Neuroimaging studies have shown that changes in brain morphology often go with chronic pain conditions. used a linear support vector machine (SVM) algorithm to differentiate gray matter images from the two groups. Regions of positive SVM excess weight included several regions within the primary somatosensory cortex pre-supplementary motor area hippocampus and amygdala were identified as important drivers of the classification with 73% overall accuracy. Thus we have recognized a preliminary classifier based on brain structure that is able to predict the presence of CPP with a good degree of predictive power. Our regional findings suggest that in individuals with AZ628 CPP greater gray matter density may be found in the recognized distributed brain regions which are consistent with some previous investigations in visceral pain syndromes. Future studies are needed to improve upon our recognized preliminary classifier with integration of additional variables and to assess whether the observed differences in brain structure are unique to CPP or generalizable to other chronic pain conditions. subjects) are used for training the SVM. The trained SVM is usually then tested using the remaining image. This process is usually repeated until all the images are used as a test image. The AZ628 LOOCV classification accuracy is calculated as the percentage of images that are correctly classified. The alternative approach of leave-pair-out cross validation (LPOCV) was also tested despite the limitation that patients and controls were not strictly age matched for each pair (age matching was +/? 5 years for each pair and balanced so that group means were not significantly different). Since there were several possible pair combinations the LPOCV classification was repeated 1000 occasions each time randomizing the pairing of subjects and the imply classification accuracy was computed. Aside from the classification accuracy we also calculated the AZ628 following classification steps: sensitivity = TP / (TP + FN) specificity = TN / (TN + FP) positive predictive value PPV = TP / (TP + FP) and unfavorable predictive value NPV = TN / (TN + FN). TP was defined as the number of images correctly classified as belonging to the patient group (true positive) TN was defined as the number of images correctly classified as belonging to the healthy control group (true unfavorable) FP was defined as the number of images from your healthy control group AZ628 misclassified as belonging to the patient group (false positives) and FN was defined as the number of images from the patient group misclassified AZ628 as belonging to the healthy control group (false negative). All of these additional values were assessed for significance level EGR1 using permutation assessments following [53]. For this analysis we used a MATLAB version of LIBSVM a library for support vector machines (http://www.csie.ntu.edu.tw/~cjlin/libsvm [14]). Permutation Test We were also interested in the significance of each voxel��s contribution to the classification accuracy. We used a permutation test [35] with 5000 iterations to generate an SVM-derived significance map a map showing regions that significantly contributed to the classification performance of the trained SVM. In brief this nonparametric test involved randomly permuting the class labels and training an SVM for each permutation. This gives an estimate of the probability distribution of the SVM weight associated with each voxel under the null hypothesis of no relationship between class labels and the global structure of the training data. The p-value is then calculated as the proportion of values in the null distribution that is greater or equal to the value obtained using the original (i.e. non-permuted) labels. Thus the farther away the weight value of a voxel from the major mass of the distribution under null hypothesis (small p-value) the more likely it is to be significantly predictive for the class label. The significance map was corrected for multiple comparisons using false discovery rate with q < 0.05 and cluster size greater than 20 voxels. We used an SPM8 extension (http://www.alivelearn.net/xjview8) for the anatomical labeling of significant brain regions. Using the same approach we also estimated the significance of the classification accuracy and other classification measures described above. For each permutation we computed the LOOCV / LPOCV accuracy sensitivity and specificity among others. The p-value associated with each measure was calculated as the proportion of values in the null distribution greater than the value obtained when using the nonpermuted.