Functional anatomy of central pain

Accurate classification of pain experiences using wearable electroencephalography in adolescents with and without chronic musculoskeletal pain

Objective

We assessed the potential of using EEG to detect cold thermal pain in adolescents with and without chronic musculoskeletal pain.

Methods

Thirty-nine healthy controls (15.2 ± 2.1 years, 18 females) and 121 chronic pain participants (15.0 ± 2.0 years, 100 females, 85 experiencing pain ≥12-months) had 19-channel EEG recorded at rest and throughout a cold-pressor task (CPT). Permutation entropy, directed phase lag index, peak frequency, and binary graph theory features were calculated across 10-second EEG epochs (Healthy: 292 baseline / 273 CPT epochs; Pain: 1039 baseline / 755 CPT epochs). Support vector machine (SVM) and logistic regression models were trained to classify between baseline and CPT conditions separately for control and pain participants.

Results

SVM models significantly distinguished between baseline and CPT conditions in chronic pain (75.2% accuracy, 95% CI: 71.4%–77.1%; p < 0.0001) and control (74.8% accuracy, 95% CI: 66.3%–77.6%; p < 0.0001) participants. Logistic regression models performed similar to the SVM (Pain: 75.8% accuracy, 95% CI: 69.5%–76.6%, p < 0.0001; Controls: 72.0% accuracy, 95% CI: 64.5%–78.5%, p < 0.0001). Permutation entropy features in the theta frequency band were the largest contributor to model accuracy for both groups.

Conclusions

Our results demonstrate that subjective pain experiences can accurately be detected from electrophysiological data, and represent the first step towards the development of a point-of-care system to detect pain in the absence of self-report.

The nucleus accumbens as a potential target for central poststroke pain

Although deep brain stimulation (DBS) has been found to be efficacious for some chronic pain syndromes, its usefulness in patients with central poststroke pain (CPSP) has been disappointing. The most common DBS targets for pain are the periventricular gray region (PVG) and the ventralis caudalis of the thalamus. Despite the limited success of DBS for CPSP, few alternative targets have been explored. The nucleus accumbens (NAC), a limbic structure within the ventral striatum that is involved in reward and pain processing, has emerged as an effective target for psychiatric disease. There is also evidence that it may be an effective target for pain. We describe a 72-year-old woman with a large right hemisphere infarct who subsequently experienced refractory left hemibody pain. She underwent placement of 3 electrodes in the right PVG, ventralis caudalis of the thalamus, and NAC. Individual stimulation of the NAC and PVG provided substantial improvement in pain rating. The patient underwent implantation of permanent electrodes in both targets, and combined stimulation has provided sustained pain relief at nearly 1 year after the procedure. These results suggest that the NAC may be an effective DBS target for CPSP.

Cancer Pain: A Critical Review of Mechanism-based Classification and Physical Therapy Management in Palliative Care

Mechanism-based classification and physical therapy management of pain is essential to effectively manage painful symptoms in patients attending palliative care. The objective of this review is to provide a detailed review of mechanism-based classification and physical therapy management of patients with cancer pain. Cancer pain can be classified based upon pain symptoms, pain mechanisms and pain syndromes. Classification based upon mechanisms not only addresses the underlying pathophysiology but also provides us with an understanding behind patient's symptoms and treatment responses. Existing evidence suggests that the five mechanisms – central sensitization, peripheral sensitization, sympathetically maintained pain, nociceptive and cognitive-affective – operate in patients with cancer pain. Summary of studies showing evidence for physical therapy treatment methods for cancer pain follows with suggested therapeutic implications. Effective palliative physical therapy care using a mechanism-based classification model should be tailored to suit each patient's findings, using a biopsychosocial model of pain.