检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Cao, Xiaozhi Liao, Congyu Zhang, Zijing Iyer, Siddharth Srinivasan Chen, Zhifeng Lo, Wei-Ching Liu, Huafeng Wang, Kang He, Hongjian Setsompop, Kawin Zhong, Jianhui Bilgic, Berkin Center for Brain Imaging Science and Technology Department of Biomedical Engineering Zhejiang University Zhejiang Hangzhou China Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School CharlestownMA United States State Key Laboratory of Modern Optical Instrumentation College of Optical Science and Engineering Zhejiang University Zhejiang Hangzhou China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou China Siemens Medical Solutions BostonMA United States Department of Neurology The First Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Harvard-MIT Department of Health Sciences and Technology CambridgeMA United States Department of Imaging Sciences University of Rochester NY United States

Purpose: To rapidly obtain high isotropic-resolution T2 maps with whole-brain coverage and high geometric fidelity. Methods: A T2 blip-up/down echo planar imaging (EPI) acquisition with generalized Slice-dithered enhanced resolution (T2-BUDA-gSlider) is proposed. A radiofrequency (RF)-encoded multi-slab spin-echo EPI acquisition with multiple echo times (TEs) was developed to obtain high SNR efficiency with reduced repetition time (TR). This was combined with an interleaved 2-shot EPI acquisition using blip-up/down phase encoding. An estimated field map was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to achieve distortion-free and robust reconstruction for each slab without navigation. A Bloch simulated subspace model was integrated into gSlider reconstruction and utilized for T2 quantification. Results: In vivo results demonstrated that the T2 values estimated by the proposed method were consistent with gold standard spin-echo acquisition. Compared to the reference 3D fast spin echo (FSE) images, distortion caused by off-resonance and eddy current effects were effectively mitigated. Conclusion: BUDA-gSlider SE-EPI acquisition and gSlider-subspace joint reconstruction enabled distortion-free whole-brain T2 mapping in 2 min at ~1 mm3 isotropic resolution, which could bring significant benefits to related clinical and neuroscience applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Spin polarization

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Identification of cancer-keeping genes as therapeutic targets by finding network control hubs

arXiv

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arXiv 2022年

作者： Zhang, Xizhe Pan, Chunyu Wei, Xinru Yu, Meng Liu, Shuangjie An, Jun Yang, Jieping Wei, Baojun Hao, Wenjun Yao, Yang Zhu, Yuyan Zhang, Weixiong Early Intervention Unit Department of Psychiatry Affiliated Nanjing Brain Hospital Nanjing Medical University Nanjing China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China Department of Laboratory Animal Science China Medical University Shenyang China Key Laboratory of Transgenetic Animal Research China Medical University Shenyang China Department of Urology First Affiliated Hospital of China Medical University Shenyang China Department of Physiology Shenyang Medical College Shenyang China Department of Health Technology and Informatics The Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Department of Genetics Washington University in St. Louis St. LouisMO United States

Finding cancer driver genes has been a focal theme of cancer research and clinical studies. One of the recent approaches is based on network structural controllability that focuses on finding a control scheme and driver genes that can steer the cell from an arbitrary state to a designated state. While theoretically sound, this approach is impractical for many reasons, e.g., the control scheme is often not unique and half of the nodes may be driver genes for the cell. We developed a novel approach that transcends structural controllability. Instead of considering driver genes for one control scheme, we considered control hub genes that reside in the middle of a control path of every control scheme. Control hubs are the most vulnerable spots for controlling the cell and exogenous stimuli on them may render the cell uncontrollable. We adopted control hubs as cancer-keep genes (CKGs) and applied them to a gene regulatory network of bladder cancer (BLCA). All the genes on the cell cycle and p53 singling pathways in BLCA are CKGs, confirming the importance of these genes and the two pathways in cancer. A smaller set of 35 sensitive CKGs (sCKGs) for BLCA was identified by removing network links. Six sCKGs (RPS6KA3, FGFR3, N-cadherin (CDH2), EP300, caspase-1, and FN1) were subjected to small-interferencing-RNA knockdown in four cell lines to validate their effects on the proliferation or migration of cancer cells. Knocking down RPS6KA3 in a mouse model of BLCA significantly inhibited the growth of tumor xenografts in the mouse model. Combined, our results demonstrated the value of CKGs as therapeutic targets for cancer therapy and the potential of CKGs as an effective means for studying and characterizing cancer etiology. Copyright © 2022, The Authors. All rights reserved.

