Barrette, L.-E. | Search bioRxiv

bioRxiv2026

A comprehensive physiological dataset of pain and aversive modalities for pain biomarkers research

While progress has been made in identifying pain biomarkers using neuroimaging, the development of reliable EEG-based biomarkers remains challenging, limited by small sample sizes and the lack of adequate control conditions. We present a comprehensive dataset from 125 pain-free adults, comprising electroencephalogram (EEG) recordings, and behavioural, sociodemographic, and psychological data. Galvanic skin response and electrocardiogram data were also recorded in a subset of participants (n = 76). Participants received two sustained painful stimulations (thermal, chemical) and two sustained aversive non-painful stimulations (auditory, taste), each delivered twice (with and without continuous intensity ratings). Data are provided in Brain Imaging Data Structure format, where all physiological, stimulus-related, and behavioural data are synchronized within the EEG data files for easy access and analysis. Technical validation confirmed data integrity, with spectral and physiological analyses showing responses associated with stimulus intensity and subjective pain/unpleasantness levels. By providing multimodal physiological data across painful and non-painful aversive conditions, this initiative represents a valuable resource for developing or validating biomarkers and exploring the multidimensional nature of pain perception.

neuroscience↗

bioRxiv2026

Distinct EEG microstate signatures across different pain types

Pain is associated with dynamic brain reorganization. Electroencephalographic microstates can characterize transient large-scale brain states, yet findings across pain studies are inconsistent. Two features may contribute: conditions have been studied in isolation, and analytical pipelines vary widely. We applied a harmonized analysis pipeline across three pain conditions: capsaicininduced pain, assessed within participants against a pain-free baseline, and non-chronic and chronic back pain, each compared with matched controls. Using a five-minute resting-state electroencephalographic recordings we examined microstate topography, temporal expression and transition probabilities, and directly compared pain related differences across pain states. We identified five microstates (A-E), corresponding to auditory, visual, salience, and attentional networks. Capsaicin pain showed increased expression of microstate C and reduced expression of microstates A, D and E. Transitions were redistributed towards microstate C, indicating a shift toward salience-related processing at the expense of sensory and attentional dynamics. In clinical back pain, effects were smaller and diverged by pain type: non-chronic back pain showed lower microstate C occurrence, opposite in direction to the capsaicin effect, whereas chronic back pain showed lower microstate D global explained variance, suggesting salience and attentional differences in clinical pain. Direct comparisons showed weaker pain-related increases in microstate C expression in both clinical back pain groups than capsaicin pain, with additional differences involving microstates A, D and E. These findings argue against a unitary EEG microstate signature of pain and instead reveal pain-type-specific patterns of large-scale brain dynamics, with microstate C providing the clearest distinction between experimental and clinical pain.

neuroscience↗