Correlation of BOLD Signal with Linear and Nonlinear Patterns of EEG in Resting State EEG-Informed fMRI

Concurrent EEG and fMRI acquisitions in resting state showed a correlation between EEG power in various bands and spontaneous BOLD fluctuations. However, there is a lack of data on how changes in the complexity of brain dynamics derived from EEG reflect variations in the BOLD signal. The purpose of our study was to correlate both spectral patterns, as linear features of EEG rhythms, and nonlinear EEG dynamic complexity with neuronal activity obtained by fMRI. We examined the relationships between EEG patterns and brain activation obtained by simultaneous EEG-fMRI during the resting state condition in 25 healthy right-handed adult volunteers. Using EEG-derived regressors, we demonstrated a substantial correlation of BOLD signal changes with linear and nonlinear features of EEG. We found the most significant positive correlation of fMRI signal with delta spectral power. Beta and alpha spectral features had no reliable effect on BOLD fluctuation. However, dynamic changes of alpha peak frequency exhibited a significant association with BOLD signal increase in right-hemisphere areas. Additionally, EEG dynamic complexity as measured by the HFD of the 2–20 Hz EEG frequency range significantly correlated with the activation of cortical and subcortical limbic system areas. Our results indicate that both spectral features of EEG frequency bands and nonlinear dynamic properties of spontaneous EEG are strongly associated with fluctuations of the BOLD signal during the resting state condition.

[1]  Dominique L. Pritchett,et al.  Cued Spatial Attention Drives Functionally Relevant Modulation of the Mu Rhythm in Primary Somatosensory Cortex , 2010, The Journal of Neuroscience.

[2]  Mark S. Cohen,et al.  Simultaneous EEG and fMRI of the alpha rhythm , 2002, Neuroreport.

[3]  K. Paller,et al.  Observing the transformation of experience into memory , 2002, Trends in Cognitive Sciences.

[4]  Per B Sederberg,et al.  Power shifts track serial position and modulate encoding in human episodic memory. , 2014, Cerebral cortex.

[5]  C. Moore,et al.  Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice , 2016, Proceedings of the National Academy of Sciences.

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

[7]  S. Iglesias-Parro,et al.  Fractal characterization of internally and externally generated conscious experiences , 2014, Brain and Cognition.

[8]  A. Engel,et al.  Neuronal Synchronization along the Dorsal Visual Pathway Reflects the Focus of Spatial Attention , 2008, Neuron.

[9]  M. Steriade Grouping of brain rhythms in corticothalamic systems , 2006, Neuroscience.

[10]  Dinesh Kumar,et al.  Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: a review , 2011, Australasian Physical & Engineering Sciences in Medicine.

[11]  M. Corbetta,et al.  Electrophysiological signatures of resting state networks in the human brain , 2007, Proceedings of the National Academy of Sciences.

[12]  Ehsan Tarkesh Esfahani,et al.  Using Brain-Computer Interfaces to Detect Human Satisfaction in Human-Robot Interaction , 2011, Int. J. Humanoid Robotics.

[13]  Srdjan Kesic,et al.  Application of Higuchi's fractal dimension from basic to clinical neurophysiology: A review , 2016, Comput. Methods Programs Biomed..

[14]  B. He,et al.  Multimodal Functional Neuroimaging: Integrating Functional MRI and EEG/MEG , 2008, IEEE Reviews in Biomedical Engineering.

[15]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[16]  F. D. Silva,et al.  EEG and MEG: Relevance to Neuroscience , 2013, Neuron.

[17]  C. J. Stam,et al.  Global dynamical analysis of the EEG in Alzheimer’s disease: Frequency-specific changes of functional interactions , 2008, Clinical Neurophysiology.

[18]  D. Tucker,et al.  Scalp electrode impedance, infection risk, and EEG data quality , 2001, Clinical Neurophysiology.

[19]  Karl J. Friston,et al.  EEG-fMRI integration: a critical review of biophysical modeling and data analysis approaches. , 2010, Journal of integrative neuroscience.

[20]  Christian Degueldre,et al.  Functional Neuroanatomy of Human Slow Wave Sleep , 1997, The Journal of Neuroscience.

[21]  Andreas Kleinschmidt,et al.  EEG Alpha Power Modulation of fMRI Resting-State Connectivity , 2012, Brain Connect..

[22]  T. Hendler,et al.  Never Resting Brain: Simultaneous Representation of Two Alpha Related Processes in Humans , 2008, PloS one.

[23]  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.

[24]  G. Buzsáki,et al.  Neuronal Oscillations in Cortical Networks , 2004, Science.

[25]  L. Lemieux,et al.  Electrophysiological correlates of the BOLD signal for EEG‐informed fMRI , 2014, Human brain mapping.

[26]  Sonia I. Gonçalves,et al.  Interactions between different EEG frequency bands and their effect on alpha - fMRI correlations , 2009, NeuroImage.

[27]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[28]  N. Shah,et al.  The Default Mode Network and EEG Regional Spectral Power: A Simultaneous fMRI-EEG Study , 2014, PloS one.

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

[30]  Mehmet Siraç Özerdem,et al.  Application of Higuchi's Fractal Dimension for the Statistical Analysis of Human EEG Responses to Odors , 2018, 2018 41st International Conference on Telecommunications and Signal Processing (TSP).

