Neuroscience and Biobehavioral Reviews Review Moment-to-moment Brain Signal Variability: a next Frontier in Human Brain Mapping?

[1]  Tetsuya Takahashi Complexity of spontaneous brain activity in mental disorders , 2013, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[2]  Roberto Hornero,et al.  Complexity and schizophrenia , 2013, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[3]  C. Grady,et al.  The modulation of BOLD variability between cognitive states varies by age and processing speed. , 2013, Cerebral cortex.

[4]  Woodrow L. Shew,et al.  The Functional Benefits of Criticality in the Cortex , 2013, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[5]  Feng Liu,et al.  Reversal alterations of amplitude of low-frequency fluctuations in early and late onset, first-episode, drug-naive depression , 2013, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[6]  Anthony Randal McIntosh,et al.  Relating brain signal variability to knowledge representation , 2012, NeuroImage.

[7]  Zufu Zhu,et al.  Spatial patterns of intrinsic neural activity in depressed patients with vascular risk factors as revealed by the amplitude of low-frequency fluctuation , 2012, Brain Research.

[8]  Christine L. Cox,et al.  The balance between feeling and knowing: affective and cognitive empathy are reflected in the brain's intrinsic functional dynamics. , 2012, Social cognitive and affective neuroscience.

[9]  Daniela Bonino,et al.  Increased BOLD Variability in the Parietal Cortex and Enhanced Parieto-Occipital Connectivity during Tactile Perception in Congenitally Blind Individuals , 2012, Neural plasticity.

[10]  C. Grady The cognitive neuroscience of ageing , 2012, Nature Reviews Neuroscience.

[11]  P. Abry,et al.  Scale-Free and Multifractal Time Dynamics of fMRI Signals during Rest and Task , 2012, Front. Physio..

[12]  John M. Beggs,et al.  Being Critical of Criticality in the Brain , 2012, Front. Physio..

[13]  Karl J. Friston,et al.  Perception and self-organized instability , 2012, Front. Comput. Neurosci..

[14]  P. Dayan,et al.  A Step-by-Step Guide to Dopamine , 2012, Biological Psychiatry.

[15]  Shu-Chen Li Neuromodulation of behavioral and cognitive development across the life span. , 2012, Developmental psychology.

[16]  Edward T. Bullmore,et al.  On the use of correlation as a measure of network connectivity , 2012, NeuroImage.

[17]  Feng Liu,et al.  Alterations of the amplitude of low-frequency fluctuations in treatment-resistant and treatment-response depression: A resting-state fMRI study , 2012, Progress in Neuro-Psychopharmacology and Biological Psychiatry.

[18]  Natasa Kovacevic,et al.  Brain signal variability relates to stability of behavior after recovery from diffuse brain injury , 2012, NeuroImage.

[19]  G. Deco,et al.  Ongoing Cortical Activity at Rest: Criticality, Multistability, and Ghost Attractors , 2012, The Journal of Neuroscience.

[20]  Ian J Deary,et al.  Neuroticism combined with slower and more variable reaction time: synergistic risk factors for 7-year cognitive decline in females. , 2012, The journals of gerontology. Series B, Psychological sciences and social sciences.

[21]  D. L. Schomer,et al.  Niedermeyer's Electroencephalography: Basic Principles, Clinical Applications, and Related Fields , 2012 .

[22]  Woodrow L. Shew,et al.  Maximal Variability of Phase Synchrony in Cortical Networks with Neuronal Avalanches , 2012, The Journal of Neuroscience.

[23]  Jordan B. Peterson,et al.  Psychological entropy: a framework for understanding uncertainty-related anxiety. , 2012, Psychological review.

[24]  Q. Xi,et al.  Spontaneous brain activity in mild cognitive impairment revealed by amplitude of low-frequency fluctuation analysis: a resting-state fMRI study , 2012, La radiologia medica.

[25]  Pablo Balenzuela,et al.  Criticality in Large-Scale Brain fMRI Dynamics Unveiled by a Novel Point Process Analysis , 2012, Front. Physio..

[26]  Karl J. Friston,et al.  Free Energy, Value, and Attractors , 2011, Comput. Math. Methods Medicine.

