Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle
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Vladimir Miskovic | L Jack Rhodes | V. Miskovic | K. Cote | K. MacDonald | L. J. Rhodes | Kimberly A Cote | Kevin J MacDonald
[1] Hamed Azami,et al. Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals , 2016, IEEE Transactions on Biomedical Engineering.
[2] Eiji Shimizu,et al. Approximate Entropy in the Electroencephalogram during Wake and Sleep , 2005, Clinical EEG and neuroscience.
[3] C. Peng,et al. What is physiologic complexity and how does it change with aging and disease? , 2002, Neurobiology of Aging.
[4] Hamed Azami,et al. Dispersion Entropy: A Measure for Time-Series Analysis , 2016, IEEE Signal Processing Letters.
[5] R. Carhart-Harris. The entropic brain - revisited , 2018, Neuropharmacology.
[6] Madalena Costa,et al. Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.
[7] Wenbin Shi,et al. Nonlinear dynamical analysis of sleep electroencephalography using fractal and entropy approaches. , 2018, Sleep medicine reviews.
[8] Manuel S. Schröter,et al. Development of a Large-Scale Functional Brain Network during Human Non-Rapid Eye Movement Sleep , 2010, The Journal of Neuroscience.
[9] Ervin Sejdic,et al. Necessity of noise in physiology and medicine , 2013, Comput. Methods Programs Biomed..
[10] U. Rajendra Acharya,et al. Non-linear analysis of EEG signals at various sleep stages , 2005, Comput. Methods Programs Biomed..
[11] W. Gerstner,et al. Temporal whitening by power-law adaptation in neocortical neurons , 2013, Nature Neuroscience.
[12] P. Achermann,et al. Coherence analysis of the human sleep electroencephalogram , 1998, Neuroscience.
[13] R. McCarley,et al. Control of sleep and wakefulness. , 2012, Physiological reviews.
[14] Sergey Borisov,et al. Large-scale brain functional modularity is reflected in slow electroencephalographic rhythms across the human non-rapid eye movement sleep cycle , 2013, NeuroImage.
[15] Karl J. Friston,et al. Broadband Cortical Desynchronization Underlies the Human Psychedelic State , 2013, The Journal of Neuroscience.
[16] R. T. Pivik,et al. Handbook of Psychophysiology: Sleep and Dreaming , 2007 .
[17] D. Chialvo,et al. Enhanced repertoire of brain dynamical states during the psychedelic experience , 2014, Human brain mapping.
[18] Ronald J Killiany,et al. Edited Magnetic Resonance Spectroscopy Detects an Age-Related Decline in Nonhuman Primate Brain GABA Levels , 2016, BioMed research international.
[19] Richard Gao,et al. Inferring synaptic excitation/inhibition balance from field potentials , 2016, NeuroImage.
[20] W. Freeman,et al. Spatial spectra of scalp EEG and EMG from awake humans , 2003, Clinical Neurophysiology.
[21] A. Engel,et al. Cortical Hypersynchrony Predicts Breakdown of Sensory Processing during Loss of Consciousness , 2011, Current Biology.
[22] G. Tononi,et al. Breakdown of Cortical Effective Connectivity During Sleep , 2005, Science.
[23] Damon G Lamb,et al. Frontal Gamma-Aminobutyric Acid Concentrations Are Associated With Cognitive Performance in Older Adults. , 2017, Biological psychiatry. Cognitive neuroscience and neuroimaging.
[24] T. Mizuno,et al. Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy , 2010, Clinical Neurophysiology.
[25] Erik W. Jensen,et al. EEG complexity as a measure of depth of anesthesia for patients , 2001, IEEE Trans. Biomed. Eng..
[26] David M. Groppe,et al. Mass univariate analysis of event-related brain potentials/fields I: a critical tutorial review. , 2011, Psychophysiology.
[27] Vasily A. Vakorin,et al. Variability of Brain Signals Processed Locally Transforms into Higher Connectivity with Brain Development , 2011, Journal of Neuroscience.
[28] Paolo Castiglioni,et al. Multiscale Sample Entropy of Cardiovascular Signals: Does the Choice between Fixed- or Varying-Tolerance among Scales Influence Its Evaluation and Interpretation? , 2017, Entropy.
[29] L de Arcangelis,et al. Balance of excitation and inhibition determines 1/f power spectrum in neuronal networks. , 2017, Chaos.
[30] Gustavo Deco,et al. Functional connectivity dynamics: Modeling the switching behavior of the resting state , 2015, NeuroImage.
[31] A L Goldberger,et al. Physiological time-series analysis: what does regularity quantify? , 1994, The American journal of physiology.
[32] Adam Gazzaley,et al. Age-Related Changes in 1/f Neural Electrophysiological Noise , 2015, The Journal of Neuroscience.
[33] E. Tagliazucchi. The signatures of conscious access and its phenomenology are consistent with large-scale brain communication at criticality , 2017, Consciousness and Cognition.
