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arXiv 2020年

作者： Kossen, Tabea Subramaniam, Pooja Madai, Vince I. Hennemuth, Anja Hildebrand, Kristian Hilbert, Adam Sobesky, Jan Livne, Michelle Galinovic, Ivana Khalil, Ahmed A. Fiebach, Jochen B. Frey, Dietmar CLAIM - Charité Lab for AI in Medicine Charité Universitätsmedizin Berlin Germany Department of Computer Engineering and Microelectronics Computer Vision & Remote Sensing Technical University Berlin Berlin Germany Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany School of Computing and Digital Technology Faculty of Computing Engineering and the Built Environment Birmingham City University Birmingham United Kingdom Institute for Imaging Science and Computational Modelling in Cardiovascular Medicine Charité Universitätsmedizin Berlin Berlin Germany Fraunhofer MEVIS Bremen Germany Department VI Computer Science and Media Beuth University of Applied Sciences Berlin Germany Johanna-Etienne-Hospital Neuss Germany Centre for Stroke Research Berlin Charité Universitätsmedizin Berlin Berlin Germany Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany Mind Brain Body Institute Berlin School of Mind and Brain Humboldt University Berlin Berlin Germany Berlin Institute of Health Berlin Germany

Anonymization and data sharing are crucial for privacy protection and acquisition of large datasets for robust medical image analysis. This represents a major challenge, especially for brain imaging research. Here, the unique structure of brain images allows for potential re-identification and thus requires anonymization beyond conventional methods. Generative adversarial networks (GANs) have the potential to provide anonymous images while maintaining their predictive properties. Analyzing brain vessel segmentation as a use case, we trained 3 GAN architectures on time-of-flight (TOF) magnetic resonance angiography (MRA) patches of patients with cerebrovascular disease for image-label pair generation: 1) Deep convolutional GAN, 2) Wasserstein-GAN with gradient penalty (WGAN-GP) and 3) WGAN-GP with spectral normalization (WGAN-GP-SN). First, the synthesized image-labels from each GAN architecture were used to train a U-net for vessel segmentation. The U-nets were then tested on real patient data. In total, 66 patients were used for this analysis. In a second step, we simulated the application of our synthetic patches in a transfer learning approach using a second, independent dataset. Here, for an increasing number of up to 15 patients we evaluated vessel segmentation model performance on real data with and without pre-training on generated patches. Finally, performance for all models was assessed by the Dice Similarity Coefficient (DSC) and the 95th percentile of the Hausdorff Distance (95HD). Comparing the 3 GAN architectures, the U-net model trained on synthetic data generated by the WGAN-GP-SN showed the highest performance to predict brain vessels (DSC/95HD 0.82/28.97) benchmarked by the U-net trained on real data (0.89/26.61). The transfer learning approach showed superior performance for the same GAN architecture compared to no pre-training, especially for one labeled patient only (DSC/95HD 0.91/25.68 compared to DSC/95HD 0.85/27.36). In a brain imaging segment

关键词： Image segmentation

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A framework and resource for global collaboration in non-human primate neuroscience

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Current Research in Neurobiology 2023年 4卷 100079页

