Towards a physiology-based measure of pain: patterns of human brain activity distinguish painful from non-painful thermal stimulation

Abstract

Pain often exists in the absence of observable injury; therefore, the gold standard for pain assessment has long been self-report. Because the inability to verbally communicate can prevent effective pain management, research efforts have focused on the development of a tool that accurately assesses pain without depending on self-report. Those previous efforts have not proven successful at substituting self-report with a clinically valid, physiology-based measure of pain. Recent neuroimaging data suggest that functional magnetic resonance imaging (fMRI) and support vector machine (SVM) learning can be jointly used to accurately assess cognitive states. Therefore, we hypothesized that an SVM trained on fMRI data can assess pain in the absence of self-report. In fMRI experiments, 24 individuals were presented painful and nonpainful thermal stimuli. Using eight individuals, we trained a linear SVM to distinguish these stimuli using whole-brain patterns of activity. We assessed the performance of this trained SVM model by testing it on 16 individuals whose data were not used for training. The whole-brain SVM was 81% accurate at distinguishing painful from non-painful stimuli (p<0.0000001). Using distance from the SVM hyperplane as a confidence measure, accuracy was further increased to 84%, albeit at the expense of excluding 15% of the stimuli that were the most difficult to classify. Overall performance of the SVM was primarily affected by activity in pain-processing regions of the brain including the primary somatosensory cortex, secondary somatosensory cortex, insular cortex, primary motor cortex, and cingulate cortex. Region of interest (ROI) analyses revealed that whole-brain patterns of activity led to more accurate classification than localized activity from individual brain regions. Our findings demonstrate that fMRI with SVM learning can assess pain without requiring any communication from the person being tested. We outline tasks that should be completed to advance this approach toward use in clinical settings.

Figure 1. Performance of the Whole-Brain SVM Classifier is Increased by a Distance Threshold.

The classifier's performance was assessed at increasing distance thresholds. As the distance threshold increased, an increasing number of stimuli were excluded on the grounds that stimuli nearest the separating hyperplane were most likely misclassified. In this figure, performance is plotted as a function of the percentage of stimuli that have been excluded from classification. Dotted lines display the performance computed at each distance threshold. Solid lines display a third degree polynomial fit to those data.

Figure 2. Brain Regions that Most Influenced the Whole-Brain SVM Classifier.

A permutation test was run to determine which brain regions significantly affected the whole-brain SVM classification. This figure illustrates brain regions that fall within the 90^th percentile of the null distribution that was determined by permutation. Regions in the 99^th percentile (p<0.01) are noted in the results section. Shades of red indicate regions where greater BOLD signal influenced the SVM to classify a stimulus as painful. Shades of blue indicate regions where greater BOLD signal influenced the SVM to classify a stimulus as non-painful.