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dc.contributor.authorHinds, Oliver
dc.contributor.authorGhosh, Satrajit S.
dc.contributor.authorThompson, Todd W.
dc.contributor.authorYoo, Julie J.
dc.contributor.authorTriantafyllou, Christina
dc.contributor.authorGabrieli, Susan
dc.contributor.authorGabrieli, John D. E.
dc.date.accessioned2016-05-11T23:44:33Z
dc.date.available2016-05-11T23:44:33Z
dc.date.issued2010-08
dc.date.submitted2010-07
dc.identifier.issn10538119
dc.identifier.urihttp://hdl.handle.net/1721.1/102456
dc.description.abstractEstimating moment-to-moment changes in blood oxygenation level dependent (BOLD) activation levels from functional magnetic resonance imaging (fMRI) data has applications for learned regulation of regional activation, brain state monitoring, and brain–machine interfaces. In each of these contexts, accurate estimation of the BOLD signal in as little time as possible is desired. This is a challenging problem due to the low signal-to-noise ratio of fMRI data. Previous methods for real-time fMRI analysis have either sacrificed the ability to compute moment-to-moment activation changes by averaging several acquisitions into a single activation estimate or have sacrificed accuracy by failing to account for prominent sources of noise in the fMRI signal. Here we present a new method for computing the amount of activation present in a single fMRI acquisition that separates moment-to-moment changes in the fMRI signal intensity attributable to neural sources from those due to noise, resulting in a feedback signal more reflective of neural activation. This method computes an incremental general linear model fit to the fMRI time series, which is used to calculate the expected signal intensity at each new acquisition. The difference between the measured intensity and the expected intensity is scaled by the variance of the estimator in order to transform this residual difference into a statistic. Both synthetic and real data were used to validate this method and compare it to the only other published real-time fMRI method.en_US
dc.description.sponsorshipAthinoula A. Martinos Center for Biomedical Imagingen_US
dc.description.sponsorshipMcGovern Institute for Brain Research at MITen_US
dc.language.isoen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.neuroimage.2010.07.060en_US
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivs Licenseen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.sourceProf. Gabrielien_US
dc.titleComputing moment-to-moment BOLD activation for real-time neurofeedbacken_US
dc.typeArticleen_US
dc.identifier.citationHinds, Oliver, Satrajit Ghosh, Todd W. Thompson, Julie J. Yoo, Susan Whitfield-Gabrieli, Christina Triantafyllou, and John D.E. Gabrieli. “Computing Moment-to-Moment BOLD Activation for Real-Time Neurofeedback.” NeuroImage 54, no. 1 (January 2011): 361–68.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Brain and Cognitive Sciencesen_US
dc.contributor.departmentMassachusetts Institute of Technology. Research Laboratory of Electronicsen_US
dc.contributor.departmentMcGovern Institute for Brain Research at MITen_US
dc.contributor.approverGabrieli, John D. E.en_US
dc.contributor.mitauthorHinds, Oliveren_US
dc.contributor.mitauthorGhosh, Satrajit S.en_US
dc.contributor.mitauthorThompson, Todd W.en_US
dc.contributor.mitauthorYoo, Julie J.en_US
dc.contributor.mitauthorGabrieli, Susanen_US
dc.contributor.mitauthorTriantafyllou, Christinaen_US
dc.contributor.mitauthorGabrieli, John D. E.en_US
dc.relation.journalNeuroImageen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dspace.orderedauthorsHinds, Oliver; Ghosh, Satrajit; Thompson, Todd W.; Yoo, Julie J.; Whitfield-Gabrieli, Susan; Triantafyllou, Christina; Gabrieli, John D.E.en_US
dspace.embargo.termsNen_US
dc.identifier.orcidhttps://orcid.org/0000-0002-5312-6729
dc.identifier.orcidhttps://orcid.org/0000-0003-1158-5692
dspace.mitauthor.errortrue
mit.licensePUBLISHER_CCen_US
mit.metadata.statusComplete


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