FROM BOLD-FMRI SIGNALS TO THE PREDICTION OF SUBJECTIVE PAIN PERCEPTION THROUGH A REGULARIZATION ALGORITHM
European Signal Processing Conference（2009）
摘要
Functional magnetic resonance imaging, in particular the BOLD-fMRI technique, plays a dominant role in hu- man brain mapping studies, mostly because of its non- invasiveness and relatively high spatio-temporal resolut ion. The main goal of fMRI data analysis has been to reveal the distributed patterns of brain areas involved in specific functions, by applying a variety of statistical methods wit h model-based or data-driven approaches. In the last years, several studies have taken a different approach, where the direction of analysis is reversed in order to probe whether fMRI signals can be used to predict perceptual or cognitive states. In this study we test the feasibility of predicting t he perceived pain intensity in healthy volunteers, based on fMRI signals collected during an experimental pain paradigm last- ing several minutes. In particular, we introduce a method- ological approach based on new regularization learning al- gorithms for regression problems.
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关键词
biomedical MRI,blood,brain,cognition,emotion recognition,medical signal processing,neurophysiology,patient diagnosis,psychology,regression analysis,BOLD-fMRI signals,BOLD-fMRI spatio-temporal resolution,blood oxygenation level dependent-fMRI technique,brain area distributed patterns,data-driven approaches,experimental pain paradigm,fMRI data analysis,fMRI signal-based pain intensity prediction,fMRI signal-probed cognitive states,fMRI signal-probed perceptual state,fMRI-based cognitive state prediction,fMRI-based perceptual state prediction,functional MRI signal-probed cognitive states,functional MRI signal-probed perceptual state,functional magnetic resonance image data analysis,functional magnetic resonance imaging,human brain mapping,model-based approaches,noninvasive BOLD-fMRI technique,pain intensity prediction feasibility,pain paradigm-collected fMRI signal,pain perception prediction,perceived pain intensity prediction,regression problems,regularization algorithm,regularization learning algorithms,statistical method application,subjective pain perception,
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