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FORSCHUNG
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, Paris, France 2Laboratoire de Physique Théorique, Ecole Normale Supérieure, PSL Research and CNRS- UMR 8549, Paris Sorbonne UPMC, Paris, France 3Donders Institute for
FORSCHUNG
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,
METHODEN
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
METHODEN
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, Stockholm, 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. Kürzlich, interest has been
FORSCHUNG
RESEARCH Fluid and flexible minds: Intelligence reflects synchrony in the brain’s intrinsic network architecture Michael A. Ferguson1,2∗ , Jeffrey S. Anderson2, and R. Nathan Spreng1∗ 1Laboratory of Brain and Cognition, Human Neuroscience Institute, Department of Human Development, Cornell University, Ithaca, New York, 14853 2Departments of Bioengineering and Neuroradiology, University of Utah, Salt Lake City, UT, 84132 Schlüsselwörter: intelligence, fMRT, resting state functional connectivity, machine learning, Erkenntnis
FORSCHUNG
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, Leipzig, Germany Keywords: Network dynamics, Perceptual decision, Oscillation, MEG, Functional connectivity a n o p e n a c c e
FORSCHUNG
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, and J. Matias Palva1 1Neuroscience Center, University of Helsinki, Finland 2BioMag laboratory, HUS Medical Imaging Center, Helsinki University Central Hospital, Finland 3Department of Computer Science, University of Helsinki, Finland 4Department of Informatics, Bioengineering, Robotics and System Engineering, University of Genova, Italy 5Claudio Munari
FORSCHUNG
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, Spanien. 2KU Leuven, Movement Control and Neuroplasticity Research Group, Group Biomedical Sciences, Leuve, Belgien. 3Dipartimento di Fisica, Universita degli Studi
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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, Melbourne, Victoria, Australia 2Department of Psychiatry, Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, Australia 3Melbourne School of Engineering, The University of Melbourne, Victoria, Australia 4Department of Neurology, Austin
ARTIKEL
ARTIKEL Übermittelt von Oleksandr Popovych Mean-Field-Approximationen mit adaptiver Kopplung für Netzwerke mit Spike-Timing-abhängiger Plastizität Benoit Duchet benoit.duchet@ndcn.ox.ac.uk Nuffield Department of Clinical Neuroscience, Universität Oxford, Oxford X3 9DU, VEREINIGTES KÖNIGREICH., and MRC Brain Network Dynamics Unit, Universität Oxford, Oxford X1 3TH, VEREINIGTES KÖNIGREICH. Christian Bick c.bick@vu.nl Department of Mathematics, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, die Niederlande; Amsterdam Neuroscience—Systems and Network Neuroscience, Amsterdam 1081 HV,
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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, Dallas, 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. Jedoch, these analyses do not provide the specific genes driving the associations, which are complicated by intergenic localization
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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, University of Pennsylvania, Philadelphia, PA, 19104 2Abteilung für Psychologie, University of Pennsylvania, Philadelphia, PA, 19104 3Department of Physics, University of Pennsylvania,
FORSCHUNG
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, University of Pennsylvania, Philadelphia, PA 19104 USA 2Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA 3Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia,
ARTIKEL
ARTIKEL Übermittelt von Joel Zylberberg Optimale Burstiness in Populationen von Spike-Neuronen erleichtert die Entschlüsselung von Rückgängen im tonischen Feuern Sylvia C. L. Durian sdurian@uchicago.edu Abteilungen für Augenheilkunde und Neurowissenschaften, Feinberg School of Medicine, Nordwestliche Universität, Chicago, IL, USA. Mark Agrios mark.agrios@northwestern.edu Northwestern Interdepartmental Neuroscience Graduate Program, Nordwestliche Universität, Evanston, IL, USA. Gregory W. Schwartz greg.schwartz@northwestern.edu Departments of Ophthalmology and Neuroscience, Feinberg School of Medicine, Northwestern
BRIEF
BRIEF Mitgeteilt von Geoffrey J. Goodhill Maximal Memory Capacity Near the Edge of Chaos in Balanced Cortical E-I Networks Takashi Kanamaru kanamaru@cc.kogakuin.ac.jp Internationales Forschungszentrum für Neurointelligenz (WPI-IRCN), Sie sollten es verwenden, Universität Tokio 113-0033, Japan, and Department of Mechanical Science and Engineering, Kogakuin University, Tokio 192-0015, Japan Takao K. Hensch hensch@ircn.jp International Research Center for Neurointelligence (WPI-IRCN), Sie sollten es verwenden, Universität Tokio 113-0033, Japan; Center for Brain
ARTIKEL
ARTIKEL Übermittelt von Ian Stevenson Skalierbare Variationsinferenz für spatiotemporale rezeptive Felder mit niedrigem Rang Lea Duncker lduncker@stanford.edu Wu Tsai Neurosciences Institute und Howard Hughes Medical Institute, Universität in Stanford, Stanford, CA 94302, USA. Kiersten M. Ruda kruda@bidmc.harvard.edu Beth Israel Deaconess Medical Center, Harvard Universität, Boston, MA 02115, USA. Greg D. Field field@neuro.duke.edu Department of Neurobiology, Duke University, Durham, NC 27708, USA. Jonathan W. Pillow pillow@princeton.edu Princeton Neuroscience
ARTIKEL
ARTIKEL Übermittelt von Tim Verbelen Belohnungsmaximierung durch diskrete aktive Inferenz Lancelot Da Costa l.da-costa@imperial.ac.uk Fakultät für Mathematik, Imperial College London, London SW7 2AZ, VEREINIGTES KÖNIGREICH. 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, University College London, London, WC1N 3AR, VEREINIGTES KÖNIGREICH. Ryan Smith rsmith@laureateinstitute.org Laureate Institute for Brain Research, Tulsa, OK 74136, USA. Active inference is a probabilistic framework for