Specificity and replicability of personalized pain prediction: a 5-year follow-up study
Poster No:
2049
Submission Type:
Abstract Submission
Authors:
Youngeun Park1, Sungwoo Lee1,2, Dong Hee Lee1, Choong-Wan Woo1,2
Institutions:
1Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea, Republic of, 2Sungkyunkwan University, Suwon, Korea, Republic of
First Author:
Youngeun Park
Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Korea, Republic of
Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Korea, Republic of
Co-Author(s):
Sungwoo Lee
Center for Neuroscience Imaging Research, Institute for Basic Science|Sungkyunkwan University
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Center for Neuroscience Imaging Research, Institute for Basic Science|Sungkyunkwan University
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Dong Hee Lee
Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Korea, Republic of
Center for Neuroscience Imaging Research, Institute for Basic Science
Suwon, Korea, Republic of
Choong-Wan Woo, Ph.D.
Center for Neuroscience Imaging Research, Institute for Basic Science|Sungkyunkwan University
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Center for Neuroscience Imaging Research, Institute for Basic Science|Sungkyunkwan University
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Introduction:
Pain is a complex and subjective experience that extends beyond nociceptive processes (Melzack 1999). This intricate nature leads to high individual variability in brain responses and subjective pain experiences (Kohoutová et al., 2022; Coghill et al., 2003). Several studies have proposed group-level neuroimaging-based pain biomarkers using experimentally evoked pain, demonstrating moderate prediction accuracy and identifying pain-predictive brain activation patterns conserved across individuals (Wager et al., 2013; Woo et al., 2017). Nevertheless, the group-level approach fails to account for individual variability in brain systems related to pain. In our ongoing study, we have developed a personalized pain signature using densely sampled fMRI data (one individual, 33 sessions), demonstrating high prediction accuracy for trial-level pain ratings within an individual (R-squared = 0.615 on the hold-out test set ). However, the model's specificity (i.e., its response to conditions that could be confused with pain) and replicability over a long test-retest interval remain unclear. To address these gaps, we collected additional fMRI data from the same individual five years after the initial experiment and tested the specificity and replicability of the personalized model.
Methods:
The same single participant, who completed 33 fMRI sessions five years ago, i.e., July 2019 – Aug 2020, underwent four additional fMRI sessions, during which multiple types of stimuli were presented, including heat, aversive images, aversive sounds, and pleasant images (fig. 1a). Thermal stimulation was delivered to the left forearm at six temperature levels, ranging from 45 to 47.5℃, with 0.5℃ increments, as in the initial study. We used the same set of aversive and pleasant images, as well as aversive sound stimuli from a previous study (Čeko et al., 2022). The images were primarily drawn from the IAPS database, and the sound stimulus was a knife scraping on a bottle. After each stimulus, the participant rated the intensity of the negative or positive emotions elicited using the generalized Labeled Magnitude Scale (gLMS) (Bartoshuk et al., 2004). The a priori personalized pain signature, developed from 18 sessions of fMRI data (1623 trials) collected in 2019, was applied to the newly collected fMRI data to test the signature's replicability and specificity.
Results:
The personalized pain signature showed higher prediction accuracy for thermal pain (r = 0.706, p < 0.0001, bootstrap test) compared to other sensory stimuli (r = 0.388-0.422, p < 0.0025, Fig. 1b), supporting the model's specificity. While the population-level model, derived from fMRI data of multiple participants (n = 80), also demonstrated high prediction accuracy (r = 0.620, p < 0.0001) and specificity (p < 0.0001) for thermal pain, the personalized pain signature exhibited significantly higher prediction accuracy than the population-level model (p < 0.0001, two-sample z-test). Although the prediction performance measured by correlation coefficients was high in the replication dataset, the explained variance was negative (R-squared = -0.051) and substantially lower than the original dataset (R-squared = 0.617), likely due to scale differences (Fig. 1c). Rescaling methods, such as baseline correction and predicted outcome calibration, improved the explained variance of the pain prediction (R-squared = 0.237 and 0.386, respectively; Fig. 1d), suggesting that the rescaling could enhance the model's generalizability for prospective testing.
Conclusions:
Overall, we demonstrated that the personalized pain signature can distinguish pain from other sensory stimuli and accurately predict trial-by-trial pain ratings even after a multi-year interval. These findings highlight the potential of neuroimaging-based personalized pain biomarkers for their precision and replicability.
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Multivariate Approaches
Novel Imaging Acquisition Methods:
BOLD fMRI
Perception, Attention and Motor Behavior:
Perception: Pain and Visceral 1
Keywords:
Computational Neuroscience
FUNCTIONAL MRI
Multivariate
Pain
1|2Indicates the priority used for review
·Figure 1. Research overview and results
Abstract Information
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Provide references using APA citation style.
Čeko, M., Kragel, P. A., Woo, C. W., López-Solà, M., & Wager, T. D. (2022). Common and stimulus-type-specific brain representations of negative affect. Nature neuroscience, 25(6), 760-770.
Coghill, R. C., McHaffie, J. G., & Yen, Y. F. (2003). Neural correlates of interindividual differences in the subjective experience of pain. Proceedings of the National Academy of Sciences, 100(14), 8538-8542.
Kohoutová, L., Atlas, L. Y., Büchel, C., Buhle, J. T., Geuter, S., Jepma, M., ... & Woo, C. W. (2022). Individual variability in brain representations of pain. Nature neuroscience, 25(6), 749-759.
Melzack, R. (1999). From the gate to the neuromatrix. Pain, 82, S121-S126.
Wager, T. D., Atlas, L. Y., Lindquist, M. A., Roy, M., Woo, C. W., & Kross, E. (2013). An fMRI-based neurologic signature of physical pain. New England Journal of Medicine, 368(15), 1388-1397.
Woo, C. W., Schmidt, L., Krishnan, A., Jepma, M., Roy, M., Lindquist, M. A., ... & Wager, T. D. (2017). Quantifying cerebral contributions to pain beyond nociception. Nature communications, 8(1), 14211.