Chair of Health Informatics MRI Technical University of Munich Germany

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

An electrocardiogram (ECG) records the electrical signals from the heart to assess various cardiovascular conditions. Deep learning methods have been proposed to model ECGs, but the insufficient availability of ECG data and annotations often hinders their performance. To address this challenge, this paper explores the latest data synthesis technique, i.e., diffusion probabilistic models (DPMs), to enable the generation of an unlimited number of ECGs representing various cardiovascular conditions. In contrast to previous approaches that treat ECGs as time series data or convert them into power spectrograms, we introduce a novel multi-channel spectrogram-based diffusion framework. In our method, the diffusion model enhances generation diversity, while the multi-channel spectrogram preserves both magnitude and phase information, ensuring high fidelity in the reconstructed ECGs. Extensive experiments conducted on real-world ECG data demonstrate the superiority of our approach. Notably, our ECG-DPM outperforms the best baseline by a margin ranging from ${1 2. 5 \%}$ to ${6 2. 5 \%}$ when generating ECGs for 30 seconds.

关键词： Training Heart Biological system modeling Time series analysis Electrocardiography Diffusion models brain modeling Data models Distance measurement Spectrogram

Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Masuyama, Naoki Nojima, Yusuke Toda, Yuichiro Loo, Chu Kiong Ishibuchi, Hisao Kubota, Naoyuki The Graduate School of Informatics Department of Core Informatics Osaka Metropolitan University 1-1 Gakuen-cho Naka-ku Sakai-Shi Osaka599-8531 Japan The Faculty of Environmental Life Natural Science and Technology Okayama University 3-1-1 Tsushima-Naka Kita-ku Okayama Okayamashi700-8530 Japan The Department of Artificial Intelligence Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia The Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Graduate School of Systems Design Department of Mechanical Systems Engineering Tokyo Metropolitan University 6-6 Asahigaoka Hino-Shi Tokyo191-0065 Japan

With the increasing importance of data privacy protection, various privacy-preserving machine learning methods have been proposed. In the clustering domain, various algorithms with a federated learning framework (i.e., federated clustering) have been actively studied and showed high clustering performance while preserving data privacy. However, most of the base clusterers (i.e., clustering algorithms) used in existing federated clustering algorithms need to specify the number of clusters in advance. These algorithms, therefore, are unable to deal with data whose distributions are unknown or continually changing. To tackle this problem, this paper proposes a privacy-preserving continual federated clustering algorithm. In the proposed algorithm, an adaptive resonance theory-based clustering algorithm capable of continual learning is used as a base clusterer. Therefore, the proposed algorithm inherits the ability of continual learning. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to state-of-the-art federated clustering algorithms while realizing data privacy protection and continual learning ability. The source code is available at https://***/Masuyama-lab/FCAC. © 2023, CC BY-NC-ND.

关键词： Clustering algorithms

SamRobNODDI: Q-Space Sampling-Augmented Continuous Representation Learning for Robust and Generalized NODDI

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Dong, Enqing Zheng, Hairong Wang, Shanshan The School of Mechanical Electrical & Information Engineering Shandong University Weihai264209 China The Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The School of Computer Science and Engineering Beihang University Beijing100191 China The State Key Laboratory of Cognitive Neuroscience and Learning IDG/McGovern Institute for Brain Research Beijing Normal University Beijing100191 China

Neurite Orientation Dispersion and Density Imaging (NODDI) microstructure estimation from diffusion magnetic resonance imaging (dMRI) is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods accelerate the speed of NODDI parameter estimation and improve the accuracy. However, most methods require the number and coordinates of gradient directions during testing and training to remain strictly consistent, significantly limiting the generalization and robustness of these models in NODDI parameter estimation. In this paper, we propose a q-space sampling augmentation-based continuous representation learning framework (SamRobNODDI) to achieve robust and generalized NODDI. Specifically, a continuous representation learning method based on q-space sampling augmentation is introduced to fully explore the information between different gradient directions in q-space. Furthermore, we design a sampling consistency loss to constrain the outputs of different sampling schemes, ensuring that the outputs remain as consistent as possible, thereby further enhancing performance and robustness to varying q-space sampling schemes. SamRobNODDI is also a flexible framework that can be applied to different backbone networks. To validate the effectiveness of the proposed method, we compared it with 7 state-of-the-art methods across 18 different q-space sampling schemes, demonstrating that the proposed SamRobNODDI has better performance, robustness, generalization, and flexibility. Copyright © 2024, The Authors. All rights reserved.