[27]  Kaustubh Supekar,et al.  Dynamic Reconfiguration of Structural and Functional Connectivity Across Core Neurocognitive Brain Networks with Development , 2011, The Journal of Neuroscience.

[28]  S. Baron-Cohen,et al.  Atypical EEG complexity in autism spectrum conditions: A multiscale entropy analysis , 2011, Clinical Neurophysiology.

[29]  Anthony R. McIntosh,et al.  Functional Embedding Predicts the Variability of Neural Activity , 2011, Front. Syst. Neurosci..

[30]  Biyu J. He Scale-Free Properties of the Functional Magnetic Resonance Imaging Signal during Rest and Task , 2011, The Journal of Neuroscience.

[31]  Xi-Nian Zuo,et al.  REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing , 2011, PloS one.

[32]  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.

[33]  Matti Laine,et al.  Effects of Working-Memory Training on Striatal Dopamine Release , 2011, Science.

[34]  T. Wolbers,et al.  Physiological Signal Variability in hMT+ Reflects Performance on a Direction Discrimination Task , 2011, Front. Psychology.

[35]  Mark D. McDonnell,et al.  The benefits of noise in neural systems: bridging theory and experiment , 2011, Nature Reviews Neuroscience.

[36]  M. D’Esposito,et al.  Inverted-U–Shaped Dopamine Actions on Human Working Memory and Cognitive Control , 2011, Biological Psychiatry.

[37]  Natasa Kovacevic,et al.  Extracting Message Inter-Departure Time Distributions from the Human Electroencephalogram , 2011, PLoS Comput. Biol..

[38]  Vasily A. Vakorin,et al.  Variability of Brain Signals Processed Locally Transforms into Higher Connectivity with Brain Development , 2011, Journal of Neuroscience.

[39]  Yasumasa Okamoto,et al.  Personality traits and the amplitude of spontaneous low-frequency oscillations during resting state , 2011, Neuroscience Letters.

[40]  C. Grady,et al.  The Importance of Being Variable , 2011, The Journal of Neuroscience.

[41]  Ying Han,et al.  Frequency-dependent changes in the amplitude of low-frequency fluctuations in amnestic mild cognitive impairment: A resting-state fMRI study , 2011, NeuroImage.

[42]  C. Nelson,et al.  EEG complexity as a biomarker for autism spectrum disorder risk , 2011, BMC medicine.

[43]  Yufeng Zang,et al.  Linking inter-individual differences in neural activation and behavior to intrinsic brain dynamics , 2011, NeuroImage.

[44]  Yasumasa Okamoto,et al.  Modulation of default-mode network activity by acute tryptophan depletion is associated with mood change: A resting state functional magnetic resonance imaging study , 2011, Neuroscience Research.

[45]  Woodrow L. Shew,et al.  Information Capacity and Transmission Are Maximized in Balanced Cortical Networks with Neuronal Avalanches , 2010, The Journal of Neuroscience.

[46]  Dante R. Chialvo,et al.  What kind of noise is brain noise: anomalous scaling behavior of the resting brain activity fluctuations , 2010, Front. Physio..

[47]  Margot J. Taylor,et al.  Brain noise is task dependent and region specific. , 2010, Journal of neurophysiology.

[48]  Jonathan D. Power,et al.  The Development of Human Functional Brain Networks , 2010, Neuron.

[49]  A. Dale,et al.  Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. , 2010, Cerebral cortex.

[50]  T. Mizuno,et al.  Assessment of EEG dynamical complexity in Alzheimer’s disease using multiscale entropy , 2010, Clinical Neurophysiology.

[51]  A. Cichocki,et al.  Diagnosis of Alzheimer's disease from EEG signals: where are we standing? , 2010, Current Alzheimer research.

[52]  N. Kovacevic,et al.  Hippocampal signal complexity in mesial temporal lobe epilepsy: a noisy brain is a healthy brain. , 2010, Archives italiennes de biologie.

[53]  A R McIntosh,et al.  The development of a noisy brain. , 2010, Archives italiennes de biologie.

