The reliability of two prospective cortical biomarkers for pain: EEG peak alpha frequency and TMS corticomotor excitability

On the (un)reliability of common behavioral and electrophysiological measures from the stop signal task: Measures of inhibition lack stability over time

Response inhibition, the intentional stopping of planned or initiated actions, is often considered a key facet of control, impulsivity, and self-regulation. The stop signal task is argued to be the purest inhibition task we have, and it is thus central to much work investigating the role of inhibition in areas like development and psychopathology. Most of this work quantifies stopping behavior by calculating the stop signal reaction time as a measure of individual stopping latency. Individual difference studies aiming to investigate why and how stopping latencies differ between people often do this under the assumption that the stop signal reaction time indexes a stable, dispositional trait. However, empirical support for this assumption is lacking, as common measures of inhibition and control tend to show low test-retest reliability and thus appear unstable over time. The reasons for this could be methodological, where low stability is driven by measurement noise, or substantive, where low stability is driven by a larger influence of state-like and situational factors. To investigate this, we characterized the split-half and test-retest reliability of a range of common behavioral and electrophysiological measures derived from the stop signal task. Across three independent studies, different measurement modalities, and a systematic review of the literature, we found a pattern of low temporal stability for inhibition measures and higher stability for measures of manifest behavior and non-inhibitory processing. This pattern could not be explained by measurement noise and low internal consistency. Consequently, response inhibition appears to have mostly state-like and situational determinants, and there is little support for the validity of conceptualizing common inhibition measures as reflecting stable traits.

Can non-invasive brain stimulation modulate peak alpha frequency in the human brain? A systematic review and meta-analysis

Peak alpha frequency (PAF), the dominant oscillatory frequency within the alpha range (8-12 Hz), is associated with cognitive function and several neurological conditions, including chronic pain. Manipulating PAF could offer valuable insight into the relationship between PAF and various functions and conditions, potentially providing new treatment avenues. This systematic review aimed to comprehensively synthesise effects of non-invasive brain stimulation (NIBS) on PAF speed. Relevant studies assessing PAF pre- and post-NIBS in healthy adults were identified through systematic searches of electronic databases (Embase, PubMed, PsychINFO, Scopus, The Cochrane Library) and trial registers. The Cochrane risk-of-bias tool was employed for assessing study quality. Quantitative analysis was conducted through pairwise meta-analysis when possible; otherwise, qualitative synthesis was performed. The review protocol was registered with PROSPERO (CRD42020190512) and the Open Science Framework (https://osf.io/2yaxz/). Eleven NIBS studies were included, all with a low risk-of-bias, comprising seven transcranial alternating current stimulation (tACS), three repetitive transcranial magnetic stimulation (rTMS), and one transcranial direct current stimulation (tDCS) study. Meta-analysis of active tACS conditions (eight conditions from five studies) revealed no significant effects on PAF (mean difference [MD] = -0.12, 95% CI = -0.32 to 0.08, p = 0.24). Qualitative synthesis provided no evidence that tDCS altered PAF and moderate evidence for transient increases in PAF with 10 Hz rTMS. However, it is crucial to note that small sample sizes were used, there was substantial variation in stimulation protocols, and most studies did not specifically target PAF alteration. Further studies are needed to determine NIBS's potential for modulating PAF.

Repetitive Transcranial Magnetic Stimulation Combined with Sling Exercise Modulates the Motor Cortex in Patients with Chronic Low Back Pain