关键词： Genes

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Using deep learning models to examine the biological impact of polygenic risks for Alzheimer’s disease

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Alzheimer's & Dementia 2023年第S1期19卷

作者： Xiaopu Zhou Fanny C. F. Ip Ge Lv Yuanbing JIANG Han Cao Huan Zhong Lei Chen Amy K.Y. Fu Nancy Y. Ip Division of Life Science State Key Laboratory of Molecular Neuroscience Molecular Neuroscience Center The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China Guangdong Provincial Key Laboratory of Brain Science Disease and Drug Development HKUST Shenzhen Research Institute Shenzhen–Hong Kong Institute of Brain Science Shenzhen Guangdong 518057 China Hong Kong Center for Neurodegenerative Diseases Hong Kong Science Park Hong Kong China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong China

Background Alzheimer’s disease (AD) is a leading cause of mortality in the elderly. Genetics studies suggest that AD is polygenic in nature; thus, an individual’s risk of AD can be predicted by polygenic risk score (PRS) models. Of these models, the deep learning models more accurately capture nonlinear interactions among different genetic risks and are more suitable for modeling disease polygenic risk. Method We used the neural network-based deep learning models to evaluate the polygenic risk of AD. Meanwhile, we examined the associations of resultant PRSs and plasma protein biomarkers for AD by regression analysis. We also identified the biological pathways that contribute to the polygenic risk using Gene Set Enrichment Analysis. Result We found that the resultant PRSs of AD are associated with the levels of specific AD plasma protein biomarkers, including amyloid beta and p-tau181, as well as changes in the activity of specific biological events, such as TNF-α and cytokine-related pathways. Conclusion Our findings suggest that AD polygenic risk is associated with AD progression and modulates immune-associated pathways. They also highlight the potential use of deep learning methods in investigating the underlying genetic mechanisms of AD.

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Recommendations for the diagnosis and treatment of paroxysmal kinesigemc dyskinesia: an expert consensus in China

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Translational Neurodegeneration 2021年第1期10卷 67-76页

作者： Li Cao Xiaojun Huang Ning Wang Zhiying Wu Cheng Zhang Weihong Gu Shuyan Cong Jianhua Ma Ling Wei Yanchun Deng Qi Fang Qi Niu Jin Wang Zhaoxia Wang You Yin Jinyong Tian Shufen Tian Hongyan Bi Hong Jiang Xiaorong Liu Yang Lu Meizhen Sun Jianjun Wu Erhe Xu Tao Chen Tao Chen Xu Chen Wei Li Shujian Li Qinghua Li Xiaonan Song Ying Tang Ping Yang Yun Yang Min Zhang Xiong Zhang Yuhu Zhang Ruxu Zhang Yi Ouyang Jintai Yu Quanzhong Hu Qing Ke Yuanrong Yao Zhe Zhao Xiuhe Zhao Guohua Zhao Furu Liang Nan Cheng Jianhong Han Rong Peng Shengdi Chen Beisha Tang Department of Neurology Ruijin Hospital Shanghai Jiao Tong University School of MedicineShanghaiChina Department of Neurology The Sixth People's Hospital Shanghai Jiaotong UniversityShanghaiChina Department of Neurology and Institute of Neurology The First Affiliated HospitalFujian Medical University/Fujian Key Laboratory of Molecular NeurologyFujian Medical UniversityFuzhouChina Department of Neurology and Research Center of Neurology The Second Affiliated Hospital/Key Laboratory of Medical Neurobiology of Zhejiang ProvinceZhejiang University School of MedicineHangzhouChina Department of Neurology The First Affiliated HospitalSun Yat-sen UniversityGuangdong Provincial Key Laboratory of Diagnosis and Treatment of Major Neurological Diseases/National Key Clinical Department and Key Discipline of NeurologyGuangzhouChina Department of Neurology China-Japan Friendship HospitalMovement Disorder and Neurogenetics Research CenterBeijingChina Department of Neurology Shengjing Hospital of China Medical UniversityShenyangChina Department of Neurology The First Affiliated Hospital of Xinjiang Medical UniversityUrumqiChina Department of Neurology The First Affiliated Hospital of Anhui Medical UniversityCollaborative Innovation Center for Neuropsychiatric Disorders and Mental HealthAnhui Medical University/Anhui Province Key Laboratory of Cognition and Neuropsychiatry DisordersHefeiChina Department of Neurology Xijing HospitalFourth Military Medical UniversityXi'anChina Department of Neurology The First Affiliated Hospital of Soochow UniversitySuzhouChina Department of Neurology The First Affiliated Hospital of Nanjing Medical UniversityNanjingChina Department of Neurology The First Affiliated Hospital of Guangxi Medical UniversityNanningChina Department of Neurology Peking University First HospitalBeijingChina Department of Neurology Changzheng Hospitalthe PLA Naval Medical UniversityShanghaiChina Department of Neurology and Department of Emergency Guizhou Provincial People's Hospital

