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|a Ou, Wanmei
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|a Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a Ou, Wanmei
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|a Golland, Polina
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|a Raij, Tommi
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|a Lin, Fa-Hsuan
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|a Golland, Polina
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|a Hamalainen, Matti S.
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|a Modeling Adaptation Effects in fMRI Analysis
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|b Springer-Verlag Berlin Heidelberg,
|c 2014-05-16T16:36:37Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/87029
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|a available in PMC 2013 June 30
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|a The standard general linear model (GLM) for rapid event-related fMRI design protocols typically ignores reduction in hemodynamic responses in successive stimuli in a train due to incomplete recovery from the preceding stimuli. To capture this adaptation effect, we incorporate a region-specific adaptation model into GLM. The model quantifies the rate of adaptation across brain regions, which is of interest in neuroscience. Empirical evaluation of the proposed model demonstrates its potential to improve detection sensitivity. In the fMRI experiments using visual and auditory stimuli, we observed that the adaptation effect is significantly stronger in the visual area than in the auditory area, suggesting that we must account for this effect to avoid bias in fMRI detection.
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|a National Institutes of Health (U.S.) (NIH R01 NS048279)
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|a National Institutes of Health (U.S.) (NIH R01 EB006385)
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|a National Institute for Biomedical Imaging and Bioengineering (U.S.) (NIH NIBIB NAMIC U54-EB005149)
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|a National Center for Research Resources (U.S.) (NIH NCRR P41-RR14075)
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|a National Science Foundation (U.S.) (NSF CAREER Award 0642971)
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|a Sigrid Jusélius Foundation
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|a Academy of Finland
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|a Finnish Cultural Foundation
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|a United States. Public Health Service (PHS training grant DA022759-03)
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|a en_US
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|a Article
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|t Medical Image Computing and Computer-Assisted Intervention - MICCAI 2009
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