Waking EEG cortical markers of chronic pain and sleepiness

Danny Camfferman1, G. Lorimer Moseley1, Kevin Gertz2, Mark W. Pettet2 and Mark P. Jensen2

  1. Body in Mind Group, School of Health Sciences, University of South Australia;
  2. Department of Rehabilitation Medicine, University of Washington, Seattle Washington.

Corresponding Author: Danny Camfferman Ph.D. email danny.camfferman@unisa.edu.au 

Purpose of the current study

The purposes of this study are to (1) evaluate whether we can distinguish between the contributions of chronic pain and sleep deficits found within the EEG bandwidth power spectrum, (2) measure the independent contributions of the chronic pain and sleep deficits to the EEG markers identified, and (3) determine the extent these markers may be viewed as measures that may be useful in the study of chronic pain. Based on the available findings, as well as current thinking regarding the effects of sleepiness on the waking EEG power spectrum, we hypothesized that higher levels of reported sleepiness would be associated with lower levels of alpha activity in an eyes closed condition. We also hypothesized that higher levels of sleepiness would be associated with higher levels of theta activity. Given that some of the findings regarding the maximal localization of both decreased alpha activity and increased theta activity are preliminary (found in only one or two studies so far), we also wanted to test the following hypotheses, which we viewed as more exploratory: (1) lower alpha activity related to increased sleepiness would be maximally found located in in activity measures by electrodes in frontal areas; and (2) higher levels of theta activity associated with increased sleepiness would be found in activity measured by electrodes placed in frontal areas.

With regard to the effects of chronic pain intensity on the waking EEG power spectrum, we hypothesized that more chronic pain intensity would be associated with higher levels of theta activity. To the extent that previous findings regarding the maximal locations of increased theta activity and chronic pain intensity, and that the associations between reduced alpha activity and chronic pain intensity are limited to only a few studies, we further wished to test the following hypotheses, which we also viewed as more exploratory: (1) more pain intensity associated with increased theta activity would be maximally located in brain activity measures from electrodes over the parietal areas; (2) more pain intensity would be related to less alpha activity; and (3) the lower levels of alpha activity associated with chronic pain intensity would be found maximally using activity measured from electrodes placed over frontal areas. Finally, given that no one has yet examined the independent contributions of both sleepiness and chronic pain intensity on the waking EEG power spectrum, we sought to explore the associations between these conditions and their relative (and independent) contributions to alpha and theta activity.

Data analysis

Data analyses were performed using SPSS for Windows (version 21). Our initial analysis was to produce a correlation matrix of pain variables and also of sleep variables to assess them both for Factor Analyses. Factor Analyses was selected as a preliminary procedure as it is a statistical method used to describe variability among observed, correlated variables to potentially reduce datasets to a lower number of unobserved latent variables. Among the various types of Factor Analyses available, Principal Axis Factor was the preferred option as it is an extraction method used for trying to understand the shared variance of the variables used. The goal in using Principal Axis Factor analysis is factor structure interpretation and data reduction, as opposed to the goal for Principle Component Analyses which is usually only data reduction.

Version 21 of SPSS offers five rotation methods: varimax, direct oblimin, quartimax, equamax, and promax. Three of those are orthogonal (varimax, quartimax, & equimax), and two are oblique (direct oblimin & promax). Tabachnick and Fidell (2006) argue that “the best way to decide between orthogonal and oblique rotation is to request oblique rotation [e.g., direct oblimin or promax from SPSS] with the desired number of factors and look at the correlations among factors. If factor correlations are not driven by the data, the solution remains nearly orthogonal. If correlations exceed .32, then there is 10% (or more) overlap in variance among factors, and enough variance to warrant oblique rotation.”

We then examined the association between demographic and descriptive variables (age, sex, type of pain [MS or SCI]) and the pain and sleep factors to determine if they needed to be controlled for in subsequent analyses.

In order to test the our hypotheses regarding sleep and pain factors and their relationship to alpha and theta bandwidth spectral power activity and their maximal source location, initial analyses were undertaken using Pearson r correlations.

The correlation data was then used to determine whether a mediation analyses was to be undertaken. As sleep characteristics had been reported to affect pain characteristics, and pain affected sleep quality, it was determined that if the sleep factor, pain factor and target bandwidth power were all found to be correlated to each other, with at least at a weak strength of .29, then a mediation analysis would be performed.

On the basis that both pain and sleep variables are hypothesised to be independently associated with reduced alpha activity localised in the frontal lobes, we performed a moderation analyses to examine if there was an interaction effect from pain and sleep factors on alpha bandwidth power. Finally, we undertook a moderation analyses to assess whether there was a latent interaction effect between pain and sleep factors on theta bandwidth power in the parietal areas.