Elucidating relations between fMRI, ECoG and EEG through a common natural stimulus

Human brain activity can be studied at different spatial and temporal scales using fMRI, ECoG and EEG. To assess how reliably these imaging methods reflect brain responses related to a complex audio-visual stimulus and how similar stimulus-related responses are across methods, we presented a movie clip twice to three different cohorts of subjects (NEEG = 45, NfMRI = 11, NECoOG = 5) and assessed correlations across viewings and imaging methods within a standardized brain space. Grand-average broad-band EEG and low-frequency (4–28 Hz) EEG power reached similar levels of inter-subject reliability as grand-average fMRI and single-subject high-frequency (28116 Hz) ECoG power. ECoG power was negatively correlated with fMRI in low frequencies and positively correlated in high frequencies in temporal and occipital brain areas. Low-frequency EEG power was negatively correlated with fMRI in occipital and parietal, but positively correlated in temporal areas. We also observed strong correlations between fMRI and infra-slow EEG voltage fluctuations.

[1]  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.

[2]  I. Fried,et al.  Coupling between Neuronal Firing Rate, Gamma LFP, and BOLD fMRI Is Related to Interneuronal Correlations , 2007, Current Biology.

[3]  W. Singer,et al.  Hemodynamic Signals Correlate Tightly with Synchronized Gamma Oscillations , 2005, Science.

[4]  D. Heeger,et al.  Slow Cortical Dynamics and the Accumulation of Information over Long Timescales , 2012, Neuron.

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

[6]  D. Kleinfeld,et al.  Entrainment of Arteriole Vasomotor Fluctuations by Neural Activity Is a Basis of Blood-Oxygenation-Level-Dependent “Resting-State” Connectivity , 2017, Neuron.

[7]  D. Leopold,et al.  Layer-Specific Entrainment of Gamma-Band Neural Activity by the Alpha Rhythm in Monkey Visual Cortex , 2012, Current Biology.

[8]  Jonathan Winawer,et al.  Asynchronous Broadband Signals Are the Principal Source of the BOLD Response in Human Visual Cortex , 2013, Current Biology.

[9]  Krish D. Singh,et al.  Functional decoupling of BOLD and gamma‐band amplitudes in human primary visual cortex , 2009, Human brain mapping.

[10]  Yuan Chang Leong,et al.  Shared memories reveal shared structure in neural activity across individuals , 2016, Nature Neuroscience.

[11]  René Scheeringa,et al.  The relationship between oscillatory EEG activity and the laminar-specific BOLD signal , 2016, Proceedings of the National Academy of Sciences.

[12]  Stefan Haufe,et al.  Multivariate Machine Learning Methods for Fusing Multimodal Functional Neuroimaging Data , 2015, Proceedings of the IEEE.

[13]  A. Villringer,et al.  Simultaneous EEG–fMRI , 2006, Neuroscience & Biobehavioral Reviews.

[14]  Anthony Randal McIntosh,et al.  Partial least squares analysis of neuroimaging data: applications and advances , 2004, NeuroImage.

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

[16]  E. Halgren,et al.  Superficial Slow Rhythms Integrate Cortical Processing in Humans , 2017, bioRxiv.

[17]  Christopher J. Aura,et al.  Divergence of fMRI and neural signals in V1 during perceptual suppression in the awake monkey , 2008, Nature Neuroscience.

[18]  Aaron T. Winder,et al.  Weak correlations between hemodynamic signals and ongoing neural activity during the resting state , 2017, Nature Neuroscience.

[19]  Yu Huang,et al.  The New York Head—A precise standardized volume conductor model for EEG source localization and tES targeting , 2015, NeuroImage.

[20]  Josef Parvizi,et al.  Corresponding ECoG and fMRI category-selective signals in human ventral temporal cortex , 2016, Neuropsychologia.

[21]  Arnaud Delorme,et al.  EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing , 2011, Comput. Intell. Neurosci..

[22]  L. Parra,et al.  Memorable Audiovisual Narratives Synchronize Sensory and Supramodal Neural Responses , 2016, eNeuro.

[23]  Janice Chen,et al.  Dynamic reconfiguration of the default mode network during narrative comprehension , 2016, Nature Communications.

[24]  Marina Schmid,et al.  An Introduction To The Event Related Potential Technique , 2016 .

[25]  Stefan Haufe,et al.  Large-scale EEG/MEG source localization with spatial flexibility , 2011, NeuroImage.

[26]  Lucas C. Parra,et al.  Recipes for the linear analysis of EEG , 2005, NeuroImage.

[27]  Stefan Haufe,et al.  Finding brain oscillations with power dependencies in neuroimaging data , 2014, NeuroImage.

[28]  Michael J. Morais,et al.  Global network influences on local functional connectivity , 2015, Nature Neuroscience.

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

[30]  Robert Oostenveld,et al.  Trial-by-trial coupling between EEG and BOLD identifies networks related to alpha and theta EEG power increases during working memory maintenance , 2009, NeuroImage.

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

[32]  Orrin Devinsky,et al.  Localization of dense intracranial electrode arrays using magnetic resonance imaging , 2012, NeuroImage.

[33]  N. Ramsey,et al.  Neurophysiologic correlates of fMRI in human motor cortex , 2012, Human brain mapping.

