Multimodal Experimental Platform to Disentangle Emotional and Physiological Pain Components.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 40720270.
- Also identified by DOI 10.1109/TBME.2025.3593280.
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Abstract
Pain is a disabling experience significantly impacting individuals' lives. The complex interplay between physiological and psychological factors poses challenges for assessing pain and developing effective therapies. Hence, healthcare providers advocate for reliable, objective, multidimensional metrics to quantify pain. Developing and validating such metrics requires standardized experimental tools capable of probing physical and emotional dimensions. To these aims, we designed a synergistic platform combining virtual reality (VR) and electro-cutaneous stimulation (ECS). The platform targeted physical (via ECS) and emotional (flames appearing on a virtual hand) pain components. We tested it with 20 participants, each undergoing 120 painful stimuli. During stimulation, we collected neural, physiological signals, and self-reported pain. We demonstrated the platform's effectiveness in modulating pain through physical (NRS<sub>HP</sub> = 5.85 ± 1.23, NRS<sub>LP</sub> = 1.69 ± 0.87, p<0.001) and emotional (NRS<sub>FIRE</sub> = 6.04 ± 1.21, NRS<sub>NEUTRAL</sub> = 5.66 ± 1.25, p<0.001) stimuli. In parallel, we leveraged explainable ML to identify objective signatures of pain modulation in neural activity and physiological signals. Using multilevel mixed-effect-models (MEM), we predicted self-reported pain based on physiological signals and a subjective bias term, showing that physiological data alone cannot fully capture pain's complexity. To bridge this gap, we calculated TIP (subjective Index of Pain) quantifying the mismatch between reported pain and objective signals. We validated TIP as a reliable indicator of subjective predisposition to pain by showing its sensitivity to emotional modulations (ΔTIP<sub>FIRE-NEUTRAL</sub> = 0.363 ± 0.270, p<0.001). We developed a robust framework to investigate distinct pain dimensions and validated TIP for assessing individuals' subjective pain. In the future, multidimensional tools and reliable metrics could foster personalized effective pain therapies.
Medical subject headings
- Emotions
- Pain Measurement
- Pain
- Signal Processing, Computer-Assisted