[31]  G. Pfurtscheller,et al.  Alpha frequency, cognitive load and memory performance , 1993, Brain Topography.

[32]  Han Yuan,et al.  Correlated slow fluctuations in respiration, EEG, and BOLD fMRI , 2013, NeuroImage.

[33]  K. Linkenkaer-Hansen,et al.  Resting-State fMRI Functional Connectivity Is Associated with Sleepiness, Imagery, and Discontinuity of Mind , 2015, PloS one.

[34]  S. Muthukumaraswamy High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations , 2013, Front. Hum. Neurosci..

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

[36]  Craig G. Richter,et al.  Interareal oscillatory synchronization in top-down neocortical processing , 2015, Current Opinion in Neurobiology.

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

[38]  S. Hughes,et al.  Thalamic Mechanisms of EEG Alpha Rhythms and Their Pathological Implications , 2005, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[39]  Ah Chung Tsoi,et al.  Fractal dimension pattern-based multiresolution analysis for rough estimator of speaker-dependent audio emotion recognition , 2017, Int. J. Wavelets Multiresolution Inf. Process..

[40]  W. Klonowski Everything you wanted to ask about EEG but were afraid to get the right answer , 2009, Nonlinear biomedical physics.

[41]  Chu Kiong Loo,et al.  Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface , 2011 .

[42]  G. Schroth,et al.  MRI of paramedian thalamic stroke with sleep disturbance , 1997, Neuroradiology.

[43]  C. Koch,et al.  The origin of extracellular fields and currents — EEG, ECoG, LFP and spikes , 2012, Nature Reviews Neuroscience.

[44]  Simon Finnigan,et al.  fMRI evidence of word frequency and strength effects during episodic memory encoding. , 2005, Brain research. Cognitive brain research.

[45]  Jed A. Meltzer,et al.  Individual differences in EEG theta and alpha dynamics during working memory correlate with fMRI responses across subjects , 2007, Clinical Neurophysiology.

[46]  Tomer Fekete,et al.  Optimizing Complexity Measures for fMRI Data: Algorithm, Artifact, and Sensitivity , 2013, PloS one.

[47]  Helmut Laufs,et al.  Where the BOLD signal goes when alpha EEG leaves , 2006, NeuroImage.

[48]  Fumikazu Miwakeichi,et al.  Concurrent EEG/fMRI analysis by multiway Partial Least Squares , 2004, NeuroImage.

[49]  Dennis D Spencer,et al.  Delta rhythm in wakefulness: evidence from intracranial recordings in human beings. , 2015, Journal of neurophysiology.

[50]  M. Kirkby The fractal geometry of nature. Benoit B. Mandelbrot. W. H. Freeman and co., San Francisco, 1982. No. of pages: 460. Price: £22.75 (hardback) , 1983 .

[51]  D. McCormick,et al.  Properties of a hyperpolarization‐activated cation current and its role in rhythmic oscillation in thalamic relay neurones. , 1990, The Journal of physiology.

[52]  E. Olejarczyk,et al.  Application of fractal dimension method of functional MRI time-series to limbic dysregulation in anxiety study , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[53]  A. Kleinschmidt,et al.  Electroencephalographic signatures of attentional and cognitive default modes in spontaneous brain activity fluctuations at rest , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[54]  Ljiljana Martac,et al.  Spectral and Fractal Analysis of Cerebellar Activity After Single and Repeated Brain Injury , 2008, Bulletin of mathematical biology.

[55]  Natasha M. Maurits,et al.  Correlating the alpha rhythm to BOLD using simultaneous EEG/fMRI: Inter-subject variability , 2006, NeuroImage.

[56]  Radek Mareček,et al.  Exploring task-related variability in fMRI data using fluctuations in power spectrum of simultaneously acquired EEG , 2015, Journal of Neuroscience Methods.

[57]  Nick F. Ramsey,et al.  Human Motor Cortical Activity Is Selectively Phase-Entrained on Underlying Rhythms , 2012, PLoS Comput. Biol..

[58]  J. Hobson,et al.  The cognitive neuroscience of sleep: neuronal systems, consciousness and learning , 2002, Nature Reviews Neuroscience.

[59]  Olga Sourina,et al.  Real-Time EEG-Based Human Emotion Recognition and Visualization , 2010, 2010 International Conference on Cyberworlds.

[60]  H. Laufs,et al.  Decoding Wakefulness Levels from Typical fMRI Resting-State Data Reveals Reliable Drifts between Wakefulness and Sleep , 2014, Neuron.

[61]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[62]  Anna M. Bianchi,et al.  EEG-informed fMRI analysis during a hand grip task: estimating the relationship between EEG rhythms and the BOLD signal , 2014, Front. Hum. Neurosci..

[63]  Paul Cisek,et al.  Nonperiodic Synchronization in Heterogeneous Networks of Spiking Neurons , 2008, The Journal of Neuroscience.

[64]  P. Agostino Accardo,et al.  Use of the fractal dimension for the analysis of electroencephalographic time series , 1997, Biological Cybernetics.

[65]  T. Higuchi Approach to an irregular time series on the basis of the fractal theory , 1988 .

[66]  T. Koenig,et al.  Topographic Electrophysiological Signatures of fMRI Resting State Networks , 2010, PloS one.