[en] Single-trial fluctuations in the EEG signal have been shown to temporally correlate with the fMRI BOLD response and are valuable for modeling trial-to-trial fluctuations in responses. The P1 and N1 components of the visual ERP are sensitive to different attentional modulations, suggesting that different aspects of stimulus processing can be modeled with these ERP parameters. As such, different patterns of BOLD covariation for P1 and N1 informed regressors would be expected; however, current findings are equivocal. We investigate the effects of variations in attention on P1 and N1 informed BOLD activation in a visual oddball task. Simultaneous EEG-fMRI data were recorded from 13 healthy participants during three conditions of a visual oddball task: Passive, Count, and Respond. We show that the P1 and N1 components of the visual ERP can be used in the integration-by-prediction method of EEG-fMRI data integration to highlight brain regions related to target detection and response production. Our data suggest that the P1 component of the ERP reflects changes in sensory encoding of stimulus features and is more informative for the Passive and Count conditions. The N1, on the other hand, was more informative for the Respond condition, suggesting that it can be used to model the processing of stimulus, meaning specifically discriminating one type of stimulus from another, and processes involved in integrating sensory information with response selection. Our results show that an understanding of the underlying electrophysiology is necessary for a thorough interpretation of EEG-informed fMRI analysis.
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
Allen, P. J., Josephs, O., & Turner, R. (2000). A method for removing imaging artifact from continuous EEG recorded during functional MRI. Neuroimage, 12, 230-239.
Bagshaw, A. P., & Warbrick, T. (2007). Single trial variability of EEG and fMRI responses to visual stimuli. Neuroimage, 38, 280-292.
Biessmann, F., Plis, S., Meinecke, F. C., Eichele, T., & Muller, K. R. (2011). Analysis of multimodal neuroimaging data. IEEE Reviews in Biomedical Engineering, 4, 26-58.
Corbetta, M., Kincade, J. M., Ollinger, J. M., McAvoy, M. P., & Shulman, G. L. (2000). Voluntary orienting is dissociated from target detection in human posterior parietal cortex. Nature Neuroscience, 3, 292-297.
Corbetta, M., & Shulman, G. L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nature Reviews Neuroscience, 3, 201-215.
Debener, S., Ullsperger, M., Siegel, M., Fiehler, K., von Cramon, D. Y., & Engel, A. K. (2005). Trial-by-trial coupling of concurrent electroencephalogram and functional magnetic resonance imaging identifies the dynamics of performance monitoring. Journal of Neuroscience, 25, 11730-11737.
Delorme, A., & Makeig, S. (2004). EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. Journal of Neuroscience Methods, 134, 9-21.
Di Russo, F., Martinez, A., Sereno, M. I., Pitzalis, S., & Hillyard, S. A. (2002). Cortical sources of the early components of the visual evoked potential. Human Brain Mapping, 15, 95-111.
Eichele, T., Specht, K., Moosmann, M., Jongsma, M. L., Quiroga, R. Q., Nordby, H., et al. (2005). Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI. Proceedings of the National Academy of Sciences, U.S.A., 102, 17798-17803.
Fell, J. (2007). Cognitive neurophysiology: Beyond averaging. Neuroimage, 37, 1069-1072.
Forman, S. D., Cohen, J. D., Fitzgerald, M., Eddy, W. F., Mintun, M. A., & Noll, D. C. (1995). Improved assessment of significant activation in functional magnetic-resonance-imaging (fMRI)-Use of a cluster-size threshold. Magnetic Resonance in Medicine, 33, 636-647.
Friston, K. J., Worsley, K. J., Frackowiak, R. S. J., Mazziotta, J. C., & Evans, A. C. (1993). Assessing the significance of focal activations using their spatial extent. Human Brain Mapping, 1, 210-220.
Fuglø, D., Pedersen, H., Rostrup, E., Hansen, A. E., & Larsson, H. B. (2012). Correlation between single-trial visual evoked potentials and the blood oxygenation level dependent response in simultaneously recorded electroencephalography-functional magnetic resonance imaging. Magnetic Resonance in Medicine, 68, 252-260.
Goldman, R. I., Wei, C. Y., Philiastides, M. G., Gerson, A. D., Friedman, D., Brown, T. R., et al. (2009). Single-trial discrimination for integrating simultaneous EEG and fMRI: Identifying cortical areas contributing to trial-to-trial variability in the auditory oddball task. Neuroimage, 47, 136-147.
Heeger, D. J., & Ress, D. (2002). What does fMRI tell us about neuronal activity? Nature Reviews Neuroscience, 3, 142-151.
