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Pain Control by Co-adaptive Learning in a Brain-Machine Interface.

Suyi Zhang¹, Wako Yoshida², Hiroaki Mano³

¹Computational and Biological Learning Laboratory, Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK; Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International, Kyoto 619-0237, Japan; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford OX3 9DU, UK.

Current Biology : CB

|August 16, 2020

Summary

This summary is machine-generated.

Brain-machine interfaces for pain management can backfire. Cognitive strategies to control the system paradoxically degraded pain signals by activating the brain's natural pain modulation. This highlights challenges in brain decoding applications.

Keywords:

PAG adaptive control brain-machine interface endogenous pain modulation fMRI insula pain pgACC real-time decoding uncertainty

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Area of Science:

Neuroscience
Biomedical Engineering
Pain Management

Background:

Brain-machine interfaces (BMIs) offer novel therapeutic approaches.
Closed-loop systems aim to modulate brain activity for pain management.
The adaptability of brain signals during BMI interaction remains unclear.

Purpose of the Study:

To investigate co-adaptive neural and behavioral changes during closed-loop pain management using BMIs.
To explore how cognitive strategies influence brain activity and pain perception within a BMI system.
To understand the fundamental challenges of brain decoding applications due to neural adaptation.

Main Methods:

Real-time decoded functional MRI (fMRI) from the insula cortex used as input for a closed-loop pain control system.
Subjects employed cognitive strategies to modulate their brain activity.
Electroencephalography (EEG)-based closed-loop system used for behavioral validation.

Main Results:

Active cognitive engagement paradoxically degraded pain encoding in the insula.
Endogenous pain modulation systems were activated, evidenced by attentional modulation and increased activity in the anterior cingulate cortex and periaqueductal gray.
Co-adaptive neural and behavioral changes interfered with BMI control.

Conclusions:

Implementing BMIs for pain management induces co-adaptive brain changes that can disrupt intended control.
The brain's inherent adaptation to decoding, especially during cognitive engagement, presents a fundamental challenge for BMI applications.
Understanding these adaptive processes is crucial for developing effective BMI strategies for pain relief.