Pain is closely linked to alpha oscillations (8 to 13 Hz) which are thought to represent a supra-modal, top-down mediated gating mechanism that shapes sensory processing. Consequently, alpha oscillations might also shape the cerebral processing of nociceptive input and eventually the perception of pain. To test this mechanistic hypothesis, we designed a sham-controlled and double-blind electroencephalography (EEG)-based neurofeedback study. In a short-term neurofeedback training protocol, healthy participants will learn to up- and downregulate somatosensory alpha oscillations using attention. Subsequently, we will investigate how this manipulation impacts experimental pain applied during neurofeedback. Using Bayesian statistics and mediation analysis, we will test whether alpha oscillations mediate attention effects on pain perception. This approach promises causal insights into the role of alpha oscillations in shaping pain, and thereby extends previous correlative evidence. Beyond, it can aid the development of novel, non-invasive modulatory treatment approaches for chronic pain, which are urgently needed.
Assessing the balance between excitation and inhibition in chronic pain through the aperiodic component of EEG
In this project, we will characterize the aperiodic component of resting-state EEG recordings in a large cohort of patients with chronic pain (N=150) and healthy participants (N=115). Based on previous literature, changes in the aperiodic exponent (χ) of the resting-state power spectrum can be interpreted as indirect changes in E/I ratio. Higher exponents, i.e. steeper slopes in the power spectrum, indicate a shift towards inhibition and lower exponents a shift towards excitation (Gao et al., 2017; Ostlund et al., 2022). We hypothesize that changes in the aperiodic exponent will particularly occur in brain areas whose function is altered in chronic pain such as the PFC. In particular, we will assess changes in the medial prefrontal cortex (mPFC), as this is a well-known area exhibiting E/I imbalances during chronic pain in rodents (Kuner and Kuner, 2021). Therefore, our aim is to explore whether previously described E/I imbalances in the rodent PFC can also be measured in the human mPFC through the aperiodic exponent of resting state EEG data. We intend to make a step forward in the development of translational, non-invasive tools to better understand chronic pain mechanisms. Such insights might pave the way for novel pharmacological and/or neurotechnological treatments of chronic pain.
To improve the diagnosis and treatment of chronic pain (CP), establishing reliable and objective biomarkers is a central challenge (Davis et al., 2020). Resting state electroencephalography (rsEEG) is a promising tool for biomarker development in CP. Recently, evidence was found for stronger oscillatory brain activity at theta (3-8 Hz) and beta (13-30 Hz) frequencies in patients compared to healthy participants (Zebhauser et al., 2023). EEG-based connectivity and network measures are other biomarkers candidates (Lee et al., 2021; Ploner and Tiemann, 2021). However, most patients with CP take centrally acting analgesics whose confounding effects on rsEEG-variables are largely unknown yet. In this study, we aim to evaluate effects of opioids, analgetic antiepileptics and analgetic antidepressants on a range of rsEEG-biomarker candidates for CP. Effects will be evaluated in two independent existing datasets of patients with CP.
Chronic pain is a multi-faceted and debilitating condition that imposes a large burden on patients and society. With a prevalence exceeding 20%, chronic pain is a major cause of disability and, as such, has a considerable socio-economic dimension (Breivik, Collett, Ventafridda, Cohen, & Gallacher, 2006; Kennedy, Roll, Schraudner, Murphy, & McPherson, 2014). However, the means for diagnosis and treatment of chronic pain remain limited. To address this deficit, we must deepen our understanding of the underlying pathology. Converging lines of evidence highlight the central role of the brain in the emergence of chronic pain (Kennedy et al., 2014; Ploner, Sorg, & Gross, 2017). However, the precise pathophysiological mechanisms that lead to chronic pain have yet to be identified. In this study, we intend to investigate the role of brain network function in chronic pain. Specifically, we want to use electroencephalography (EEG) to assess the function of intrinsic brain networks in chronic pain. Intrinsic brain networks are spatially distributed networks of connected brain areas that are synchronously activated during rest or particular tasks (Uddin, Yeo, & Spreng, 2019; Yeo et al., 2011). Abnormalities of the function of intrinsic brain networks have been observed in different neuropsychiatric disorders including chronic pain (Menon, 2011). While these abnormalities have originally been identified using hemodynamic signals obtained from fMRI, we want to assess their involvement in chronic pain using EEG. EEG has a higher temporal resolution than fMRI and can, thus, specify the temporal and spectral characteristics of intrinsic brain network function. Moreover, since EEG is broadly available, potentially mobile, and cost-efficient, it can be easily scaled to large patient numbers in different settings. Here, we will adopt the most common definition of intrinsic brain networks as provided in (Yeo et al., 2011), which divides the brain into seven intrinsic brain networks. We will focus on four of these networks which figure prominently in the pathology of neuropsychiatric disorders (Menon, 2011) and chronic pain (Brandl et al., 2022): the somatomotor (a.k.a. pericentral) network, the frontoparietal (a.k.a. lateral frontoparietal/ control/ central executive) network, the ventral attention (a.k.a. midcingulo-insular/ salience) network, and the default (a.k.a. medial frontoparietal) network. To analyze the function of these networks, we have developed a pipeline to evaluate EEG activity within and across networks. We will relate the function of the networks to the intensity of the pain experience. Moreover, we will relate brain network function to other dependent variables such as group (heathy vs. control) and depression. To assess the robustness of the effects, we will include multiple data sets from different sites. To this end, we obtained five additional data sets, which we will use to validate findings in our own data sets.