Heekeren, H. R., Marrett, S., & Ungerleider, L. G. (2008). The neural systems that mediate human perceptual decision making. Nature Reviews Neuroscience, 9, 467-479.
Herrmann, C. S., & Knight, R. T. (2001). Mechanisms of human attention: Event-related potentials and oscillations. Neuroscience and Biobehavioral Reviews, 25, 465-476.
Hou, Y., & Liu, T. (2012). Neural correlates of object-based attentional selection in human cortex. Neuropsychologia, 50, 2916-2925.
Huettel, S. A., & McCarthy, G. (2004). What is odd in the oddball task? Prefrontal cortex is activated by dynamic changes in response strategy. Neuropsychologia, 42, 379-386.
Huster, R. J., Debener, S., Eichele, T., & Herrmann, C. S. (2012). Methods for simultaneous EEG-fMRI: An introductory review. Journal of Neuroscience, 32, 6053-6060.
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17, 825-841.
Jenkinson, M., & Smith, S. (2001). A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5, 143-156.
Karch, S., Feuerecker, R., Leicht, G., Meindl, T., Hantschk, I., Kirsch, V., et al. (2010). Separating distinct aspects of the voluntary selection between response alternatives: N2-and P3-related BOLD responses. Neuroimage, 51, 356-364.
Lauritzen, M., & Gold, L. (2003). Brain function and neurophysiological correlates of signals used in functional neuroimaging. Journal of Neuroscience, 23, 3972-3980.
Liu, T., Hospadaruk, L., Zhu, D. C., & Gardner, J. L. (2011). Feature-specific attentional priority signals in human cortex. Journal of Neuroscience, 31, 4484-4495.
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T., & Oeltermann, A. (2001). Neurophysiological investigation of the basis of the fMRI signal. Nature, 412, 150-157.
Luck, S. J., Woodman, G. F., & Vogel, E. K. (2000). Event-related potential studies of attention. Trends in Cognitive Sciences, 4, 432-440.
Musall, S., von Pfostl, V., Rauch, A., Logothetis, N. K., & Whittingstall, K. (2012). Effects of neural synchrony on surface EEG. Cerebral Cortex. doi:101093/cercor/bhs389.
Niazy, R. K., Beckmann, C. F., Iannetti, G. D., Brady, J. M., & Smith, S. M. (2005). Removal of fMRI environment artifacts from EEG data using optimal basis sets. Neuroimage, 28, 720-737.
Nolan, H., Whelan, R., & Reilly, R. B. (2010). FASTER: Fully automated statistical thresholding for EEG artifact rejection. Journal of Neuroscience Methods, 192, 152-162.
Novitskiy, N., Ramautar, J. R., Vanderperren, K., De Vos, M., Mennes, M., Mijovic, B., et al. (2011). The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task. Neuroimage, 54, 824-835.
Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Neuropsychologia, 9, 97-113.
Oostenveld, R., & Praamstra, P. (2001). The five percent electrode system for high-resolution EEG and ERP measurements. Clinical Neurophysiology, 112, 713-719.
Rorden, C., Karnath, H. O., & Bonilha, L. (2007). Improving lesion-symptom mapping. Journal of Cognitive Neuroscience, 19, 1081-1088.
Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17, 143-155.
Vogel, E. K., & Luck, S. J. (2000). The visual N1 component as an index of a discrimination process. Psychophysiology, 37, 190-203.
Warbrick, T., Mobascher, A., Brinkmeyer, J., Musso, F., Richter, N., Stoecker, T., et al. (2009). Single-trial P3 amplitude and latency informed event-related fMRI models yield different BOLD response patterns to a target detection task. Neuroimage, 47, 1532-1544.
Warbrick, T., Reske, M., & Shah, J. (2013). Do EEG paradigms work in fMRI? Varying task demands in the visual oddball paradigm: Implications for task design and results interpretation. Neuroimage, 77, 177-185.
Weissman, D. H., Roberts, K. C., Visscher, K. M., & Woldorff, M. G. (2006). The neural bases of momentary lapses in attention. Nature Neuroscience, 9, 971-978.
Woolrich, M. W., Ripley, B. D., Brady, M., & Smith, S. M. (2001). Temporal autocorrelation in univariate linear modeling of fMRI data. Neuroimage, 14, 1370-1386.
Worsley, K. J., Evans, A. C., Marrett, S., & Neelin, P. (1992). A three-dimensional statistical analysis for CBF activation studies in human brain. Journal of Cerebral Blood Flow and Metabolism, 12, 900-918.
Similar publications
Sorry the service is unavailable at the moment. Please try again later.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.