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A prototype closed-loop brain-machine interface for the study and treatment of pain

PainSci » bibliography » Zhang et al 2021
updated
Tags: chronic pain, neat, pain problems

Two articles on PainSci cite Zhang 2021: 1. Zapped! Does TENS work for pain?2. What Works for Pain?

PainSci notes on Zhang 2021:

This study advances the science of brain-machine interfaces with a test of an implanted computer chip in rat brains, designed to treat chronic pain. The chip reads the rat’s minds: it detects patterns of brain activity in the cingulate gyrus that are consistent with pain, and then stimulates part of the frontal lobe to mute pain: specifically, to “exert top-down nociceptive regulation.” Obviously this is very invasive, and even if it works there’s a risk of adaptation and dependence, and human applications are many years off.

Doing this for humans is probably still many years away. But if it works? It’s extremely precise, responding in real-time, only working when there’s pain to treat — completely unlike the continuous, always-zapping approach that has dominated the field so far.

And it really did work amazingly well. The treated rats withdrew from painful stimuli about 40% slower, and greatly preferred spending time in a chamber where the implant was functional to one where it wasn’t. These were strong results, and a very promising demonstration of the principle.

This kind of approach is likely to improve as we continue to improve brain-machine interface technology, and knowledge of brain circuity gets more precise.

See neurologist Dr. Steve Novella’s more detailed explanation of this experiment.

original abstract Abstracts here may not perfectly match originals, for a variety of technical and practical reasons. Some abstacts are truncated for my purposes here, if they are particularly long-winded and unhelpful. I occasionally add clarifying notes. And I make some minor corrections.

Chronic pain is characterized by discrete pain episodes of unpredictable frequency and duration. This hinders the study of pain mechanisms and contributes to the use of pharmacological treatments associated with side effects, addiction and drug tolerance. Here, we show that a closed-loop brain-machine interface (BMI) can modulate sensory-affective experiences in real time in freely behaving rats by coupling neural codes for nociception directly with therapeutic cortical stimulation. The BMI decodes the onset of nociception via a state-space model on the basis of the analysis of online-sorted spikes recorded from the anterior cingulate cortex (which is critical for pain processing) and couples real-time pain detection with optogenetic activation of the prelimbic prefrontal cortex (which exerts top-down nociceptive regulation). In rats, the BMI effectively inhibited sensory and affective behaviours caused by acute mechanical or thermal pain, and by chronic inflammatory or neuropathic pain. The approach provides a blueprint for demand-based neuromodulation to treat sensory-affective disorders, and could be further leveraged for nociceptive control and to study pain mechanisms.

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