Local temporal variability reflects functional integration in the human brain
暂无分享,去创建一个
Ulman Lindenberger | Douglas D. Garrett | Alistair Perry | Samira M. Epp | U. Lindenberger | A. Perry | D. Garrett | Alistair Perry
[1] S. MacDonald,et al. Neuroscience and Biobehavioral Reviews Review Moment-to-moment Brain Signal Variability: a next Frontier in Human Brain Mapping? , 2022 .
[2] Marisa O. Hollinshead,et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.
[3] R. Douglas,et al. A Quantitative Map of the Circuit of Cat Primary Visual Cortex , 2004, The Journal of Neuroscience.
[4] Mark W. Woolrich,et al. Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.
[5] James M. Shine,et al. Subcortical contributions to large-scale network communication , 2016, Neuroscience & Biobehavioral Reviews.
[6] Michael Breakspear,et al. The modulation of neural gain facilitates a transition between functional segregation and integration in the brain , 2017, bioRxiv.
[7] O. Sporns,et al. Network hubs in the human brain , 2013, Trends in Cognitive Sciences.
[8] A. Babloyantz,et al. Low-dimensional chaos in an instance of epilepsy. , 1986, Proceedings of the National Academy of Sciences of the United States of America.
[9] K. H. Britten,et al. Responses of neurons in macaque MT to stochastic motion signals , 1993, Visual Neuroscience.
[10] Douglas D Garrett,et al. Brain signal variability is parametrically modifiable. , 2014, Cerebral cortex.
[11] M. Carandini,et al. Cortical State Determines Global Variability and Correlations in Visual Cortex , 2015, The Journal of Neuroscience.
[12] C. Grady,et al. Blood Oxygen Level-Dependent Signal Variability Is More than Just Noise , 2010, The Journal of Neuroscience.
[13] Rasmus M. Birn,et al. The role of physiological noise in resting-state functional connectivity , 2012, NeuroImage.
[14] Zhihao Li,et al. Dynamic thalamus parcellation from resting‐state fMRI data , 2016, Human brain mapping.
[15] T. Sejnowski,et al. Synchrony of Thalamocortical Inputs Maximizes Cortical Reliability , 2010, Science.
[16] Timothy Edward John Behrens,et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging , 2003, Nature Neuroscience.
[17] J. V. Haxby,et al. Spatial Pattern Analysis of Functional Brain Images Using Partial Least Squares , 1996, NeuroImage.
[18] C. Grady,et al. The Importance of Being Variable , 2011, The Journal of Neuroscience.
[19] L. Nyberg,et al. Elevated hippocampal resting-state connectivity underlies deficient neurocognitive function in aging , 2014, Proceedings of the National Academy of Sciences.
[20] M. Greicius,et al. Decoding subject-driven cognitive states with whole-brain connectivity patterns. , 2012, Cerebral cortex.
[21] Denise C. Park,et al. Neural Broadening or Neural Attenuation? Investigating Age-Related Dedifferentiation in the Face Network in a Large Lifespan Sample , 2012, The Journal of Neuroscience.
[22] Mark W. Woolrich,et al. FSL , 2012, NeuroImage.
[23] Ninon Burgos,et al. New advances in the Clinica software platform for clinical neuroimaging studies , 2019 .
[24] Anthony Randal McIntosh,et al. Partial Least Squares (PLS) methods for neuroimaging: A tutorial and review , 2011, NeuroImage.
[25] C. Grady,et al. The modulation of BOLD variability between cognitive states varies by age and processing speed. , 2013, Cerebral cortex.
[26] R. Freeman,et al. Neurometabolic coupling in cerebral cortex reflects synaptic more than spiking activity , 2007, Nature Neuroscience.
[27] Xiao-Jing Wang,et al. The importance of mixed selectivity in complex cognitive tasks , 2013, Nature.
[28] Woodrow L. Shew. Neuronal Avalanches , 2014, Encyclopedia of Computational Neuroscience.
[29] Irene E. Nagel,et al. Amphetamine modulates brain signal variability and working memory in younger and older adults , 2015, Proceedings of the National Academy of Sciences.
[30] R. Reid,et al. Low Response Variability in Simultaneously Recorded Retinal, Thalamic, and Cortical Neurons , 2000, Neuron.
[31] Jonas Obleser,et al. Dopaminergic modulation of hemodynamic signal variability and the functional connectome during cognitive performance , 2017, NeuroImage.
[32] Timothy O. Laumann,et al. Sources and implications of whole-brain fMRI signals in humans , 2017, NeuroImage.
[33] A. Litwin-Kumar,et al. Slow dynamics and high variability in balanced cortical networks with clustered connections , 2012, Nature Neuroscience.
[34] R. Douglas,et al. Neuronal circuits of the neocortex. , 2004, Annual review of neuroscience.
[35] K. Martin,et al. Termination of the geniculocortical projection in the striate cortex of macaque monkey: A quantitative immunoelectron microscopic study , 2000, The Journal of comparative neurology.
[36] Bharat B. Biswal,et al. Functional Covariance Networks: Obtaining Resting-State Networks from Intersubject Variability , 2012, Brain Connect..
[37] Maxwell A. Bertolero,et al. The Human Thalamus Is an Integrative Hub for Functional Brain Networks , 2016, The Journal of Neuroscience.
