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研究
RESEARCH Functional coupling networks inferred from prefrontal cortex activity show experience-related effective plasticity Gaia Tavoni1,2, Ulisse Ferrari1,2, Francesco P. Battaglia3, Simona Cocco1, and Rémi Monasson2 1Laboratoire de Physique Statistique, Ecole Normale Supérieure, PSL Research and CNRS – UMR 8550, Paris Sorbonne UPMC, 巴黎, France 2Laboratoire de Physique Théorique, Ecole Normale Supérieure, PSL Research and CNRS- UMR 8549, Paris Sorbonne UPMC, 巴黎, France 3Donders Institute for
研究
RESEARCH Consistency of Regions of Interest as nodes of fMRI functional brain networks Onerva Korhonen 1,2 , Heini Saarimäki 1 , Enrico Glerean 1 1 , Mikko Sams , and Jari Saramäki 2 1Department of Neuroscience and Biomedical Engineering, School of Science, Aalto University, Espoo, Finland 2Department of Computer Science, School of Science, Aalto University, Espoo, Finland Keywords: Functional magnetic resonance imaging, Functional brain networks,
方法
METHODS Visibility graphs for fMRI data: Multiplex temporal graphs and their modulations across resting-state networks Speranza Sannino1,4, Sebastiano Stramaglia2, Lucas Lacasa3, and Daniele Marinazzo1 1Department of Data Analysis, Faculty of Psychology and Educational Sciences, University of Ghent, Belgium 2Department of Physics, University of Bari and INFN Section of Bari, Italy 3School of Mathematical Sciences, Queen Mary University of London, United Kingdom 4Department of Electric and
方法
METHODS From static to temporal network theory: Applications to functional brain connectivity William Hedley Thompson1, Per Brantefors1, and Peter Fransson1 1Department of Clinical Neuroscience, Karolinska Institutet, 斯德哥尔摩, Sweden Keywords: Resting-state, Temporal network theory, Temporal networks, Functional connectome, Dynamic functional connectivity ABSTRACT Network neuroscience has become an established paradigm to tackle questions related to the functional and structural connectome of the brain. 最近, interest has been
研究
RESEARCH Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture Michael A. Ferguson1,2∗ , Jeffrey S. Anderson2, 和R. Nathan Spreng1∗ 1Laboratory of Brain and Cognition, Human Neuroscience Institute, Department of Human Development, 康奈尔大学, 伊萨卡岛, 纽约, 14853 2Departments of Bioengineering and Neuroradiology, University of Utah, Salt Lake City, UT, 84132 关键词: 智力, 功能磁共振成像, resting state functional connectivity, 机器学习, 认识
研究
RESEARCH Large-scale network dynamics of beta-band oscillations underlie auditory perceptual decision-making Mohsen Alavash1,2, Christoph Daube2, Malte Wöstmann1,2, Alex Brandmeyer2, and Jonas Obleser1,2 1Department of Psychology, University of Lübeck, Germany 2Max Planck Research Group “Auditory Cognition,” Max Planck Institute for Human Cognitive and Brain Sciences, 莱比锡, Germany Keywords: Network dynamics, Perceptual decision, Oscillation, 乙二醇, Functional connectivity a n o p e n a c c e
研究
RESEARCH Modular co-organization of functional connectivity and scale-free dynamics in the human brain Alexander Zhigalov1,2,3∗ , Gabriele Arnulfo1,4, Lino Nobili5, Satu Palva1, 和 J. Matias Palva1 1Neuroscience Center, 赫尔辛基大学, Finland 2BioMag laboratory, HUS医学影像中心, 赫尔辛基大学中心医院, Finland 3Department of Computer Science, 赫尔辛基大学, Finland 4Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Italy 5Claudio Munari
研究
RESEARCH Enhanced prefrontal functional–structural networks to support postural control deficits after traumatic brain injury in a pediatric population Ibai Diez1, David Drijkoningen2, Sebastiano Stramaglia3,4, Paolo Bonifazi1,7, Daniele Marinazzo5, Jolien Gooijers2, Stephan P. Swinnen2,6, and Jesus M. Cortes1,7,8 1Biocruces Health Research Institute, Cruces University Hospital, Barakaldo, 西班牙. 2KU Leuven, Movement Control and Neuroplasticity Research Group, Group Biomedical Sciences, Leuve, 比利时. 3Dipartimento di Fisica, Universita degli Studi
研究
RESEARCH Spontaneous brain network activity: Analysis of its temporal complexity Mangor Pedersen1∗ , Amir Omidvarnia1, Jennifer M. Walz1, Andrew Zalesky2,3, and Graeme D. Jackson1,4 1The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, 墨尔本, 维多利亚, Australia 2Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne, 维多利亚, Australia 3Melbourne School of Engineering, The University of Melbourne, 维多利亚, Australia 4Department of Neurology, Austin
文章
