The early stages of a new romantic relationship are characterized by intense feelings of euphoria, well-being, and preoccupation with the romantic partner. Neuroimaging research has linked those feelings to activation of reward systems in the human brain. The results of those studies may be relevant to pain management in humans, as basic animal research has shown that pharmacologic activation of reward systems can substantially reduce pain. Indeed, viewing pictures of a romantic partner was recently demonstrated to reduce experimental thermal pain. We hypothesized that pain relief evoked by viewing pictures of a romantic partner would be associated with neural activations in reward-processing centers. In this functional magnetic resonance imaging (fMRI) study, we examined fifteen individuals in the first nine months of a new, romantic relationship. Participants completed three tasks under periods of moderate and high thermal pain: 1) viewing pictures of their romantic partner, 2) viewing pictures of an equally attractive and familiar acquaintance, and 3) a word-association distraction task previously demonstrated to reduce pain. The partner and distraction tasks both significantly reduced self-reported pain, although only the partner task was associated with activation of reward systems. Greater analgesia while viewing pictures of a romantic partner was associated with increased activity in several reward-processing regions, including the caudate head, nucleus accumbens, lateral orbitofrontal cortex, amygdala, and dorsolateral prefrontal cortex – regions not associated with distraction-induced analgesia. The results suggest that the activation of neural reward systems via non-pharmacologic means can reduce the experience of pain.

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.

Complex regional pain syndrome (CRPS) is a chronic condition that involves significant hyperalgesia of the affected limb, typically accompanied by localized autonomic abnormalities, and frequently motor dysfunction. Although central brain systems are thought to play a role in the development and maintenance of CRPS, these systems have not been well characterized. In this study, we used structural magnetic resonance imaging (sMRI) to characterize differences in gray matter volume between patients with right upper extremity CRPS and matched controls . Analyses were carried out using a whole brain voxel-based morphometry (VBM) approach. The CRPS group showed decreased gray matter volume in several pain-affect regions, including the dorsal insula, left orbitofrontal cortex, and several aspects of the cingulate cortex. Greater gray matter volume in CRPS patients was seen in the bilateral dorsal putamen and right hypothalamus. Correlation analyses with self-reported pain were then performed on the CRPS group. Pain duration was associated with decreased gray matter in the left dorsolateral prefrontal cortex. Pain intensity was positively correlated with volume in the left posterior hippocampus and left amygdala, and negatively correlated with the bilateral dorsolateral prefrontal cortex. Our findings demonstrate that CRPS is associated with abnormal brain system morphology, particularly pain-related sensory, affect, motor, and autonomic systems.

Background and Purpose Limited data exist regarding the relationship between acute infarct volume and health-related quality of life (HRQOL) measures after ischemic stroke. We evaluated whether acute infarct volume predicts domain-specific Neuro-Quality of Life (Neuro-QOL) scores at 3 months after stroke. Methods Between 2012 and 2014, we prospectively enrolled consecutive patients with ischemic stroke and calculated infarct volume. Outcome scores at 3 months included modified Rankin score (mRS) and Neuro-QOL T-scores. We evaluated whether volume organized by quartiles predicted mRS and HRQOL scores at 3 months using logistic and linear regression as appropriate, adjusting for relevant covariates. We calculated variance accounted for (R2) overall and by volume for each domain of HRQOL. Results Among 490 patients (mean age 64.2 ± 15.86 years; 51.2% male; 63.3% Caucasian) included for analysis, 58 (11.8%) were disabled (mRS score >2) at 3 months. In unadjusted analysis, the highest volume quartile remained a significant predictor of one HRQOL domain, applied cognition-general concerns (R2 0.06, p<0.001). Our fully-adjusted prediction model explained 32–51% of the variance in HRQOL: upper extremity (R2 0.32), lower extremity (R2 0.51), executive function (R2 0.45), and general concerns (R2 0.34). Conclusions Acute infarct volume is a poor predictor of HRQOL domains after ischemic stroke, with the exception of the cognitive domain. Overall, clinical and imaging variables explained <50% of the variance in HRQOL outcomes at 3 months. Our data imply that a broad range of factors, some known and others undiscovered, may better predict post-stroke HRQOL than what is currently available.