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Cited by 166Advanced MRI Techniques and ApplicationsEEG and Brain-Computer InterfacesAdvanced Neuroimaging Techniques and Applications

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Electrophysiological correlates of the BOLD signal for EEG-informed fMRI

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Murta, Teresa, Leite, Marco, Carmichael, David W., Figueiredo, Patrícia, Lemieux, Louis·2015·Human Brain Mapping
DOI 10.1002/hbm.22623PMC PMC4280889
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Abstract

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are important tools in cognitive and clinical neuroscience. Combined EEG-fMRI has been shown to help to characterise brain networks involved in epileptic activity, as well as in different sensory, motor and cognitive functions. A good understanding of the electrophysiological correlates of the blood oxygen level-dependent (BOLD) signal is necessary to interpret fMRI maps, particularly when obtained in combination with EEG. We review the current understanding of electrophysiological-haemodynamic correlates, during different types of brain activity. We start by describing the basic mechanisms underlying EEG and BOLD signals and proceed by reviewing EEG-informed fMRI studies using fMRI to map specific EEG phenomena over the entire brain (EEG-fMRI mapping), or exploring a range of EEG-derived quantities to determine which best explain colocalised BOLD fluctuations (local EEG-fMRI coupling). While reviewing studies of different forms of brain activity (epileptic and nonepileptic spontaneous activity; cognitive, sensory and motor functions), a significant attention is given to epilepsy because the investigation of its haemodynamic correlates is the most common application of EEG-informed fMRI. Our review is focused on EEG-informed fMRI, an asymmetric approach of data integration. We give special attention to the invasiveness of electrophysiological measurements and the simultaneity of multimodal acquisitions because these methodological aspects determine the nature of the conclusions that can be drawn from EEG-informed fMRI studies. We emphasise the advantages of, and need for, simultaneous intracranial EEG-fMRI studies in humans, which recently became available and hold great potential to improve our understanding of the electrophysiological correlates of BOLD fluctuations.

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Crossref · 10.1002/hbm.22623

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Figures

Figure 1 Common pipeline of EEG‐informed fMRI in epilepsy. Functional MRI is used to map the generators of interictal activity (IEDs), visually identified in the simultaneously recorded EEG. Matrix design example borrowed with permission from Chaudhary et al., Neuroimage, 2012, 61, 1383–1393. EEG traces and BOLD fMRI maps illustrations borrowed with permission from Vulliemoz et al., Neuroimage, 2011, 54, 182–190. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]
Figure 1 Common pipeline of EEG‐informed fMRI in epilepsy. Functional MRI is used to map the generators of interictal activity (IEDs), visually identified in the simultaneously recorded EEG. Matrix design example borrowed with permission from Chaudhary et al., Neuroimage, 2012, 61, 1383–1393. EEG traces and BOLD fMRI maps illustrations borrowed with permission from Vulliemoz et al., Neuroimage, 2011, 54, 182–190. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]CC BY · PubMed Central
Figure 2 Brain signals at multiple scales: from thousands of neurons to whole‐brain. Illustration of recording of scalp EEG, ECoG, depth EEG and LFP signals, and three hypothetical BOLD‐fMRI clusters, in different lobes, on different sides of the brain. The spatial sensitivity profiles of the four electrophysiological techniques are sketched in shaded grey: mainly neocortical for scalp EEG and ECoG, local to each electrode pair for depth EEG (∼1 cm3), and local to each microelectrode for LFP (∼100 μm3). LFP recording is illustrated with a draw from Ramón y Cajal, 1899. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]
Figure 2 Brain signals at multiple scales: from thousands of neurons to whole‐brain. Illustration of recording of scalp EEG, ECoG, depth EEG and LFP signals, and three hypothetical BOLD‐fMRI clusters, in different lobes, on different sides of the brain. The spatial sensitivity profiles of the four electrophysiological techniques are sketched in shaded grey: mainly neocortical for scalp EEG and ECoG, local to each electrode pair for depth EEG (∼1 cm3), and local to each microelectrode for LFP (∼100 μm3). LFP recording is illustrated with a draw from Ramón y Cajal, 1899. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]CC BY · PubMed Central
Figure 3 General EEG‐informed fMRI integration scheme, highlighting the mechanisms underlying each signal. In a given voxel, the neuronal activity generates an ensemble of postsynaptic potentials (ePSP). The temporarily and spatially synchronised summated PSPs produce the primary current sources (PCD). The head volume conductor properties transform the PCD into EEG. The ePSP generates a vasomotor feed forward signal (VFFS), via its own forward model, which is in turn transformed, via the haemodynamic forward model, into the observed BOLD signal. ePSP, PCD, VFFS, EEG and BOLD are time‐dependent. The EEG is considered to have the same time evolution as the PCD, which is considered to be a driver for the BOLD signal. This is an asymmetrical EEG and fMRI data integration approach because the EEG temporal dynamics are taken as surrogates for the VFFS. Diagram was adapted with permission from Valdes‐Sosa et al., Human Brain Mapping, 2009, 30, 2701–2721.
Figure 3 General EEG‐informed fMRI integration scheme, highlighting the mechanisms underlying each signal. In a given voxel, the neuronal activity generates an ensemble of postsynaptic potentials (ePSP). The temporarily and spatially synchronised summated PSPs produce the primary current sources (PCD). The head volume conductor properties transform the PCD into EEG. The ePSP generates a vasomotor feed forward signal (VFFS), via its own forward model, which is in turn transformed, via the haemodynamic forward model, into the observed BOLD signal. ePSP, PCD, VFFS, EEG and BOLD are time‐dependent. The EEG is considered to have the same time evolution as the PCD, which is considered to be a driver for the BOLD signal. This is an asymmetrical EEG and fMRI data integration approach because the EEG temporal dynamics are taken as surrogates for the VFFS. Diagram was adapted with permission from Valdes‐Sosa et al., Human Brain Mapping, 2009, 30, 2701–2721.CC BY · PubMed Central