[54]  John A. E. Anderson,et al.  A multivariate analysis of age-related differences in default mode and task-positive networks across multiple cognitive domains. , 2010, Cerebral cortex.

[55]  Koichi Takahashi,et al.  Antipsychotics reverse abnormal EEG complexity in drug-naive schizophrenia: A multiscale entropy analysis , 2010, NeuroImage.

[56]  C. Grady,et al.  Blood Oxygen Level-Dependent Signal Variability Is More than Just Noise , 2010, The Journal of Neuroscience.

[57]  L. Nyberg,et al.  Linking cognitive aging to alterations in dopamine neurotransmitter functioning: Recent data and future avenues , 2010, Neuroscience & Biobehavioral Reviews.

[58]  Shu-Chen Li,et al.  Dopaminergic modulation of cognition across the life span , 2010, Neuroscience & Biobehavioral Reviews.

[59]  M. Raichle Two views of brain function , 2010, Trends in Cognitive Sciences.

[60]  Xi-Nian Zuo,et al.  Amplitude of low-frequency oscillations in schizophrenia: A resting state fMRI study , 2010, Schizophrenia Research.

[61]  Karl J. Friston The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.

[62]  Camelia M. Kuhnen,et al.  Variability in Nucleus Accumbens Activity Mediates Age-Related Suboptimal Financial Risk Taking , 2010, The Journal of Neuroscience.

[63]  Bharat B. Biswal,et al.  The oscillating brain: Complex and reliable , 2010, NeuroImage.

[64]  S. MacDonald,et al.  Neural underpinnings of within-person variability in cognitive functioning. , 2009, Psychology and aging.

[65]  Reza Tafreshi,et al.  An entropy-based approach to predict seizures in temporal lobe epilepsy using scalp EEG , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[66]  M. Fukunaga,et al.  Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study. , 2009, Magnetic resonance imaging.

[67]  Natasa Kovacevic,et al.  Differential Maturation of Brain Signal Complexity in the Human Auditory and Visual System , 2009, Frontiers in human neuroscience.

[68]  Kaustubh Supekar,et al.  Development of Large-Scale Functional Brain Networks in Children , 2009, NeuroImage.

[69]  A. Hariri The neurobiology of individual differences in complex behavioral traits. , 2009, Annual review of neuroscience.

[70]  O. Sporns,et al.  Key role of coupling, delay, and noise in resting brain fluctuations , 2009, Proceedings of the National Academy of Sciences.

[71]  Woodrow L. Shew,et al.  Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality , 2009, The Journal of Neuroscience.

[72]  Derek Abbott,et al.  What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology , 2009, PLoS Comput. Biol..

[73]  Jonathan D. Power,et al.  Functional Brain Networks Develop from a “Local to Distributed” Organization , 2009, PLoS Comput. Biol..

[74]  T. Mizuno,et al.  Age-related variation in EEG complexity to photic stimulation: A multiscale entropy analysis , 2009, Clinical Neurophysiology.

[75]  O. Sporns,et al.  Complex brain networks: graph theoretical analysis of structural and functional systems , 2009, Nature Reviews Neuroscience.

[76]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[77]  Timothy D. Hanks,et al.  Probabilistic Population Codes for Bayesian Decision Making , 2008, Neuron.

[78]  Viktor K. Jirsa,et al.  Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire , 2008, PLoS Comput. Biol..

[79]  E. Bullmore,et al.  Endogenous multifractal brain dynamics are modulated by age, cholinergic blockade and cognitive performance , 2008, Journal of Neuroscience Methods.

[80]  I. Fried,et al.  Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex , 2008, Nature Neuroscience.

[81]  Rafael Doti,et al.  Ubiquitous Crossmodal Stochastic Resonance in Humans: Auditory Noise Facilitates Tactile, Visual and Proprioceptive Sensations , 2008, PloS one.

[82]  Chaozhe Zhu,et al.  An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF , 2008, Journal of Neuroscience Methods.

[83]  Natasa Kovacevic,et al.  Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development , 2008, PLoS Comput. Biol..

[84]  Y. Niv,et al.  Dialogues on prediction errors , 2008, Trends in Cognitive Sciences.

