Weichen Huang

All Publications

• Naturalistic acute pain states decoded from neural and facial dynamics. Nature communications Huang, Y., Gopal, J., Kakusa, B., Li, A. H., Huang, W., Wang, J. B., Persad, A., Ramayya, A., Parvizi, J., Buch, V. P., Keller, C. J. 2025; 16 (1): 4371

  Abstract

  Pain remains poorly understood in task-free contexts, limiting our understanding of its neurobehavioral basis in naturalistic settings. Here, we use a multimodal, data-driven approach with intracranial electroencephalography, pain self-reports, and facial expression analysis to study acute pain in twelve epilepsy patients under continuous neural and audiovisual monitoring. Using machine learning, we successfully decode individual participants' high versus low pain states from distributed neural activity, involving mesolimbic regions, striatum, and temporoparietal cortex. Neural representation of pain remains stable for hours and is modulated by pain onset and relief. Objective facial expressions also classify pain states, concordant with neural findings. Importantly, we identify transient periods of momentary pain as a distinct naturalistic acute pain measure, which can be reliably discriminated from affect-neutral periods using neural and facial features. These findings reveal reliable neurobehavioral markers of acute pain across naturalistic contexts, underscoring the potential for monitoring and personalizing pain interventions in real-world settings.

  View details for DOI 10.1038/s41467-025-59756-5

  View details for PubMedID 40350488

  View details for PubMedCentralID 6146950

• Naturalistic acute pain states decoded from neural and facial dynamics. bioRxiv : the preprint server for biology Huang, Y., Gopal, J., Kakusa, B., Li, A. H., Huang, W., Wang, J. B., Persad, A., Ramayya, A., Parvizi, J., Buch, V. P., Keller, C. 2024

  Abstract

  Pain is a complex experience that remains largely unexplored in naturalistic contexts, hindering our understanding of its neurobehavioral representation in ecologically valid settings. To address this, we employed a multimodal, data-driven approach integrating intracranial electroencephalography, pain self-reports, and facial expression quantification to characterize the neural and behavioral correlates of naturalistic acute pain in twelve epilepsy patients undergoing continuous monitoring with neural and audiovisual recordings. High self-reported pain states were associated with elevated blood pressure, increased pain medication use, and distinct facial muscle activations. Using machine learning, we successfully decoded individual participants' high versus low self-reported pain states from distributed neural activity patterns (mean AUC = 0.70), involving mesolimbic regions, striatum, and temporoparietal cortex. High self-reported pain states exhibited increased low-frequency activity in temporoparietal areas and decreased high-frequency activity in mesolimbic regions (hippocampus, cingulate, and orbitofrontal cortex) compared to low pain states. This neural pain representation remained stable for hours and was modulated by pain onset and relief. Objective facial expression changes also classified self-reported pain states, with results concordant with electrophysiological predictions. Importantly, we identified transient periods of momentary pain as a distinct naturalistic acute pain measure, which could be reliably differentiated from affect-neutral periods using intracranial and facial features, albeit with neural and facial patterns distinct from self-reported pain. These findings reveal reliable neurobehavioral markers of naturalistic acute pain across contexts and timescales, underscoring the potential for developing personalized pain interventions in real-world settings.

  View details for DOI 10.1101/2024.05.10.593652

  View details for PubMedID 38766098

  View details for PubMedCentralID PMC11100805