The analgesic effect of pregabalin in patients with chronic pain is reflected by changes in pharmaco‐EEG spectral indices

Abstract

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Pregabalin is an anticonvulsive agent prescribed as a secondary analgesic for patients when standard pain treatment is insufficient.• The analgesic effect resides in the central nervous system. • The central analgesic effect can be evaluated by electroencephalography. WHAT THIS STUDY ADDS• The analgesic effect of pregabalin is reflected as a slowing of brain oscillations.• The slowing of brain oscillations for each individual patient is correlated with subjective pain scores.• The developed methodology may be used as a mechanistic approach to monitor the analgesic effect of pregabalin in pharmacological studies. AIMTo identify electroencephalographic (EEG) biomarkers for the analgesic effect of pregabalin in patients with chronic visceral pain. METHODSThis was a double-blind, placebo-controlled study in 31 patients suffering from visceral pain due to chronic pancreatitis. Patients received increasing doses of pregabalin (75 mg-300 mg twice a day) or matching placebo during 3 weeks of treatment. Pain scores were documented in a diary based on a visual analogue scale. In addition, brief pain inventory-short form (BPI) and quality of life questionnaires were collected prior to and after the study period. Multi-channel resting EEG was recorded before treatment onset and at the end of the study. Changes in EEG spectral indices were extracted, and individual changes were classified by a support vector machine (SVM) to discriminate the pregabalin and placebo responses. Changes in individual spectral indices and pain scores were correlated. RESULTSPregabalin increased normalized intensity in low spectral indices, most prominent in the theta band (3.5-7.5 Hz), difference of -3.18, 95% CI -3.57, -2.80; P = 0.03. No changes in spectral indices were seen for placebo. The maximum difference between pregabalin and placebo treated patients was seen in the parietal region, with a classification accuracy of 85.7% (P = 0.009). Individual changes in EEG indices were correlated with changes in pain diary (P = 0.04) and BPI pain composite scores (P = 0.02). CONCLUSIONSChanges in spectral indices caused by slowing of brain oscillations were identified as a biomarker for the central analgesic effect of pregabalin. The developed methodology may provide perspectives to assess individual responses to treatment in personalized medicine.

Altered Frequency Distribution in the Electroencephalogram is Correlated to the Analgesic Effect of Remifentanil

“…To obtain a trade-off between time and frequency resolution, a complex Morlet wavelet function was chosen for this study [16]. The Morlet function is defined by two design parameters, which were set to have a bandwidth parameter of 128 Hz and wavelet centre frequency of 0.5 Hz [12,17,18]. This selection of the mother wavelet function is similar to previous studies on pharmaco-EEG and hence makes results comparable [12].…”

“…The overall flow of the MVPA was to extract the individual alterations in normalized frequency distribution for each channel separately, and use the frequency ratio (treatment/baseline) for both remifentanil and placebo as input to the SVM. The SVM is a binary classifier taking two classes as input, in this study remifentanil alterations as one class and placebo alterations as the second class, to calculate an optimal separating hyperplane [12,19,20]. In the leave-oneout approach, data from one volunteer were eliminated from the calculation of the hyperplane, and data from this volunteer were then classified to determine the predicted class for both treatments.…”

“…The Morlet function is defined by two design parameters, which were set to have a bandwidth parameter of 128 Hz and wavelet centre frequency of 0.5 Hz [12,17,18]. This selection of the mother wavelet function is similar to previous studies on pharmaco-EEG and hence makes results comparable [12]. The coefficients were calculated in the following bands: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), beta (12-32 Hz) and gamma .…”

“…Resting EEG is characterized by brain oscillations present in distinct frequency bands such as delta (0.5-4 Hz), theta (4)(5)(6)(7)(8), alpha (8)(9)(10)(11)(12), beta and gamma reflecting the sum of excitatory and inhibitory post-synaptic activities [8]. However, as the oscillations are widely distributed in the brain, the scalp potentials provide distinct information depending on the site of the scalp.…”

“…However, as multi-channel recordings generate a separate frequency distribution per channel, selection of the most appropriate channels for analysis is complicated [11]. To overcome this obstacle, we have previously developed a method to identify the most relevant channels in a multivariate pattern analysis (MVPA) [12]. This method is based on extracting the frequency distribution by a wavelet transformation followed by statistical learning by a support vector machine (SVM).…”