Human brain mapping
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Human brain mapping · Jan 1998
Comparative Study Clinical TrialSomatosensory cortex: a comparison of the response to noxious thermal, mechanical, and electrical stimuli using functional magnetic resonance imaging.
In the present study, functional magnetic resonance imaging (fMRI) was used to examine pain perception in humans. Three types of noxious stimuli were presented: electric shock (20.8 mA, 2 Hz), heat (48 degrees C), and mechanical, as well as a control tactile stimulus. The significance of activation at the level of the voxel was determined using correlation analysis. ⋯ Lack of detectable activation in response to pure noxious stimuli supports the idea that nociceptive and nonnociceptive fibers are interspersed in the somatosensory cortex. Conflicting results from recent functional imaging studies of pain perception regarding cortical activation indicate that it is essential to consider both the tactile and nociceptive components of the stimuli used, the spatial extent of stimulation, and the possibility of adaptation to the response. Furthermore, these results suggest that subtractive or correlative methods may not be sufficiently sensitive to image the activity of nociceptive cells, which are sparsely distributed throughout the somatosensory cortex.
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Human brain mapping · Jan 1998
Clinical TrialAnalysis of fMRI data by blind separation into independent spatial components.
Current analytical techniques applied to functional magnetic resonance imaging (fMRI) data require a priori knowledge or specific assumptions about the time courses of processes contributing to the measured signals. Here we describe a new method for analyzing fMRI data based on the independent component analysis (ICA) algorithm of Bell and Sejnowski ([1995]: Neural Comput 7:1129-1159). We decomposed eight fMRI data sets from 4 normal subjects performing Stroop color-naming, the Brown and Peterson work/number task, and control tasks into spatially independent components. ⋯ Simulated movement artifact and simulated task-related activations added to actual fMRI data were clearly separated by the algorithm. ICA can be used to distinguish between nontask-related signal components, movements, and other artifacts, as well as consistently or transiently task-related fMRI activations, based on only weak assumptions about their spatial distributions and without a priori assumptions about their time courses. ICA appears to be a highly promising method for the analysis of fMRI data from normal and clinical populations, especially for uncovering unpredictable transient patterns of brain activity associated with performance of psychomotor tasks.