作者： Hartig, Renée Klink, P. Christiaan Polyakova, Zlata Dehaqani, Mohammad-Reza A. Bondar, Igor Merchant, Hugo Vanduffel, Wim Roe, Anna Wang Nambu, Atsushi Thirumala, M. Shmuel, Amir Kapoor, Vishal Gothard, Katalin M. Evrard, Henry C. Basso, Michele A. Petkov, Christopher I. Mitchell, Anna S. Max Planck Institute for Biological Cybernetics Max-Planck-Ring 11 Tübingen 72076 Germany Functional and Comparative Neuroanatomy Laboratory Werner Reichardt Centre for Integrative Neuroscience Eberhard Karls University of Tübingen Otfried-Müller-Straße 25 Tübingen 72076 Germany Department of Psychiatry and Psychotherapy University Medical Center Johannes Gutenberg-University Mainz 55131 Germany Nathan S. Kline Institute for Psychiatric Research Center for Biomedical Imaging and Neuromodulation Orangeburg New York 10962 United States Department of Vision and Cognition Netherlands Institute for Neuroscience Royal Netherlands Academy of Arts and Sciences Meibergdreef 47 Amsterdam 1105 BA Netherlands Laboratory of Visual Brain Therapy Sorbonne Université Institut National de la Santé et de la Recherche Médicale Centre National de la Recherche Scientifique Institut de la Vision Paris F-75012 France Experimental Psychology Helmholtz institute Utrecht University Heidelberglaan 1 Utrecht 3584 CS Netherlands Center for Human Nature Artificial Intelligence and Neuroscience Hokkaido University Sapporo 060-0812 Japan School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Brain and Cognitive Sciences Cell Science Research Center Royan Institute for Stem Cell Biology and Technology ACECR Tehran Iran Institute of Higher Nervous Activity and Neurophysiology Russian Academy of Sciences Moscow Russian Federation Institute of Neurobiology Universidad Nacional Autónoma de México Campus Juriquilla Department of Neurophysiology Querétaro 76230 Mexico Laboratory for Neuro- and Psychophysiology Department of Neurosciences KU Leuven Medical School Leuven 3000 Belgium Leuven Brain Institute KU Leuven Leuven 3000 Belgium Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlest

As science and technology evolve, there is an increasing need for promotion of international scientific exchange. Collaborations, while offering substantial opportunities for scientists and benefit to society, also present challenges for those working with animal models, such as non-human primates (NHPs). Diversity in regulation of animal research is sometimes mistaken for the absence of common international welfare standards. Here, the ethical and regulatory protocols for 13 countries that have guidelines in place for biomedical research involving NHPs were assessed with a focus on neuroscience. Review of the variability and similarity in trans-national NHP welfare regulations extended to countries in Asia, Europe and North America. A tabulated resource was established to advance solution-oriented discussions and scientific collaborations across borders. Our aim is to better inform the public and other stakeholders. Through cooperative efforts to identify and analyze information with reference to evidence-based discussion, the proposed key ingredients may help to shape and support a more informed, open framework. This framework and resource can be expanded further for biomedical research in other countries. © 2023

关键词： Ethics Macaque Marmoset Regulations Requirements Welfare

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Author Correction: Longitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture

Communications biology

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Communications biology 2024年第1期7卷 85页

作者： Guozheng Feng Rui Chen Rui Zhao Yuanyuan Li Leilei Ma Yanpei Wang Weiwei Men Jiahong Gao Shuping Tan Jian Cheng Yong He Shaozheng Qin Qi Dong Sha Tao Ni Shu State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Beijing Normal University Beijing China. BABRI Centre Beijing Normal University Beijing China. Beijing Key Laboratory of Brain Imaging and Connectomics Beijing Normal University Beijing China. College of Life Sciences Beijing Normal University Beijing China. Beijing Key Laboratory of Gene Resource and Molecular Development Beijing China. Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China. Beijing Huilongguan Hospital Peking University Huilongguan Clinical Medical School Beijing China. School of Computer Science and Engineering Beihang University Beijing China. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Beijing Normal University Beijing China. taosha@***. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Beijing Normal University Beijing China. nshu@***. BABRI Centre Beijing Normal University Beijing China. nshu@***. Beijing Key Laboratory of Brain Imaging and Connectomics Beijing Normal University Beijing China. nshu@***.

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Structured dialogue policy with graph neural networks 27

Structured dialogue policy with graph neural networks

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27th International Conference on Computational Linguistics, COLING 2018

作者： Chen, Lu Tan, Bowen Long, Sishan Yu, Kai Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Eng. SpeechLab Department of Computer Science and Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781948087506

Recently, deep reinforcement learning (DRL) has been used for dialogue policy optimization. However, many DRL-based policies are not sample-efficient. Most recent advances focus on improving DRL optimization algorithms to address this issue. Here, we take an alternative route of designing neural network structure that is better suited for DRL-based dialogue management. The proposed structured deep reinforcement learning is based on graph neural networks (GNN), which consists of some sub-networks, each one for a node on a directed graph. The graph is defined according to the domain ontology and each node can be considered as a sub-agent. During decision making, these sub-agents have internal message exchange between neighbors on the graph. We also propose an approach to jointly optimize the graph structure as well as the parameters of GNN. Experiments show that structured DRL significantly outperforms previous state-of-the-art approaches in almost all of the 18 tasks of the PyDial benchmark. © 2018 COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings. All rights reserved.

