Computing moment-to-moment BOLD activation for real-time neurofeedback
| Autor: | Julie J. Yoo, Satrajit S. Ghosh, Susan Whitfield-Gabrieli, John D. E. Gabrieli, Oliver Hinds, Todd W. Thompson, Christina Triantafyllou |
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| Přispěvatelé: | Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology. Research Laboratory of Electronics, McGovern Institute for Brain Research at MIT, Gabrieli, John D. E., Hinds, Oliver, Ghosh, Satrajit S., Thompson, Todd W., Yoo, Julie J., Gabrieli, Susan, Triantafyllou, Christina |
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Computer science
Feedback Psychological Cognitive Neuroscience Speech recognition Residual Computing Methodologies Signal Article Image Processing Computer-Assisted medicine Humans Feedback Physiological medicine.diagnostic_test Noise (signal processing) business.industry Reproducibility of Results Estimator Biofeedback Psychology Magnetic resonance imaging Pattern recognition Magnetic Resonance Imaging Intensity (physics) Oxygen Moment (mathematics) Kinetics Neurology Artificial intelligence Neurofeedback business Functional magnetic resonance imaging Signal Transduction |
| Zdroj: | Prof. Gabrieli |
| Popis: | Estimating 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. Athinoula A. Martinos Center for Biomedical Imaging McGovern Institute for Brain Research at MIT |
| Databáze: | OpenAIRE |
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