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International Conference on Display Technology, ICDT 2018

作者： Hu, Haichen Wang, Danli Yue, Kang Yang, Xinpan Chiu, Steve C. The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China College of Science and Engineering Idaho State University Pocatello United States

ISBN: (纸本)9781510867659

3D display has become increasingly popular in many fields. It can provide incredible immersion compared with 2D display. There are many researches referring to the change of user about no matter subjective experience or objective physiology signal while watching 3D content. Similarly, in this paper, we study the variation of user’s EEG when the user watched 3D content. However, we only focus on parallax cue and avoid other cues of 3D content by using Random Dot Stereogram(RDS) as visual stimulus. We designed an experiment which asked the subject to watch RDS with nine kinds of parallaxes(0,±0.5,±1.1,±1.3,±1.7). The users watched one RDS for 1 minute and then closed eyes and rest for 1 minute every time. During watching, EEG signal with 30-channel was recorded. The result suggests that significant variation of the user’s EEG data occurred in the Alpha(8-13Hz) and Beta(14-30Hz) frequency band by different parallax’s comparison. And we found not only parallax but also electrode position can impact the user’s EEG data significantly. Accurately, the EEG data has a significant change in six electrodes position in both frequency bands including CZ 、 PZ 、 OZ 、 O2 in international 10-20 system. It means those electrode positions we detect can be regarded as typical positions where we record EEG signal. It will simplify the process of EEG signal acquisition when we need to study the relationship between parallax cue of 3D display and the user further. © 2018 SID.

关键词： Three dimensional displays

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Non-Frontal Facial Expression Recognition Using a Depth-Patch Based Deep Neural Network

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Journal of computer science & Technology 2017年第6期32卷 1172-1185页

作者： Nai-Ming Yao Hui Chen Qing-Pei Guo Hong-An Wang Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Bcijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China

The challenge of coping with non-frontal head poses during facial expression recognition results in considerable reduction of accuracy and robustness when capturing expressions that occur during natural communications. In this paper, we attempt to recognize facial expressions under poses with large rotation angles from 2D videos. A depth^patch based 4D expression representation model is proposed. It was reconstructed from 2D dynamic images for delineating continuous spatial changes and temporal context under non-frontal cases. Furthermore, we present an effective deep neural network classifier, which can accurately capture pose-variant expression features from the depth patches and recognize non-frontal expressions. Experimental results on the BU-4DFE database show that the proposed method achieves a high recognition accuracy of 86.87% for non-frontal facial expressions within a range of head rotation angle of up to 52%, outperforming existing methods. We also present a quantitative analysis of the components contributing to the performance gain through tests on the BU-4DFE and Multi-PIE datasets.

关键词： facial expression recognition non-frontal head pose depth spatial-temporal convolutional neural network

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Aberrant Functional Network Connectivity Transition Probability in Major Depressive Disorder

Aberrant Functional Network Connectivity Transition Probabil...

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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

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Scenario Design Tool Based on Hybrid Input

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Ruan Jian Xue Bao/Journal of Software 2019年 30卷 48-61页

作者： Tong, Qing-Shan Zhang, Zong-Qi Huang, Jin Tian, Feng Liu, Jie Dai, Guo-Zhong Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100190 China

Pen-based user interface relying on touch technology is one of the Post-WIMP interfaces. It discards the physical keyboard and mouse, which changed the method of human-computer interaction to some extent. Though sketch drawing software and recognition software are constantly emerging, there is no mature pen-based interface design development tool. Based on the PGIS interaction paradigm and scenario design method, this paper develops a tool named with SDT that allows hybrid input of graphics and sketchs based on pen-based interaction primitives. Firstly, based on the principle of high-cohesion and low-coupling in the field of software engineering, the "Separation-Fusion" design method is proposed. Accordingly, the overall architecture of the system is put forward. Secondly, the essential technologies are elaborated from three aspects: user interface description language, pen-based interactive primitive and mono-case, hybrid input. Thirdly, an example of a complete application is built by the SDT, which makes the availability and feasibility of the system more convincible. Finally, the advantage and effectiveness of the tool are verified by two evaluation experiments. © Copyright 2019, Institute of Software, the Chinese Academy of sciences. All rights reserved.