The study aims to explore the effects of combining repetitive transcranial magnetic stimulation (rTMS) with sling exercise (SE) intervention in patients with chronic low back pain (CLBP). This approach aims to directly stimulate brain circuits and indirectly activate trunk muscles to influence motor cortex plasticity. However, the impact of this combined intervention on motor cortex organization and clinical symptom improvement is still unclear, as well as whether it is more effective than either intervention alone. To investigate this, patients with CLBP were randomly assigned to three groups: SE/rTMS, rTMS alone, and SE alone. Motor cortical organization, numerical pain rating scale (NPRS), Oswestry Disability Index (ODI), and postural balance stability were measured before and after a 2-week intervention. The results showed statistically significant differences in the representative location of multifidus on the left hemispheres, as well as in NPRS and ODI scores, in the combined SE/rTMS group after the intervention. When compared to the other two groups, the combined SE/rTMS group demonstrated significantly different motor cortical organization, sway area, and path range from the rTMS alone group, but not from the SE alone group. These findings highlight the potential benefits of a combined SE/rTMS intervention in terms of clinical outcomes and neuroadaptive changes compared to rTMS alone. However, there was no significant difference between the combined intervention and SE alone. Therefore, our research does not support the use of rTMS as a standalone treatment for CLBP. Our study contributed to optimizing treatment strategies for individuals suffering from CLBP.

Combined transcranial magnetic stimulation and electroencephalography reveals alterations in cortical excitability during pain

Transcranial magnetic stimulation (TMS) has been used to examine inhibitory and facilitatory circuits during experimental pain and in chronic pain populations. However, current applications of TMS to pain have been restricted to measurements of motor evoked potentials (MEPs) from peripheral muscles. Here, TMS was combined with electroencephalography (EEG) to determine whether experimental pain could induce alterations in cortical inhibitory/facilitatory activity observed in TMS-evoked potentials (TEPs). In Experiment 1 (n=29), multiple sustained thermal stimuli were administered to the forearm, with the first, second, and third block of thermal stimuli consisting of warm but non-painful (pre-pain block), painful (pain block) and warm but non-painful (post-pain block) temperatures, respectively. During each stimulus, TMS pulses were delivered while EEG (64 channels) was simultaneously recorded. Verbal pain ratings were collected between TMS pulses. Relative to pre-pain warm stimuli, painful stimuli led to an increase in the amplitude of the frontocentral negative peak ~45 ms post-TMS (N45), with a larger increase associated with higher pain ratings. Experiments 2 and 3 (n=10 in each) showed that the increase in the N45 in response to pain was not due to changes in sensory potentials associated with TMS, or a result of stronger reafferent muscle feedback during pain. This is the first study to use combined TMS-EEG to examine alterations in cortical excitability in response to pain. These results suggest that the N45 TEP peak, which indexes GABAergic neurotransmission, is implicated in pain perception and is a potential marker of individual differences in pain sensitivity.

Alterations in cortical excitability during pain: A combined TMS-EEG Study

Transcranial magnetic stimulation (TMS) has been used to examine inhibitory and facilitatory circuits during experimental pain and in chronic pain populations. However, current applications of TMS to pain have been restricted to measurements of motor evoked potentials (MEPs) from peripheral muscles. Here, TMS was combined with electroencephalography (EEG) to determine whether experimental pain could induce alterations in cortical inhibitory/facilitatory activity observed in TMS-evoked potentials (TEPs). In Experiment 1 (n = 29), multiple sustained thermal stimuli were administered to the forearm, with the first, second and third block of thermal stimuli consisting of warm but non-painful (pre-pain block), painful (pain block) and warm but non-painful (post-pain block) temperatures respectively. During each stimulus, TMS pulses were delivered while EEG (64 channels) was simultaneously recorded. Verbal pain ratings were collected between TMS pulses. Relative to pre-pain warm stimuli, painful stimuli led to an increase in the amplitude of the frontocentral negative peak ~45ms post-TMS (N45), with a larger increase associated with higher pain ratings. Experiments 2 and 3 (n = 10 in each) showed that the increase in the N45 in response to pain was not due to changes in sensory potentials associated with TMS, or a result of stronger reafferent muscle feedback during pain. This is the first study to use combined TMS-EEG to examine alterations in cortical excitability in response to pain. These results suggest that the N45 TEP peak, which indexes GABAergic neurotransmission, is implicated in pain perception and is a potential marker of individual differences in pain sensitivity.

In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?

Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.