Spatial-temporal-spectral EEG patterns of BOLD functional network connectivity dynamics

OBJECTIVE Growing interest in the examination of large-scale brain network functional connectivity dynamics is accompanied by an effort to find the electrophysiological correlates. The commonly used constraints applied to spatial and spectral domains during electroencephalogram (EEG) data analysis may leave part of the neural activity unrecognized. We propose an approach that blindly reveals multimodal EEG spectral patterns that are related to the dynamics of the BOLD functional network connectivity. APPROACH The blind decomposition of EEG spectrogram by parallel factor analysis has been shown to be a useful technique for uncovering patterns of neural activity. The simultaneously acquired BOLD fMRI data were decomposed by independent component analysis. Dynamic functional connectivity was computed on the component's time series using a sliding window correlation, and between-network connectivity states were then defined based on the values of the correlation coefficients. ANOVA tests were performed to assess the relationships between the dynamics of between-network connectivity states and the fluctuations of EEG spectral patterns. MAIN RESULTS We found three patterns related to the dynamics of between-network connectivity states. The first pattern has dominant peaks in the alpha, beta, and gamma bands and is related to the dynamics between the auditory, sensorimotor, and attentional networks. The second pattern, with dominant peaks in the theta and low alpha bands, is related to the visual and default mode network. The third pattern, also with peaks in the theta and low alpha bands, is related to the auditory and frontal network. SIGNIFICANCE Our previous findings revealed a relationship between EEG spectral pattern fluctuations and the hemodynamics of large-scale brain networks. In this study, we suggest that the relationship also exists at the level of functional connectivity dynamics among large-scale brain networks when no standard spatial and spectral constraints are applied on the EEG data.

[1]  Brigitte Röder,et al.  On the relationship between slow cortical potentials and BOLD signal changes in humans. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[2]  Xiao Liu,et al.  EEG correlates of time-varying BOLD functional connectivity , 2013, NeuroImage.

[3]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[4]  David T. Jones,et al.  Non-Stationarity in the “Resting Brain’s” Modular Architecture , 2012, PloS one.

[5]  Shella D. Keilholz,et al.  Dynamic Properties of Functional Connectivity in the Rodent , 2013, Brain Connect..

[6]  Stephen M. Smith,et al.  Investigations into resting-state connectivity using independent component analysis , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[7]  Ludovica Griffanti,et al.  Hand classification of fMRI ICA noise components , 2017, NeuroImage.

[8]  Waqas Majeed,et al.  Spatiotemporal dynamics of low frequency fluctuations in BOLD fMRI of the rat , 2009, Journal of magnetic resonance imaging : JMRI.

[9]  Jiří Jan,et al.  Digital signal filtering, analysis and restoration , 2000 .

[10]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

[11]  Stephen M. Smith,et al.  fMRI resting state networks define distinct modes of long-distance interactions in the human brain , 2006, NeuroImage.

[12]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[13]  Daniel A. Handwerker,et al.  Periodic changes in fMRI connectivity , 2012, NeuroImage.

[14]  S. Rombouts,et al.  Consistent resting-state networks across healthy subjects , 2006, Proceedings of the National Academy of Sciences.

[15]  Enzo Tagliazucchi,et al.  Dynamic BOLD functional connectivity in humans and its electrophysiological correlates , 2012, Front. Hum. Neurosci..

[16]  Xi-Nian Zuo,et al.  Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach , 2010, NeuroImage.

[17]  P. Hluštík,et al.  Effects of spatial smoothing on fMRI group inferences. , 2008, Magnetic resonance imaging.

[18]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

[19]  Dieter Jaeger,et al.  Infraslow LFP correlates to resting-state fMRI BOLD signals , 2013, NeuroImage.

[20]  R. Bro,et al.  A new efficient method for determining the number of components in PARAFAC models , 2003 .