Paroxysmal dyskinesias are a group of neurological diseases characterized by intermittent episodes of involuntary movements with different *** kinesigenic dyskinesia(PKD)is the most common type of paroxysmal dyskinesia and can be divided into primary and secondary types based on the ***,PKD is characterized by recurrent and transient attacks of involuntary movements precipitated by a sudden voluntary *** major cause of primary PKD is genetic abnormalities,and the inheritance pattern of PKD is mainly autosomal-dominant with incomplete *** proline-rich transmembrane protein 2(PRRT2)was the first identified causative gene of PKD,accounting for the majority of PKD cases *** increasing number of studies has revealed the clinical and genetic characteristics,as well as the underlying mechanisms of *** seeking the views of domestic experts,we propose an expert consensus regarding the diagnosis and treatment of PKD to help establish standardized clinical evaluation and therapies for *** this consensus,we review the clinical manifestations,etiology,clinical diagnostic criteria and therapeutic recommendations for PKD,and results of genetic analyses in PKD patients performed in domestic hospitals.

关键词： Paroxysmal kinesigenic dyskinesia Diagnosis and treatment Expert consensus China

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En route for molecular dynamics simulation of a living cell

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Fundamental Research 2022年

作者： Yibo Wang Cong Zhang Ke Tang Xiaohui Wang Laboratory of Chemical Biology Changchun Institute of Applied Chemistry Chinese Academy of Sciences Changchun Jilin 130022 China State Key Laboratory of Natural and Biomimetic Drugs Peking University Beijing China School of Applied Chemistry and Engineering University of Science and Technology of China Hefei Anhui 230026 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Beijing National Laboratory for Molecular Sciences Beijing 100190 China

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CMR×Recon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

arXiv

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arXiv 2023年

作者： Wang, Chengyan Lyu, Jun Wang, Shuo Qin, Chen Guo, Kunyuan Zhang, Xinyu Yu, Xiaotong Li, Yan Wang, Fanwen Jin, Jianhua Shi, Zhang Xu, Ziqiang Tian, Yapeng Hua, Sha Chen, Zhensen Liu, Meng Sun, Mengting Kuang, Xutong Wang, Kang Wang, Haoran Li, Hao Chu, Yinghua Yang, Guang Bai, Wenjia Zhuang, Xiahai Wang, He Qin, Jing Qu, Xiaobo Human Phenome Institute Fudan University Shanghai China School of Nursing The Hong Kong Polytechnic University Hong Kong Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Electrical and Electronic Engineering & I-X Imperial College London United Kingdom Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Institute of Artificial Intelligence Xiamen University Xiamen China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Department of Bioengineering/˜Imperial-X Imperial College London United Kingdom School of Data Science Fudan University Shanghai China Department of Radiology Zhongshan Hospital Fudan University Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China Department of Computer Science The University of Texas Dallas United States Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai200433 China Simens Healthineers Ltd. China Department of Brain Sciences Imperial College London London United Kingdom Department of Computing Imperial College London London United Kingdom

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://***/#!Synapse:syn51471091/wiki/. Copyright © 2023, The Authors. All rights reserved.

