Low-Dimensional Spatiotemporal Dynamics Underlie Cortex-wide Neural Activity

[1]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[2]  A. Grinvald,et al.  Dynamics of Ongoing Activity: Explanation of the Large Variability in Evoked Cortical Responses , 1996, Science.

[3]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[4]  E. Miller,et al.  An integrative theory of prefrontal cortex function. , 2001, Annual review of neuroscience.

[5]  E. Miller,et al.  Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices , 2007, Science.

[6]  Karl J. Friston,et al.  Functional Connectivity: Eigenimages and multivariate analyses , 2003 .

[7]  M. Botvinick.  Hierarchical models of behavior and prefrontal function , 2008, Trends in Cognitive Sciences.

[8]  E. Miller,et al.  All My Circuits: Using Multiple Electrodes to Understand Functioning Neural Networks , 2008, Neuron.

[9]  V. Carchiolo,et al.  Extending the definition of modularity to directed graphs with overlapping communities , 2008, 0801.1647.

[10]  Tom M. Mitchell,et al.  Machine learning classifiers and fMRI: A tutorial overview , 2009, NeuroImage.

[11]  W. Maass,et al.  State-dependent computations: spatiotemporal processing in cortical networks , 2009, Nature Reviews Neuroscience.

[12]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[13]  A. Engel,et al.  Cortical Network Dynamics of Perceptual Decision-Making in the Human Brain , 2011, Frontiers in Human Neuroscience.

[14]  Anne-Marie Le Sourd,et al.  Autistic-like behaviours and hyperactivity in mice lacking ProSAP1/Shank2 , 2012, Nature.

[15]  Karl J. Friston,et al.  Structural and Functional Brain Networks: From Connections to Cognition , 2013, Science.

[16]  Stefan R. Pulver,et al.  Ultra-sensitive fluorescent proteins for imaging neuronal activity , 2013, Nature.

[17]  R. Romo,et al.  Conversion of sensory signals into perceptual decisions , 2013, Progress in Neurobiology.

[18]  D. McVea,et al.  Spontaneous cortical activity alternates between motifs defined by regional axonal projections , 2013, Nature Neuroscience.

[19]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[20]  Dimitri Van De Ville,et al.  Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest , 2013, NeuroImage.

[21]  Brenda C. Shields,et al.  Thy1-GCaMP6 Transgenic Mice for Neuronal Population Imaging In Vivo , 2014, PloS one.

[22]  Stefano Panzeri,et al.  A unifying model of concurrent spatial and temporal modularity in muscle activity. , 2014, Journal of neurophysiology.

[23]  T. Murphy,et al.  Mesoscale Transcranial Spontaneous Activity Mapping in GCaMP3 Transgenic Mice Reveals Extensive Reciprocal Connections between Areas of Somatomotor Cortex , 2014, The Journal of Neuroscience.

[24]  Allan R. Jones,et al.  A mesoscale connectome of the mouse brain , 2014, Nature.

[25]  Zengcai V. Guo,et al.  Flow of Cortical Activity Underlying a Tactile Decision in Mice , 2014, Neuron.

[26]  Sean C. Bendall,et al.  Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis , 2015, Cell.

[27]  Talia N. Lerner,et al.  Intact-Brain Analyses Reveal Distinct Information Carried by SNc Dopamine Subcircuits , 2015, Cell.

[28]  Ryan P. Adams,et al.  Mapping Sub-Second Structure in Mouse Behavior , 2015, Neuron.

[29]  Sharon L. Thompson-Schill,et al.  A Functional Cartography of Cognitive Systems , 2015, PLoS Comput. Biol..

[30]  Theodoros P. Zanos,et al.  A Sensorimotor Role for Traveling Waves in Primate Visual Cortex , 2015, Neuron.

[31]  P. Fries.  Rhythms for Cognition: Communication through Coherence , 2015, Neuron.

[32]  T. Bourgeron,et al.  mouseTube – a database to collaboratively unravel mouse ultrasonic communication , 2016, F1000Research.

[33]  K. Ohki,et al.  Transient neuronal coactivations embedded in globally propagating waves underlie resting-state functional connectivity , 2016, Proceedings of the National Academy of Sciences.

[34]  Ying Ma,et al.  Wide-field optical mapping of neural activity and brain haemodynamics: considerations and novel approaches , 2016, Philosophical Transactions of the Royal Society B: Biological Sciences.

[35]  Timothy H. Murphy,et al.  High-throughput automated home-cage mesoscopic functional imaging of mouse cortex , 2016, Nature Communications.

[36]  Andrew C. N. Chen,et al.  Intact skull chronic windows for mesoscopic wide-field imaging in awake mice , 2016, Journal of Neuroscience Methods.

[37]  Lena S. Geiger,et al.  Dynamic brain network reconfiguration as a potential schizophrenia genetic risk mechanism modulated by NMDA receptor function , 2016, Proceedings of the National Academy of Sciences.

[38]  Danielle S Bassett,et al.  Detection of functional brain network reconfiguration during task-driven cognitive states , 2016, NeuroImage.

[39]  Stefano Panzeri,et al.  Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains , 2016, PLoS Comput. Biol..

