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Cited by 105Advanced MRI Techniques and ApplicationsOptical Imaging and Spectroscopy TechniquesAdvanced Neuroimaging Techniques and Applications

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Estimating the transfer function from neuronal activity to BOLD using simultaneous EEG-fMRI

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Rosa, M.J., Kilner, J., Blankenburg, F., Josephs, O., Penny, W.·2010·NeuroImage
DOI 10.1016/j.neuroimage.2009.09.011PMC PMC2793371
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Abstract

Previous studies using combined electrical and hemodynamic measurements of brain activity, such as EEG and (BOLD) fMRI, have yielded discrepant results regarding the relationship between neuronal activity and the associated BOLD response. In particular, some studies suggest that this link, or transfer function, depends on the frequency content of neuronal activity, while others suggest that total neuronal power accounts for the changes in BOLD. Here we explored this dependency by comparing different frequency-dependent and -independent transfer functions, using simultaneous EEG-fMRI. Our results suggest that changes in BOLD are indeed associated with changes in the spectral profile of neuronal activity and that these changes do not arise from one specific spectral band. Instead they result from the dynamics of the various frequency components together, in particular, from the relative power between high and low frequencies. Understanding the nature of the link between neuronal activity and BOLD plays a crucial role in improving the interpretability of BOLD images as well as on the design of more robust and realistic models for the integration of EEG and fMRI.

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Crossref · 10.1016/j.neuroimage.2009.09.011

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Figures

Fig. 1 Example of artefact-corrected EEG time series for the first 10 seconds of the first visual stimulation period: (a) mean activity of electrodes O1 and O2. (b) Projection onto first principal component (SVD time series).
Fig. 1 Example of artefact-corrected EEG time series for the first 10 seconds of the first visual stimulation period: (a) mean activity of electrodes O1 and O2. (b) Projection onto first principal component (SVD time series).CC BY · PubMed Central
Fig. 2 Topography (2D) of the EEG first principal component for a representative subject. The locations of the occipital and frontal electrodes are indicated by their respective names.
Fig. 2 Topography (2D) of the EEG first principal component for a representative subject. The locations of the occipital and frontal electrodes are indicated by their respective names.CC BY · PubMed Central
Fig. 3 Power spectra of the SSVERs for EEG acquired outside (left) and inside the scanner (right) averaged over the three sessions of one representative subject. The frequencies on top of each plot correspond to the reversing frequencies of the visual flicker stimuli.
Fig. 3 Power spectra of the SSVERs for EEG acquired outside (left) and inside the scanner (right) averaged over the three sessions of one representative subject. The frequencies on top of each plot correspond to the reversing frequencies of the visual flicker stimuli.CC BY · PubMed Central
Fig. 4 Effect of visual flicker stimulation on fMRI data. Single-subject analyses (3 sessions per subject) and fixed-effects group analysis (9 sessions in total), p < 0.05 (FWE). The voxel locations on the left correspond to the most significant cluster maximum for the group analysis (Talairach space).
Fig. 4 Effect of visual flicker stimulation on fMRI data. Single-subject analyses (3 sessions per subject) and fixed-effects group analysis (9 sessions in total), p < 0.05 (FWE). The voxel locations on the left correspond to the most significant cluster maximum for the group analysis (Talairach space).CC BY · PubMed Central
Fig. 5 Example regressors for (a) Total Power, (b) Heuristic, and (c) Frequency Response (3 bands) models after convolution with the HRF (subject 2). (d) Example BOLD time series for the same period of time and subject, at the most significant cluster maximum ([12, − 101, 18] mm, Talairach space) from the fixed-effects group analysis of the main effects of visual stimulation (Fig. 4).
Fig. 5 Example regressors for (a) Total Power, (b) Heuristic, and (c) Frequency Response (3 bands) models after convolution with the HRF (subject 2). (d) Example BOLD time series for the same period of time and subject, at the most significant cluster maximum ([12, − 101, 18] mm, Talairach space) from the fixed-effects group analysis of the main effects of visual stimulation (Fig. 4).CC BY · PubMed Central
Fig. 6 Fixed-effects SPM analyses (p < 0.001 (uncorrected)) for the Heuristic, Total Power, and Frequency Response (3 bands) models analysed in separate design matrices. The voxel locations on the left correspond to the most significant cluster maximum after small volume correction with the BOLD activation mask (Talairach space).
Fig. 6 Fixed-effects SPM analyses (p < 0.001 (uncorrected)) for the Heuristic, Total Power, and Frequency Response (3 bands) models analysed in separate design matrices. The voxel locations on the left correspond to the most significant cluster maximum after small volume correction with the BOLD activation mask (Talairach space).CC BY · PubMed Central
Fig. 7 Contrast estimates and 90% CI for (a) Heuristic, (b) Total Power, and (c) Frequency Response with 3 bands (analysed individually). The estimates include the canonical HRF, as well as its temporal and dispersion derivatives.
Fig. 7 Contrast estimates and 90% CI for (a) Heuristic, (b) Total Power, and (c) Frequency Response with 3 bands (analysed individually). The estimates include the canonical HRF, as well as its temporal and dispersion derivatives.CC BY · PubMed Central
Fig. 8 Two-way model comparison between (a) Total Power versus Heuristic and (b) Total Power versus Frequency Response (fixed-effects SPM analyses (p < 0.001 (uncorrected)). The voxel locations on the left correspond to the most significant cluster maximum after small volume correction with the BOLD activation mask (Talairach space). These F-maps show the correlations between EEG and BOLD that are uniquely attributable to each model within a pair.
Fig. 8 Two-way model comparison between (a) Total Power versus Heuristic and (b) Total Power versus Frequency Response (fixed-effects SPM analyses (p < 0.001 (uncorrected)). The voxel locations on the left correspond to the most significant cluster maximum after small volume correction with the BOLD activation mask (Talairach space). These F-maps show the correlations between EEG and BOLD that are uniquely attributable to each model within a pair.CC BY · PubMed Central
Fig. 9 Three-way model comparison: fixed-effects SPM analyses (p < 0.001 (uncorrected)). Heuristic, Total Power, and Frequency Response (3 bands). The voxel locations on the left correspond to the most significant cluster maximum after small volume correction with the BOLD activation mask (Talairach space). These F-maps show correlations between EEG and BOLD that are uniquely attributable to each model.
Fig. 9 Three-way model comparison: fixed-effects SPM analyses (p < 0.001 (uncorrected)). Heuristic, Total Power, and Frequency Response (3 bands). The voxel locations on the left correspond to the most significant cluster maximum after small volume correction with the BOLD activation mask (Talairach space). These F-maps show correlations between EEG and BOLD that are uniquely attributable to each model.CC BY · PubMed Central
Fig. 10 Comparison between Heuristic and its un-normalised version, the u-Heuristic: fixed-effects SPM analysis (p < 0.001 (uncorrected)). The voxel locations on the left correspond to the most significant cluster maximum after small volume correction with the BOLD activation mask (Talairach space). These F-maps show correlations between EEG and BOLD that are uniquely attributable to each model.
Fig. 10 Comparison between Heuristic and its un-normalised version, the u-Heuristic: fixed-effects SPM analysis (p < 0.001 (uncorrected)). The voxel locations on the left correspond to the most significant cluster maximum after small volume correction with the BOLD activation mask (Talairach space). These F-maps show correlations between EEG and BOLD that are uniquely attributable to each model.CC BY · PubMed Central