[34]  L. Parra,et al.  Human Neuroscience Original Research Article Correlated Components of Ongoing Eeg Point to Emotionally Laden Attention – a Possible Marker of Engagement? , 2022 .

[35]  Riitta Hari,et al.  Intersubject consistency of cortical MEG signals during movie viewing , 2014, NeuroImage.

[36]  Roberto D. Pascual-Marqui,et al.  Discrete, 3D distributed, linear imaging methods of electric neuronal activity. Part 1: exact, zero error localization , 2007, 0710.3341.

[37]  N. Logothetis.  The Underpinnings of the BOLD Functional Magnetic Resonance Imaging Signal , 2003, The Journal of Neuroscience.

[38]  Lucas C. Parra,et al.  Extracting multidimensional stimulus-response correlations using hybrid encoding-decoding of neural activity , 2017, NeuroImage.

[39]  A. Villringer,et al.  Rolandic alpha and beta EEG rhythms' strengths are inversely related to fMRI‐BOLD signal in primary somatosensory and motor cortex , 2009, Human brain mapping.

[40]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[41]  R. Eckhorn,et al.  Visual stimulation elicits locked and induced gamma oscillations in monkey intracortical- and EEG-potentials, but not in human EEG , 1999, Experimental Brain Research.

[42]  N. Logothetis,et al.  The Amplitude and Timing of the BOLD Signal Reflects the Relationship between Local Field Potential Power at Different Frequencies , 2012, The Journal of Neuroscience.

[43]  Jason J. Ki,et al.  Attention Strongly Modulates Reliability of Neural Responses to Naturalistic Narrative Stimuli , 2016, The Journal of Neuroscience.

[44]  Bin He,et al.  Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: An EEG and fMRI study of motor imagery and movements , 2010, NeuroImage.

[45]  Klaus-Robert Müller,et al.  Temporal kernel CCA and its application in multimodal neuronal data analysis , 2010, Machine Learning.

[46]  Biyu J. He,et al.  The fMRI signal, slow cortical potential and consciousness , 2009, Trends in Cognitive Sciences.

[47]  I. Fried,et al.  Coupling Between Neuronal Firing, Field Potentials, and fMRI in Human Auditory Cortex , 2005, Science.

[48]  Jeremy R. Manning,et al.  Broadband Shifts in Local Field Potential Power Spectra Are Correlated with Single-Neuron Spiking in Humans , 2009, The Journal of Neuroscience.

[49]  Karl J. Friston,et al.  Measurement of the mapping between intracranial EEG and fMRI recordings in the human brain , 2017, bioRxiv.

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

[51]  Clare Grall,et al.  Reliability of fMRI time series: Similarity of neural processing during movie viewing , 2017, bioRxiv.

[52]  John S. Johnson,et al.  Audience preferences are predicted by temporal reliability of neural processing , 2014, Nature Communications.

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

[54]  J. Winawer,et al.  Neuronal synchrony and the relation between the blood-oxygen-level dependent response and the local field potential , 2017, PLoS biology.

[55]  John Ashburner,et al.  A fast diffeomorphic image registration algorithm , 2007, NeuroImage.

[56]  R. Malach,et al.  Intersubject Synchronization of Cortical Activity During Natural Vision , 2004, Science.

[57]  J. Matias Palva,et al.  Infra-Slow EEG Fluctuations Are Correlated with Resting-State Network Dynamics in fMRI , 2014, The Journal of Neuroscience.

[58]  Nick F. Ramsey,et al.  Frequency specific spatial interactions in human electrocorticography: V1 alpha oscillations reflect surround suppression , 2013, NeuroImage.

[59]  D. Louis Collins,et al.  Unbiased average age-appropriate atlases for pediatric studies , 2011, NeuroImage.

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

[61]  S. Yantis,et al.  Control of Attention Shifts between Vision and Audition in Human Cortex , 2004, The Journal of Neuroscience.

[62]  Andreas Ziehe,et al.  Combining sparsity and rotational invariance in EEG/MEG source reconstruction , 2008, NeuroImage.

[63]  D. Heeger,et al.  Reliability of cortical activity during natural stimulation , 2010, Trends in Cognitive Sciences.

[64]  Andrea Bergmann,et al.  Statistical Parametric Mapping The Analysis Of Functional Brain Images , 2016 .

[65]  R. Oostenveld,et al.  Neuronal Dynamics Underlying High- and Low-Frequency EEG Oscillations Contribute Independently to the Human BOLD Signal , 2011, Neuron.

[66]  N. Logothetis,et al.  Frequency-Band Coupling in Surface EEG Reflects Spiking Activity in Monkey Visual Cortex , 2009, Neuron.

[67]  M. Corbetta,et al.  Inter-species activity correlations reveal functional correspondences between monkey and human brain areas , 2012, Nature Methods.

[68]  Denis G. Pelli,et al.  ECVP '07 Abstracts , 2007, Perception.

[69]  Gabriel Curio,et al.  Monochromatic Ultra-Slow (~0.1Hz) Oscillations in the human electroencephalogram and their relation to hemodynamics , 2014, NeuroImage.

[70]  M. Sams,et al.  Inter-Subject Synchronization of Prefrontal Cortex Hemodynamic Activity During Natural Viewing , 2008, The open neuroimaging journal.