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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
Open accessAbstract
Similar to the occipital alpha rhythm, electroencephalographic (EEG) signals in the alpha- and beta-frequency bands can be suppressed by movement or motor imagery and have thus been thought to represent the "idling state" of the sensorimotor cortex. A negative correlation between spontaneous alpha EEG and blood-oxygen-level-dependent (BOLD) signals has been reported in combined EEG and fMRI (functional Magnetic Resonance Imaging) experiments when subjects stayed at the resting state or alternated between the resting state and a task. However, the precise nature of the task-induced alpha modulation remains elusive. It was not clear whether alpha/beta rhythm suppressions may co-vary with BOLD when conducting tasks involving varying activations of the cortex. Here, we quantified the task-evoked responses of BOLD and alpha/beta-band power of EEG directly in the cortical source domain, by using source imaging technology, and examined their covariation across task conditions in a mixed block and event-related design. In this study, 13 subjects performed tasks of right-hand, right-foot or left-hand movement and motor imagery when EEG and fMRI data were separately collected. Task-induced increase of BOLD signal and decrease of EEG amplitudes in alpha and beta bands were shown to be co-localized at the somatotopic sensorimotor cortex. At the corresponding regions, the reciprocal changes of the two signals co-varied in the magnitudes across imagination and movement conditions. The spatial correspondence and negative covariation between the two measurements were further shown to exist at somatotopic brain regions associated with different body parts. These results suggest an inverse functional coupling relationship between task-induced changes of BOLD and low-frequency EEG signals.
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“Combining ESI and fMRI data has also shed light on the nature of event related de/synchronization (75).”
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Cites
The assessment and analysis of handedness: The Edinburgh inventory
Oldfield, R. C. (1971) · Neuropsychologia · doi:10.1016/0028-3932(71)90067-4 · Cited by 35,900
Statistical parametric maps in functional imaging: A general linear approach
Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. ‐., Frith, C. D., & Frackowiak, R. S. J. (1994) · Human Brain Mapping · doi:10.1002/hbm.460020402 · Cited by 9,688
Event-related EEG/MEG synchronization and desynchronization: basic principles
Pfurtscheller, G., & Lopes da Silva, F. H. (1999) · Clinical Neurophysiology · doi:10.1016/s1388-2457(99)00141-8 · Cited by 7,328
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Neurophysiological investigation of the basis of the fMRI signal
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
Hämäläinen, M., Hari, R., Ilmoniemi, R. J., Knuutila, J., & Lounasmaa, O. V. (1993) · Reviews of Modern Physics · doi:10.1103/revmodphys.65.413 · Cited by 4,684
Über das Elektrenkephalogramm des Menschen
Berger, H. (1929) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/bf01797193 · Cited by 4,266
What we can do and what we cannot do with fMRI
Logothetis, N. K. (2008) · Nature · doi:10.1038/nature06976 · Cited by 3,612
Event-Related fMRI: Characterizing Differential Responses
Friston, K. J., Fletcher, P. C., Josephs, O., Holmes, A., Rugg, M. D., & Turner, R. (1998) · NeuroImage · doi:10.1006/nimg.1997.0306 · Cited by 2,017
Dale, A. M., & Sereno, M. I. (1993) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.1993.5.2.162 · Cited by 2,003
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He, B., Sohrabpour, A., Brown, E., & Liu, Z. (2018) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-062117-120853 · Cited by 297
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Goldfine, A. M., Victor, J. D., Conte, M. M., Bardin, J. C., & Schiff, N. D. (2011) · Clinical Neurophysiology · doi:10.1016/j.clinph.2011.03.022 · Cited by 240
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Wolpaw, J. R. (2013) · Handbook of clinical neurology · doi:10.1016/b978-0-444-52901-5.00006-x · Cited by 231
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Zich, C., Debener, S., Kranczioch, C., Bleichner, M. G., Gutberlet, I., & Vos, M. D. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.020 · Cited by 186
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Mennella, R., Patron, E., & Palomba, D. (2017) · Behaviour Research and Therapy · doi:10.1016/j.brat.2017.02.002 · Cited by 183
Mullinger, K. J., Mayhew, S. D., Bagshaw, A. P., Bowtell, R. W., & Francis, S. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.02.029 · Cited by 165
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Yuan, H., Perdoni, C., & He, B. (2010) · Journal of Neural Engineering · doi:10.1088/1741-2560/7/2/026001 · Cited by 154
Self-citationA novel approach of decoding EEG four-class motor imagery tasks via scout ESI and CNN
Hou, Y., Zhou, L., Jia, S., & Lun, X. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab4af6 · Cited by 152
He, B., Yuan, H., Meng, J., & Gao, S. (2020) · Springer eBooks · doi:10.1007/978-3-030-43395-6_4 · Cited by 150
Self-citationScheeringa, R., Mazaheri, A., Bojak, I., Norris, D. G., & Kleinschmidt, A. K. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.4697-10.2011 · Cited by 141
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Tzagarakis, C., West, S. L., & Pellizzer, G. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00246 · Cited by 119
