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Intracranial Pharmacotherapy and Pain Assays in Rodents
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Closed-loop stimulation using a multiregion brain-machine interface has analgesic effects in rodents.

Guanghao Sun1,2,3, Fei Zeng2, Michael McCartin2

  • 1Department of Psychiatry, New York University Grossman School of Medicine, New York, NY 10016, USA.

Science Translational Medicine
|June 29, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a brain-machine interface (BMI) for effective chronic pain treatment. The system accurately detects pain signals and delivers rapid, stable relief, offering a promising new therapeutic approach.

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Pain Management

Background:

  • Effective chronic pain treatments are limited.
  • Closed-loop neural interfaces offer potential for timely pain relief by integrating sensory detection and therapeutic delivery.
  • Challenges include accurate pain detection and rapid analgesic delivery.

Purpose of the Study:

  • To design and test a brain-machine interface (BMI) for detecting and treating pain.
  • To combine sensory signal detection from the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) with therapeutic delivery via prefrontal cortex (PFC) stimulation.
  • To evaluate the BMI's efficacy in acute and chronic pain models in freely behaving rats.

Main Methods:

  • Developed a multiregion neural interface combining automated pain detection (using local field potential signals from S1 and ACC) with a treatment arm (optogenetic activation or deep brain stimulation (DBS) of the PFC).
  • Tested the system in freely behaving rats with acute evoked pain and chronic pain models.
  • Recorded local field potential (LFP) signals for pain detection and utilized optogenetics or DBS for pain treatment.

Main Results:

  • The brain-machine interface accurately detected and treated both acute evoked pain and chronic pain.
  • The system demonstrated rapid activation and stable efficacy over time.
  • The neural interface successfully integrated pain signal detection with targeted therapeutic intervention.

Conclusions:

  • The developed brain-machine interface is a promising approach for pain treatment.
  • The system's ability to accurately detect pain and deliver rapid, stable relief addresses key challenges in pain management.
  • The clinical feasibility of LFP recordings and DBS supports the potential translation of this BMI for human pain therapy.