Paul Theo Zebhauser

Cognitive dysfunction is increasingly recognized as an important feature of chronic pain (CP). However, subjective cognitive complaints and objectively measured cognitive performance frequently diverge. Whether and how these two aspects of cognitive functioning differentially relate to the broad symptomatology and brain function in CP remains unclear. Here, 114 individuals with CP completed patient-reported outcome measures on cognitive functioning and multidimensional CP symptoms, as well as a visuospatial working memory task, and resting-state EEG. Bayesian correlations, network analyses, and Bayesian regression models examined how subjective and objective cognitive functioning relate to multidimensional CP symptoms and EEG activity/connectivity, while controlling for age and sex. Additional models tested whether EEG associations were independent of broader symptom burden. Results indicated that subjective and objective cognitive functioning were uncorrelated. Subjective cognitive functioning was strongly associated with psychosocial symptoms, whereas objective cognitive functioning was largely independent of broader symptom burden. EEG revealed associations between subjective cognitive functioning and bilateral frontotemporal beta connectivity; however, these relationships were substantially attenuated after accounting for broader CP symptom burden. Objective cognitive functioning showed no robust associations with EEG. These findings indicate a dissociation between subjective cognitive complaints and objective cognitive performance in CP. Subjective cognitive complaints were primarily associated with psychosocial symptom burden and beta-band hypoconnectivity. In contrast, objective cognitive performance was unrelated to the broader symptomatology of CP and EEG measures. This dissociation may inform more targeted interventions, optimize the allocation of cognitive assessment resources, and ultimately improve long-term functional outcomes in CP.

Chronic pain (CP) is a multidimensional condition characterized by physical, emotional, and cognitive symptoms. However, many neuroimaging studies investigating the brain mechanisms of CP have focused on single-domain measures, most commonly pain intensity. Incorporating multidimensional symptom profiles may advance the understanding of CP, its neural underpinnings, and the development of clinically actionable biomarkers. Here, we aimed to empirically derive symptom dimensions of CP and relate them to resting-state brain activity and connectivity measured by electroencephalography (EEG). Using a data-driven approach, we identified latent symptom dimensions in 207 individuals with CP based on the brief, internationally validated PROMIS-29 profile, which assesses general health across key physical, mental, and social domains. Principal component analysis revealed two dimensions, affective burden and physical burden, closely corresponding to established PROMIS-derived health dimensions in other clinical populations. Resting-state EEG was obtained in a subsample of 116 participants using a mobile, rapid-to-deploy 29-channel dry-electrode system. Bayesian regression analyses provided moderate to strong evidence for a negative association between affective burden and beta-band connectivity, particularly in left frontal and somatomotor regions. Together, these findings demonstrate how empirically derived symptom dimensions captured by PROMIS-29 can be linked to scalable, network-level EEG biomarkers. This framework illustrates an EEG-informed strategy for biopsychosocial stratification in CP, with potential relevance for personalized symptom-targeted interventions.

Chronic pain is a prevalent and debilitating condition whose neural mechanisms are incompletely understood. An imbalance of cerebral excitation and inhibition (E/I), particularly in the medial prefrontal cortex (mPFC), is believed to represent a crucial mechanism in the development and maintenance of chronic pain. Thus, identifying a non-invasive, scalable marker of E/I could provide valuable insights into the neural mechanisms of chronic pain and aid in developing clinically useful biomarkers. Recently, the aperiodic component of the electroencephalography (EEG) power spectrum has been proposed to represent a non-invasive proxy for E/I. We, therefore, assessed the aperiodic component in the mPFC of resting-state EEG recordings in 149 people with chronic pain and 115 healthy participants. We found robust evidence against differences in the aperiodic component in the mPFC between people with chronic pain and healthy participants, and no correlation between the aperiodic component and pain intensity. These findings were consistent across different subtypes of chronic pain and were similarly found in a whole-brain analysis. Their robustness was supported by preregistration and multiverse analyses across many different methodological choices. Together, our results suggest that the EEG aperiodic component does not differentiate between people with chronic pain and healthy individuals. These findings and the rigorous methodological approach can guide future studies investigating non-invasive, scalable markers of cerebral dysfunction in people with chronic pain and beyond.

