Cluster analysis: a useful tool for the analysis of cerebral laser-Doppler scanning data.

Laser-Doppler (LD) fluxmetry (LDF) is a widely used method for the measurement of relative tissue perfusion. Assessing LD-flux at multiple locations using a scanning technique greatly reduces movement artefacts and makes repetitive measurements at the same location possible. However, measurements in brain are often confounded by superficial cortical vessels. Commonly applied strategies to circumvent this problem, such as defining a cut-off point to exclude the high flux data of vessels or calculating the median from multiple locations to estimate regional cerebral blood flow (rCBF) all have specific shortcomings. The aim of this study was to analyse LD-data by mathematically discriminating between parenchymal and vessel data based on the distribution of flux data. Data was obtained by scanning the cortex of 15 male Sprague-Dawley rats using a matrix of 6x10 equidistant (500 microm) points. Standard statistical analysis as well as cluster analysis using the complete linkage algorithm was performed. The LD-data showed a bimodal frequency distribution with low values representing parenchymal and high values representing vessel flux. Parenchyma and vessels were reliably discriminated by cluster analysis. This was shown by mapping the vessel clusters on the scan matrix with the location of the superficial cortical vessels using Chi-square testing (p<0.0001). The parenchymal data followed a Gaussian normal distribution (p<0.851), whereas the vessel data did not (p<0.0001). Thus, cluster analysis is useful to discriminate parenchymal from vessel flux, thereby significantly improving the accuracy of LD-scanning data.