Cluster analysis of muscle functional MRI data

Abstract

magnetic resonance imaging (mfMRI) is frequently used to determine spatial patterns of muscle involvement in exercising humans. A frequent finding in mfMRI is that, even within synergistic muscle groups, signal intensity (SI) data from individual voxels can be quite heterogeneous. The purpose of this study was to develop a novel method for organizing heterogeneous mfMRI data into clusters whose members behave similarly to each other but distinctly from members of other clusters and apply it in studies of functional compartmentalization in the anterior compartment of the leg. An algorithm was developed that compared the SI time courses of adjacent voxels and grouped together voxels that were sufficiently similar. The algorithm's performance was verified by using simulated data sets with known regional differences in SI time courses that were then applied to experimental mfMRI data acquired from six male subjects (age 22.6 Ϯ 0.9 yr, mean Ϯ SE) who sustained isometric contractions of the dorsiflexors at 40% of maximum voluntary contraction. The experimental data were also characterized by using a traditional analysis (userspecified regions of interest from a single image), in which the relative change in SI and the contrast-to-noise ratio [CNR; 100%ϫ(SI RESTING Ϫ SIACTIVE)/(noise standard deviation)] were measured. In general, clusters were found in areas in which the CNR exceeded 5. Cluster analysis made functional distinctions between regions of muscle that were not seen with traditional analysis. In conclusion, cluster analysis's use of the full SI time course provides more sensitivity to muscle functional compartmentation than traditional analysis.transverse relaxation time constant; image processing; time series; exercise; dorsiflexors DURING THE PAST 15 YEARS, muscle functional magnetic resonance imaging (mfMRI) has emerged as a promising tool for studying muscle involvement during exercise because it is sensitive to the spatial pattern and extent of muscle utilization. For example, mfMRI has been used to identify different muscle utilization patterns in novice and elite rowers (9) and in uphill and horizontal running (25). It has also been used to detect changes in muscle function consequent to clinical conditions such as peripheral vascular disease (31). The interpretation of these data, however, is limited by the absence of a full theoretical understanding of which physiological variables produce the mfMRI response and how their contributions may differ during and after exercise.Multiexponential relaxation analysis has shown that the major contribution to signal intensity (SI) changes in mfMRI is made by the increase in the transverse relaxation time constant (T2) of intracellular water protons (4,22,23). Proposed explanations for intracellular T2 increases include the accumulation of end products of cellular energy metabolism, which cause water to move into the cell (4, 21), decreased intracellular pH (4, 8), and water shifts from an intracellular compartment possessing a short T2 (ϳ20 ms) to a second int...

Spatial heterogeneity in the muscle functional MRI signal intensity time course: effect of exercise intensity

“…Qualitatively, this finding is similar to that of Akima et al [19] in their study of intermittent isotonic dorsiflexion exercise and to that of our previous study of sustained isometric dorsiflexion at 40% MVC [18]. Moreover, this study demonstrates that this spatial heterogeneity is exercise intensity dependent, because it exists during contractions at 60% of MVC but not during contractions at 30% of MVC.…”

“…Moreover, this study demonstrates that this spatial heterogeneity is exercise intensity dependent, because it exists during contractions at 60% of MVC but not during contractions at 30% of MVC. Finally, the findings confirm those of our previous study, in which we observed heterogeneity in the mfMRI SI time course during exercise but not following exercise [18].…”

“…While postexercise T 2 -weighted SI changes are useful as a noninvasive indication of the spatial pattern of muscle activation during a previous bout of exercise, it has also been shown that postexercise T 2 -weighted SI changes may not fully capture the spatially heterogeneous functional properties of exercising muscles [18]. For such applications, it may be necessary to examine the time course of SI changes, obtained during the exercise bout itself [18,19].…”

“…While postexercise T 2 -weighted SI changes are useful as a noninvasive indication of the spatial pattern of muscle activation during a previous bout of exercise, it has also been shown that postexercise T 2 -weighted SI changes may not fully capture the spatially heterogeneous functional properties of exercising muscles [18]. For such applications, it may be necessary to examine the time course of SI changes, obtained during the exercise bout itself [18,19]. For instance, regional variations in the mfMRI SI time course obtained during sustained isometric dorsiflexion exercise at 40% of MVC have been observed in the anterior compartment muscles that may reflect heterogeneity in the metabolic, hemodynamic and/or mechanical responses to neural activation [18].…”

“…For such applications, it may be necessary to examine the time course of SI changes, obtained during the exercise bout itself [18,19]. For instance, regional variations in the mfMRI SI time course obtained during sustained isometric dorsiflexion exercise at 40% of MVC have been observed in the anterior compartment muscles that may reflect heterogeneity in the metabolic, hemodynamic and/or mechanical responses to neural activation [18].…”