Multimodal Experimental Platform to Disentangle Emotional and Physiological Pain Components

Area of Science:

• Neuroscience
• Pain Research
• Human-Computer Interaction

Background:

• Pain is a complex experience influenced by physiological and psychological factors.
• Objective, multidimensional pain assessment tools are needed for effective therapy development.
• Current pain metrics face challenges in capturing the full spectrum of pain perception.

Purpose of the Study:

• To develop and validate a synergistic platform integrating virtual reality (VR) and electro-cutaneous stimulation (ECS).
• To objectively quantify physical and emotional pain dimensions.
• To introduce and validate a novel metric, the subjective Index of Pain (TIP), for personalized pain assessment.

Main Methods:

• Designed a VR-ECS platform to deliver physical and emotional pain stimuli.
• Collected neural, physiological, and self-reported pain data from 20 participants.
• Utilized explainable machine learning and multilevel mixed-effect models to analyze data and develop the TIP metric.

Main Results:

• The VR-ECS platform effectively modulated both physical and emotional pain perception.
• Objective signatures of pain modulation were identified in neural and physiological signals.
• The subjective Index of Pain (TIP) was validated as a reliable indicator sensitive to emotional pain modulation.

Conclusions:

• A robust framework for investigating distinct pain dimensions was developed.
• The subjective Index of Pain (TIP) offers a reliable method for assessing individual subjective pain experiences.
• Future multidimensional tools and metrics hold promise for personalized pain therapies.