Classification of primate spinothalamic and somatosensory thalamic neurons based on cluster analysis

Abstract

Data analyzed in this study were derived from the responses of 128 spinothalamic tract (STT) cells and 110 thalamic neurons recorded in 75 anesthetized monkeys. A k-means cluster analysis, a nonhierarchical clustering technique, was performed using the relative magnitudes of responses to a graded series of innocuous and noxious mechanical stimuli applied to the receptive field. For comparison, a parallel analysis was performed based on definitions of low-threshold (LT), wide dynamic range (WDR), and high-threshold (HT) cells used by our laboratory. For 128 STT cells, a classification scheme with three clusters was found statistically to be the best. This yielded groups of 22, 57, and 49 cells in clusters 1, 2, and 3, respectively. Cluster 1 cells were activated best by low-intensity mechanical stimuli, whereas cluster 3 cells were activated primarily by nociceptive stimuli. Cluster 2 cells had intermediate characteristics. When the classification scheme based on the cluster analysis was compared with the classification of the same neurons as LT, WDR, and HT cells, cluster 1 cells were divided into LT and WDR cells, whereas cluster 2 and 3 cells included WDR and HT cells. For 110 thalamic neurons, a classification scheme with five clusters was found statistically to be the best. Clusters 1-5 contained 25, 34, 17, 10, and 24 cells, respectively. Response characteristics of cells in each group indicated a gradual change in sensitivity to higher intensities of peripheral input from cluster 1 to 5. When this classification scheme was compared with the classification scheme previously used by our laboratory, cluster 1 cells belonged to the LT group, clusters 2 and 3 split into LT and WDR cells, and clusters 4 and 5 included WDR and HT cells. It is concluded that a classification scheme based on a cluster analysis of the responses of neurons to standardized stimuli may provide an objective and functionally meaningful way to categorize somatosensory neurons.