CoChat
Sign Up
Cited by 384EEG and Brain-Computer InterfacesAdvanced MRI Techniques and ApplicationsOptical Imaging and Spectroscopy Techniques

Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations

Open access
Liu, Arthur K., Belliveau, John W., Dale, Anders M.·1998·Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.95.15.8945PMC PMC21182
Open this paper in CoChat — free account

Abstract

The goal of our research is to develop an experimental and analytical framework for spatiotemporal imaging of human brain function. Preliminary studies suggest that noninvasive spatiotemporal maps of cerebral activity can be produced by combining the high spatial resolution (millimeters) of functional MRI (fMRI) with the high temporal resolution (milliseconds) of electroencephalography (EEG) and magnetoencephalography (MEG). Although MEG and EEG are sensitive to millisecond changes in mental activity, the ability to resolve source localization and timing is limited by the ill-posed “inverse” problem. We conducted Monte Carlo simulations to evaluate the use of MRI constraints in a linear estimation inverse procedure, where fMRI weighting, cortical location and orientation, and sensor noise statistics were realistically incorporated. An error metric was computed to quantify the effects of fMRI invisible (“missing”) sources, “extra” fMRI sources, and cortical orientation errors. Our simulation results demonstrate that prior anatomical and functional information from MRI can be used to regularize the EEG/MEG inverse problem, giving an improved solution with high spatial and temporal resolution. An fMRI weighting of approximately 90% was determined to provide the best compromise between separation of activity from correctly localized sources and minimization of error caused by missing sources. The accuracy of the estimate was relatively independent of the number and extent of the sources, allowing for incorporation of physiologically realistic multiple distributed sources. This linear estimation method provides an operator-independent approach for combining information from fMRI, MEG, and EEG and represents a significant advance over traditional dipole modeling.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.95.15.8945

Cites

41 of 41 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • fMRI-biased regularization algorithms share the assumption that EEG/MEG sources are more likely to occur where fMRI shows activation.

    “such algorithms all share and utilize a common assumption that EEG/MEG sources are more likely to occur at where fMRI views as activated.”

    From Electrophysiological source imaging: A noninvasive window to brain dynamics (2018) · cites this paper as [148]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 17

  1. [3]Raichle M E. In: Handbook of Physiology: The Nervous System V, Higher Functions of the Brain. Plum F, editor. Bethesda, MD: Am. Physiol. Soc.; 1987. pp. 643–674. [Google Scholar]
  2. [11]Hamalainen M, Hari R, Ilmoniemi R J, Knuutila J, Lounasmaa O V. Rev Mod Physics. 1993;65:413–497. [Google Scholar]
  3. [12]Ilmoniemi R J. In: The Inverse Problem. Lubbig H, editor. Berlin: Akademie Verlag; 1995. pp. 89–106. [Google Scholar]
  4. [13]George J S, Lewis P S, Ranken D M, Kaplan L, Wood C C. SPIE Med Imaging V: Image Physics. 1991;1443:37–51. [Google Scholar]
  5. [14]Belliveau J W. In: Quantification of Brain Function: Tracer Kinetics and Image Analysis in Brain PET. Uemura K, Lassen N A, Jones T, Kanno I, editors. Amsterdam: Elsevier; 1993. pp. 639–667. [Google Scholar]
  6. [20]Nunez P L. Electric Fields of the Brain. New York: Oxford Univ. Press; 1981. [Google Scholar]
  7. [21]Helmholtz H. Ann Phys Chem. 1853;89:211–233. , 353–377. [Google Scholar]
  8. [26]Hamalainen M S, Ilmoniemi R J. Interpreting Measured Magnetic Fields of the Brain: Estimates of Current Distributions. Finland: Helsinki Univ. of Technology; 1984. [Google Scholar]
  9. [28]Belliveau J W, Baker J R, Buchbinder B R, Stern C E, Kwong K K, Cosgrove G R, Alpert N M, Rosen B R. In: Cerebrovascular Diseases: Nineteenth Princeton Stroke Conference. Moskowitz M A, Caplan L R, editors. Boston: Butterworth-Heinemann; 1995. pp. 519–539. [Google Scholar]
  10. [32]FitzGerald D B, Cosgrove G R, Ronner S, Jiang H, Buchbinder B R, Belliveau J W, Rosen B R, Benson R R. AJNR. 1997;18:1529–1539. [PMC free article] [PubMed] [Google Scholar]
  11. [33]Knuutila J E T, Ahonen A I, Hamalainen M S, Kajola M J, Laine P O, Lounasmaa O V, Parkkonen L T, Simola J T A, Tesche C D. IEEE Trans Magn. 1993;29:3315–3320. [Google Scholar]
  12. [37]Dale A M. Ph.D. thesis. San Diego: Univ. of California; 1994. [Google Scholar]
  13. [40]Deutsch R. Estimation Theory. Englewood Cliffs, NJ: Prentice Hall; 1965. [Google Scholar]
  14. [42]Backus G E, Gilbert F. Geophys J R Astronom Soc. 1968;16:169–205. [Google Scholar]
  15. [43]Backus G E, Gilbert F. Philos Trans R Soc London A. 1970;266:123–192. [Google Scholar]
  16. [45]Grave de Peralta Menendez R, Gonzalez Andino S, Lutkenhoner B. Brain Topography. 1996;9:117–124. [Google Scholar]
  17. [47]Belliveau J W, Simpson G V, Baker J R, Huang-Hellinger F R, Wedeen V J, George J S. Hum Brain Mapp. 1995;S1:88. (Abstr.). [Google Scholar]

Cited by

Sorted by citations

Related

Authors
Liu, A. K., Belliveau, J. W., & Dale, A. M.
Affiliations
Massachusetts General Hospital (US)
ORCID
Venue
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences
Published
1998-07-21
Volume
95
Issue
15
Pages
8945-8950
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
DOI
10.1073/pnas.95.15.8945
PMC
PMC21182
OpenAlex
W2072786348
Cited by
384
References
41
Citations by year
2026: 3 · 2025: 2 · 2024: 5 · 2023: 3 · 2022: 8 · 2021: 3 · 2020: 1 · 2019: 16 · 2018: 10 · 2017: 10 · 2016: 6 · 2015: 11 · 2014: 11 · 2013: 17 · 2012: 15
FWCI
7.85
Type
journal article
Language
en
Open access
Locations
  • Proceedings of the National Academy of Sciences (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • 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
Keywords
functional MRI, magnetoencephalography, EEG, inverse problem regularization, linear estimation, sensor noise, dipole modeling
MeSH terms
Brain, Humans, Magnetic Resonance Imaging, Monte Carlo Method, Radiography, Magnetoencephalography

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review