Cortical activity in multiple motor areas during sequential finger movements: an application of independent component analysis.

Multiple cortical regions such as the supplementary motor area (SMA), premotor cortex (PM), and primary motor cortex (M1) are involved in the sequential execution of hand movements, but it is unclear how these areas collaborate in the preparation and execution of ipsilateral and contralateral hand movements. In this study, we used right-handed subjects to examine the spatial distribution and temporal profiles of motor-related activity during visually cued sequential finger movements by applying independent component analysis (ICA) to event-related functional magnetic resonance imaging (fMRI) signals. The particular merit of the ICA method is that it allows brain activity in individual subjects to be elucidated without making a priori assumptions about the anatomical areas that are activated or the temporal profile of activity. By applying ICA, we found that (1) the SMA contributed to both the preparation and execution of movements of the right and left hand; (2) the left M1 and dorsal premotor cortex (PMd) contributed to both the preparation and execution of movements of the right and left hand, whereas the right M1 and PMd contributed mainly to the execution of movements of the left hand; (3) pre-SMA areas were activated in some subjects in concert with the posterior parietal and prefrontal cortex; and (4) fMRI signals over superficial cortical draining veins could be distinguished from cortical activation. We suggest that ICA is useful for categorizing distributed task-related activities in individual subjects into several spatially independent activities that represent functional units in motor control.

Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter.

An application of independent component analysis (ICA) was attempted to develop a method of processing magnetic resonance (MR) images to extract physiologically independent components representing tissue relaxation times and achieve improved visualization of normal and pathologic structures. Anatomical T1-weighted, T2-weighted and proton density images were obtained from 10 normal subjects, 3 patients with brain tumors and 1 patient with multiple sclerosis. The data sets were analyzed using ICA based on the learning rule of Bell and Sejnowski after prewhitening operations. The three independent components obtained from the three original data sets corresponded to (1) short T1 components representing myelin of white matter and lipids, (2) relatively short T1 components representing gray matter and (3) long T2 components representing free water. The involvement of gray or white matter in brain tumor cases and the demyelination in the case of multiple sclerosis were enhanced and visualized in independent component images. ICA can potentially achieve separation of tissues with different relaxation characteristics and generate new contrast images of gray and white matter. With the proper choice of contrast for the original images, ICA may be useful not only for extracting subtle or hidden changes but also for preprocessing transformation before clustering and segmenting the structure of the human brain.