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Cites

Research works printed but not linked: 3

  1. Ritter, 2010, EEG quality: The image acquisiton artefact. In: Mulert C, Lemieux L, editors. EEG-fMRI Physiological Basis, Technique, and Applications
  2. Vaughn, 1974, Bioelectric Recording Tecniques, Part B. Electroencephalography and Human Brain Potentials, 158
  3. Villringer, 2010, EEG-fMRI Physiological Basis, Technique, and Applications

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Metadata

Authors
Murta, T., Leite, M., Carmichael, D. W., Figueiredo, P., & Lemieux, L.
Affiliations
  • University of Lisbon (PT)
  • University College London (GB)
  • National Hospital for Neurology and Neurosurgery (GB)
Corresponding author
Murta, Teresa
ORCID
Venue
Human Brain Mapping
Publisher
Wiley
Published
2014-10-03
Volume
36
Issue
1
Pages
391-414
ISSN
1065-9471 (ISSN-L)
DOI
10.1002/hbm.22623
PMC
PMC4280889
OpenAlex
W1591966483
Semantic Scholar
213e8a7e4f617a884af93681624845d67264d85a
Cited by
165
References
249
Citations by year
2026: 16 · 2025: 12 · 2024: 7 · 2023: 4 · 2022: 11 · 2021: 17 · 2020: 20 · 2019: 21 · 2018: 26 · 2017: 10 · 2016: 13 · 2015: 8
FWCI
1.61
Type
journal article
Language
en
Open access
Licence
doi.wiley.com/10.1002/tdm_license_1.1 (tdm, 333 d)
Locations
  • Human Brain Mapping (publishedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
Funders
  • Department of Health's NIHR Biomedical Research Centres funding scheme
  • Portuguese Science Foundation (FCT) (SFRH/BD/80421/2011 and PTDC/SAU-ENB/112294/2009)
  • Action Medical Research (SP4646)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
BOLD signal, epilepsy, hemodynamic response, spontaneous brain activity
MeSH terms
Brain, Brain Mapping, Electroencephalography, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Oxygen, Brain Waves

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