Analytic consistency and neural correlates of peak alpha frequency in the study of pain
Introduction
Chronic pain is very impactful. A challenge of studying chronic pain is that it is an inherently subjective experience. Therefore, identifying objective markers is a pressing need. Markers of particular interest would be those that could measure the predisposition to developing chronic pain. Resting-state peak alpha frequency (PAF) measured from electroencephalography (EEG) has been proposed as such a marker (Furman et al., 2019). Previous work has also shown that resting-state PAF is reduced in populations with neuropathic pain in persistent abdominal pain as a result of chronic pancreatitis (Vries et al., 2013), neuropathic pain as a result of spinal cord injury (Boord et al., 2007, Sato et al., 2017), and increased subjective perception of tonic heat pain (Nir et al., 2010, Raghuraman et al., 2019). Interindividual differences in PAF may relate to awareness and the sampling of sensory inputs (Angelakis et al., 2004, Mierau et al., 2017), and thus may reflect an individual’s response to acute pain in a way that may influence the transition to chronic pain.
However, the existing literature is not conclusive in establishing a relationship between PAF and pain: studies on chronic back pain (Schmidt et al., 2012), central neuropathic pain in multiple sclerosis patients (Krupina et al., 2019), and persistent pain after breast cancer treatment (van den Broeke et al., 2013) did not find a relationship between slowed PAF and pain. Together the positive and negative findings of the existing literature are often interpreted as suggesting either a differential relationship between PAF and pain of different origins, or as lack of a strong relationship between PAF and pain altogether. Within the pain field there is also substantial variation in how PAF is computed and interpreted, which complicates the comparison of results across studies.
In order to interpret and compare results across the field in a meaningful way, we must first be certain that differences in findings are not simply a byproduct of differences in processing pipelines. While previously published papers have focused on developing a method that best estimates the “true” PAF value (Corcoran et al., 2018), the goal of this paper is instead to analyze previously employed methods and determine whether differences in processing decisions may impact the final PAF calculation. We reviewed 17 papers with complete available methods investigating the relationship between PAF and pain and found that there were major differences in data filtering, whether data were re-referenced, what kind of artifact removal was performed, epoching, alpha band bounds, and formula for calculating PAF. Furthermore, comparisons between PAF and other measures of neural activity are infrequent, limiting the interpretation of PAF in terms of underlying cortical and subcortical activity patterns. The thalamus is a hypothesized generator of the alpha rhythm and has been implicated in alpha rhythm alterations, but the association between thalamic activity and PAF in humans has not been thoroughly explored.
Given these uncertainties, in this study we aimed to assess the robustness of PAF to three different pre-processing pipelines as well as four processing variables (seven epoch lengths, two formulas for calculating PAF, three different alpha bands, and five regions-of-interest [ROIs]: grand-average, frontal, parietal, occipital, sensorimotor) representative of what is currently being used in the pain literature. Additionally, we aimed to associate PAF with fractional amplitude of low-frequency fluctuations (fALFF), a resting-state functional magnetic resonance imaging (rs-fMRI)-derived measure of local activity. To accomplish these aims, we analyzed data from 47 healthy men, a subset of an ongoing study of urologic chronic pelvic pain. The dataset included 64-channel resting-state electroencephalographic (EEG) and resting-state functional magnetic resonance imaging (rs-fMRI) from all participants. We first ran three of the most representative EEG pre-processing pipelines and then varied post-processing parameters (epoch length, PAF formula, alpha band bounds, and ROI) as determined by our review of the PAF and pain literature. We then determined whether there was a relationship between the ROI averages and whole brain as well as thalamic activity in healthy men.
Section snippets
Participants
Forty-seven healthy men who were the control group with complete EEG and rs-fMRI data for an ongoing study of chronic prostatitis were entered into this study. Participants were included if they were older than 18 years of age, able to participate in the informed consent process, safe to be scanned by magnetic resonance imaging, had no diagnosis of chronic prostatitis, had no active urinary, anal, or genital infection, and no severe, urgent, or debilitating medical condition. All aspects of the
Results
All processing variables of interest are reported in Table 1, and information about subject populations (sample size, clinical versus healthy, pathology if applicable) as well as the relationship between PAF and pain are reported in Supplementary Table 1. Detailed criteria and steps for the literature review process are reported in Supplementary Fig. 1.
The mean number of ICs removed using MARA for all participants was 27.85 ± 9.54 (mean±STD). The total amount of data removed due to the 80 mV
Discussion
Despite the novelty and promise of PAF, a few key issues complicate the interpretation of past data and comparison across publications in the pain field. The first issue is a lack of consistency in the pre-processing pipelines and post-processing variables for EEG data. We reviewed 17 papers investigating the relationship between PAF and pain and found that there were major differences in how labs were pre-processing their data, (re-referencing, artifact removal, etc), epoch length, bounds of
CRediT authorship contribution statement
Natalie McLain: Conceptualization, Methodology, Formal analysis, Visualization, Writing – original draft. Moheb Yani.: Software, Formal analysis, Investigation, Writing – Review & Editing. Jason Kutch: Conceptualization, Methodology, Software, Writing – original draft, Funding acquisition.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We would like to thank Arike Coker for manuscript support and helpful discussions. This work was supported, in part, by grants DK110669 and DK121724 from the National Institute of Diabetes and Digestive and Kidney Diseases (NIH/NIDDK).
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