[85]  Hiroshi Otsubo,et al.  Fluctuations in cortical synchronization in pediatric traumatic brain injury. , 2008, Journal of neurotrauma.

[86]  Cees van Leeuwen,et al.  Chaos breeds autonomy: connectionist design between bias and baby-sitting , 2008, Cognitive Processing.

[87]  A. Faisal,et al.  Noise in the nervous system , 2008, Nature Reviews Neuroscience.

[88]  K. Doya Modulators of decision making , 2008, Nature Neuroscience.

[89]  E. Bullmore,et al.  Fractal connectivity of long-memory networks. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[90]  Partha P. Mitra,et al.  Observed Brain Dynamics , 2007 .

[91]  Justin L. Vincent,et al.  Disruption of Large-Scale Brain Systems in Advanced Aging , 2007, Neuron.

[92]  Elizabeth Redcay,et al.  fMRI during natural sleep as a method to study brain function during early childhood , 2007, NeuroImage.

[93]  Soo Yong Kim,et al.  MULTISCALE ENTROPY ANALYSIS OF EEG FROM PATIENTS UNDER DIFFERENT PATHOLOGICAL CONDITIONS , 2007 .

[94]  Khader M Hasan,et al.  Development and organization of the human brain tissue compartments across the lifespan using diffusion tensor imaging , 2007, Neuroreport.

[95]  Abraham Z. Snyder,et al.  A default mode of brain function: A brief history of an evolving idea , 2007, NeuroImage.

[96]  Alexa M. Morcom,et al.  Cognitive neuroscience: The case for design rather than default , 2007, NeuroImage.

[97]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

[98]  P. Dayan,et al.  Tonic dopamine: opportunity costs and the control of response vigor , 2007, Psychopharmacology.

[99]  D. Plenz,et al.  The organizing principles of neuronal avalanches: cell assemblies in the cortex? , 2007, Trends in Neurosciences.

[100]  Yong He,et al.  Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. , 2007, Brain & development.

[101]  L. Nyberg,et al.  The correlative triad among aging, dopamine, and cognition: Current status and future prospects , 2006, Neuroscience & Biobehavioral Reviews.

[102]  N. Raz,et al.  Differential Aging of the Brain: Patterns, Cognitive Correlates and Modifiers , 2022 .

[103]  E. Bullmore,et al.  Adaptive reconfiguration of fractal small-world human brain functional networks , 2006, Proceedings of the National Academy of Sciences.

[104]  Keiichi Kitajo,et al.  Neural synchrony in stochastic resonance, attention, and consciousness. , 2006, Canadian journal of experimental psychology = Revue canadienne de psychologie experimentale.

[105]  Wei Ji Ma,et al.  Bayesian inference with probabilistic population codes , 2006, Nature Neuroscience.

[106]  J. Escudero,et al.  Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy , 2006, Physiological measurement.

[107]  Ulman Lindenberger,et al.  A neurocomputational model of stochastic resonance and aging , 2006, Neurocomputing.

[108]  S. MacDonald,et al.  Intra-individual variability in behavior: links to brain structure, neurotransmission and neuronal activity , 2006, Trends in Neurosciences.

[109]  A. Toga,et al.  Mapping brain maturation , 2006, Trends in Neurosciences.

[110]  Cheryl L. Dahle,et al.  Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. , 2005, Cerebral cortex.

[111]  Emilio Salinas,et al.  When Response Variability Increases Neural Network Robustness to Synaptic Noise , 2005, Neural Computation.

[112]  C. Stam,et al.  Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field , 2005, Clinical Neurophysiology.

[113]  Michael D. Robinson,et al.  Neuroticism as mental noise: a relation between neuroticism and reaction time standard deviations. , 2005, Journal of personality and social psychology.

[114]  Eiji Shimizu,et al.  Approximate Entropy of the Electroencephalogram in Healthy Awake Subjects and Absence Epilepsy Patients , 2005, Clinical EEG and neuroscience.

[115]  Kelvin E. Jones,et al.  Neuronal variability: noise or part of the signal? , 2005, Nature Reviews Neuroscience.

