Researchers at New York University (NYU) reported that a computerized brain implant effectively relieved short-term and chronic pain in rodents, according to a press release issued by NYU Langone Health.1,2
The experiments were conducted by investigators at NYU Grossman School of Medicine and published on June 21 in the journal Nature Biomedical Engineering. The researchers said the study offers 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 authors reported that device-implanted rats withdrew their paws 40% more slowly from sudden pain, compared with when their device was turned off. According to the study authors, this suggests that the device reduced the intensity of the pain the rodents experienced. Animals in sudden or continuous pain spent about two-thirds more time in a chamber where the computer-controlled device was turned on than in a chamber where it was not.
"Our findings show that this implant offers an effective strategy for pain therapy, even in cases where symptoms are traditionally difficult to pinpoint or manage," said senior study author Jing Wang, MD, PhD, the Valentino D.B. Mazzia, MD, JD, Associate Professor in the Department of Anesthesiology at NYU Langone Health. Wang is also the Vice Chair for Clinical and Translational Research at NYU Langone.
Researchers said the investigation is the first to use a computerized brain implant to detect and relieve bursts of pain in real time, and that it is also the first of its kind to target chronic pain.
Computerized brain implants, previously investigated to prevent epileptic seizures and control prosthetic devices, may avert the risk of addiction and other issues associated with opioids, said Wang. The technology, known as a closed-loop brain-machine interface, detects brain activity in the anterior cingulate cortex, a region of the brain that is critical for pain processing. A computer linked to the device then automatically identifies electrical patterns in the brain closely linked to pain. When signs of pain are detected, the computer triggers therapeutic stimulation of another region of the brain, the prefrontal cortex, to ease it.
Because the device is only activated in the presence of pain, Wang said, it lessens the risk of overuse and any potential for tolerance to develop. Furthermore, because the implant does not offer a reward beyond pain relief, as do opioids, the risk of addiction is minimized.
As part of the study, the researchers installed tiny electrodes in the brains of dozens of rats and then exposed them to carefully measured amounts of pain. The animals were closely monitored for how quickly they moved away from the pain source. This allowed the investigators to track how often the device correctly identified pain-based brain activity in the anterior cingulate cortex and how effectively it could lessen the resulting sensation.
According to the study authors, the implant accurately detected pain up to 80% of the time.
"Our results demonstrate that this device may help researchers better understand how pain works in the brain," said lead study investigator Qiaosheng Zhang, PhD, a doctoral fellow in the Department of Anesthesiology, Perioperative Care and Pain at NYU Langone. "Moreover, it may allow us to find non-drug therapies for other neuropsychiatric disorders, such as anxiety, depression, and post-traumatic stress."
Zhang added that the implant's pain detection properties could be improved by installing electrodes in other regions of the brain beyond the anterior cingulate cortex. He cautioned, however, that the technology is not yet suitable for use in people, but said plans are underway to investigate less invasive forms with potential to be adapted for human use.
Funding for the study was provided by National Institutes of Health grants R01 NS100065, R01 GM115384, and R01 MH118928 and National Science Foundation grant CBET 1835000.
In addition to Wang and Zhang, other NYU Langone researchers included Sile Hu, MS; Robert Talay; Amrita Singh, BA; Bassir Caravan, BS; Zhengdong Xiao, MS; David Rosenberg, BS; Anna Li, BM; Johnathan D. Gould; Yaling Liu; Guanghao Sun; and Zhe S. Chen, PhD.
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