Precision dynamical mapping using topological data analysis reveals a hub-like transition state at rest
暂无分享,去创建一个
[1] Richard F. Betzel,et al. Individualized event structure drives individual differences in whole-brain functional connectivity , 2021, NeuroImage.
[2] S. Ganguli,et al. Coupling of activity, metabolism and behaviour across the Drosophila brain , 2021, Nature.
[3] G. Deco,et al. Anatomical and Functional Gradients Shape Dynamic Functional Connectivity in the Human Brain , 2021, bioRxiv.
[4] Jonathan D. Power,et al. Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI , 2020, Cell reports.
[5] Olaf Sporns,et al. High-amplitude cofluctuations in cortical activity drive functional connectivity , 2020, Proceedings of the National Academy of Sciences.
[6] Farnaz Zamani Esfahlani,et al. Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture , 2020, Nature Neuroscience.
[7] Luke J. Chang,et al. Movie viewing elicits rich and reliable brain state dynamics , 2020, Nature Communications.
[8] Xenophon Papademetris,et al. Simultaneous cortex-wide fluorescence Ca2+ imaging and whole-brain fMRI , 2020, Nature Methods.
[9] Jessica A. Turner,et al. Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia , 2020, NeuroImage.
[10] Timothy O. Laumann,et al. Towards Reproducible Brain-Wide Association Studies , 2020, bioRxiv.
[11] V. Calhoun,et al. Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises , 2020, Biological Psychiatry.
[12] Vince D. Calhoun,et al. Questions and controversies in the study of time-varying functional connectivity in resting fMRI , 2020, Network Neuroscience.
[13] L. Uddin,et al. Pushing the Boundaries of Psychiatric Neuroimaging to Ground Diagnosis in Biology , 2019, eNeuro.
[14] Olaf Sporns,et al. Edge-centric functional network representations of human cerebral cortex reveal overlapping system-level architecture , 2019, Nature Neuroscience.
[15] Abraham Z. Snyder,et al. On time delay estimation and sampling error in resting-state fMRI , 2019, NeuroImage.
[16] T. Ge,et al. Resting brain dynamics at different timescales capture distinct aspects of human behavior , 2019, Nature Communications.
[17] Oluwasanmi Koyejo,et al. Human cognition involves the dynamic integration of neural activity and neuromodulatory systems , 2019, Nature Neuroscience.
[18] Olaf Sporns,et al. Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis , 2019, Network Neuroscience.
[19] Dimitri Van De Ville,et al. Dynamic mode decomposition of resting-state and task fMRI , 2018, NeuroImage.
[20] Leonardo L. Gollo,et al. Metastable brain waves , 2018, Nature Communications.
[21] Anders M. Dale,et al. Correction of respiratory artifacts in MRI head motion estimates , 2018, bioRxiv.
[22] Satrajit S. Ghosh,et al. FMRIPrep: a robust preprocessing pipeline for functional MRI , 2018, bioRxiv.
[23] Nicholas A. Steinmetz,et al. Spontaneous behaviors drive multidimensional, brainwide activity , 2019, Science.
[24] O. Sporns,et al. Towards a new approach to reveal dynamical organization of the brain using topological data analysis , 2018, Nature Communications.
[25] M. Kunitski,et al. Double-slit photoelectron interference in strong-field ionization of the neon dimer , 2018, Nature Communications.
[26] Oliver Y. Chén,et al. The human cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychosis , 2018, Nature Communications.
[27] Anders M. Dale,et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites , 2018, Developmental Cognitive Neuroscience.
[28] Angkoon Phinyomark,et al. Resting-State fMRI Functional Connectivity: Big Data Preprocessing Pipelines and Topological Data Analysis , 2017, IEEE Transactions on Big Data.
[29] Stephen M. Smith,et al. Brain network dynamics are hierarchically organized in time , 2017, Proceedings of the National Academy of Sciences.
[30] Dimitri Van De Ville,et al. The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.
[31] Dongdong Lin,et al. Dynamic functional connectivity impairments in early schizophrenia and clinical high-risk for psychosis , 2017, NeuroImage.
[32] Evan M. Gordon,et al. Precision Functional Mapping of Individual Human Brains , 2017, Neuron.
[33] B. T. Thomas Yeo,et al. Interpreting temporal fluctuations in resting-state functional connectivity MRI , 2017, NeuroImage.
[34] M. Breakspear. Dynamic models of large-scale brain activity , 2017, Nature Neuroscience.
[35] 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.
[36] Danielle S Bassett,et al. Brain network analysis: a practical tutorial. , 2016, Brain : a journal of neurology.
