Cited by 308Neural and Behavioral Psychology StudiesNeural dynamics and brain functionFunctional Brain Connectivity Studies

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Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI

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Eichele, Tom, Specht, Karsten, Moosmann, Matthias, Jongsma, Marijtje L. A., Quiroga, Rodrigo Quian, Nordby, Helge, Hugdahl, Kenneth·2005·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.0505508102PMC PMC1295592

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Abstract

The brain acts as an integrated information processing system, which methods in cognitive neuroscience have so far depicted in a fragmented fashion. Here, we propose a simple and robust way to integrate functional MRI (fMRI) with single trial event-related potentials (ERP) to provide a more complete spatiotemporal characterization of evoked responses in the human brain. The idea behind the approach is to find brain regions whose fMRI responses can be predicted by paradigm-induced amplitude modulations of simultaneously acquired single trial ERPs. The method was used to study a variant of a two-stimulus auditory target detection (odd-ball) paradigm that manipulated predictability through alternations of stimulus sequences with random or regular target-to-target intervals. In addition to electrophysiologic and hemodynamic evoked responses to auditory targets per se , single-trial modulations were expressed during the latencies of the P2 (170-ms), N2 (200-ms), and P3 (320-ms) components and predicted spatially separated fMRI activation patterns. These spatiotemporal matches, i.e., the prediction of hemodynamic activation by time-variant information from single trial ERPs, permit inferences about regional responses using fMRI with the temporal resolution provided by electrophysiology.

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Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
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Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001) · Nature · doi:10.1038/35084005 · Cited by 6,551
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578
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Is the P300 component a manifestation of context updating?
Donchin, E., & Coles, M. G. H. (1988) · Behavioral and Brain Sciences · doi:10.1017/s0140525x00058027 · Cited by 3,585
The N1 wave of the human electric and magnetic response to sound: A review and an analysis of the component structure
Näätänen, R., & Picton, T. (1987) · Psychophysiology · doi:10.1111/j.1469-8986.1987.tb00311.x · Cited by 3,321
Evoked-potential correlates of stimulus uncertainty
Sutton, S., Braren, M., Zubin, J., & John, E. R. (1965) · Science · doi:10.1126/science.150.3700.1187 · Cited by 3,061
Perception and the Conditioned Reflex
Hay, J. C., & Sokolov, Y. N. (1966) · The American Journal of Psychology · doi:10.2307/1420734 · Cited by 2,123

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Spencer K. M. (2005) in Event Related Potentials A Methods Handbook ed. Handy T. C. (MIT Press Cambridge MA) pp. 209-228.
Halgren E. & Marinkovic K. (1995) in Recent Advances in Event-Related Brain Potential Research eds. Ogura C. Koga Y. & Shimokochi M. (Elsevier Amsterdam) pp. 1072-1084.

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Authors: Eichele, T., Specht, K., Moosmann, M., Jongsma, M. L. A., Quiroga, R. Q., Nordby, H., & Hugdahl, K.
Affiliations: Radboud University Nijmegen (NL)
ORCID: Specht, Karsten
Jongsma, Marijtje L. A.
Quiroga, Rodrigo Quian
Hugdahl, Kenneth
Venue: Proceedings of the National Academy of Sciences
Publisher: National Academy of Sciences
Published: 2005-11-28
Volume: 102
Issue: 49
Pages: 17798-17803
ISSN: 0027-8424 (ISSN-L)
1091-6490
DOI: 10.1073/pnas.0505508102
PMC: PMC1295592
OpenAlex: W2130038497
Cited by: 308
References: 59
Citations by year: 2026: 5 · 2025: 8 · 2023: 4 · 2022: 13 · 2021: 6 · 2020: 2 · 2019: 11 · 2018: 9 · 2017: 11 · 2016: 15 · 2015: 12 · 2014: 19 · 2013: 24 · 2012: 26
FWCI: 7.98
Type: journal article
Language: en
Open access: green
europepmc.org
Locations: Proceedings of the National Academy of Sciences (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Leicester Research Archive (University of Leicester) (submittedVersion)
Radboud Repository (Radboud University) (submittedVersion)
Funders: Bundesministerium für Bildung und Forschung
Norges Forskningsråd
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords: single-trial event-related potentials, functional MRI, spatiotemporal integration, evoked potentials, oddball paradigm, auditory target detection, predictability, P2 component, N2 component, P300
MeSH terms: Adult, Brain, Electroencephalography, Evoked Potentials, Female, Humans, Magnetic Resonance Imaging, Male, Neurons, Time Factors

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Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI

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