Person:

Treister, Roi

The term “neuropathic pain” (NP) refers to chronic pain caused by illnesses or injuries that damage peripheral or central pain-sensing neural pathways to cause them to fire inappropriately and signal pain without cause. Neuropathic pain is common, complicating diabetes, shingles, HIV, and cancer. Medications are often ineffective or cause various adverse effects, so better approaches are needed. Half a century ago, electrical stimulation of specific brain regions (neuromodulation) was demonstrated to relieve refractory NP without distant effects, but the need for surgical electrode implantation limited use of deep brain stimulation. Next, electrodes applied to the dura outside the brain’s surface to stimulate the motor cortex were shown to relieve NP less invasively. Now, electromagnetic induction permits cortical neurons to be stimulated entirely non-invasively using transcranial magnetic stimulation (TMS). Repeated sessions of many TMS pulses (rTMS) can trigger neuronal plasticity to produce long-lasting therapeutic benefit. Repeated TMS already has US and European regulatory approval for treating refractory depression, and multiple small studies report efficacy for neuropathic pain. Recent improvements include “frameless stereotactic” neuronavigation systems, in which patients’ head MRIs allow TMS to be applied to precise underlying cortical targets, minimizing variability between sessions and patients, which may enhance efficacy. Transcranial magnetic stimulation appears poised for the larger trials necessary for regulatory approval of a NP indication. Since few clinicians are familiar with TMS, we review its theoretical basis and historical development, summarize the neuropathic pain trial results, and identify issues to resolve before large-scale clinical trials.

Background: A model for measuring temporal summation (TS) by tonic noxious stimulation was recently proposed. However, methodological variations between studies make it difficult to reach a consensus regarding the way TS should be applied and calculated. The present study aimed to present a calculation method of TS magnitude produced by a tonic heat model in a large cohort of healthy subjects. Methods: Noxious heat stimulation (46.5°C/2 minutes) was applied to the forearm of 154 subjects who continuously rated pain intensity using a computerized visual analog scale. TS was calculated by “mean group” and “individual” approaches. Results: A “typical” pattern of pain response, characterized by a peak pain followed by a decrease in intensity to a nadir and subsequently a progressive increase in pain scores, was exhibited by 86.4% of the subjects. Using the “mean group” and “individual” calculation approaches, the mean ± standard deviation magnitudes of TS were 31.4±27.5 and 41.0±26.0, respectively (P<0.001). Additionally, using the individualized approach, we identified a different (“atypical”) response pattern among the rest of the subjects (13.6%). Conclusion: The results support the tonic heat model of TS for future utilization. The individualized TS calculation method seems advantageous since it better reflects individual magnitudes of TS.

Abstract Recognizing that electrically stimulating the motor cortex could relieve chronic pain sparked development of noninvasive technologies. In transcranial magnetic stimulation (TMS), electromagnetic coils held against the scalp influence underlying cortical firing. Multiday repetitive transcranial magnetic stimulation (rTMS) can induce long-lasting, potentially therapeutic brain plasticity. Nearby ferromagnetic or electronic implants are contraindications. Adverse effects are minimal, primarily headaches. Single provoked seizures are very rare. Transcranial magnetic stimulation devices are marketed for depression and migraine in the United States and for various indications elsewhere. Although multiple studies report that high-frequency rTMS of the motor cortex reduces neuropathic pain, their quality has been insufficient to support Food and Drug Administration application. Harvard's Radcliffe Institute therefore sponsored a workshop to solicit advice from experts in TMS, pain research, and clinical trials. They recommended that researchers standardize and document all TMS parameters and improve strategies for sham and double blinding. Subjects should have common well-characterized pain conditions amenable to motor cortex rTMS and studies should be adequately powered. They recommended standardized assessment tools (eg, NIH's PROMIS) plus validated condition-specific instruments and consensus-recommended metrics (eg, IMMPACT). Outcomes should include pain intensity and qualities, patient and clinician impression of change, and proportions achieving 30% and 50% pain relief. Secondary outcomes could include function, mood, sleep, and/or quality of life. Minimum required elements include sample sources, sizes, and demographics, recruitment methods, inclusion and exclusion criteria, baseline and posttreatment means and SD, adverse effects, safety concerns, discontinuations, and medication-usage records. Outcomes should be monitored for at least 3 months after initiation with prespecified statistical analyses. Multigroup collaborations or registry studies may be needed for pivotal trials.

Background: The clinically used methods of pain diagnosis do not allow for objective and robust measurement, and physicians must rely on the patient’s report on the pain sensation. Verbal scales, visual analog scales (VAS) or numeric rating scales (NRS) count among the most common tools, which are restricted to patients with normal mental abilities. There also exist instruments for pain assessment in people with verbal and / or cognitive impairments and instruments for pain assessment in people who are sedated and automated ventilated. However, all these diagnostic methods either have limited reliability and validity or are very time-consuming. In contrast, biopotentials can be automatically analyzed with machine learning algorithms to provide a surrogate measure of pain intensity. Methods: In this context, we created a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. Eighty-five participants were subjected to painful heat stimuli (baseline, pain threshold, two intermediate thresholds, and pain tolerance threshold) under controlled conditions and the signals of electromyography, skin conductance level, and electrocardiography were collected. A total of 159 features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, variability, and similarity. Results: We achieved classification rates of 90.94% for baseline vs. pain tolerance threshold and 79.29% for baseline vs. pain threshold. The most selected pain features stemmed from the amplitude and similarity group and were derived from facial electromyography. Conclusion: The machine learning measurement of pain in patients could provide valuable information for a clinical team and thus support the treatment assessment.