PainRelief.com Interview with:
Jing Wang MD PhD
Department of Anesthesiology, Perioperative Care and Pain
Department of Neuroscience & Physiology
NYU Langone
Neuroscience Institute, New York University School of Medicine
New York, NY
PainRelief.com: What is the background for this study? What are the main findings?
Response: The motivation for this study is three fold.
First, there are no objective ways to measure pain in preclinical models that could facilitate study of pain mechanisms and analgesic screening.
Secondly, while pain is assessed by patient report, a lack of alternative pain measures in humans hinders clinical treatment of pain in patients whom we cannot assess pain readily, such as patients who suffer from dementia or very young children.
Thirdly, chronic pain patients often complain of spontaneously occurring pain episodes which are unpredictable, and we currently do have a way to target specific pain episodes, and so we treat pain with scheduled drugs, leading to under- or over-treatment. We designed a prototype closed-loop neural interface, employing computerized brain implants, to address these challenges. We found that this interface quite effectively relieves short-term and chronic pain in rodents. In this study, we designed a computerized brain implant to detect and relieve bursts of pain in real time. We implanted electrodes in a region of the brain called anterior cingulate cortex, an important area for the processing the emotional component of pain. We used these implanted electrodes to measure neural activity in this brain region, and then applied machine learning algorithm to detect a change in neural activity in this region which signals the onset of pain experience. The detected pain signal then triggered stimulation of another brain region, called prefrontal cortex, which is known to suppress pain. In this way, our device automatically detected and treated pain with minimal delay, as shown by a number of pain behavior assays in rats. The device is also the first of its kind to target chronic pain, which often occurs without being prompted by a known trigger.
PainRelief.com: What should readers take away from your report?
Response: Our experiments offer a blueprint for the development of brain implants to treat pain syndromes and other brain-based disorders, such as anxiety, depression, and panic attacks. The advantage of our approach is that it targets symptoms in a time-sensitive manner. Our approach can detect pain as it occurs in real time. In its current form, it already becomes a powerful tool to screen drugs. In our current system, pain detection is coupled with neurostimulation treatment. But it can also be coupled with drug delivery. In this way, our system can be used to screen new analgesic drugs. It can also be used to screen other neurostimulation techniques.
PainRelief.com: What recommendations do you have for future research as a result of this work?
Response: We are already working on modifications of our system to move it closer to translation to the bedside.
First, we would like to improve pain decoding accuracy. We are doing that be recording from multiple brain regions.
Second, the current treatment requires injection of viral vectors and foreign proteins, which are not realistic in human use, and thus we are working to use more clinically feasible approaches to treat pain in our closed-loop device.
Finally, we are working on making the device non-invasive – free of brain implants.
Citation:
Zhang, Q., Hu, S., Talay, R. et al. A prototype closed-loop brain–machine interface for the study and treatment of pain. Nat Biomed Eng (2021). https://doi.org/10.1038/s41551-021-00736-7
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