[21]  Fabrice Wendling,et al.  Simultaneous Intracranial EEG-fMRI Shows Inter-Modality Correlation in Time-Resolved Connectivity Within Normal Areas but Not Within Epileptic Regions , 2017, Brain Topography.

[22]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[23]  M. Fox,et al.  Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging , 2007, Nature Reviews Neuroscience.

[24]  Alberto Leal,et al.  Characterisation and Reduction of the EEG Artefact Caused by the Helium Cooling Pump in the MR Environment: Validation in Epilepsy Patient Data , 2014, Brain Topography.

[25]  Andreas Kleinschmidt,et al.  EEG-correlated fMRI of human alpha activity , 2003, NeuroImage.

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

[27]  Michal Mikl,et al.  Mask_explorer: A tool for exploring brain masks in fMRI group analysis , 2016, Comput. Methods Programs Biomed..

[28]  Karl J. Friston,et al.  Hemodynamic correlates of EEG: A heuristic , 2005, NeuroImage.

[29]  R. Bro.  PARAFAC. Tutorial and applications , 1997 .

[30]  William D. Penny,et al.  Estimating the transfer function from neuronal activity to BOLD using simultaneous EEG-fMRI , 2010, NeuroImage.

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

[32]  Waqas Majeed,et al.  Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans , 2011, NeuroImage.

[33]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[34]  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.

[35]  S. Bressler,et al.  Large-scale brain networks in cognition: emerging methods and principles , 2010, Trends in Cognitive Sciences.

[36]  Michal Mikl,et al.  The impact of diverse preprocessing pipelines on brain functional connectivity , 2017, 2017 25th European Signal Processing Conference (EUSIPCO).

[37]  Vince D. Calhoun,et al.  The spatiospectral characterization of brain networks: Fusing concurrent EEG spectra and fMRI maps , 2013, NeuroImage.

[38]  Rupert Lanzenberger,et al.  Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies , 2009, NeuroImage.

[39]  Louis Lemieux,et al.  Identification of EEG Events in the MR Scanner: The Problem of Pulse Artifact and a Method for Its Subtraction , 1998, NeuroImage.

[40]  Dieter Jaeger,et al.  Neural correlates of time-varying functional connectivity in the rat , 2013, NeuroImage.

[41]  Eduardo Martínez-Montes,et al.  Identifying Complex Brain Networks Using Penalized Regression Methods , 2008, Journal of biological physics.

[42]  Michal Mikl,et al.  Stable Scalp EEG Spatiospectral Patterns Across Paradigms Estimated by Group ICA , 2017, Brain Topography.

[43]  Wenxian Yu,et al.  Variational Bayesian PARAFAC decomposition for Multidimensional Harmonic Retrieval , 2011, Proceedings of 2011 IEEE CIE International Conference on Radar.

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

[45]  Aapo Hyvärinen,et al.  Independent component analysis of nondeterministic fMRI signal sources , 2003, NeuroImage.

[46]  M. Murray,et al.  EEG source imaging , 2004, Clinical Neurophysiology.

[47]  M. Mikl,et al.  Sensitivity of PPI analysis to differences in noise reduction strategies , 2015, Journal of Neuroscience Methods.

[48]  Stephen M. Smith,et al.  Temporally-independent functional modes of spontaneous brain activity , 2012, Proceedings of the National Academy of Sciences.

[49]  Ravi S. Menon,et al.  Resting‐state networks show dynamic functional connectivity in awake humans and anesthetized macaques , 2013, Human brain mapping.

[50]  G H Glover,et al.  Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR , 2000, Magnetic resonance in medicine.

[51]  M. Barth,et al.  Electrophysiological Correlation Patterns of Resting State Networks in Single Subjects: A Combined EEG–fMRI Study , 2012, Brain Topography.

[52]  Leonardo L. Gollo,et al.  Time-resolved resting-state brain networks , 2014, Proceedings of the National Academy of Sciences.

[53]  Andrzej Cichocki,et al.  Multiway array decomposition analysis of EEGs in Alzheimer's disease , 2012, Journal of Neuroscience Methods.