Pain is a highly subjective phenomenon which varies within and across individuals. Extensive work has addressed whether and how the functional state of the brain shapes variations of pain perception. One target of these investigations has been oscillatory brain activity in different frequency bands (Kim & Davis, 2021; Ploner et al., 2017). Two features defining oscillatory brain activity are the amplitude (or power) and the frequency of oscillations. Most previous investigations focused on relations between the amplitude of oscillatory activity and pain perception (Schulz et al., 2011; Tiemann et al., 2015; Tu et al., 2016; Zhang et al., 2012). Only recently, studies have begun to systematically investigate the relationship between the frequency of oscillatory activity and pain perception. These recent studies have focused on the individual alpha peak frequency (APF) in sensorimotor brain regions. They revealed that an individual's sensorimotor APF during the pain-free resting state relates to the individual sensitivity to longer lasting pain. More precisely, a slower resting state APF was related to higher pain ratings during experimental pain lasting from minutes to weeks (Furman et al., 2018; Furman et al., 2020; Furman et al., 2019, but see Nir et al., 2010, for different findings). Most recently, it has been shown that the pre-operative resting state APF predicted pain severity 3 days after surgery in lung cancer patients (Millard et al., 2022), extending previous experimental findings to the clinical setting. Since a heightened pain sensitivity represents a risk factor for chronic pain, the APF is increasingly investigated as a biomarker for an individual's sensitivity to pain as well as for the susceptibility to develop chronic pain (Mazaheri et al., 2022; Seminowicz et al., 2020). However, it is not yet clear whether the APF relates to pain only under certain conditions or whether it represents a general marker of pain sensitivity. In particular, it is unknown whether the APF plays a similar role for both intra-individual, moment-to-moment variations of pain as well as for inter-individual, person-to-person variations of pain. Demonstrating such a general role of the APF would strengthen its importance as a pain sensitivity biomarker. This project represents a secondary data analysis of a large electroencephalography (EEG) study applying brief painful stimuli of milliseconds duration in healthy human participants. The study design and the planned primary analyses are preregistered at clinicaltrials.gov (https://clinicaltrials.gov/ct2/show/NCT05616091). The primary analysis will focus on how different pain-evoked brain responses encode intra-individual, inter-individual, and inter-session variations of pain perception. In contrast, the present secondary data analysis aims to investigate the predictive value of the APF for pain perception. Analyses will be twofold. First, to investigate whether the APF predicts intra-individual variations of pain perception, the APF immediately prior to a brief painful laser stimulus will be related to the subsequently perceived pain intensity within participants. To the best of our knowledge, this question has not been addressed before. Second, to test whether the APF predicts the general sensitivity to brief painful stimuli, i.e. inter-individual variations of the perception of pain, the resting state APF prior to the experimental pain paradigm will be assessed and related to average perceived pain intensity across participants. This analysis is intended to replicate previous work and extend it to a different type of noxious stimulus. Moreover, since the APF can be computed using different approaches, we will perform a multiverse (specification curve) analysis to test the robustness of the results against different analysis approaches (Hardwicke & Wagenmakers, 2023; Simonsohn et al., 2020).