关键词： Diffusion tensor imaging

Author Correction: A novel interface for cortical columnar neuromodulation with multipoint infrared neural stimulation

学校读者我要写书评

暂无评论

Nature communications 2024年第1期15卷 9742页

作者： Feiyan Tian Ying Zhang Kenneth E Schriver Jia Ming Hu Anna Wang Roe Department of Neurosurgery of the Second Affiliated Hospital Interdisciplinary Institute of Neuroscience and Technology School of Medicine Zhejiang University Hangzhou 310029 China. Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou 310027 China. MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Hangzhou 310012 China. Department of Neurosurgery of the Second Affiliated Hospital Interdisciplinary Institute of Neuroscience and Technology School of Medicine Zhejiang University Hangzhou 310029 China. hujiaming@***. MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Hangzhou 310012 China. hujiaming@***. Department of Neurosurgery of the Second Affiliated Hospital Interdisciplinary Institute of Neuroscience and Technology School of Medicine Zhejiang University Hangzhou 310029 China. annawang@***. Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou 310027 China. annawang@***. MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Hangzhou 310012 China. annawang@***. National Key Laboratory of Brain and Computer Intelligence Zhejiang University Hangzhou 310058 China. annawang@***.

Improvement of Resting-state EEG Analysis Process with Spectrum Weight-Voting based on LES

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Ye, Yumeng Liu, Haichun Zhang, TianHong Pan, Changchun Yang, Genke Wang, JiJun Qiu, Robert C. Key Laboratory of System Control and Information Processing Shanghai Jiaotong University Ministry of Education of China Shanghai200240 China Department of Automation and Shanghai Key Laboratory of Navigation and Location Based Services Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center Shanghai Jiaotong University School of Medicine Shanghai200030 China Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiaotong University Shanghai200240 China Department of Electrical Engineering Shanghai Jiaotong University Shanghai200240 China Department of Electrical and Computer Engineering Tennessee Technological University CookevilleTN38505 United States

Electroencephalography is a non-invasive technique for recording brain bioelectric activity, which has potential applications in various fields such as human-computer interaction and neuroscience. Among them, analysis of the risk of schizophrenia using EEG data is a relatively new research topic. However, there are many difficulties in analyzing EEG data, including its complex composition, low amplitude as well as low signal-to-noise ratio. Some of the existing methods of analysis are based on feature extraction and machine learning to differentiate the phase of schizophrenia (First-episode schizophrenia, Healthy controls or Clinical high-risk) that samples belong to. However, medical research requires the use of machine learning not only to give more accurate classification results, but also to give the results that can be applied to pathological studies. The main purpose of this study is to obtain the weight values as the representation of influence of each frequency band on the classification of schizophrenia phases on the basis of a more effective classification method using the LES feature extraction, and then the weight values are processed and applied to improve the accuracy of machine learning classification. We propose a method called weight-voting to obtain the weights of sub-bands features by using results of classification for voting to fit the actual categories of EEG data, and using weights for reclassification. Through this method, we can first obtain the influence of each band in distinguishing three schizophrenia phases, and analyze the effect of band features on the risk of schizophrenia contributing to the study of psychopathology. In addition, the weights applied to the original classifier can achieve the upgrade of the classification effect, which contributes to the BCI-assisted system of diagnosis and treatment. Our results show that there is a high correlation between the change of weight of low gamma band and the difference between HC, CHR an

关键词： Electroencephalography

Aberrant Functional Network Connectivity Transition Probability in Major Depressive Disorder

学校读者我要写书评

暂无评论

Aberrant Functional Network Connectivity Transition Probabil...

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

作者： Elaheh Zendehrouh Mohammad. S. E. Sendi Jing Sui Zening Fu Dongmei Zhi Luxian Lv Xiaohong Ma Qing Ke Xianbin Li Chuanyue Wang Christopher. C. Abbott Jessica A. Turner Robyn. L Miller Vince D. Calhoun Department of Computer Science Georgia State University Atlanta Georgia Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta Georgia Tri-institutional Center for Translational Research in Neuroimaging and Data Science: Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia Brainnetome Center and National Laboratory of Pattern Recognition Beijing China and University of Chinese Academy of Sciences Beijing China Psychiatric Laboratory and Mental Health Center the State Key Laboratory of Biotherapy West China Hospital of Sichuan University Chengdu China Huaxi Brain Research Center West China Hospital of Sichuan University Chengdu China Beijing Key Lab of Mental Disorders Capital Medical University Beijing China Department of Psychiatry University of New Mexico Albuquerque New Mexico

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Major depressive disorder (MDD) is a common and serious mental disorder characterized by a persistent negative feeling and tremendous sadness. In recent decades, several studies used functional network connectivity (FNC), estimated from resting state functional magnetic resonance imaging (fMRI), to investigate the biological signature of MDD. However, the majority of them have ignored the temporal change of brain interaction by focusing on static FNC (sFNC). Dynamic functional network connectivity (dFNC) that explores temporal patterns of functional connectivity (FC) might provide additional information to its static counterpart. In the current study, by applying k-means clustering on dFNC of MDD and healthy subjects (HCs), we estimated 5 different states. Next, we use the hidden Markov model as a potential biomarker to differentiate the dFNC pattern of MDD patients from HCs. Comparing MDD and HC subjects' hidden Markov model (HMM) features, we have highlighted the role of transition probabilities between states as potential biomarkers and identified that transition probability from a lightly- connected state to highly connected one reduces as symptom severity increases in MDD subjects.

关键词： Hidden Markov models Hospitals Functional magnetic resonance imaging Correlation Probability Microsoft Windows Psychiatry