[54]  V. Calhoun,et al.  EEG Signatures of Dynamic Functional Network Connectivity States , 2017, Brain Topography.

[55]  Robert Turner,et al.  A Method for Removing Imaging Artifact from Continuous EEG Recorded during Functional MRI , 2000, NeuroImage.

[56]  Christoph M. Michel,et al.  Epileptic source localization with high density EEG: how many electrodes are needed? , 2003, Clinical Neurophysiology.

[57]  Michal Mikl,et al.  Multiway Array Decomposition of EEG Spectrum: Implications of Its Stability for the Exploration of Large-Scale Brain Networks , 2017, Neural Computation.

[58]  Archana Venkataraman,et al.  Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. , 2010, Journal of neurophysiology.

[59]  Vesa Kiviniemi,et al.  A Sliding Time-Window ICA Reveals Spatial Variability of the Default Mode Network in Time , 2011, Brain Connect..

[60]  N. Logothetis,et al.  Neurophysiological investigation of the basis of the fMRI signal , 2001, Nature.

[61]  Shella D. Keilholz,et al.  Infraslow Electroencephalographic and Dynamic Resting State Network Activity , 2017, Brain Connect..

[62]  V. Calhoun,et al.  The Chronnectome: Time-Varying Connectivity Networks as the Next Frontier in fMRI Data Discovery , 2014, Neuron.

[63]  Fumikazu Miwakeichi,et al.  Decomposing EEG data into space–time–frequency components using Parallel Factor Analysis , 2004, NeuroImage.

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

[65]  Yong He,et al.  Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns , 2018, Human brain mapping.

[66]  V D Calhoun,et al.  Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms , 2001, Human brain mapping.

[67]  Fernando Henrique Lopes da Silva,et al.  Interactions between different EEG frequency bands and their effect on alpha–fMRI correlations , 2009, NeuroImage.

[68]  Dimitri Van De Ville,et al.  On spurious and real fluctuations of dynamic functional connectivity during rest , 2015, NeuroImage.

[69]  Yihong Yang,et al.  Spontaneous functional network dynamics and associated structural substrates in the human brain , 2015, Front. Hum. Neurosci..

[70]  Alan J. Lee,et al.  Linear Regression Analysis: Seber/Linear , 2003 .

[71]  Arno Villringer,et al.  Internal ventilation system of MR scanners induces specific EEG artifact during simultaneous EEG-fMRI , 2013, NeuroImage.

[72]  Stephen D. Mayhew,et al.  Dynamic spatiotemporal variability of alpha-BOLD relationships during the resting-state and task-evoked responses , 2017, NeuroImage.

[73]  Yong He,et al.  Individual differences and time-varying features of modular brain architecture , 2017, NeuroImage.

[74]  M. Brázdil,et al.  What can be found in scalp EEG spectrum beyond common frequency bands. EEG–fMRI study , 2016, Journal of neural engineering.

[75]  George A. F. Seber,et al.  Linear regression analysis , 1977 .

[76]  Rex E. Jung,et al.  A Baseline for the Multivariate Comparison of Resting-State Networks , 2011, Front. Syst. Neurosci..

[77]  J. Vandewalle,et al.  An introduction to independent component analysis , 2000 .

[78]  Aapo Hyvärinen,et al.  Icasso: software for investigating the reliability of ICA estimates by clustering and visualization , 2003, 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718).

[79]  Olaf Sporns,et al.  Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks , 2015, NeuroImage.

[80]  Sylvain Chartier,et al.  An Introduction to Independent Component Analysis: InfoMax and FastICA algorithms , 2010 .

[81]  B. Biswal,et al.  The resting brain: unconstrained yet reliable. , 2009, Cerebral cortex.

[82]  Bernard Ng,et al.  Optimization of rs-fMRI Pre-processing for Enhanced Signal-Noise Separation, Test-Retest Reliability, and Group Discrimination , 2015, NeuroImage.