关键词： human computer interaction

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Revisiting metric learning for few-shot image classification

arXiv

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arXiv 2019年

作者： Li, Xiaomeng Yu, Lequan Fu, Chi-Wing Fang, Meng Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Tencent AI Lab United States

The goal of few-shot learning is to recognize new visual concepts with just a few amount of labeled samples in each class. Recent effective metric-based few-shot approaches employ neural networks to learn a feature similarity comparison between query and support examples. However, the importance of feature embedding, i.e., exploring the relationship among training samples, is neglected. In this work, we present a simple yet powerful baseline for few-shot classification by emphasizing the importance of feature embedding. Specifically, we revisit the classical triplet network from deep metric learning, and extend it into a deep K-tuplet network for few-shot learning, utilizing the relationship among the input samples to learn a general representation learning via episode-training. Once trained, our network is able to extract discriminative features for unseen novel categories and can be seamlessly incorporated with a non-linear distance metric function to facilitate the few-shot classification. Our result on the miniImageNet benchmark outperforms other metric-based few-shot classification methods. More importantly, when evaluated on completely different datasets (Caltech-101, CUB-200, Stanford Dogs and Cars) using the model trained with miniImageNet, our method significantly outperforms prior methods, demonstrating its superior capability to generalize to unseen classes. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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Learning to predict explainable plots for neural story generation

arXiv

引用

arXiv 2019年

作者： Chen, Gang Liu, Yang Luan, Huanbo Zhang, Meng Liu, Qun Sun, Maosong Institute for Artificial Intelligence State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing China Beijing National Research Center for Information Science and Technology Noah's Ark Lab Paris Huawei Technologies Ltd

Story generation is an important natural language processing task that aims to generate coherent stories automatically. While the use of neural networks has proven effective in improving story generation, how to learn to generate an explainable high-level plot still remains a major challenge. In this work, we propose a latent variable model for neural story generation. The model treats an outline, which is a natural language sentence explainable to humans, as a latent variable to represent a high-level plot that bridges the input and output. We adopt an external summarization model to guide the latent variable model to learn how to generate outlines from training data. Experiments show that our approach achieves significant improvements over state-of-the-art methods in both automatic and human evaluations. Copyright © 2019, The Authors. All rights reserved.

关键词： Natural language processing systems

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Fetal Brain Tissue Annotation and Segmentation Challenge Results

arXiv

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arXiv 2022年

作者： Payette, Kelly Li, Hongwei De Dumast, Priscille Licandro, Roxane Ji, Hui Siddiquee, Md Mahfuzur Rahman Xu, Daguang Myronenko, Andriy Liu, Hao Pei, Yuchen Wang, Lisheng Peng, Ying Xie, Juanying Zhang, Huiquan Dong, Guiming Fu, Hao Wang, Guotai Rieu, ZunHyan Kim, Donghyeon Kim, Hyun Gi Karimi, Davood Gholipour, Ali Torres, Helena R. Oliveira, Bruno Vilaça, João L. Lin, Yang Avisdris, Netanell Ben-Zvi, Ori Bashat, Dafna Ben Fidon, Lucas Aertsen, Michael Vercauteren, Tom Sobotka, Daniel Langs, Georg Alenyà, Mireia Villanueva, Maria Inmaculada Camara, Oscar Fadida, Bella Specktor Joskowicz, Leo Weibin, Liao Yi, Lv Xuesong, Li Mazher, Moona Qayyum, Abdul Puig, Domenec Kebiri, Hamza Zhang, Zelin Xu, Xinyi Wu, Dan Liao, KuanLun Wu, YiXuan Chen, JinTai Xu, Yunzhi Zhao, Li Vasung, Lana Menze, Bjoern Cuadra, Meritxell Bach Jakab, Andras Center for MR Research University Children's Hospital Zurich University of Zurich Zurich Switzerland Neuroscience Center Zurich University of Zurich Zurich Switzerland Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University of Munich Munich Germany Medical Image Analysis Laboratory Department of Diagnostic and Interventional Radiology Lausanne University Hospital University of Lausanne Lausanne Switzerland CIBM Center for Biomedical Imaging Lausanne Switzerland Laboratory for Computational Neuroimaging Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School CharlestownMA United States Medical University of Vienna Vienna Austria Arizona State University United States NVIDIA United States Shanghai Jiaotong University China School of Computer Science Shaanxi Normal University Xi'An710119 China Research Institute NEUROPHET Inc. Seoul06247 Korea Republic of Department of Radiology The Catholic University of Korea Eunpyeong St. Mary's Hospital Seoul06247 Korea Republic of Boston Children's Hospital Harvard Medical School BostonMA United States 2Ai - School of Technology IPCA Barcelos Portugal Algoritmi Center School of Engineering University of Minho Guimarães Portugal School of Medicine University of Minho Braga Portugal ICVS 3B's - PT Government Associate Laboratory Guimarães Braga Portugal School of Computer Science and Engineering The Hebrew University of Jerusalem Israel Sagol Brain Institute Tel Aviv Sourasky Medical Center Israel Sagol School of Neuroscience Tel Aviv University Israel Sackler Faculty of Medicine Tel Aviv University Israel School of Biomedical Engineering & Imaging Sciences King's College London LondonSE1 7EU United Kingdom Department of Radiology University Hospitals Leuven Leuven3000 Belgium Computational Imaging Research Lab Department of Biomedical Imaging and Image-guided Therapy Medical

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero. © 2022, CC BY.

