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The use of single event ƒMRI and fuzzy clustering analysis to examine haemodynamic response timecourses in supplementary motor and primary motor cortical areas.

The roles of premotor, supplementary motor and primary motor cortical areas in the Organisation and control of volunlary movement have been studied using many different methods, all of which have differing strengths and weaknesses. Electrophysiological studies, examining cortical activity recorded from the scalp surface, have shown that a slowly increasing negative potential, termed the Bereitschaftspotential, precedes movement onset by l to 2 seconds [1]. This increasing pre-movement activity is thought to reflect processes associated with movement preparation involving mainly the supplementary motor area (SMA); however, it is difficult to accurately localise the cortical source of this pre-movement activity from surface potentials. Studies of regional cerebral blood flow, using PET, have shown activation of both the SMA and primary motor cortex during voluntary movement compared with rest, and activation mainly in the SMA when movements are imagined (assumed to involve similar processes of movement preparation), but not actually executed [2, 3]; however, this technique lacks the temporal resolution to make conclusions regarding the roles of various cortical areas in processes preceding movement onset. Functional MRI provides better spatial resolution for localising sources of activation, and has the potential to provide better temporal resolution than PET; however, most studies of motor activation to date have used relatively long image repetition times and box-car type designs alternating between periods of rest and activation, thereby providing little improvement in temporal resolution compared with previous PET studies. Echo planar functional MRI, using fast repetition times, has the potential to reveal haemodynamic response characteristics for single events or actions [4], however new methods for analysing such data are required. Standard fMRI designs which use relatively long repetition times and consist of alternating rest and activation periods are commonly analysed by correlation with a simple box-car reference function. Singleevent fMRI data can also be analysed by correlation with some modelled reference function [5]; however, such an approach requires prior knowledge of the expected time and duration of the haemodynamic response, which is not always available. Alternately, average timecourses within predefmed anatomical regions of interest can be examined. A study by Richter et al. [6], using time-resolved fMRI to examine the temporal characteristics of the haemodynamic response to a single movement, was able to separate activation occurring during periods of movement preparation compared with movement execution. By examining average timecourses within regions of interest, activity of the SMA and premotor cortex were found to be similar during both preparation and execution periods, whereas activity of the primary motor cortex was reduced during movement preparation. Similarly, a study by Wildgruber et ah [7] examined average haemodynamic response timecourses within the supplementary motor area and primary motor cortex (äs predefined regions of interest) associated with single self-paced finger movements, showing that activation within supplementary motor area begins significantly earlier than that in primary motor cortex. Such studies using region of interest analyses have provided important Information regarding differences in activation of areas for which involvement in particular tasks is already known. This analysis approach, however, does not take füll advantage of the ability of high resolution function MRI to localise activity without relying on prior assumptions of activity sources. Fuzzy clustering analysis [8] is a model-free analysis method which separates different timecourses present within a data set into clusters such that variability of timecourses within clusters is minimised and variability between clusters is maximised. In this way, fuzzy clustering analysis is able to separate timecourses associated with the activation task from those associated with artefact (e.g. physiological artefact associated with heart beat or respiration [9]) or drift, and can show the extent to which voxels within the whole brain space acquired show task-related haemodynamic response timecourses. The analysis of results, therefore, is not restricted only to particular regions of interest, nor only to particular timecourse characteristics. The aim of this study was to examine activation associated with single movements of the 5 finger with both high temporal and spatial resolution. Single slice EPI images were acquired with both 64 and 128 matrix sizes with fast repetition rate (80 ms and 160 ms