Decoding the Neural Representation of Pain Using a Brain-Machine Interface

Post by Amanda McFarlan

What's the science?

The lack of adequate treatments to safely manage acute and chronic pain is a serious public health issue. Recent advances in machine-learning technology have shown that it may be possible to decode a neural representation of pain by analyzing brain responses. Using this technology, researchers may be able to identify potential biomarkers for pain that could be used to design new therapeutic treatments. This week in Current Biology, Zhang and colleagues built a machine-learning based decoder to investigate whether brain activity measured with functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) could be used to predict pain in real-time.  

How did they do it?

The authors recruited healthy participants for their study. On the first experiment day, the participants’ brain activity was measured using fMRI while they received a painful electrical stimulus of high or low intensity to their left hand. The blood oxygen level-dependent (BOLD) responses from the insula (known to play a role in pain encoding) were used to build a multivoxel-pattern analysis decoder that would later be used to predict the intensity of a painful stimulus. On the second day of the experiment, participants’ brain activity was measured using fMRI while receiving a painful stimulus of either high or low intensity (a ‘high intensity stimulator’ and a ‘low intensity stimulator’). Based on real-time fMRI responses, the control computer used the decoder (built from the participants' fMRI data on the first day) to predict whether the participant perceived the stimulus to be of high or low intensity, with the goal of identifying the stimulator that was associated with the low intensity stimulus so that it would be chosen for the next trial. The participants were encouraged to attend to the painful stimuli and use their brain responses to help the computer learn so that it could administer lower pain stimuli.

Next, the authors had participants complete a similar adaptive control task in which they applied a painful electrical stimulus to participants’ lower backs while their brain activity was measured using EEG (experimental group) or while listening to a podcast (control group). Both the experimental and control groups also completed a temporal contrast enhancement task before and after the main experimental task, in which they applied and modulated the intensity of a tonic heat pain stimulus before and after the main experimental task in both the control and experimental groups. Contrast enhancement is a known phenomenon in which small changes in tonic pain result in unexpectedly large effects on pain ratings.

What did they find?

The authors showed that the decoder’s accuracy for predicting the participants’ perception of pain intensity was above chance. Because of this, the control computer was able to deliver significantly more pain stimuli of low intensity than of high intensity, suggesting that it is possible to use brain activity to decode and predict pain. The authors found that the decoder’s accuracy for predicting pain level was higher in the pregenual anterior cingulate cortex and lower in the left anterior insula. They also determined that the level of uncertainty (which quantifies the amount of new acquired information that will be used to improve learning) was positively correlated with BOLD responses from the pregenual anterior cingulate cortex and participants’ pain ratings. Finally, the authors found enhanced responses of the periaqueductal gray (which is known to mediate pain control) to high pain intensity on the second day of the experiment. This suggests that participants adaptively controlled their brain response to pain. 

In the EEG experiment, uncertainty was also positively correlated with participants’ pain ratings. In the temporal contrast enhancement task, pain ratings were lower in the experimental group after they performed the adaptive control task — evidence of endogenous pain control system engagement. Together, these findings suggest that the participants’ attention to pain stimuli may change the neural representation and encoding of pain in the brain by engaging endogenous pain modulation mechanisms.

What’s the impact?

This study shows that a multivoxel-pattern analysis decoder can use brain activity to identify an individual’s perceived pain intensity and use that information to reduce pain in real time. The authors found that the neural encoding of pain is subject to change when the brain’s activity is being decoded. Notably, pain encoding in the insula was disrupted, which suggests this region would not likely make a good biomarker for pain. Together, these findings provide insight into how brain-machine interfaces may be developed to help alleviate pain.   

Zhang et al. Pain Control by Co-adaptive Learning in a Brain-Machine Interface. Current Biology (2020). Access the original scientific publication here.