[38] J. Duyn,et al. Investigation of Low Frequency Drift in fMRI Signal , 1999, NeuroImage.
[39] Denise C Park,et al. Neural Specificity Predicts Fluid Processing Ability in Older Adults , 2010, The Journal of Neuroscience.
[40] Eero P. Simoncelli,et al. Partitioning neuronal variability , 2014, Nature Neuroscience.
[41] G. Sapiro,et al. Comprehensive in vivo Mapping of the Human Basal Ganglia and Thalamic Connectome in Individuals Using 7T MRI , 2012, PloS one.
[42] Woodrow L. Shew,et al. Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches , 2010, The Journal of Neuroscience.
[43] Stefano Fusi,et al. Why neurons mix: high dimensionality for higher cognition , 2016, Current Opinion in Neurobiology.
[44] B. Boots,et al. Spatial Pattern Analysis , 2016 .
[45] S M Pincus,et al. Greater signal regularity may indicate increased system isolation. , 1994, Mathematical biosciences.
[46] Andreas Horn,et al. Toward a standardized structural–functional group connectome in MNI space , 2016, NeuroImage.
[47] N. Logothetis. What we can do and what we cannot do with fMRI , 2008, Nature.
[48] R Cameron Craddock,et al. A whole brain fMRI atlas generated via spatially constrained spectral clustering , 2012, Human brain mapping.
[49] M Corbetta,et al. A Dynamic Core Network and Global Efficiency in the Resting Human Brain. , 2016, Cerebral cortex.
[50] Margaret D. King,et al. The NKI-Rockland Sample: A Model for Accelerating the Pace of Discovery Science in Psychiatry , 2012, Front. Neurosci..
[51] R. Kötter,et al. Connecting Mean Field Models of Neural Activity to EEG and fMRI Data , 2010, Brain Topography.
[52] Viviana Betti,et al. Cortical cores in network dynamics , 2018, NeuroImage.
[53] Anders M. Dale,et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.
[54] S. Rombouts,et al. Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.
[55] H. Sompolinsky,et al. Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity , 1996, Science.
[56] D. Ferster,et al. Feedforward Origins of Response Variability Underlying Contrast Invariant Orientation Tuning in Cat Visual Cortex , 2012, Neuron.
[57] D. McCormick,et al. Actions of norepinephrine in the cerebral cortex and thalamus: implications for function of the central noradrenergic system. , 1991, Progress in brain research.
[58] A. Faisal,et al. Noise in the nervous system , 2008, Nature Reviews Neuroscience.
[59] S Murray Sherman,et al. Thalamus plays a central role in ongoing cortical functioning , 2016, Nature Neuroscience.
[60] Anbupalam Thalamuthu,et al. The organisation of the elderly connectome , 2015, NeuroImage.
[61] Stephen M. Smith,et al. Probabilistic independent component analysis for functional magnetic resonance imaging , 2004, IEEE Transactions on Medical Imaging.
[62] Anthony R. McIntosh,et al. Functional Embedding Predicts the Variability of Neural Activity , 2011, Front. Syst. Neurosci..
[63] Haim Sompolinsky,et al. Chaotic Balanced State in a Model of Cortical Circuits , 1998, Neural Computation.
[64] Brent Doiron,et al. The mechanics of state-dependent neural correlations , 2016, Nature Neuroscience.
[65] Brent Doiron,et al. Balanced neural architecture and the idling brain , 2014, Front. Comput. Neurosci..
[66] Douglas D Garrett,et al. Moment-to-moment signal variability in the human brain can inform models of stochastic facilitation now , 2011, Nature Reviews Neuroscience.
[67] Peter H. Schurr,et al. Human Thalamus , 1970 .
[68] Jonathan D. Power,et al. Evidence for Hubs in Human Functional Brain Networks , 2013, Neuron.
[69] M. Fox,et al. Intrinsic functional relations between human cerebral cortex and thalamus. , 2008, Journal of neurophysiology.
[70] Richard S. J. Frackowiak,et al. Evidence for Segregated and Integrative Connectivity Patterns in the Human Basal Ganglia , 2008, The Journal of Neuroscience.
[71] M. Schölvinck,et al. Tracking brain arousal fluctuations with fMRI , 2016, Proceedings of the National Academy of Sciences.
[72] P. Blier,et al. Relevance of Norepinephrine–Dopamine Interactions in the Treatment of Major Depressive Disorder , 2010, CNS neuroscience & therapeutics.
[73] L. Pinneo. On noise in the nervous system. , 1966, Psychological review.
[74] Margot J. Taylor,et al. Brain noise is task dependent and region specific. , 2010, Journal of neurophysiology.
[75] M. Carandini,et al. The Nature of Shared Cortical Variability , 2015, Neuron.
[76] Natasa Kovacevic,et al. Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development , 2008, PLoS Comput. Biol..
[77] Thomas T. Liu,et al. The global signal in fMRI: Nuisance or Information? , 2017, NeuroImage.
[78] Zul Merali,et al. Functional interactions between dopamine, serotonin and norepinephrine neurons: an in-vivo electrophysiological study in rats with monoaminergic lesions. , 2008, The international journal of neuropsychopharmacology.
[79] Woodrow L. Shew,et al. Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality , 2009, The Journal of Neuroscience.
[80] Michael N. Shadlen,et al. Noise, neural codes and cortical organization , 1994, Current Opinion in Neurobiology.