关键词： Reinforcement learning

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Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 — Ex vivo imaging: data processing, comparisons with microscopy, and tractography

arXiv

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arXiv 2024年

作者： Schilling, Kurt G. Howard, Amy F.D. Grussu, Francesco Ianus, Andrada Hansen, Brian Barrett, Rachel L.C. Aggarwal, Manisha Michielse, Stijn Nasrallah, Fatima Syeda, Warda Wang, Nian Veraart, Jelle Roebroeck, Alard Bagdasarian, Andrew F. Eichner, Cornelius Sepehrband, Farshid Zimmermann, Jan Soustelle, Lucas Bowman, Christien Tendler, Benjamin C. Hertanu, Andreea Jeurissen, Ben Verhoye, Marleen Frydman, Lucio van de Looij, Yohan Hike, David Dunn, Jeff F. Miller, Karla Landman, Bennett A. Shemesh, Noam Anderson, Adam McKinnon, Emilie Farquharson, Shawna Dell' Acqua, Flavio Pierpaoli, Carlo Drobnjak, Ivana Leemans, Alexander Harkins, Kevin D. Descoteaux, Maxime Xu, Duan Huang, Hao Santin, Mathieu D. Grant, Samuel C. Obenaus, Andre Kim, Gene S. Wu, Dan Le Bihan, Denis Blackband, Stephen J. Ciobanu, Luisa Fieremans, Els Bai, Ruiliang Leergaard, Trygve B. Zhang, Jiangyang Dyrby, Tim B. Johnson, G. Allan Cohen-Adad, Julien Budde, Matthew D. Jelescu, Ileana O. Radiology and Radiological Sciences Vanderbilt University Medical Center NashvilleTN United States Vanderbilt University Institute of Imaging Science Vanderbilt University NashvilleTN United States Department of Bioengineering Imperial College London London United Kingdom FMRIB Centre Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences University of Oxford Oxford United Kingdom Radiomics Group Vall d’Hebron Institute of Oncology Vall d’Hebron Barcelona Hospital Campus Barcelona Spain Queen Square MS Centre Queen Square Institute of Neurology Faculty of Brain Sciences University College London London United Kingdom Champalimaud Research Champalimaud Foundation Lisbon Portugal Center of Functionally Integrative Neuroscience Aarhus University Aarhus Denmark Department of Neuroimaging Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom NatBrainLab Department of Forensics and Neurodevelopmental Sciences Institute of Psychiatry Psychology and Neuroscience King's College London London United Kingdom Russell H. Morgan Department of Radiology and Radiological Science Johns Hopkins University School of Medicine BaltimoreMD United States Maastricht University Medical Center Maastricht Netherlands The Queensland Brain Institute The University of Queensland Queensland Australia Melbourne Neuropsychiatry Centre The University of Melbourne ParkvilleVIC Australia Department of Radiology and Imaging Sciences Indiana University IN United States Stark Neurosciences Research Institute Indiana University School of Medicine IN United States Center for Biomedical Imaging NYU Grossman School of Medicine New YorkNY United States Faculty of psychology and Neuroscience Maastricht University Maastricht Netherlands Department of Chemical & Biomedical Engineering FAMU-FSU College of Engineering Florida State University TallahasseeFL United States Center for Interdisciplinary Magnetic Res

Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging. © 2024, CC BY.