[116]  Madalena Costa,et al.  Multiscale entropy analysis of biological signals. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[117]  D. Knill,et al.  The Bayesian brain: the role of uncertainty in neural coding and computation , 2004, Trends in Neurosciences.

[118]  Jonathan D. Cohen,et al.  Computational roles for dopamine in behavioural control , 2004, Nature.

[119]  Suzanne E. Welcome,et al.  Longitudinal Mapping of Cortical Thickness and Brain Growth in Normal Children , 2022 .

[120]  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.

[121]  John M. Beggs,et al.  Neuronal Avalanches in Neocortical Circuits , 2003, The Journal of Neuroscience.

[122]  L. M. Ward,et al.  Synchronous neural oscillations and cognitive processes , 2003, Trends in Cognitive Sciences.

[123]  A. A. Ioannides,et al.  Single-trial variability in early visual neuromagnetic responses: an explorative study based on the regional activation contributing to the N70m peak , 2003, NeuroImage.

[124]  M. Egan,et al.  Catechol O-methyltransferase val158-met genotype and individual variation in the brain response to amphetamine , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[125]  Suzanne E. Welcome,et al.  Mapping cortical change across the human life span , 2003, Nature Neuroscience.

[126]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[127]  P. Sanberg,et al.  Neuroscience and Biobehavioral Reviews , 2002, Physiology & Behavior.

[128]  S. Sikström,et al.  Aging cognition: from neuromodulation to representation , 2001, Trends in Cognitive Sciences.

[129]  G. Glover,et al.  Physiological noise in oxygenation‐sensitive magnetic resonance imaging , 2001, Magnetic resonance in medicine.

[130]  Mark H. Johnson Functional brain development in humans , 2001, Nature Reviews Neuroscience.

[131]  J. Martinerie,et al.  The brainweb: Phase synchronization and large-scale integration , 2001, Nature Reviews Neuroscience.

[132]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[133]  J. Richman,et al.  Physiological time-series analysis using approximate entropy and sample entropy. , 2000, American journal of physiology. Heart and circulatory physiology.

[134]  G. Edelman,et al.  Complexity and coherency: integrating information in the brain , 1998, Trends in Cognitive Sciences.

[135]  Stephen F. Traynelis,et al.  Getting the most out of noise in the central nervous system , 1998, Trends in Neurosciences.

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

[137]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[138]  G. Edelman,et al.  A measure for brain complexity: relating functional segregation and integration in the nervous system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[139]  Viktor K. Jirsa,et al.  A theoretical model of phase transitions in the human brain , 1994, Biological Cybernetics.

[140]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[141]  R. Cremer,et al.  What kind of noise increases with age? , 1987, Journal of gerontology.

[142]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[143]  秦 浩起,et al.  Characterization of Strange Attractor (カオスとその周辺(基研長期研究会報告)) , 1987 .

[144]  T. Salthouse,et al.  Tests of the neural noise hypothesis of age-related cognitive change. , 1985, Journal of gerontology.

[145]  P. Grassberger,et al.  Characterization of Strange Attractors , 1983 .

[146]  A T Welford,et al.  Signal, Noise, Performance, and Age , 1981, Human factors.

[147]  L. Pinneo On noise in the nervous system. , 1966, Psychological review.

[148]  R. H. Kent,et al.  The Mean Square Successive Difference , 1941 .

[149]  T. Brown On the nature of the fundamental activity of the nervous centres; together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system , 1914, The Journal of physiology.

[150]  Gerald Langner,et al.  The oscillating brain , 2015 .

[151]  A. Hariri,et al.  Toward a mechanistic understanding of how variability in neurobiology shapes individual differences in behavior. , 2012, Current topics in behavioral neurosciences.

[152]  G. Deco,et al.  Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.

[153]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[154]  Jeffrey M. Zacks,et al.  Coherent spontaneous activity accounts for trial-to-trial variability in human evoked brain responses , 2006, Nature Neuroscience.

[155]  A. Arduini The Tonic Discharge of the Retina and its Central Effects , 1963 .

[156]  K. Lashley The problem of serial order in behavior , 1951 .

[157]  R. K. Simpson Nature Neuroscience , 2022 .