[40]  Mariel G Kozberg,et al.  Resting-state hemodynamics are spatiotemporally coupled to synchronized and symmetric neural activity in excitatory neurons , 2016, Proceedings of the National Academy of Sciences.

[41]  Navvab Afrashteh,et al.  Optical-flow analysis toolbox for characterization of spatiotemporal dynamics in mesoscale optical imaging of brain activity , 2016, NeuroImage.

[42]  Jean M. Vettel,et al.  The energy landscape underpinning module dynamics in the human brain connectome , 2016, NeuroImage.

[43]  Pierre Bellec,et al.  Resting-state network dysfunction in Alzheimer's disease: A systematic review and meta-analysis , 2017, Alzheimer's & dementia.

[44]  Dimitri Van De Ville,et al.  The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.

[45]  Morten L. Kringelbach,et al.  Hierarchy of Information Processing in the Brain: A Novel ‘Intrinsic Ignition’ Framework , 2017, Neuron.

[46]  T. Murphy,et al.  Mesoscale Mapping of Mouse Cortex Reveals Frequency-Dependent Cycling between Distinct Macroscale Functional Modules , 2017, The Journal of Neuroscience.

[47]  Maria Giulia Preti,et al.  Dynamics of functional connectivity at high spatial resolution reveal long-range interactions and fine-scale organization , 2017, Scientific Reports.

[48]  Shrikanth Narayanan,et al.  MUPET—Mouse Ultrasonic Profile ExTraction: A Signal Processing Tool for Rapid and Unsupervised Analysis of Ultrasonic Vocalizations , 2017, Neuron.

[49]  T. Komiyama,et al.  Transformation of Cortex-wide Emergent Properties during Motor Learning , 2017, Neuron.

[50]  Stephen M. Smith,et al.  Brain network dynamics are hierarchically organized in time , 2017, Proceedings of the National Academy of Sciences.

[51]  Tsai-Wen Chen,et al.  A Map of Anticipatory Activity in Mouse Motor Cortex , 2017, Neuron.

[52]  William E. Allen,et al.  Global Representations of Goal-Directed Behavior in Distinct Cell Types of Mouse Neocortex , 2017, Neuron.

[53]  Elina A K Jacobs,et al.  Aberrant Cortical Activity in Multiple GCaMP6-Expressing Transgenic Mouse Lines , 2017, eNeuro.

[54]  Majid H. Mohajerani,et al.  New waves: Rhythmic electrical field stimulation systematically alters spontaneous slow dynamics across mouse neocortex , 2017, NeuroImage.

[55]  G. Sumbre,et al.  Whole-Brain Neuronal Activity Displays Crackling Noise Dynamics , 2018, Neuron.

[56]  Andrew W. Kraft,et al.  Spontaneous Infra-slow Brain Activity Has Unique Spatiotemporal Dynamics and Laminar Structure , 2018, Neuron.

[57]  Matthias Bethge,et al.  DeepLabCut: markerless pose estimation of user-defined body parts with deep learning , 2018, Nature Neuroscience.

[58]  Terrence J. Sejnowski,et al.  Cortical travelling waves: mechanisms and computational principles , 2018, Nature Reviews Neuroscience.

[59]  Danielle S Bassett,et al.  Multi-scale detection of hierarchical community architecture in structural and functional brain networks , 2017, PloS one.

[60]  Matthias Bethge,et al.  Using DeepLabCut for 3D markerless pose estimation across species and behaviors , 2018, Nature Protocols.

[61]  Carlos D. Brody,et al.  Task-Dependent Changes in the Large-Scale Dynamics and Necessity of Cortical Regions , 2019, Neuron.

[62]  Nicholas A. Steinmetz,et al.  The impact of bilateral ongoing activity on evoked responses in mouse cortex , 2018, bioRxiv.

[63]  Martin Dichgans,et al.  In vivo widefield calcium imaging of the mouse cortex for analysis of network connectivity in health and brain disease , 2018, NeuroImage.

[64]  Alex H. Williams,et al.  Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience , 2018, bioRxiv.

[65]  Nicholas A. Steinmetz,et al.  Spontaneous behaviors drive multidimensional, brainwide activity , 2019, Science.

[66]  Sina Farsiu,et al.  Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning , 2019, Proceedings of the National Academy of Sciences.

[67]  Sliman J. Bensmaia,et al.  High-dimensional representation of texture in somatosensory cortex of primates , 2018, Proceedings of the National Academy of Sciences.

[68]  Nicholas A. Steinmetz,et al.  High-dimensional geometry of population responses in visual cortex , 2018, Nature.

[69]  P. K. Vinod,et al.  Atypical Flexibility in Dynamic Functional Connectivity Quantifies the Severity in Autism Spectrum Disorder , 2018, bioRxiv.

[70]  Matthew T. Kaufman,et al.  Single-trial neural dynamics are dominated by richly varied movements , 2019, Nature Neuroscience.

[71]  H. Baier,et al.  Deconstructing Hunting Behavior Reveals a Tightly Coupled Stimulus-Response Loop , 2019, Current Biology.