ARTICLE Communicated by Oleksandr Popovych Mean-Field Approximations With Adaptive Coupling for Networks With Spike-Timing-Dependent Plasticity Benoit Duchet benoit.duchet@ndcn.ox.ac.uk Nuffield Department of Clinical Neuroscience, 牛津大学, Oxford X3 9DU, 英国。, and MRC Brain Network Dynamics Unit, 牛津大学, Oxford X1 3TH, U.K. Christian Bick c.bick@vu.nl Department of Mathematics, Vrije Universiteit Amsterdam, 阿姆斯特丹 1081 HV, 荷兰人; Amsterdam Neuroscience—Systems and Network Neuroscience, 阿姆斯特丹 1081 HV,
研究
RESEARCH Nonrandom network connectivity comes in pairs Felix Z. Hoffmann1,2 and Jochen Triesch1 1Frankfurt Institute for Advanced Studies (FIAS), Johann Wolfgang Goethe University, Frankfurt am Main, Germany 2International Max Planck Research School for Neural Circuits, Max Planck Institute for Brain Research, Frankfurt am Main, Germany Keywords: Nonrandom connectivity, Cortical circuit, Bidirectional connections, Random graph model ABSTRACT Overrepresentation of bidirectional connections in local cortical networks has
PERSPECTIVE
PERSPECTIVE Cognitive genomics: Linking genes to behavior in the human brain Genevieve Konopka Department of Neuroscience, UT Southwestern Medical Center, 达拉斯, TX 75390-9111, USA ABSTRACT Correlations of genetic variation in DNA with functional brain activity have already provided a starting point for delving into human cognitive mechanisms. 然而, these analyses do not provide the specific genes driving the associations, which are complicated by intergenic localization
研究
RESEARCH The modular organization of human anatomical brain networks: Accounting for the cost of wiring Richard F. Betzel1, John D. Medaglia1,2, Lia Papadopoulos3, Graham L. Baum4, Ruben C. Gur4, Raquel E. Gur4, David Roalf4, Theodore D. Satterthwaite4, and Danielle S. Bassett1,5 1Department of Bioengineering, 宾夕法尼亚大学, 费城, PA, 19104 2心理学系, 宾夕法尼亚大学, 费城, PA, 19104 3Department of Physics, 宾夕法尼亚大学,
研究
RESEARCH Evolution of brain network dynamics in neurodevelopment Lucy R. Chai1, Ankit N. Khambhati1, Rastko Ciric2, Tyler M. Moore2, Ruben C. Gur2, Raquel E. Gur2, Theodore D. Satterthwaite2, and Danielle S. Bassett1,3 1Department of Bioengineering, 宾夕法尼亚大学, 费城, PA 19104 USA 2Brain Behavior Laboratory, Department of Psychiatry, 宾夕法尼亚大学, 费城, PA 19104 USA 3Department of Electrical & Systems Engineering, 宾夕法尼亚大学, 费城,
文章
ARTICLE Communicated by Joel Zylberberg Optimal Burstiness in Populations of Spiking Neurons Facilitates Decoding of Decreases in Tonic Firing Sylvia C. L. Durian sdurian@uchicago.edu Departments of Ophthalmology and Neuroscience, Feinberg School of Medicine, Northwestern University, 芝加哥, 伊尔, 美国. Mark Agrios mark.agrios@northwestern.edu Northwestern Interdepartmental Neuroscience Graduate Program, Northwestern University, Evanston, 伊尔, 美国. Gregory W. Schwartz greg.schwartz@northwestern.edu Departments of Ophthalmology and Neuroscience, Feinberg School of Medicine, Northwestern
信
LETTER Communicated by Geoffrey J. Goodhill Maximal Memory Capacity Near the Edge of Chaos in Balanced Cortical E-I Networks Takashi Kanamaru kanamaru@cc.kogakuin.ac.jp International Research Center for Neurointelligence (WPI-IRCN), UTIAS, University of Tokyo 113-0033, 日本, and Department of Mechanical Science and Engineering, Kogakuin University, 东京 192-0015, Japan Takao K. Hensch hensch@ircn.jp International Research Center for Neurointelligence (WPI-IRCN), UTIAS, University of Tokyo 113-0033, 日本; Center for Brain
文章
ARTICLE Communicated by Ian Stevenson Scalable Variational Inference for Low-Rank Spatiotemporal Receptive Fields Lea Duncker lduncker@stanford.edu Wu Tsai Neurosciences Institute and Howard Hughes Medical Institute, 斯坦福大学, 斯坦福大学, CA 94302, 美国. Kiersten M. Ruda kruda@bidmc.harvard.edu Beth Israel Deaconess Medical Center, 哈佛大学, 波士顿, 嘛 02115, 美国. Greg D. Field field@neuro.duke.edu Department of Neurobiology, 杜克大学, 达勒姆, NC 27708, 美国. Jonathan W. Pillow pillow@princeton.edu Princeton Neuroscience
文章
ARTICLE Communicated by Tim Verbelen Reward Maximization Through Discrete Active Inference Lancelot Da Costa l.da-costa@imperial.ac.uk Department of Mathematics, Imperial College London, London SW7 2AZ, U.K. Noor Sajid noor.sajid.18@ucl.ac.uk Thomas Parr thomas.parr.12@ucl.ac.uk Karl Friston k.friston@ucl.ac.uk Wellcome Centre for Human Neuroimaging, 伦敦大学学院, 伦敦, WC1N 3AR, U.K. Ryan Smith rsmith@laureateinstitute.org Laureate Institute for Brain Research, Tulsa, OK 74136, 美国. Active inference is a probabilistic framework for