Capturing Pain-Induced Changes in Visual Processing Through Manifold Geometry
Poster No:
2060
Submission Type:
Late-Breaking Abstract Submission
Authors:
Lada Kohoutova1, Royoung Kim2, Chi-Ning Chou3, Youngeun Park2, SueYeon Chung3, Won Mok Shim4, Choong-Wan Woo5
Institutions:
1EPFL, Geneva, Switzerland, 2Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea, Republic of, 3Center for Computational Neuroscience, Flatiron Institute, New York, NY, 4Sungkyunkwan University, Suwon, Korea, Republic of, 5Sungkyunkwan University, Seoul, N/A
First Author:
Co-Author(s):
Royoung Kim
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
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
Late Breaking Reviewer(s):
Introduction:
As pain is a crucial signal for survival, it captures attention and influences not only cognitive but also multimodal sensory processing (Eccleston & Crombez, 1999; Buhle & Wager, 2010). For example, pain interferes with visual processing in the lateral occipital complex (LOC) (Bingel et al. 2007), yet the impact of pain on early visual processes remains unexplored though visual areas are among pain-predictive brain regions (Kohoutova et al., 2022). Building on the theory of object recognition, which posits that objects are represented by manifolds that progressively untangle along the ventral visual stream (DiCarlo & Cox, 2007), we investigated how pain shapes the neural representations of visual stimuli using a functional Magnetic Resonance Imaging (fMRI) experiment that combined painful thermal stimulation with a picture-viewing task. Beyond traditional multivariate analysis methods such as support vector machines (SVMs) and representational similarity analysis (RSA), we applied the Geometry from Classification Manifold Capacity (GCMC) framework (Chou et al., 2024), a state-of-the-art technique to quantify modulatory effects of pain on visual representations in terms of manifold geometry.
Methods:
A total of 37 participants (17 females, mean age = 22.8 ± 2.6 years) were included in the study, which comprised two fMRI sessions. Session 1 involved an experiment with image viewing and simultaneous thermal stimulation (Fig. 1). There were three levels of thermal stimulation: non-painful warmth (42 °C), intense but non-painful heat (44.5 °C), and painful heat (47 °C). During the thermal stimulus plateau, three images were presented, selected from the THINGS database (Hebart et al., 2019). Session 2 included three pain-only runs, as well as standard retinotopic mapping and category localizer runs to identify visual regions of interest (ROIs). The pain experiment in Session 2 followed the same structure as in Session 1 but included continuous pain ratings. Early and high-level visual ROIs, including V1, V2, hV4, and LOC, were defined for each individual. Common ROI masks were then created using voxels present in at least 20 % of participants. We first tested whether information on different heat levels was encoded in the ROIs using SVMs. To analyse the effects of pain on the neural representations of visual stimuli, we first performed image category classification using SVMs and RSA. Finally, we applied the GCMC framework, specifically examining radius, dimensions, and manifold capacity, to quantify the effects of pain on manifold geometry in the ventral visual stream.
Results:
Heat level classification in the ROIs revealed that only hV4 and LOC distinguished warmth from low heat above chance, while all ROIs successfully classified warmth vs. low/high heat. Image category classification was also above chance in all ROIs, with higher accuracy in LOC, as expected, and no significant effects of the levels of thermal stimulation were observed. RSA showed lower similarity between categories in LOC but no heat-related differences. Manifold geometry analysis revealed a significant increase in capacity and a decrease in radius and dimensions along the ventral visual stream (Fig. 2a). Crucially, high heat lowered classification capacity and increased the combined effect of radius and dimensions, known as the effective margin (Fig. 2b).
Conclusions:
While traditional methods such as SVM and RSA failed to capture the effects of pain on the neural representations in the ventral visual stream, changes in manifold geometry revealed that pain decreased classification capacity due to an increased effective margin. These findings suggest that in the presence of potential harm, the details of visual inputs may be disregarded, possibly to facilitate more efficient processing. Further research is needed to confirm this hypothesis and explore the link between pain-induced changes at the low level of sensory processing and cognitive performance.
Modeling and Analysis Methods:
Multivariate Approaches
Other Methods
Novel Imaging Acquisition Methods:
BOLD fMRI
Perception, Attention and Motor Behavior:
Perception: Pain and Visceral 1
Perception: Visual 2
Keywords:
Pain
Vision
Other - manifold geometry; multivariate analysis
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
2. Buhle, J., & Wager, T. D. (2010). Performance-dependent inhibition of pain by an executive working memory task. Pain, 149(1), 19–26. https://doi.org/10.1016/j.pain.2009.10.027
3. Chou, C.-N., Arend, L., Wakhloo, A. J., Kim, R., Slatton, W., & Chung, S. (2024). Neural Manifold Capacity Captures Representation Geometry, Correlations, and Task-Efficiency Across Species and Behaviors (p. 2024.02.26.582157). bioRxiv. https://doi.org/10.1101/2024.02.26.582157
4. DiCarlo, J. J., & Cox, D. D. (2007). Untangling invariant object recognition. Trends in Cognitive Sciences, 11(8), 333–341. https://doi.org/10.1016/j.tics.2007.06.010
5. Eccleston, C., & Crombez, G. (1999). Pain demands attention: A cognitive–affective model of the interruptive function of pain. Psychological Bulletin, 125(3), 356–366. https://doi.org/10.1037/0033-2909.125.3.356
6. Hebart, M. N., Dickter, A. H., Kidder, A., Kwok, W. Y., Corriveau, A., Wicklin, C. V., & Baker, C. I. (2019). THINGS: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PLOS ONE, 14(10), e0223792. https://doi.org/10.1371/journal.pone.0223792
7. Kohoutová, L., Atlas, L. Y., Büchel, C., Buhle, J. T., Geuter, S., Jepma, M., Koban, L., Krishnan, A., Lee, D. H., Lee, S., Roy, M., Schafer, S. M., Schmidt, L., Wager, T. D., & Woo, C.-W. (2022). Individual variability in brain representations of pain. Nature Neuroscience, 25(6), 749–759. https://doi.org/10.1038/s41593-022-01081-x