EEG Reveals Robust Within-Person but Unstable Between-Person Neural Encoding of Pain
Tiemann, L.; Bott, F. S.; May, E. S.; Nickel, M. M.; Hohn, V. D.; Gil Avila, C.; Bruna, N.; Zebhauser, P. T.; Ploner, M.
Show abstract
The perception of pain varies both within and between individuals, even when sensory input remains constant. Understanding how the brain encodes these intra- and interindividual variations is essential for elucidating the neural mechanisms of pain in health and disease. Yet, previous findings have been inconsistent, and their robustness, in terms of both repeatability and replicability, has remained unclear. Here, we used electroencephalography (EEG) in 161 healthy participants to re-investigate the neural correlates of intra- and inter-individual variations in the perception of brief painful stimuli, independent of stimulus intensity. Using Bayesian multivariate multi-model regression, we related pain ratings to evoked (N1, N2, P2) and induced oscillatory responses (alpha, beta, gamma). To assess the robustness of our findings, the experiment was repeated after four weeks in the same participants and replicated in an independent cohort (n = 111). This design allowed us to examine within-person variability at both short (moment-to-moment) and long (day-to-day) timescales. Inter-individual differences in pain were primarily associated with the P2 response, an effect that was repeatable in the same but not replicable in the independent cohort. In contrast, intra-individual variations were explained by a multicomponent EEG pattern that was both repeatable across time and replicable across cohorts. These findings demonstrate that intra- and inter-individual variability in pain is differentially encoded in the human brain and reveal greater robustness of within-person brain-behavior associations. EEG markers may therefore be more suitable for tracking longitudinal changes in pain within individuals than for comparing pain sensitivity across individuals.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Assessing the predictive value of peak alpha frequency for the sensitivity to pain 98%
- Dynamics of brain function in chronic pain patients assessed by microstate analysis of resting-state electroencephalography 97%
- Baseline Resting-State Functional Connectivity Determines Subsequent Pain Ratings to a Tonic Ecologically Valid Experimental Model of Orofacial Pain 97%
Similar papers in this journal
Similar papers in this journal
- Acute pain drives different effects on local and global cortical excitability in motor and prefrontal areas: Insights into interregional and interpersonal differences in pain processing 96%
- Sensorimotor peak alpha frequency is a reliable biomarker of pain sensitivity 95%
- Multiple brain networks mediating stimulus-pain relationships in humans 94%
Similar papers in this journal
- Effect sizes and test-retest reliability of the fMRI-based Neurologic Pain Signature 95%
- A machine learning based approach towards high-dimensional mediation analysis 95%
- Another's pain in my brain: No evidence that placebo analgesia affects the sensory-discriminative component in empathy for pain 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.