[34] M. Boly,et al. Complexity of Multi-Dimensional Spontaneous EEG Decreases during Propofol Induced General Anaesthesia , 2015, PloS one.
[35] Biyu J. He. Scale-free brain activity: past, present, and future , 2014, Trends in Cognitive Sciences.
[36] D. de Araújo,et al. Shannon entropy of brain functional complex networks under the influence of the psychedelic Ayahuasca , 2016, Scientific Reports.
[37] P. F. Meier,et al. Dimensional complexity and spectral properties of the human sleep EEG , 2003, Clinical Neurophysiology.
[38] Pengjian Shang,et al. A comparison study on stages of sleep: Quantifying multiscale complexity using higher moments on coarse-graining , 2017, Commun. Nonlinear Sci. Numer. Simul..
[39] G. Biggio,et al. Neurochemical action of the general anaesthetic propofol on the chloride ion channel coupled with GABAA receptors , 1991, Brain Research.
[40] S. MacDonald,et al. Neuroscience and Biobehavioral Reviews Review Moment-to-moment Brain Signal Variability: a next Frontier in Human Brain Mapping? , 2022 .
[41] Soo Yong Kim,et al. MULTISCALE ENTROPY ANALYSIS OF EEG FROM PATIENTS UNDER DIFFERENT PATHOLOGICAL CONDITIONS , 2007 .
[42] D. M. Mateos,et al. Measures of entropy and complexity in altered states of consciousness , 2017, Cognitive Neurodynamics.
[43] S. Pati,et al. Phase Synchronization Analysis of Natural Wake and Sleep States in Healthy Individuals Using a Novel Ensemble Phase Synchronization Measure , 2017, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.
[44] Richard Gao,et al. Interpreting the electrophysiological power spectrum. , 2016, Journal of neurophysiology.
[45] Selen Atasoy,et al. Human brain networks function in connectome-specific harmonic waves , 2016, Nature Communications.
[46] Biyu J. He,et al. The Temporal Structures and Functional Significance of Scale-free Brain Activity , 2010, Neuron.
[47] W. Freeman,et al. Simulated power spectral density (PSD) of background electrocorticogram (ECoG) , 2008, Cognitive Neurodynamics.
[48] G. Tononi,et al. Molecular and electrophysiological evidence for net synaptic potentiation in wake and depression in sleep , 2008, Nature Neuroscience.
[49] Gustavo Deco,et al. Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics , 2017, bioRxiv.
[50] Vasily A. Vakorin,et al. Spatiotemporal Dependency of Age-Related Changes in Brain Signal Variability , 2013, Cerebral cortex.
[51] Madalena Costa,et al. Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.
[52] Maria V. Sanchez-Vives,et al. Cellular and network mechanisms of rhythmic recurrent activity in neocortex , 2000, Nature Neuroscience.
[53] G. Tononi,et al. Lempel-Ziv complexity of cortical activity during sleep and waking in rats , 2015, Journal of neurophysiology.
[54] Anthony R. McIntosh,et al. Mapping the Multiscale Information Content of Complex Brain Signals , 2012 .
[55] Viktor K. Jirsa,et al. The multiscale entropy: Guidelines for use and interpretation in brain signal analysis , 2016, Journal of Neuroscience Methods.
[56] Christopher G. Wilson,et al. The effect of time delay on Approximate & Sample Entropy calculations , 2008 .
[57] R. Huber,et al. Diurnal changes in glutamate + glutamine levels of healthy young adults assessed by proton magnetic resonance spectroscopy , 2018, Human brain mapping.
[58] Peter Achermann,et al. Temporal evolution of coherence and power in the human sleep electroencephalogram , 1998, Journal of sleep research.
[59] N. Nicolaou,et al. The Use of Permutation Entropy to Characterize Sleep Electroencephalograms , 2011, Clinical EEG and neuroscience.
[60] J. Obleser,et al. States and traits of neural irregularity in the age-varying human brain , 2017, Scientific Reports.
[61] R. Knight,et al. Dynamic Network Communication as a Unifying Neural Basis for Cognition, Development, Aging, and Disease , 2015, Biological Psychiatry.
[62] A. Gruber,et al. Serotonin modulation of cortical neurons and networks , 2013, Front. Integr. Neurosci..
[63] T. Roth,et al. Neurophysiology of Sleep and Wakefulness: Basic Science and Clinical Implications , 2008, Current neuropharmacology.
[64] A. Seth,et al. Increased spontaneous MEG signal diversity for psychoactive doses of ketamine, LSD and psilocybin , 2017, Scientific Reports.
[65] Reto Huber,et al. Electroencephalogram approximate entropy influenced by both age and sleep , 2013, Front. Neuroinform..
[66] K. Cote,et al. Probing awareness during sleep with the auditory odd-ball paradigm. , 2002, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.
