Decomposing spatiotemporal patterns of pain in the brain
Poster No:
2040
Submission Type:
Abstract Submission
Authors:
Yuseon Park1,2, Jungwoo Kim1,3,2, Eunjin Lee1, Choong-Wan Woo1,3,2,4
Institutions:
1Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea, Republic of, 2Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea, Republic of, 3Department of Biomedical engineering, Sungkyunkwan University, Suwon, Korea, Republic of, 4life- inspired neural network for Prediction and Optimization Research Group, Suwon, Korea, Republic of
First Author:
Yuseon Park
Center for Neuroscience Imaging Research, Institute for Basic Science|Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Center for Neuroscience Imaging Research, Institute for Basic Science|Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University
Suwon, Korea, Republic of|Suwon, Korea, Republic of
Co-Author(s):
Jungwoo Kim
Center for Neuroscience Imaging Research, Institute for Basic Science|Department of Biomedical engineering, Sungkyunkwan University|Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University
Suwon, Korea, Republic of|Suwon, Korea, Republic of|Suwon, Korea, Republic of
Center for Neuroscience Imaging Research, Institute for Basic Science|Department of Biomedical engineering, Sungkyunkwan University|Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University
Suwon, Korea, Republic of|Suwon, Korea, Republic of|Suwon, Korea, Republic of
Eunjin 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|Department of Biomedical engineering, Sungkyunkwan University|Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University|life- inspired neural network for Prediction and Optimization Research Group
Suwon, Korea, Republic of|Suwon, Korea, Republic of|Suwon, Korea, Republic of|Suwon, Korea, Republic of
Center for Neuroscience Imaging Research, Institute for Basic Science|Department of Biomedical engineering, Sungkyunkwan University|Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University|life- inspired neural network for Prediction and Optimization Research Group
Suwon, Korea, Republic of|Suwon, Korea, Republic of|Suwon, Korea, Republic of|Suwon, Korea, Republic of
Introduction:
Pain is constructed through multiple components of brain activity, with different regions engaging at distinct time points (Gim et al., 2024). Previous studies have largely focused on the spatial patterns of brain activation during the stimulation period and identified pattern signatures predictive of pain levels. However, the way in which multiple spatiotemporal patterns of brain activity dynamically construct the entire pain experience-ranging from pain onset to the return to baseline-remains unclear. The components associated with the return to baseline may be particularly important for understanding variability in pain ratings, as they likely play a critical role in pain relief and the homeostatic regulation of pain. This aspect, however, has not been well examined due to the short post-stimulus durations typically employed in pain studies. Here, we aim to decompose spatiotemporal patterns of brain activity to explain the continuous changes in pain perception, encompassing the full range of the pain experience, from onset to the return to the resting state, by including an extended post-stimulus period.
Methods:
Sixty-six healthy and right-handed participants (age = 22.4 ± 1.9 [mean ± SD], 31 female) participated in a heat pain task with fMRI scans. The thermal stimulation lasted 12 seconds, comprising 2.5 seconds of ramp-up, 7 seconds of plateau, and 2.5 seconds of ramp-down. We applied four levels of thermal stimulation (45–48°C in 1°C increments) and included a 60-second post-stimulus period to capture the full return to baseline (Fig. 1a left). We divided the data into training (n = 36), validation (n =12), and test sets (n =12) and first decomposed the fMRI data of the training set into 30 spatiotemporal components using a group independent component analysis (ICA). We also applied principal component analysis (PCA) to the continuous pain rating data and identified two principal components (Fig. 1b left): the first component represented the early response to thermal stimulation, while the second reflected the late response, potentially capturing the return to the baseline. We then predicted the rating PC scores for each trial using the 30 ICs of brain activity, employing leave-one-participant-out cross-validation.
Results:
The spatiotemporal predictive models for the two rating PC scores demonstrated significant predictive accuracies: prediction-outcome correlations were r = 0.693, p =7.657e-08 for the first PC score and r = 0.203, p = 0.0053 for the second PC score (Fig. 1c). Bootstrap tests on the predictive weights revealed significant contributions of brain ICs at different time points, suggesting the dynamic engagement of multiple spatiotemporal patterns in the construction of pain (Fig. 1d). We then interpreted spatiotemporal patterns of brain activity explaining the early and late responses to painful stimulation.
Conclusions:
In this study, we identified spatiotemporal patterns of brain activity that account for distinct components of continuous pain ratings. Notably, we identified a brain map involved during the period of painful stimulus, as well as a distinct brain map engaged in the the post-stimulus period. These findings offering new insights into brain dynamics underlying pain recovery and relief.
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Multivariate Approaches
Perception, Attention and Motor Behavior:
Perception: Pain and Visceral 1
Keywords:
Computational Neuroscience
Data analysis
FUNCTIONAL MRI
Multivariate
Pain
1|2Indicates the priority used for review
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Provide references using APA citation style.
cortical hierarchy in human pain experience. PLoS Biol 22(11): e3002910
2. Calhoun, V. D., Liu, J., & Adalı, T. (2009). A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. NeuroImage, S163-S172.
3. Woo CW, Schmidt L, Krishnan A, Jepma M, Roy M, Lindquist MA, et al. Quantifying cerebral contribu- tions to pain beyond nociception. Nat Commun. 2017; 8:14211. Epub 20170214.
4. WagerTD, Atlas LY, Lindquist MA, Roy M, Woo C-W, Kross E. An fMRI-Based Neurologic Signature of PhysicalPain. N Engl J Med. 2013; 368(15):1388–1397.
5. Cecchi GA, Huang L, Hashmi JA, Baliki M, Centeno MV, et al. (2012) Predictive Dynamics of Human Pain Perception. PLoS Comput Biol 8(10): e1002719. doi:10.1371/journal.pcbi.1002719