[37] Elizabeth Jefferies,et al. Situating the default-mode network along a principal gradient of macroscale cortical organization , 2016, Proceedings of the National Academy of Sciences.
[38] Paul Antoine Salin,et al. Highly Dynamic Spatiotemporal Organization of Low‐Frequency Activities During Behavioral States in the Mouse Cerebral Cortex , 2016, Cerebral cortex.
[39] Alexander J. E. Kell,et al. Measuring and Modeling Nonlinear Interactions Between Brain Regions with fMRI , 2016, bioRxiv.
[40] Satrajit S. Ghosh,et al. The Healthy Brain Network Serial Scanning Initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions , 2016, bioRxiv.
[41] Evan M. Gordon,et al. On the Stability of BOLD fMRI Correlations , 2016, Cerebral cortex.
[42] Eric Halgren,et al. Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night , 2016, eLife.
[43] Steen Moeller,et al. The Human Connectome Project's neuroimaging approach , 2016, Nature Neuroscience.
[44] Jesper Andersson,et al. A multi-modal parcellation of human cerebral cortex , 2016, Nature.
[45] Laurent Thoraval,et al. Identifying Dynamic Functional Connectivity Changes in Dementia with Lewy Bodies Based on Product Hidden Markov Models , 2016, Front. Comput. Neurosci..
[46] Olaf Sporns,et al. Comparative Connectomics , 2016, Trends in Cognitive Sciences.
[47] G. Deco,et al. Dynamic functional connectivity reveals altered variability in functional connectivity among patients with major depressive disorder , 2016, Human brain mapping.
[48] Edward T. Bullmore,et al. Fundamentals of Brain Network Analysis , 2016 .
[49] M. Frank,et al. Computational psychiatry as a bridge from neuroscience to clinical applications , 2016, Nature Neuroscience.
[50] Russell A. Poldrack,et al. Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives , 2015, NeuroImage.
[51] Krzysztof J. Gorgolewski,et al. The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance , 2015, Neuron.
[52] Ann S. Choe,et al. Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years , 2015, PloS one.
[53] Jingyuan E. Chen,et al. Functional Magnetic Resonance Imaging Methods , 2015, Neuropsychology Review.
[54] Evan M. Gordon,et al. Functional System and Areal Organization of a Highly Sampled Individual Human Brain , 2015, Neuron.
[55] Gustavo Deco,et al. Functional connectivity dynamics: Modeling the switching behavior of the resting state , 2015, NeuroImage.
[56] M. Sigman,et al. Signature of consciousness in the dynamics of resting-state brain activity , 2015, Proceedings of the National Academy of Sciences.
[57] Shella D. Keilholz,et al. The Neural Basis of Time-Varying Resting-State Functional Connectivity , 2014, Brain Connect..
[58] V. Calhoun,et al. Dynamic connectivity states estimated from resting fMRI Identify differences among Schizophrenia, bipolar disorder, and healthy control subjects , 2014, Front. Hum. Neurosci..
[59] Martin A. Lindquist,et al. Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach , 2014, NeuroImage.
[60] V. Calhoun,et al. The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery , 2014, Neuron.
[61] A. Belger,et al. Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia , 2014, NeuroImage: Clinical.
[62] Leonardo L. Gollo,et al. Time-resolved resting-state brain networks , 2014, Proceedings of the National Academy of Sciences.
[63] Stephen M Smith,et al. Fast transient networks in spontaneous human brain activity , 2014, eLife.
[64] Timothy O. Laumann,et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.
[65] Catie Chang,et al. Decomposition of spontaneous brain activity into distinct fMRI co-activation patterns , 2013, Front. Syst. Neurosci..
[66] Thomas E. Nichols,et al. Functional connectomics from resting-state fMRI , 2013, Trends in Cognitive Sciences.
[67] O. Sporns,et al. Network hubs in the human brain , 2013, Trends in Cognitive Sciences.
[68] Mark W. Woolrich,et al. Resting-state fMRI in the Human Connectome Project , 2013, NeuroImage.
[69] Essa Yacoub,et al. The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.
[70] Mark Jenkinson,et al. The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.
[71] Abraham Z. Snyder,et al. Steps toward optimizing motion artifact removal in functional connectivity MRI; a reply to Carp , 2013, NeuroImage.
[72] Olaf Sporns,et al. Network maps of the human brain's rich club , 2013, Network Science.
[73] C. C. Gaudes,et al. Periods of rest in fMRI contain individual spontaneous events which are related to slowly fluctuating spontaneous activity , 2013, Human brain mapping.
[74] J. Duyn,et al. Time-varying functional network information extracted from brief instances of spontaneous brain activity , 2013, Proceedings of the National Academy of Sciences.