关键词： Image reconstruction

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROscience 2017年第Sup1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Kings County Hospital Center Brooklyn NY 11203 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics

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Corrections to "Identification of the General Anesthesia Induced Loss of Consciousness by Cross Fuzzy Entropy-Based brain Network"

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IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 2022年 30卷 2970页

作者： Fali Li Yuqin Li Hui Zheng Lin Jiang Dongrui Gao Cunbo Li Yueheng Peng Zehong Cao Yangsong Zhang Dezhong Yao Tao Xu Ti-Fei Yuan Peng Xu MOE Key Laboratory for Neuroinformation The Clinical Hospital of Chengdu Brain Science Institute and the Center for Information in Medicine School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China Shanghai Key Laboratory of Psychotic Disorders Shanghai Mental Health Center School of Medicine Shanghai Jiao Tong University Shanghai China School of Computer Science Chengdu University of Information Technology Chengdu China STEM University of South Australia Mawson Lakes Campus Adelaide SA Australia School of Computer Science and Technology Southwest University of Science and Technology Mianyang China Department of Anesthesiology Affiliated Shanghai Sixth People’s Hospital Shanghai Jiao Tong University Shanghai China

In the above article [1], to track the loss of consciousness (LOC) induced by general anesthesia (GA), we first developed the multi-channel cross fuzzy entropy method to construct the time- varying networks, whose temporal fluctuations were then explored and quantitatively evaluated. Since time-varying network topologies were found to fluctuate from long-range frontal-occipital to short-range prefrontal-frontal connectivity during the LOC period, a new parameter, i.e., the long-range connectivity (LRC) that measured the number of frontal-occipital connectivity, was accordingly calculated and then investigated between the coherence (COH) and cross fuzzy entropy (C-FuzzyEn) approaches, as displayed in Fig. 1. The distinct time-varying fluctuations of both approaches were indeed found within this period, where only C-FuzzyEn effectively captured the consciousness fluctuation induced by the GA.

关键词： Fluctuations Network topology Coherence Anesthesia Entropy Time-varying systems

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Author Correction: Two distinct trajectories of clinical and neurodegeneration events in Parkinson's disease

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NPJ Parkinson's disease 2024年第1期10卷 22页

作者： Cheng Zhou Linbo Wang Wei Cheng JinChao Lv Xiaojun Guan Tao Guo Jingjing Wu Wei Zhang Ting Gao Xiaocao Liu Xueqin Bai Haoting Wu Zhengye Cao Luyan Gu Jingwen Chen Jiaqi Wen Peiyu Huang Xiaojun Xu Baorong Zhang Jianfeng Feng Minming Zhang Department of Radiology The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou 310000 China. Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai 200433 China. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education Shanghai China. MOE Frontiers Center for Brain Science Fudan University Shanghai China. Zhangjiang Fudan International Innovation Center Shanghai China. Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai 200433 China. wcheng.fdu@***. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education Shanghai China. wcheng.fdu@***. MOE Frontiers Center for Brain Science Fudan University Shanghai China. wcheng.fdu@***. Zhangjiang Fudan International Innovation Center Shanghai China. wcheng.fdu@***. Department of Computer Science University of Warwick Coventry CV4 7AL United Kingdom. wcheng.fdu@***. Department of Neurology The Second Affiliated Hospital Zhejiang University School of Medicine 310000 Hangzhou China. Institute of Science and Technology for Brain-inspired Intelligence Fudan University Shanghai 200433 China. jffeng@***. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education Shanghai China. jffeng@***. MOE Frontiers Center for Brain Science Fudan University Shanghai China. jffeng@***. Zhangjiang Fudan International Innovation Center Shanghai China. jffeng@***. Department of Computer Science University of Warwick Coventry CV4 7AL United Kingdom. jffeng@***. Department of Radiology The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou 310000 China. zhangminming@***.

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