[67] Vladimir Miskovic,et al. A practical comparison of algorithms for the measurement of multiscale entropy in neural time series data , 2018, Brain and Cognition.
[68] Sampsa Vanhatalo,et al. Early development of spatial patterns of power-law frequency scaling in FMRI resting-state and EEG data in the newborn brain. , 2013, Cerebral cortex.
[69] Arnaud Delorme,et al. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.
[70] W. Pritchard,et al. The brain in fractal time: 1/f-like power spectrum scaling of the human electroencephalogram. , 1992, The International journal of neuroscience.
[71] Vishnu Sreekumar,et al. Signal Complexity of Human Intracranial EEG Tracks Successful Associative-Memory Formation across Individuals , 2018, The Journal of Neuroscience.
[72] Amir H. Omidvarnia,et al. Spontaneous brain network activity: Analysis of its temporal complexity , 2017, Network Neuroscience.
[73] James Theiler,et al. Testing for nonlinearity in time series: the method of surrogate data , 1992 .
[74] Schreiber,et al. Improved Surrogate Data for Nonlinearity Tests. , 1996, Physical review letters.
[75] Natasa Kovacevic,et al. Age-related Multiscale Changes in Brain Signal Variability in Pre-task versus Post-task Resting-state EEG , 2016, Journal of Cognitive Neuroscience.
[76] R. Llinás,et al. Of dreaming and wakefulness , 1991, Neuroscience.
[77] G. Deco,et al. Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.
[78] Rajesh P. N. Rao,et al. Broadband changes in the cortical surface potential track activation of functionally diverse neuronal populations , 2014, NeuroImage.
[79] E. Bruce,et al. Sample Entropy Tracks Changes in Electroencephalogram Power Spectrum With Sleep State and Aging , 2009, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.
[80] R. Blair,et al. An alternative method for significance testing of waveform difference potentials. , 1993, Psychophysiology.
[81] M. Struys,et al. Behavior of Entropy/Complexity Measures of the Electroencephalogram during Propofol-induced Sedation: Dose-dependent Effects of Remifentanil , 2007, Anesthesiology.
[82] Claude Bédard,et al. Comparative power spectral analysis of simultaneous elecroencephalographic and magnetoencephalographic recordings in humans suggests non-resistive extracellular media , 2010, Journal of Computational Neuroscience.
[83] G. Heinzel,et al. Improved spectrum estimation from digitized time series on a logarithmic frequency axis , 2006 .
[84] Ian M. McDonough,et al. Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project , 2014, Front. Hum. Neurosci..
[85] Manuel Schabus,et al. Hierarchical clustering of brain activity during human nonrapid eye movement sleep , 2012, Proceedings of the National Academy of Sciences.
[86] Sleep physiology predicts memory retention after reactivation , 2016, Journal of sleep research.
[87] Haiguang Wen,et al. Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal , 2015, Brain Topography.
[88] Julián J. González,et al. Non-linear behaviour of human EEG: fractal exponent versus correlation dimension in awake and sleep stages , 1998, Neuroscience Letters.
[89] M. Steriade,et al. Natural waking and sleep states: a view from inside neocortical neurons. , 2001, Journal of neurophysiology.
[90] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[91] Jeremy R. Manning,et al. Broadband Shifts in Local Field Potential Power Spectra Are Correlated with Single-Neuron Spiking in Humans , 2009, The Journal of Neuroscience.
[92] R. Manfredini,et al. Unravelling the Complexity of Inherited Retinal Dystrophies Molecular Testing: Added Value of Targeted Next-Generation Sequencing , 2016, BioMed research international.
[93] Anthony Randal McIntosh,et al. Age-related Shift in Neural Complexity Related to Task Performance and Physical Activity , 2015, Journal of Cognitive Neuroscience.
[94] C. Koch,et al. The Neural Correlates of Consciousness , 2008, Annals of the New York Academy of Sciences.
[95] Gal Chechik,et al. A unifying principle underlying the extracellular field potential spectral responses in the human cortex. , 2015, Journal of neurophysiology.
[96] G. Tononi,et al. Long-Term Homeostasis of Extracellular Glutamate in the Rat Cerebral Cortex across Sleep and Waking States , 2009, The Journal of Neuroscience.
[97] K. Campbell,et al. Neurophysiological evidence for the detection of external stimuli during sleep. , 2001, Sleep.
[98] Enzo Tagliazucchi,et al. Sleep Neuroimaging and Models of Consciousness , 2013, Front. Psychol..
[99] A. Seth,et al. Global and local complexity of intracranial EEG decreases during NREM sleep , 2017, Neuroscience of consciousness.
[100] Chung-Kang Peng,et al. Adaptive Data Analysis of Complex Fluctuations in physiologic Time Series , 2009, Adv. Data Sci. Adapt. Anal..
[101] E. Wolpert. A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .