Decomposing alpha and 1/f brain activities reveals their differential associations with cognitive processing speed

Research in cognitive neuroscience has extensively demonstrated that the temporal dynamics of brain activity are associated with cognitive functioning. The temporal dynamics mainly include oscillatory and 1/f noise-like, non-oscillatory brain activities that coexist in many forms of brain activity and confound each other's variability. As such, observed functional associations of narrowband oscillations might have been confounded with the broadband 1/f component. Here, we investigated the relationship between resting-state EEG activity and the efficiency of cognitive functioning in N = 180 individuals. We show that 1/f brain activity plays an essential role in accounting for between-person variability in cognitive speed - a relationship that can be mistaken as originating from brain oscillations using conventional power spectrum analysis. At first glance, the power of alpha oscillations appeared to be predictive of cognitive speed. However, when dissociating pure alpha oscillations from 1/f brain activity, only the 1/f predicted cognitive speed, whereas the predictive power of alpha vanished. With this highly powered study, we disambiguate the functional relevance of the 1/f power law pattern in resting state neural activities and substantiate the necessity of isolating the 1/f component from oscillatory activities when studying the functional relevance of spontaneous brain activities.

[1]  D. Marković,et al.  Power laws and Self-Organized Criticality in Theory and Nature , 2013, 1310.5527.

[2]  G. V. Simpson,et al.  Anticipatory Biasing of Visuospatial Attention Indexed by Retinotopically Specific α-Bank Electroencephalography Increases over Occipital Cortex , 2000, The Journal of Neuroscience.

[3]  Thomas S. Redick,et al.  Lapses in sustained attention and their relation to executive control and fluid abilities: An individual differences investigation , 2010 .

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

[5]  P Walla,et al.  Early cortical activation indicates preparation for retrieval of memory for faces: an event-related potential study , 1998, Neuroscience Letters.

[6]  Xiao-Jing Wang.  Neurophysiological and computational principles of cortical rhythms in cognition. , 2010, Physiological reviews.

[7]  Steven Laureys,et al.  The spectral exponent of the resting EEG indexes the presence of consciousness during unresponsiveness induced by propofol, xenon, and ketamine , 2019, NeuroImage.

[8]  C. Braun,et al.  Prestimulus oscillatory power and connectivity patterns predispose conscious somatosensory perception , 2014, Proceedings of the National Academy of Sciences.

[9]  P. Bentler,et al.  Comparative fit indexes in structural models. , 1990, Psychological bulletin.

[10]  Gal Chechik,et al.  A unifying principle underlying the extracellular field potential spectral responses in the human cortex. , 2015, Journal of neurophysiology.

[11]  E. Peterson,et al.  Inferring Synaptic Excitation/Inhibition Balance from Field Potentials , 2016, bioRxiv.

[12]  Arno Villringer,et al.  Power and temporal dynamics of alpha oscillations at rest differentiate cognitive performance involving sustained and phasic cognitive control , 2019, NeuroImage.

[13]  O. Wilhelm,et al.  Modeling Mental Speed: Decomposing Response Time Distributions in Elementary Cognitive Tasks and Correlations with Working Memory Capacity and Fluid Intelligence , 2016 .

[14]  T. Thiagarajan,et al.  Modernization, wealth and the emergence of strong alpha oscillations in the human EEG , 2017, bioRxiv.

[15]  G. Buzsáki.  Rhythms of the brain , 2006 .

[16]  S. Dave,et al.  1/f neural noise and electrophysiological indices of contextual prediction in aging , 2018, Brain Research.

[17]  Robert T. Knight,et al.  Parameterizing neural power spectra , 2018, bioRxiv.

[18]  David T. J. Liley,et al.  1/f electrophysiological spectra in resting and drug-induced states can be explained by the dynamics of multiple oscillatory relaxation processes , 2018, NeuroImage.

[19]  Sam M. Doesburg,et al.  Top-down alpha oscillatory network interactions during visuospatial attention orienting , 2016, NeuroImage.

[20]  R. Oostenveld,et al.  Somatosensory working memory performance in humans depends on both engagement and disengagement of regions in a distributed network , 2009, Human brain mapping.

[21]  R. Duncan Luce,et al.  Response Times: Their Role in Inferring Elementary Mental Organization , 1986 .

[22]  Thilo Gross,et al.  Self-organized criticality as a fundamental property of neural systems , 2014, Front. Syst. Neurosci..

[23]  Adam Gazzaley,et al.  Age-Related Changes in 1/f Neural Electrophysiological Noise , 2015, The Journal of Neuroscience.

[24]  Chao Wang,et al.  The frequency of alpha oscillations: Task-dependent modulation and its functional significance , 2018, NeuroImage.

[25]  Yves Rosseel,et al.  lavaan: An R Package for Structural Equation Modeling , 2012 .

[26]  N. Logothetis,et al.  Scaling Brain Size, Keeping Timing: Evolutionary Preservation of Brain Rhythms , 2013, Neuron.

[27]  C. Bédard,et al.  Macroscopic models of local field potentials and the apparent 1/f noise in brain activity. , 2008, Biophysical journal.

[28]  Haiguang Wen,et al.  Separating Fractal and Oscillatory Components in the Power Spectrum of Neurophysiological Signal , 2015, Brain Topography.

[29]  T. Ergenoğlu,et al.  Alpha rhythm of the EEG modulates visual detection performance in humans. , 2004, Brain research. Cognitive brain research.

[30]  R. Knight,et al.  Dynamic Network Communication as a Unifying Neural Basis for Cognition, Development, Aging, and Disease , 2015, Biological Psychiatry.

[31]  K. Linkenkaer-Hansen,et al.  Neuronal long-range temporal correlations and avalanche dynamics are correlated with behavioral scaling laws , 2013, Proceedings of the National Academy of Sciences.

[32]  Werner Sommer,et al.  Individual Differences in Face Cognition: Brain–Behavior Relationships , 2010, Journal of Cognitive Neuroscience.

[33]  Biyu J. He.  Scale-free brain activity: past, present, and future , 2014, Trends in Cognitive Sciences.

[34]  W. Klimesch,et al.  EEG alpha oscillations: The inhibition–timing hypothesis , 2007, Brain Research Reviews.

[35]  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.

[36]  Ulman Lindenberger,et al.  Individual alpha peak frequency is related to latent factors of general cognitive abilities , 2013, NeuroImage.

[37]  Clayton E. Curtis,et al.  Lateralization in Alpha-Band Oscillations Predicts the Locus and Spatial Distribution of Attention , 2016, PloS one.

[38]  J. Palva,et al.  New vistas for α-frequency band oscillations , 2007, Trends in Neurosciences.

[39]  Dorothy V. M. Bishop,et al.  Journal of Neuroscience Methods , 2015 .

[40]  Yan Zhang,et al.  Detection of a Weak Somatosensory Stimulus: Role of the Prestimulus Mu Rhythm and Its Top–Down Modulation , 2010, Journal of Cognitive Neuroscience.

[41]  J. Palva,et al.  New vistas for alpha-frequency band oscillations. , 2007, Trends in neurosciences.

[42]  M. Frank,et al.  Frontal theta as a mechanism for cognitive control , 2014, Trends in Cognitive Sciences.

[43]  Simon Hanslmayr,et al.  Prestimulus oscillations predict visual perception performance between and within subjects , 2007, NeuroImage.

[44]  Zhongming Liu,et al.  Broadband Electrophysiological Dynamics Contribute to Global Resting-State fMRI Signal , 2016, The Journal of Neuroscience.

[45]  J. Lange,et al.  Fluctuations of prestimulus oscillatory power predict subjective perception of tactile simultaneity. , 2012, Cerebral cortex.

[46]  J. Obleser,et al.  States and traits of neural irregularity in the age-varying human brain , 2017, Scientific Reports.

[47]  Christopher J James,et al.  Distinguishing low frequency oscillations within the 1/f spectral behaviour of electromagnetic brain signals , 2007, Behavioral and Brain Functions.

[48]  H. Laufs,et al.  Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep , 2013, Proceedings of the National Academy of Sciences.

[49]  J. T. Enright,et al.  Are the electroencephalograms mainly rhythmic? Assessment of periodicity in wide-band time series , 2003, Neuroscience.

