Electrophysiological Correlation Patterns of Resting State Networks in Single Subjects: A Combined EEG–fMRI Study

With combined EEG–fMRI a powerful combination of methods was developed in the last decade that seems promising for answering fundamental neuroscientific questions by measuring functional processes of the human brain simultaneously with two complementary modalities. Recently, resting state networks (RSNs), representing brain regions of coherent BOLD fluctuations, raised major interest in the neuroscience community. Since RSNs are reliably found across subjects and reflect task related networks, changes in their characteristics might give insight to neuronal changes or damage, promising a broad range of scientific and clinical applications. The question of how RSNs are linked to electrophysiological signal characteristics becomes relevant in this context. In this combined EEG–fMRI study we investigated the relationship of RSNs and their correlated electrophysiological signals [electrophysiological correlation patterns (ECPs)] using a long (34 min) resting state scan per subject. This allowed us to study ECPs on group as well as on single subject level, and to examine the temporal stability of ECPs within each subject. We found that the correlation patterns obtained on group level show a large inter-subject variability. During the long scan the ECPs within a subject show temporal fluctuations, which we interpret as a result of the complex temporal dynamic of the RSNs.

[1]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[2]  M. D’Esposito,et al.  The variability of human BOLD hemodynamic responses , 1998, NeuroImage.

[3]  R. Turner,et al.  Event-Related fMRI: Characterizing Differential Responses , 1998, NeuroImage.

[4]  Karl J. Friston,et al.  Nonlinear event‐related responses in fMRI , 1998, Magnetic resonance in medicine.

[5]  M. D’Esposito,et al.  The Variability of Human, BOLD Hemodynamic Responses , 1998, NeuroImage.

[6]  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.

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

[8]  J. Lurito,et al.  Correlations in Low-Frequency BOLD Fluctuations Reflect Cortico-Cortical Connections , 2000, NeuroImage.

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

[10]  V. Haughton,et al.  Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data. , 2001, AJNR. American journal of neuroradiology.

[11]  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.

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

[13]  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.

[14]  Hellmuth Obrig,et al.  Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy , 2003, NeuroImage.

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

[16]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[17]  B. Feige,et al.  Cortical and subcortical correlates of electroencephalographic alpha rhythm modulation. , 2005, Journal of neurophysiology.

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

[19]  D. Norris,et al.  BOLD contrast sensitivity enhancement and artifact reduction with multiecho EPI: Parallel‐acquired inhomogeneity‐desensitized fMRI , 2006, Magnetic resonance in medicine.

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

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

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

[23]  Karl J. Friston,et al.  Comparing hemodynamic models with DCM , 2007, NeuroImage.

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

[25]  Bart Vanrumste,et al.  Review on solving the forward problem in EEG source analysis , 2007, Journal of NeuroEngineering and Rehabilitation.

[26]  Fetsje Bijma,et al.  A Data and Model-Driven Approach to Explore Inter-Subject Variability of Resting-State Brain Activity Using EEG-fMRI , 2008, IEEE Journal of Selected Topics in Signal Processing.

[27]  R. Oostenveld,et al.  Frontal theta EEG activity correlates negatively with the default mode network in resting state. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[28]  R. Quiroga,et al.  Unmixing concurrent EEG-fMRI with parallel independent component analysis. , 2008, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

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

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

[31]  Xiangyu Long,et al.  Functional segmentation of the brain cortex using high model order group PICA , 2009, Human brain mapping.

[32]  P. Matthews,et al.  Distinct patterns of brain activity in young carriers of the APOE e4 allele , 2009, NeuroImage.

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

[34]  Dimitri Van De Ville,et al.  BOLD correlates of EEG topography reveal rapid resting-state network dynamics , 2010, NeuroImage.

[35]  Scott Makeig,et al.  Neuroelectromagnetic Forward Head Modeling Toolbox , 2010, Journal of Neuroscience Methods.

[36]  M. Schölvinck,et al.  Neural basis of global resting-state fMRI activity , 2010, Proceedings of the National Academy of Sciences.

[37]  Helmut Laufs,et al.  Multimodal analysis of resting state cortical activity: What does EEG add to our knowledge of resting state BOLD networks? , 2010, NeuroImage.

[38]  Remco J. Renken,et al.  The effect of intra- and inter-subject variability of hemodynamic responses on group level Granger causality analyses , 2011, NeuroImage.

[39]  Thom F. Oostendorp,et al.  Towards a model-based integration of co-registered electroencephalography/functional magnetic resonance imaging data with realistic neural population meshes , 2011, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[40]  Jessica A. Turner,et al.  Behavioral Interpretations of Intrinsic Connectivity Networks , 2011, Journal of Cognitive Neuroscience.

[41]  David A. Leopold,et al.  Ongoing physiological processes in the cerebral cortex , 2012, NeuroImage.