关键词： Data Sharing

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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The 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 [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental brain Chart

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Topographic deep artificial neural networks (TDANNs) predict face selectivity topography in primate inferior temporal (IT) cortex

arXiv

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arXiv 2019年

作者： Lee, Hyodong DiCarlo, James J. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States McGovern Institute for Brain Research Department of Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA02139 United States

Deep convolutional neural networks are biologically driven models that resemble the hierarchical structure of primate visual cortex and are the current best predictors of the neural responses measured along the ventral stream. However, the networks lack topographic properties that are present in the visual cortex, such as orientation maps in primary visual cortex and category-selective maps in inferior temporal (IT) cortex. In this work, the minimum wiring cost constraint was approximated as an additional learning rule in order to generate topographic maps of the networks. We found that our topographic deep artificial neural networks (ANNs) can reproduce the category selectivity maps of the primate IT cortex. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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EEG Data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN 40

EEG Data Augmentation for Emotion Recognition Using a Condit...

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40th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2018

作者： Luo, Yun Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

ISBN: (纸本)9781538636466

Due to the lack of electroencephalography (EEG) data, it is hard to build an emotion recognition model with high accuracy from EEG signals using machine learning approach. Inspired by generative adversarial networks (GANs), we introduce a Conditional Wasserstein GAN (CWGAN) framework for EEG data augmentation to enhance EEG-based emotion recognition. A Wasserstein GAN with gradient penalty is adopted to generate realistic-like EEG data in differential entropy (DE) form. Three indicators are used to judge the qualities of the generated high-dimensional EEG data, and only high quality data are appended to supplement the data manifold, which leads to better classification of different emotions. We evaluate the proposed CWGAN framework on two public EEG datasets for emotion recognition, namely SEED and DEAP. The experimental results demonstrate that using the EEG data generated by CWGAN significantly improves the accuracies of emotion recognition models. © 2018 IEEE.

关键词： Electroencephalography Gallium nitride Training Generators Emotion recognition Generative adversarial networks brain modeling

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Explanations can be manipulated and geometry is to blame

arXiv

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arXiv 2019年

作者： Dombrowski, Ann-Kathrin Alber, Maximilian Anders, Christopher J. Ackermann, Marcel Müller, Klaus-Robert Kessel, Pan Department of Computer Science Technische Universität Berlin Department of Video Coding & Analytics Fraunhofer Heinrich-Hertz-Institute Max-Planck-Institut für Informatik Department of Brain and Cognitive Engineering Korea University

Explanation methods aim to make neural networks more trustworthy and interpretable. In this paper, we demonstrate a property of explanation methods which is disconcerting for both of these purposes. Namely, we show that explanations can be manipulated arbitrarily by applying visually hardly perceptible perturbations to the input that keep the network's output approximately constant. We establish theoretically that this phenomenon can be related to certain geometrical properties of neural networks. This allows us to derive an upper bound on the susceptibility of explanations to manipulations. Based on this result, we propose effective mechanisms to enhance the robustness of explanations. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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A closed-loop all-electronic pixel-wise adaptive imaging system for high dynamic range video

arXiv

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arXiv 2019年

作者： Zhang, Jie [Jack] Newman, Jonathan P. Wang, Xiao Thakur, Chetan Singh Rattray, John Etienne-Cummings, Ralph Wilson, Matthew A. Picower Institute for Learning and Memory Department of Brain and Cognitive Sciences Massachusetts Institute of Technology CambridgeMA United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD United States Department of Computer Science Boston University BostonMA United States Department of Electronic Systems Engineering Indian Institute of Science Bangalore India

We demonstrated a CMOS imaging system that adapts each pixels exposure and sampling rate to capture high dynamic range (HDR) videos. The system consist of a custom designed image sensor with pixel-wise exposure configurability and a real-time pixel exposure controller. These parts operate in a closed-loop to sample, detect and optimize each pixels exposure and sampling rate to minimize local regions underexposure, overexposure and motion blurring. Exposure control is implemented using all-integrated electronics without external optical modulation. This reduces overall system size and power consumption. The image sensor is implemented using a standard 130nm CMOS process while the exposure controller is implemented on a computer. We performed experiments under complex lighting and motion condition to test performance of the system, and demonstrate the benefit of pixel-wise adaptive imaging on the performance of computer vision tasks such as segmentation, motion estimation and object recognition. Copyright © 2019, The Authors. All rights reserved.

关键词： Pixels

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