Biomechanical methods and error analysis related to chronic musculoskeletal pain

Abstract: Background Spinal pain is one of humanity’s most frequent complaints with high costs for the individual and society, and is commonly related to spinal disorders. There are many origins behind these disorders e.g., trauma, disc hernia or of other organic origins. However, for many of the disorders, the origin is not known. Thus, more knowledge is needed about how pain affects the neck and neural function in pain affected regions. The purpose of this dissertation was to improve the medical examination of patients suffering from chronic whiplash-associated disorders or other pain related neck-disorders.Methods A new assessment tool for objective movement analysis was developed. In addition, basic aspects of proprioceptive information transmission, which can be of relevance for muscular tension and pain, are investigated by studying the coding of populations of different types of sensory afferents by using a new spike sorting method. Both experiments in animal models and humans were studied to accomplish the goals of this dissertation. Four cats where were studied in acute animal experiments. Mixed ensembles of afferents were recorded from L7-S1 dorsal root filaments when mechanical stimulating the innervated muscle. A real-time spike sorting method was developed to sort units in a multi-unit recording. The quantification of population coding was performed using a method based on principal component analysis. In the human studies, 3D neck movement data were collected from 59 subjects with whiplash-associated disorders (WAD) and 56 control subjects. Neck movement patterns were identified by processing movement data into parameters describing the rotation of the head for each subject. Classification of neck movement patterns was performed using a neural network using processed collected data as input. Finally, the effect of marker position error on the estimated rotation of the head was evaluated by computer simulations.Results Animal experiments showed that mixed ensembles of different types of afferents discriminated better between different muscle stimuli than ensembles of single types of these afferents. All kinds of ensembles showed an increase in discriminative ability with increased ensemble size. It is hypothesized that the main reason for the greater discriminative ability might be the variation in sensitivity tuning among the individual afferents of the mixed ensemble will be larger than that for ensembles of only one type of afferent. In the human studies, the neural networks had a predictivity of 0.89, a sensitivity of 0.90 and a specificity of 0.88 when discriminating between control and WAD subjects. Also, a systematic error along the radial axis of the rigid body added to a single marker had no affect on the estimated rotation of the head.Conclusion The developed spike sorting method, using neural networks, was suitable for sorting a multiunit recording into single units when performing neurophysiological experiments. Also, it was shown that neck movement analysis combined with a neural network could build the basis of a decision support system for classifying suspected WAD or other pain related neck-disorders.