In this study, we compared EEG power feature (PSE) in HZ patients with different effects of drug treatment. We observed that the beta-band PSE at the central-parietal region was significantly different between MSP and MRP patients, implying that EEG beta rhythm could be a possible neural correlate of effective drug treatment. More importantly, our study used machine learning models to classify MSP and MRP patients with 80% ± 11.7% accuracy, 82.5% ± 14.7% sensitivity, 77.7% ± 27.3% specificity and 0.85 AUC, suggesting EEG has the potential to predict the drug treatment effect on HZ patients in an early stage.
EEG as a Signature of Pain Treatment
EEG has been widely used in clinical pain research to find out EEG activity pattern of subjective pain experience. For example, some studies have shown that pain patients displayed enhanced power spectra at frontal and parieto-occipital regions [22,24,36]. According to these results, EEG spectral power could be used for diagnosis of pain disease and for monitoring the postoperative development of patients. The present study is different from previous EEG-based clinical pain studies in two aspects. First, this study is aimed to reveal EEG features that are related to different medication effects on HZ-associated pain, not to identify EEG patterns that are different between pain patients and healthy people, patients of different pain diseases, or patients at different stage of a pain disease. Second, we proposed to used PSE, instead of the conventional power spectrum, as EEG features [37]. Literature has shown that the entropy-type parameters have a better ability to identify the complexity of EEG than spectral power [34,38]. For example, some studies reported that PSE was a sensitive parameter for EEG of imaginary hand movements, and was an effective index of focal ischemic cerebral injure [34,39]. We actually also compared EEG power and other spectral features between two MSP and MRP patients but did not find any difference.
Despite the fact that medication is the most common intervention for many pain-related diseases, including HZ, our knowledge of medication effects on pain and on brain activity remains limited. Many EEG studies were aimed to identify the relationship between EEG and clinical outcomes [41,42], and they indicated that the pre- or post-treatment EEG changes could be used to evaluate drug treatment outcomes. For example, some studies have demonstrated that drugs, such as pregabalin, influenced EEG characteristics [43,44], suggesting that the changes of EEG activity are predictive of the drug effectiveness. A study explored the influence of gabapentin on healthy people’s EEG activity showed that the peak frequency of alpha rhythm in the posterior region significantly decreases between healthy volunteer [45]. More recently, a paper reviewed EEG findings in analgesics and found that opioids could influence delta-band activity [46]. These studies suggested that EEG could reflect the overall altered neural activity including depression of behavior and mood in patients, which may predict the clinical outcome of drug treatment.
Our study found that, MSP patients showed significantly lower PSE in the beta band than MRP patients, which implies that if medication is effctive, the information (or the disorder/randomness) of the brain activity in the beta-band is suppressed. In another word, medication can restrain the beta-band EEG activity, if it is effective. This result is partly consistent with several similar studies which observed high frequency beta-band oscillations associated with pain processing [18,20,23,25]. An experimental pain study also showed that beta-band EEG activity in frontal/parietal regions is related to conscious somatic perception [40]. The results of our study implied that beta oscillatory activity in central-parietal regions may have an important role in the perception and processing of pain.
Using EEG to Predict Drug Effectiveness
Early prediction of drug treatment would aid in reducing the risk of side effects from inefficient treatment for patients. However, there is no study concerning the use of EEG as a predictor to drug treatment on pain diseases. According to our results, EEG features recorded several days before discrimination showed different patterns between MSP and MRP patients. So, the significant different features between MSP and MRP patients can be treated as characteristic inputs to build a classification (prediction) model to distinguish the effects of drug treatment. Our study provided a classification model with 80% ± 11.7% accuracy, 82.5% ± 14.7% sensitivity, 77.7% ± 27.3% specificity and 0.85 AUC. This EEG prediction model may help doctors decide whether drug treatment is useful or not for HZ patients in an earlier stage.
It is important to mention that, EEG data we used in the study were recorded 1-2 days after drug treatment and 5-6 days before discrimination into MSP and MRP patients. By using pain ratings measured right before EEG acquisition, it was also possible to predict the medication responses. But our results in Table 2 showed that, using pain ratings VASEEG (which was measured 1-2 days after medication and 5-6 days before discrimination) to predict medication response, the accuracy, sensitivity, and specificity were all lower than the prediction results from EEG features. The possible reason is that, pain ratings were subjective and could be biased by placebo effects in medication. Hence, the EEG PSE feature we found in the study may be more objective, making it suitable for building a prediction model.
Wireless Dry-electrode EEG
In clinical practice, the acquisition of EEG is normally not convenient because it needs a long preparation time (for example, to inject conductive gel into electrodes) and the whole system (including the amplifier, cables, and computers) is cumbersome. Therefore, in this study we used a new wireless and dry-electrode EEG system, LiveAmp 32-channel (Brain Product GmbH, Munich, Germany) to acquire patients’ EEG data. The wireless and portable EEG device with dry-contact sensors could significantly reduce the time of preparatory work. We compared the preparation time of the wireless dry-electrode system and a conventional wet-electrode EEG system, BrainAmp 32-channel (Brain Product GmbH, Munich, Germany), on 3 normal subjects and found the preparation time of LiveAmp is around 15-20 minutes while the average preparation time of BrainAmp is around 30-50 minutes. Importantly, patients did not report any feeling of uncomfortableness when wearing the wireless dry-electrode cap. Recently, a study systematically compared the wet-electrode wired EEG device and dry-electrode wireless EEG device in many aspects, and the results indicated that dry-electrode EEG device can effectively record EEG for research and clinical purposes [47]. So, dry-electrode wireless EEG devices are a promising tool for the EEG experiment on HZ patients.
Limitations and Future Work
The present study still has some limitations. First, the HZ patients took different medications during hospitalization. Different medications interaction or the use of a single medication may affect the EEG changes, which is a confounding factor of our study. Second, the repeatability and reliability of the identified EEG correlates of drug effectiveness should be validated using repeatedly measured data from the same cohort and multi-center data from different cohorts. Third, the neural mechanism underlying our finding remains unclear. It may be necessary to record multimodality data (such as MRI, fMRI, biochemical and genomic data) on a large sample to do a more comprehensive investigation of the neural mechanism of drug effectiveness on HZ patients. Finally, the sensitivity and specificity of this predict model are still not sufficient for clinical diagnosis, and we need to further improve the technique and to validate the results in a rigorous way.