[50]  Barbara F. Händel,et al.  Top-Down Controlled Alpha Band Activity in Somatosensory Areas Determines Behavioral Performance in a Discrimination Task , 2011, The Journal of Neuroscience.

[51]  Gaute T. Einevoll,et al.  Intrinsic dendritic filtering gives low-pass power spectra of local field potentials , 2010, Journal of Computational Neuroscience.

[52]  Jeffrey G. Ojemann,et al.  Power-Law Scaling in the Brain Surface Electric Potential , 2009, PLoS Comput. Biol..

[53]  Klaus Oberauer,et al.  Individual differences in components of reaction time distributions and their relations to working memory and intelligence. , 2007, Journal of experimental psychology. General.

[54]  Gerald E. Larson,et al.  Reaction time variability and intelligence: A “worst performance” analysis of individual differences , 1990 .

[55]  John M Beggs,et al.  The criticality hypothesis: how local cortical networks might optimize information processing , 2008, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[56]  H. Berger,et al.  Über das Elektrenkephalogramm des Menschen , 1937, Archiv für Psychiatrie und Nervenkrankheiten.

[57]  Werner Sommer,et al.  Lateralization of posterior alpha EEG reflects the distribution of spatial attention during saccadic reading. , 2017, Psychophysiology.

[58]  Richard L. Hughson,et al.  Extracting fractal components from time series , 1993 .

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

[60]  L. Leyman,et al.  The Karolinska Directed Emotional Faces: A validation study , 2008 .

[61]  W. Freeman,et al.  Spatial spectra of scalp EEG and EMG from awake humans , 2003, Clinical Neurophysiology.

[62]  K. Linkenkaer-Hansen,et al.  Prestimulus Oscillations Enhance Psychophysical Performance in Humans , 2004, The Journal of Neuroscience.

[63]  W. Klimesch,et al.  Upper alpha ERD and absolute power: their meaning for memory performance. , 2006, Progress in brain research.

[64]  W. Klimesch.  EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[65]  Á. Pascual-Leone,et al.  α-Band Electroencephalographic Activity over Occipital Cortex Indexes Visuospatial Attention Bias and Predicts Visual Target Detection , 2006, The Journal of Neuroscience.

[66]  J. H. Steiger.  Statistically based tests for the number of common factors , 1980 .

[67]  Simon Hanslmayr,et al.  Oscillatory correlates of intentional updating in episodic memory , 2008, NeuroImage.

[68]  J. Schoffelen,et al.  Prestimulus Oscillatory Activity in the Alpha Band Predicts Visual Discrimination Ability , 2008, The Journal of Neuroscience.

[69]  E. Basar.  A review of alpha activity in integrative brain function: fundamental physiology, sensory coding, cognition and pathology. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[70]  T. Zandt,et al.  How to fit a response time distribution , 2000, Psychonomic bulletin & review.

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

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

[73]  Luc H. Arnal,et al.  Cortical oscillations and sensory predictions , 2012, Trends in Cognitive Sciences.

[74]  Robert Riener,et al.  Decrypting the electrophysiological individuality of the human brain: Identification of individuals based on resting-state EEG activity , 2019, NeuroImage.

[75]  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.

[76]  O. Bertrand,et al.  Oscillatory gamma activity in humans and its role in object representation , 1999, Trends in Cognitive Sciences.

[77]  Biyu J. He,et al.  The Temporal Structures and Functional Significance of Scale-free Brain Activity , 2010, Neuron.

[78]  Juliane Britz,et al.  EEG microstate sequences in healthy humans at rest reveal scale-free dynamics , 2010, Proceedings of the National Academy of Sciences.

[79]  R. T. Pivik,et al.  Guidelines for the recording and quantitative analysis of electroencephalographic activity in research contexts. , 1993, Psychophysiology.

[80]  Steven Garcia,et al.  Pre-target alpha power predicts the speed of cued target discrimination , 2019, NeuroImage.

[81]  D. Mewhort,et al.  Analysis of Response Time Distributions: An Example Using the Stroop Task , 1991 .

[82]  Edward T. Bullmore,et al.  Failure of Adaptive Self-Organized Criticality during Epileptic Seizure Attacks , 2011, PLoS Comput. Biol..