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Metadata

Authors
Rosa, M. J., Kilner, J., Blankenburg, F., Josephs, O., & Penny, W.
Affiliations
  • Wellcome Centre for Human Neuroimaging (GB)
  • Bernstein Center for Computational Neuroscience Berlin (DE)
Corresponding author
Rosa, M.J.
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2010-01
Volume
49
Issue
2
Pages
1496-1509
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2009.09.011
PMC
PMC2793371
OpenAlex
W2011544331
Cited by
105
References
59
Citations by year
2026: 2 · 2025: 4 · 2024: 5 · 2023: 3 · 2022: 5 · 2021: 4 · 2020: 4 · 2019: 7 · 2018: 6 · 2017: 4 · 2016: 9 · 2015: 6 · 2014: 8 · 2013: 10 · 2012: 9
FWCI
7.02
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • UCL Discovery (University College London) (submittedVersion)
  • Research Portal (King's College London) (submittedVersion, other-oa)
Funders
Wellcome Trust and the Portuguese Foundation for Science and Technology (FCT)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Optical Imaging and Spectroscopy Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Keywords
BOLD signal, neuronal activity, transfer function, EEG-fMRI integration, spectral power, hemodynamic response, simultaneous EEG-fMRI
MeSH terms
Adult, Algorithms, Brain, Electroencephalography, Humans, Magnetic Resonance Imaging, Male, Models, Theoretical, Neurons, Oxygen, Photic Stimulation, Signal Processing, Computer-Assisted, Time Factors, Visual Perception, Cluster Analysis, Artifacts, Nonlinear Dynamics, Principal Component Analysis

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