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Vasilyev, A. N., Либуркина, С. П., Yakovlev, L., Perepelkina, O. S., & Kaplan, A. (2017) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2017.02.005 · Cited by 98
Brookshire, G., & Casasanto, D. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036036 · Cited by 92
Luft, C. D. B., Nolte, G., & Bhattacharya, J. (2013) · Journal of Neuroscience · doi:10.1523/jneurosci.2565-12.2013 · Cited by 90
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Formaggio, E., Storti, S. F., Cerini, R., Fiaschi, A., & Manganotti, P. (2010) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2010.06.030 · Cited by 90
Yi, W., Qiu, S., Wang, K., Qi, H., Zhang, L., Zhou, P., He, F., & Ming, D. (2014) · PLoS ONE · doi:10.1371/journal.pone.0114853 · Cited by 86
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Yuan, H., Ding, L., Zhu, M., Zotev, V. S., Phillips, R., & Bodurka, J. (2015) · Brain Connectivity · doi:10.1089/brain.2014.0336 · Cited by 98
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Self-citationZhang, C. H., Sohrabpour, A., Lu, Y., & He, B. (2016) · Human Brain Mapping · doi:10.1002/hbm.23220 · Cited by 80
Self-citationOfner, P., & Müller-Putz, G. (2014) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2014.2377023 · Cited by 73
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Toriyama, H., Ushiba, J., & Ushiyama, J. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00295 · Cited by 55
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Meng, J., Streitz, T. D., Gulachek, N., Suma, D., & He, B. (2018) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2018.2872855 · Cited by 59
Self-citationBowers, A. L., Saltuklaroglu, T., Harkrider, A. W., & Cuellar, M. E. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072024 · Cited by 61
Yuan, H., Perdoni, C., Yang, L., & He, B. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.5312-10.2011 · Cited by 55
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Tsuchimoto, S., Shindo, K., Hotta, F., Hanakawa, T., Liu, M., & Ushiba, J. (2019) · Neuroscience · doi:10.1016/j.neuroscience.2019.07.037 · Cited by 47
Gwon, D., & Ahn, M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118403 · Cited by 48
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Hu, Y., Liu, Y., Zhang, S., Zhang, T., Dai, B., Peng, B., Yang, H., & Dai, Y. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3249831 · Cited by 45
Ono, T., Tomita, Y., Inose, M., Ota, T., Kimura, A., Liu, M., & Ushiba, J. (2014) · Brain Topography · doi:10.1007/s10548-014-0382-6 · Cited by 45
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Marchesotti, S., Martuzzi, R., Schurger, A., Blefari, M. L., Millán, J. D. R., Bleuler, H., & Blanke, O. (2017) · Human Brain Mapping · doi:10.1002/hbm.23566 · Cited by 42
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Magnuson, J. R., Doesburg, S. M., & McNeil, C. J. (2021) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108076 · Cited by 42
Pinti, P., SIDDIQUI, M., Levy, A. D., Jones, E. J. H., & Tachtsidis, I. (2021) · Scientific Reports · doi:10.1038/s41598-021-83420-9 · Cited by 48
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Lin, J., Lu, J., Shu, Z., Yu, N., & Han, J. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106968 · Cited by 39
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Conti, E., Scaffei, E., Bosetti, C., Marchi, V., Costanzo, V., Dell’Oste, V., Mazziotti, R. M., Dell’Osso, L., Carmassi, C., Muratori, F., et al. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.785993 · Cited by 34
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Kanthack, T. F. D., Guillot, A., Altimari, L. R., Núñez-Nagy, S., Collet, C., & Rienzo, F. D. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149654 · Cited by 33
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Forenzo, D., Zhu, H., Shanahan, J., Lim, J., & He, B. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae145 · Cited by 36
Self-citationBowers, A. L., Saltuklaroglu, T., Harkrider, A. W., Wilson, M., & Toner, M. A. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00366 · Cited by 32
Li, W., Li, C., Xu, Q., & Ji, L. (2019) · Journal of Healthcare Engineering · doi:10.1155/2019/9167028 · Cited by 32
Metadata
- Authors
- Yuan, H., Liu, T., Szarkowski, R., Rios, C., Ashe, J., & He, B.
- Affiliations
- University of Minnesota (US)
- Veterans Health Administration (US)
- Corresponding author
- He, Bin
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-02
- Volume
- 49
- Issue
- 3
- Pages
- 2596-2606
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2818527
- OpenAlex
- W2029934372
- Cited by
- 274
- References
- 55
- Citations by year
- 2026: 11 · 2025: 20 · 2024: 14 · 2023: 21 · 2022: 14 · 2021: 16 · 2020: 16 · 2019: 19 · 2018: 15 · 2017: 25 · 2016: 18 · 2015: 18 · 2014: 15 · 2013: 20 · 2012: 15
- FWCI
- 5.00
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (RO1EB007920, RO1EB006433, P41RR008079, P30NS057091)
- NSF (CBET-0933067)
- NIH Neuro-Physical-Computational Sciences Fellowship (R90 DK070106)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Neurology › Transcranial Magnetic Stimulation Studies
- Keywords
- alpha rhythm, beta band, BOLD signal, sensorimotor cortex, motor imagery, motor movement, somatotopic mapping, cortical source localization
- MeSH terms
- Adult, Brain Mapping, Electroencephalography, Humans, Image Interpretation, Computer-Assisted, Imagination, Functional Laterality, Magnetic Resonance Imaging, Middle Aged, Movement, Signal Processing, Computer-Assisted, Somatosensory Cortex, Young Adult
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