Magnetoencephalography/electroencephalography (M/EEG) can provide insights into migraine pathophysiology and help develop clinically valuable biomarkers. To integrate and summarize the existing evidence on changes in brain function in migraine, we performed a systematic review and meta-analysis (PROSPERO CRD42021272622) of resting-state M/EEG findings in migraine. We included 27 studies after searching MEDLINE, Web of Science Core Collection, and EMBASE. Risk of bias was assessed using a modified Newcastle–Ottawa Scale. Semi-quantitative analysis was conducted by vote counting, and meta-analyses of M/EEG differences between people with migraine and healthy participants were performed using random-effects models. In people with migraine during the interictal phase, meta-analysis revealed higher power of brain activity at theta frequencies (3-8 Hz) than in healthy participants. Furthermore, we found evidence for lower alpha and beta connectivity in people with migraine in the interictal phase. No associations between M/EEG features and disease severity were observed. Moreover, some evidence for higher delta and beta power in the premonitory compared to the interictal phase was found. Strongest risk of bias of included studies arose from a lack of controlling for comorbidities and non-automatized or non-blinded M/EEG assessments. These findings can guide future M/EEG studies on migraine pathophysiology and brain-based biomarkers, which should consider comorbidities and aim for standardized, collaborative approaches.

Chronic pain is a prevalent and debilitating condition whose neural mechanisms are incompletely understood. An imbalance of cerebral excitation and inhibition (E/I), particularly in the medial prefrontal cortex (mPFC), is believed to represent a crucial mechanism in the development and maintenance of chronic pain. Thus, identifying a non-invasive, scalable marker of E/I could provide valuable insights into the neural mechanisms of chronic pain and aid in developing clinically useful biomarkers. Recently, the aperiodic component of the electroencephalography (EEG) power spectrum has been proposed to represent a noninvasive proxy for E/I. We, therefore, assessed the aperiodic component in the mPFC of resting-state EEG recordings in 149 people with chronic pain and 115 healthy participants. We found robust evidence against differences in the aperiodic component in the mPFC between people with chronic pain and healthy participants, and no correlation between the aperiodic component and pain intensity. These findings were consistent across different subtypes of chronic pain and were similarly found in a whole-brain analysis. Their robustness was supported by preregistration and multiverse analyses across many different methodological choices. Together, our results suggest that the EEG aperiodic component does not differentiate between people with chronic pain and healthy individuals. These findings and the rigorous methodological approach can guide future studies investigating non-invasive, scalable markers of cerebral dysfunction in people with chronic pain and beyond.

Resting-state electroencephalography (rsEEG) holds promise as a biomarker of chronic pain. However, the impact of centrally acting analgesics like opioids, antiepileptics, and antidepressants on rsEEG remains unclear. This confounds and limits the interpretability of previous studies and questions the validity of rsEEG biomarker candidates of chronic pain. We, therefore, aimed to elucidate the effects of opioids, antiepileptics, and antidepressants on common rsEEG biomarker candidates of chronic pain. To this end, we analyzed two large, independent rsEEG datasets, including 220 people with chronic pain. We performed preregistered multivariate Bayesian analyses and controlled for the potential confounds of age, pain intensity, and depression. The results predominantly provided evidence against effects of centrally acting analgesics on peak alpha frequency, oscillatory power in different frequency bands, and connectivity-based network measures. Although these findings do not rule out any effects of analgesics on rsEEG, they argue against medium to large effects of centrally acting analgesics on rsEEG. Thus, the findings strengthen the validity of rsEEG biomarker candidates of chronic pain and might thereby help to develop clinically valuable biomarkers of chronic pain.