The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
Dynamic Statistical Parametric Mapping
Open accessAbstract
Functional magnetic resonance imaging (fMRI) can provide maps of brain activation with millimeter spatial resolution but is limited in its temporal resolution to the order of seconds. Here, we describe a technique that combines structural and functional MRI with magnetoencephalography (MEG) to obtain spatiotemporal maps of human brain activity with millisecond temporal resolution. This new technique was used to obtain dynamic statistical parametric maps of cortical activity during semantic processing of visually presented words. An initial wave of activity was found to spread rapidly from occipital visual cortex to temporal, parietal, and frontal areas within 185 ms, with a high degree of temporal overlap between different areas. Repetition effects were observed in many of the same areas following this initial wave of activation, providing evidence for the involvement of feedback mechanisms in repetition priming.
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“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 [149]
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Cites
Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. (1994) · Cited by 15,411
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
Self-citationCortical Surface-Based Analysis
Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147
Self-citation(2005) · Choice Reviews Online · doi:10.5860/choice.43-0626 · Cited by 4,726
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
Ungerleider, L. G. (1982) · Medical Entomology and Zoology · Cited by 4,531
Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation.
Kwong, K. K., Belliveau, J. W., Chesler, D. A., Goldberg, I. E., Weisskoff, R. M., Poncelet, B. P., Kennedy, D. N., Hoppel, B. E., Cohen, M. S., & Turner, R. (1992) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.89.12.5675 · Cited by 4,212
Self-citationIngle, D., Goodale, M. A., & Mansfield, R. J. W. (1982) · The MIT Press eBooks · Cited by 4,118
Large-scale neurocognitive networks and distributed processing for attention, language, and memory
Mesulam, M. (1990) · Annals of Neurology · doi:10.1002/ana.410280502 · Cited by 2,684
Borders of Multiple Visual Areas in Humans Revealed by Functional Magnetic Resonance Imaging
Sereno, M. I., Dale, A. M., Reppas, J. B., Kwong, K. K., Belliveau, J. W., Brady, T. J., Rosen, B. R., & Tootell, R. B. H. (1995) · Science · doi:10.1126/science.7754376 · Cited by 2,663
Self-citation
Research works printed but not linked: 4
- Chauvel, 1987, The “epileptogenic zone” in humans, Rev. Neurol. (Paris), 143, 443
- George, 1995, Functional Neuroimaging by Combined MRI, MEG and fMRI, Hum. Brain Mapp, S1, 89
- Mazziotta, 1983, Local cerebral glucose metabolic response to audiovisual stimulation and deprivation, Hum. Neurobiol, 2, 11
- Press, 1994
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Grill-Spector, K., Henson, R., & Martin, A. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.11.006 · Cited by 2,256
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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
Bar, M. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1476 · Cited by 1,613
Research on attention networks as a model for the integration of psychological science.
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Review on solving the inverse problem in EEG source analysis
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Babiloni, F., Cincotti, F., Babiloni, C., Carducci, F., Mattia, D., Astolfi, L., Basilisco, A., Rossini, P. M., Ding, L., Ni, Y., et al. (2005) · NeuroImage · doi:10.1016/j.neuroimage.2004.09.036 · Cited by 412
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Ritter, P., & Villringer, A. (2006) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2006.06.008 · Cited by 294
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Metadata
- Authors
- Dale, A. M., Liu, A. K., Fischl, B. R., Buckner, R. L., Belliveau, J. W., Lewine, J. D., & Halgren, E.
- Affiliations
- Massachusetts General Hospital (US)
- Washington University in St. Louis (US)
- University of Utah (US)
- Inserm (FR)
- Corresponding author
- Dale, Anders M.
- Venue
- Neuron
- Publisher
- Elsevier BV
- Published
- 2000-04
- Volume
- 26
- Issue
- 1
- Pages
- 55-67
- ISSN
- 0896-6273 (ISSN-L)
- OpenAlex
- W2013150886
- Cited by
- 1,787
- References
- 84
- Citations by year
- 2026: 77 · 2025: 99 · 2024: 90 · 2023: 89 · 2022: 98 · 2021: 83 · 2020: 94 · 2019: 91 · 2018: 83 · 2017: 66 · 2016: 64 · 2015: 71 · 2014: 70 · 2013: 70 · 2012: 66
- FWCI
- 18.78
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Neuron (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke (R01NS18741, R01NS018741)
- National Center for Research Resources (R01RR013609, R01-RR13609)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neurobiology of Language and Bilingualism
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- spatiotemporal mapping, semantic processing, word recognition, visual word processing, repetition priming, feedback mechanisms, multimodal neuroimaging, magnetoencephalography
- MeSH terms
- Brain, Brain Mapping, Cerebral Cortex, Cognition, Humans, Magnetic Resonance Imaging, Magnetoencephalography
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