The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Cortical and Subcortical Correlates of Electroencephalographic Alpha Rhythm Modulation
Abstract
Neural correlates of electroencephalographic (EEG) alpha rhythm are poorly understood. Here, we related EEG alpha rhythm in awake humans to blood-oxygen-level-dependent (BOLD) signal change determined by functional magnetic resonance imaging (fMRI). Topographical EEG was recorded simultaneously with fMRI during an open versus closed eyes and an auditory stimulation versus silence condition. EEG was separated into spatial components of maximal temporal independence using independent component analysis. Alpha component amplitudes and stimulus conditions served as general linear model regressors of the fMRI signal time course. In both paradigms, EEG alpha component amplitudes were associated with BOLD signal decreases in occipital areas, but not in thalamus, when a standard BOLD response curve (maximum effect at ∼6 s) was assumed. The part of the alpha regressor independent of the protocol condition, however, revealed significant positive thalamic and mesencephalic correlations with a mean time delay of ∼2.5 s between EEG and BOLD signals. The inverse relationship between EEG alpha amplitude and BOLD signals in primary and secondary visual areas suggests that widespread thalamocortical synchronization is associated with decreased brain metabolism. While the temporal relationship of this association is consistent with metabolic changes occurring simultaneously with changes in the alpha rhythm, sites in the medial thalamus and in the anterior midbrain were found to correlate with short time lag. Assuming a canonical hemodynamic response function, this finding is indicative of activity preceding the actual EEG change by some seconds.
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Cites
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
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
Dynamic Brain Sources of Visual Evoked Responses
Makeig, S., Westerfield, M., Jung, T.-P., Enghoff, S., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2002) · Science · doi:10.1126/science.1066168 · Cited by 1,630
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Shaping Functional Architecture by Oscillatory Alpha Activity: Gating by Inhibition
Jensen, O., & Mazaheri, A. (2010) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2010.00186 · Cited by 3,237
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
Intelligence and neural efficiency
Neubauer, A. C., & Fink, A. (2009) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2009.04.001 · Cited by 878
Power-Law Scaling in the Brain Surface Electric Potential
Miller, K. J., Sorensen, L. B., Ojemann, J. G., & den Nijs, M. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000609 · Cited by 742
Kelly, S. P., Lalor, E. C., Reilly, R. B., & Foxe, J. J. (2006) · Journal of Neurophysiology · doi:10.1152/jn.01234.2005 · Cited by 715
Cortical activity during motor execution, motor imagery, and imagery-based online feedback
Miller, K. J., Schalk, G., Fetz, E. E., den Nijs, M., Ojemann, J. G., & Rao, R. P. N. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0913697107 · Cited by 622
Thalamic Mechanisms of EEG Alpha Rhythms and Their Pathological Implications
Hughes, S. W., & Crunelli, V. (2005) · The Neuroscientist · doi:10.1177/1073858405277450 · Cited by 565
Fink, A., Grabner, R. H., Benedek, M., Reishofer, G., Hauswirth, V., Fally, M., Neuper, C., Ebner, F., & Neubauer, A. C. (2009) · Human Brain Mapping · doi:10.1002/hbm.20538 · Cited by 505
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Debener, S., Ullsperger, M., Siegel, M., & Engel, A. K. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.09.010 · Cited by 428
Neuronal Mechanisms of Cortical Alpha Oscillations in Awake-Behaving Macaques
Bollimunta, A., Chen, Y., Schroeder, C. E., & Ding, M. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.2699-08.2008 · Cited by 434
Ritter, P., Moosmann, M., & Villringer, A. (2009) · Human Brain Mapping · doi:10.1002/hbm.20585 · Cited by 400
Frontal theta EEG activity correlates negatively with the default mode network in resting state
Scheeringa, R., Bastiaansen, M. C. M., Petersson, K. M., Oostenveld, R., Norris, D. G., & Hagoort, P. (2008) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2007.05.017 · Cited by 400
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
Ritter, P., & Villringer, A. (2006) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2006.06.008 · Cited by 294
BOLD correlates of EEG alpha phase-locking and the fMRI default mode network
Jann, K., Dierks, T., Boesch, C., Kottlow, M., Strik, W., & Koenig, T. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.001 · Cited by 286
Yuan, H., Liu, T., Szarkowski, R., Rios, C., Ashe, J., & He, B. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.028 · Cited by 274
Connectivity in Autism: A Review of MRI Connectivity Studies
Rane, P., Cochran, D., Hodge, S. M., Haselgrove, C., Kennedy, D. N., & Frazier, J. A. (2015) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000072 · Cited by 234
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Laufs, H. (2008) · Human Brain Mapping · doi:10.1002/hbm.20600 · Cited by 194
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Tinguely, G., Finelli, L. A., Landolt, H.-P., Borbély, A. A., & Achermann, P. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.03.017 · Cited by 147
Luft, C. D. B., & Bhattacharya, J. (2015) · Scientific Reports · doi:10.1038/srep15717 · Cited by 143
“…study, parietal alpha was higher during LA than HA mood induction, which could be associated with decreased cortical excitability and higher thalamic activation (specifically dorsal thalamus) since this brain area is deemed as one of the main sources of parietal and occipital alpha rhythms 34,35 .”
Lindahl, J. R., Kaplan, C. T., Winget, E. M., & Britton, W. B. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00973 · Cited by 120
Finding thalamic BOLD correlates to posterior alpha EEG
Liu, Z., de Zwart, J. A., Yao, B., van Gelderen, P., Kuo, L.-W., & Duyn, J. H. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.025 · Cited by 104
Infraslow (<0.1Hz) oscillations in thalamic relay nuclei
Hughes, S. W., Lőrincz, M. L., Parri, H. R., & Crunelli, V. (2011) · Progress in Brain Research · doi:10.1016/b978-0-444-53839-0.00010-7 · Cited by 104
ATP-Dependent Infra-Slow (<0.1 Hz) Oscillations in Thalamic Networks
Lőrincz, M. L., Geall, F., Bao, Y., Crunelli, V., & Hughes, S. W. (2009) · PLoS ONE · doi:10.1371/journal.pone.0004447 · Cited by 103
Wan, L., Huang, H., Schwab, N., Tanner, J., Rajan, A., Lam, N. B., Zaborszky, L., Li, C. R., Price, C. C., & Ding, M. (2019) · Human Brain Mapping · doi:10.1002/hbm.24395 · Cited by 96
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Maier, S., Szalkowski, A., Kamphausen, S., Perlov, E., Feige, B., Blechert, J., Philipsen, A., van Elst, L. T., Kalisch, R., & Tüscher, O. (2012) · PLoS ONE · doi:10.1371/journal.pone.0050120 · Cited by 77
Mash, L. E., Reiter, M. A., Linke, A. C., Townsend, J., & Müller, R. (2018) · Developmental Neurobiology · doi:10.1002/dneu.22570 · Cited by 52
Chien, H., Lin, H., Lai, M., Gau, S. S., & Tseng, W. I. (2015) · Autism Research · doi:10.1002/aur.1457 · Cited by 54
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Metadata
- Authors
- Feige, B., Scheffler, K., Esposito, F., Di Salle, F., Hennig, J., & Seifritz, E.
- Venue
- Journal of Neurophysiology
- Publisher
- American Physiological Society
- Published
- 2005-05
- Volume
- 93
- Issue
- 5
- Pages
- 2864-2872
- ISSN
- 0022-3077 (ISSN-L)
- 1522-1598
- OpenAlex
- W2047737075
- Cited by
- 364
- References
- 3
- FWCI
- 6.24
- Type
- journal article
- Language
- en
- Open access
- closed
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