[75] P. Y. Lum,et al. Extracting insights from the shape of complex data using topology , 2013, Scientific Reports.
[76] Enzo Tagliazucchi,et al. Dynamic BOLD functional connectivity in humans and its electrophysiological correlates , 2012, Front. Hum. Neurosci..
[77] Michael Breakspear,et al. A Canonical Model of Multistability and Scale-Invariance in Biological Systems , 2012, PLoS Comput. Biol..
[78] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[79] Wen-Ming Luh,et al. Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI , 2012, NeuroImage.
[80] James J. DiCarlo,et al. How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.
[81] Abraham Z. Snyder,et al. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.
[82] Steen Moeller,et al. The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.
[83] Marisa O. Hollinshead,et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.
[84] D. Yurgelun-Todd,et al. Reproducibility of Single-Subject Functional Connectivity Measurements , 2011, American Journal of Neuroradiology.
[85] Waqas Majeed,et al. Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans , 2011, NeuroImage.
[86] József Fiser,et al. Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment , 2011, Science.
[87] Stephen M. Smith,et al. Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging , 2010, PloS one.
[88] J. O’Neill,et al. Play it again: reactivation of waking experience and memory , 2010, Trends in Neurosciences.
[89] Bruce Fischl,et al. Accurate and robust brain image alignment using boundary-based registration , 2009, NeuroImage.
[90] Dario L Ringach,et al. Spontaneous and driven cortical activity: implications for computation , 2009, Current Opinion in Neurobiology.
[91] M. Breakspear,et al. Bistability and Non-Gaussian Fluctuations in Spontaneous Cortical Activity , 2009, The Journal of Neuroscience.
[92] K. Harris,et al. Spontaneous Events Outline the Realm of Possible Sensory Responses in Neocortical Populations , 2009, Neuron.
[93] Jonathan D. Power,et al. Functional Brain Networks Develop from a “Local to Distributed” Organization , 2009, PLoS Comput. Biol..
[94] P. Matthews,et al. Distinct patterns of brain activity in young carriers of the APOE e4 allele , 2009, NeuroImage.
[95] Gunnar E. Carlsson,et al. Topology and data , 2009 .
[96] Feng Qi Han,et al. Reverberation of Recent Visual Experience in Spontaneous Cortical Waves , 2008, Neuron.
[97] S. Petersen,et al. A dual-networks architecture of top-down control , 2008, Trends in Cognitive Sciences.
[98] Brian B. Avants,et al. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain , 2008, Medical Image Anal..
[99] O. Sporns,et al. Identification and Classification of Hubs in Brain Networks , 2007, PloS one.
[100] Thomas T. Liu,et al. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.
[101] Justin L. Vincent,et al. Distinct brain networks for adaptive and stable task control in humans , 2007, Proceedings of the National Academy of Sciences.
[102] E. Seidemann,et al. Optimal decoding of correlated neural population responses in the primate visual cortex , 2006, Nature Neuroscience.
[103] Mark W. Woolrich,et al. Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.
[104] Michael Brady,et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.
[105] Mukund Balasubramanian,et al. The Isomap Algorithm and Topological Stability , 2002, Science.
[106] S T Roweis,et al. Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.
[107] Todd B. Parrish,et al. Impact of signal‐to‐noise on functional MRI , 2000 .
[108] Juan C. Jiménez,et al. Nonlinear EEG analysis based on a neural mass model , 1999, Biological Cybernetics.
[109] Karl J. Friston,et al. Movement‐Related effects in fMRI time‐series , 1996, Magnetic resonance in medicine.
[110] A Grinvald,et al. Coherent spatiotemporal patterns of ongoing activity revealed by real-time optical imaging coupled with single-unit recording in the cat visual cortex. , 1995, Journal of neurophysiology.
[111] Theiler,et al. Generating surrogate data for time series with several simultaneously measured variables. , 1994, Physical review letters.
[112] Timothy O. Laumann,et al. Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. , 2016, Cerebral cortex.
[113] Geoffrey E. Hinton,et al. Visualizing Data using t-SNE , 2008 .
[114] P. Mahadevan,et al. An overview , 2007, Journal of Biosciences.
[115] Facundo Mémoli,et al. Topological Methods for the Analysis of High Dimensional Data Sets and 3D Object Recognition , 2007, PBG@Eurographics.
[116] Margaret King,et al. State of the art and perspectives , 2004, Machine Translation.
[117] Stephen M. Smith,et al. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm , 2001, IEEE Transactions on Medical Imaging.
[118] N. Tatarenko. [Pathophysiology of schizophrenia]. , 1954, Zhurnal nevropatologii i psikhiatrii imeni S.S. Korsakova.