The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage
Open accessAbstract
A number of recent studies have begun to show the promise of magnetoencephalography (MEG) as a means to non-invasively measure functional connectivity within distributed networks in the human brain. However, a number of problems with the methodology still remain--the biggest of these being how to deal with the non-independence of voxels in source space, often termed signal leakage. In this paper we demonstrate a method by which non-zero lag cortico-cortical interactions between the power envelopes of neural oscillatory processes can be reliably identified within a multivariate statistical framework. The method is spatially unbiased, moderately conservative in false positive rate and removes linear signal leakage between seed and target voxels. We demonstrate this methodology in simulation and in real MEG data. The multivariate method offers a powerful means to capture the high dimensionality and rich information content of MEG signals in a single imaging statistic. Given a significant interaction between two areas, we go on to show how classical statistical tests can be used to quantify the importance of the data features driving the interaction.
Overview
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.
Integrity signals
Registry
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150
High Gamma Power Is Phase-Locked to Theta Oscillations in Human Neocortex
Canolty, R. T., Edwards, E., Dalal, S. S., Soltani, M., Nagarajan, S. S., Kirsch, H. E., Berger, M. S., Barbaro, N. M., & Knight, R. T. (2006) · Science · doi:10.1126/science.1128115 · Cited by 2,650
Neuronal Synchrony: A Versatile Code for the Definition of Relations?
Singer, W. (1999) · Neuron · doi:10.1016/s0896-6273(00)80821-1 · Cited by 2,581
Van Veen, B. D., Van Drongelen, W., Yuchtman, M., & Suzuki, A. (1997) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.623056 · Cited by 2,561
Electrophysiological signatures of resting state networks in the human brain
Mantini, D., Perrucci, M. G., Del Gratta, C., Romani, G. L., & Corbetta, M. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0700668104 · Cited by 1,938
Perception's shadow: long-distance synchronization of human brain activity
Rodriguez, E., George, N., Lachaux, J.-P., Martinerie, J., Renault, B., & Varela, F. J. (1999) · Nature · doi:10.1038/17120 · Cited by 1,915
Basic mathematical and electromagnetic concepts of the biomagnetic inverse problem
Sarvas, J. (1987) · Physics in Medicine and Biology · doi:10.1088/0031-9155/32/1/004 · Cited by 1,843
Dynamic imaging of coherent sources: studying neural interactions in the human brain
Gross, J., Kujala, J., Hämäläinen, M., Timmermann, L., Schnitzler, A., & Salmelin, R. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.694 · Cited by 1,810
Introduction to Multivariate Analysis
Lee, J. C. (1983) · Technometrics · doi:10.1080/00401706.1983.10487903 · Cited by 1,783
Identifying true brain interaction from EEG data using the imaginary part of coherency
Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S., & Hallett, M. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.04.029 · Cited by 1,756
Cited by
Moving magnetoencephalography towards real-world applications with a wearable system
Boto, E., Holmes, N., Leggett, J., Roberts, G., Shah, V., Meyer, S. S., Muñoz, L. D., Mullinger, K. J., Tierney, T. M., Bestmann, S., et al. (2018) · Nature · doi:10.1038/nature26147 · Cited by 1,377
Self-citationIntrinsic Coupling Modes: Multiscale Interactions in Ongoing Brain Activity
Engel, A. K., Gerloff, C., Hilgetag, C. C., & Nolte, G. (2013) · Neuron · doi:10.1016/j.neuron.2013.09.038 · Cited by 471
van Diessen, E., Numan, T., van Dellen, E., van der Kooi, A. W., Boersma, M., Hofman, D., van Lutterveld, R., van Dijk, B. W., van Straaten, E. C. W., Hillebrand, A., et al. (2015) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.11.018 · Cited by 417
Exploring the network dynamics underlying brain activity during rest
Cabral, J., Kringelbach, M. L., & Deco, G. (2014) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2013.12.005 · Cited by 351
Zhang, Y., Wu, W., Toll, R. T., Naparstek, S., Maron-Katz, A., Watts, M., Gordon, J., Jeong, J., Astolfi, L., Shpigel, E., et al. (2020) · Nature Biomedical Engineering · doi:10.1038/s41551-020-00614-8 · Cited by 241
Metadata
- Authors
- Brookes, M. J., Woolrich, M. W., & Barnes, G. R.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-11
- Volume
- 63
- Issue
- 2
- Pages
- 910-920
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2063171028
- Cited by
- 375
- References
- 43
- FWCI
- 6.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage. https://app.cochat.ai/papers/10.1016/j.neuroimage.2012.03.048
- The paper
- Brookes, M. J., Woolrich, M. W., & Barnes, G. R. (2012). Measuring functional connectivity in MEG: A multivariate approach insensitive to linear source leakage. NeuroImage. https://doi.org/10.1016/j.neuroimage.2012.03.048
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