关键词： Magnetic resonance imaging

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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Author Correction: π-HuB: the proteomic navigator of the human body

引用

Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Small P values may not yield robust findings:an example using REST-meta-PD

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science Bulletin 2021年第21期66卷 2148-2152,M0003页

作者： Xi-Ze Jia Na Zhao Hao-Ming Dong Jia-Wei Sun Marek Barton Roxana Burciu Nicolas Carrière Antonio Cerasa Bo-Yu Chen Jun Chen Stephen Coombes Luc Defebvre Christine Delmaire Kathy Dujardin Fabrizio Esposito Guo-Guang Fan Federica Di Nardo Yi-Xuan Feng Brett W.Fling Saurabh Garg Moran Gilat Martin Gorges Shu-Leong Ho Fay BHorak Xiao Hu Xiao-Fei Hu Biao Huang Pei-Yu Huang Ze-Juan Jia Christina Jones Jan Kassubek Lenka Krajcovicova Ajay Kurani Jing Li Qing Li Ai-Ping Liu Bo Liu Hu Liu Wei-Guo Liu Renaud Lopes Yu-Ting Lou Wei Luo Tara Madhyastha Ni-Ni Mao Grainne McAlonan Martin J.McKeown Shirley Pang Andrea Quattrone Irena Rektorova Alessia Sarica Hui-Fang Shang James M.Shine Priyank Shukla Tomas Slavicek Xiao-Peng Song Gioacchino Tedeschi Alessandro Tessitore David Vaillancourt Jian Wang Jue Wang Z.Jane Wang Lu-Qing Wei Xia Wu Xiao-Jun Xu Lei Yan Jing Yang Wan-Qun Yang Nai-Lin Yao De-Long Zhang Jiu-Quan Zhang Min-Ming Zhang Yan-Ling Zhang Cai-Hong Zhou Chao-Gan Yan Xi-Nian Zuo Mark Hallett Tao Wu Yu-Feng Zang Center for Cognition and Brain Disorders the Affiliated HospitalHangzhou Normal UniversityHangzhou 310015China Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou 311121China National Basic Science Data Center Beijing 100190China State Key Laboratory of Cognitive Neuroscience and Learning&McGovern Institute for Brain Research Beijing Normal UniversityBeijing 100875China School of Information and Electronics Technology Jiamusi UniversityJiamusi 154007China Neuroscience Program Central European Institute of TechnologyCEITECMasaryk UniversityBrno 62500Czech Republic Department of Applied Physiology and Kinesiology University of FloridaGainesville FL 32611USA Univ.Lille InsermCHU LilleU1172-LilNCog-Lille Neuroscience&CognitionLille F-59000France The Institute for Biomedical Research and Innovation National Research CouncilMangone CS 87050Italy Department of Radiology The First Affiliated Hospital of China Medical UniversityShenyang 110001China Department of Radiology Guangdong Provincial Hospital of Chinese MedicineGuangzhou 510120China Department of Medicine Surgery and DentistryScuola Medica SalernitanaUniversity of SalernoFiscianoSA 132-84084Italy Department of Advanced Medical and Surgery Sciences University of Campania“Luigi Vanvitelli”Caserta 81100Italy Eye Center of the 2nd Affiliated Hospital Medical College of Zhejiang UniversityHangzhou 310020China Zhejiang Provincial Key Lab of Ophthalmology Hangzhou 310020China Department of Health and Exercise Science Colorado State UniversityFort Collins CO 80523USA Pacific Parkinson’s Research Centre University of British ColumbiaVancouver BC V6E 2M6Canada Department of Medicine(Neurology)University of British Columbia Vancouver BC V6T 1B7Canada Brain and Mind Center The University of SydneySydney NSW 2006Australia Department of Neurology Ulm UniversityUlm 89081Germany Division of Neurology Department of MedicineQueen Mary HospitalUniversity of Hong KongHong Kong 999077China Department of

Thousands of resting state functional magnetic resonance imaging(RS-f MRI)articles have been published on brain *** precise localization of abnormal brain activity,a voxel-level comparison is *** of the large number of voxels in the brain,multiple comparison correction(MCC)must be performed to reduce false positive rates,and a smaller P value(usually including either liberal or stringent MCC)is widely recommended[1].

关键词：帕金森病 precise correction

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