Poster No:
2057
Submission Type:
Abstract Submission
Authors:
Marie-Eve Hoeppli1, Hadas Nahman-Averbuch2, Christopher King3, Robert Coghill3
Institutions:
1CCHMC, Cincinnati, OH, 2Washington University, St. Louis, MO, 3Cincinnati Children's Hospital, Cincinnati, OH
First Author:
Co-Author(s):
Introduction:
Individual differences in sensory processing are extensive. These differences have been consistently observed in pain and have been shown to complicate pain management. A better understanding of underlying mechanisms is essential to improve diagnosis of chronic pain conditions and define more individualized treatment targets. Our previous results highlighted a lack of relationship between pain-related brain activation and pain sensitivity using traditional fMRI analyses and question the reliability of machine-learning approaches to define brain markers of individual differences in pain (Hoeppli et al., 2022). Here we aim to characterize prevalence analysis as a new approach to investigate individual differences. To evaluate the sensitivity of this approach, we applied it to noxious and innocuous modalities.
Methods:
101 healthy volunteers (43 M and 58 F, age: 28.5 ± 7.7, mean ± SD) were included in this study. Participants underwent an fMRI session, including 3 series of heat stimuli, 1 series of cold stimuli, and 1 series of auditory stimuli. Together, the 3 heat series included 17 48˚C stimuli and 4 47˚C stimuli. The cold series included 4 0.5˚C stimuli and 1 3˚C stimulus. The auditory series included 5 90dB stimuli and 2 80dB stimulus. Only the high intensity stimuli were included in the analyses below. Due to technical issues, 28 of these participants had missing data in the cold series and 4 had missing data in the auditory series.
After preprocessing, first- and second-level (heat only) GLM analyses were performed on individual fMRI series using FEAT (Woolrich et al., 2001, 2004). The percentage of participants exhibiting similar pattern of brain activation in response to high-intensity noxious and innocuous stimuli was calculated following the FSL-based procedure described in (Coghill et al., 2003). Individual copes were concatenated into a 4D dataset, then binarized and averaged across time.
Results:
Our results show that the prevalence of participants exhibiting similar pattern of brain activation varies greatly between modalities. In the heat series, up to 95%, respectively 70%, of participants exhibited similar pattern of increased, respectively decreased activation. In addition, higher percentages of participants exhibited changes contralaterally in sensorimotor areas than in areas essential to the cognitive processing of pain, e.g. insula or ACC. Only a low percentage of participants had changes in areas involved in the fear circuit. In the cold series, similar pattern of increased activation was detected in 48%, while pattern of decreased activation was detected in 56% of participants. In this modality, patterns of increased and decreased activation overlapped considerably, suggesting extensive individual variability in response to high-intensity noxious cold stimuli. Finally, in the auditory series, up to 50% of participants exhibited similar pattern of increased activation and only 36% of participants exhibited similar pattern of decreased activation. Although the prevalence was greater in patterns of increased activation, there was an overlap between patterns of increased and decreased activation, suggesting individual variability in brain mechanisms underlying auditory processing.
Conclusions:
Our findings suggest that prevalence analyses are a valid and sensitive approach to investigate individual differences in sensory noxious and innocuous processing. Interestingly, greater variability in pattern of activation was detected in the cold and auditory modalities compared to the heat modality. These results might have been impacted by the lower number of stimuli in those modalities and their lower salience. Further studies are needed to confirm these results. In conclusion, this approach provides a new insight into individual differences in sensory processing and in pain, which might prove useful to better understand their underlying mechanisms.
Modeling and Analysis Methods:
Methods Development
Univariate Modeling
Novel Imaging Acquisition Methods:
BOLD fMRI 2
Perception, Attention and Motor Behavior:
Perception: Pain and Visceral 1
Keywords:
ADULTS
FUNCTIONAL MRI
NORMAL HUMAN
Pain
Univariate
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Task-activation
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
Yes
Are you Internal Review Board (IRB) certified?
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Yes, I have IRB or AUCC approval
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Yes
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.
Not applicable
Please indicate which methods were used in your research:
Functional MRI
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
FSL
Provide references using APA citation style.
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 of the United States of America, 100(14), 8538–8542. https://doi.org/10.1073/pnas.1430684100
Hoeppli, M. E., Nahman-Averbuch, H., Hinkle, W. A., Leon, E., Peugh, J., Lopez-Sola, M., King, C. D., Goldschneider, K. R., & Coghill, R. C. (2022). Dissociation between individual differences in self-reported pain intensity and underlying fMRI brain activation. Nature Communications, 13(1), 3569. https://doi.org/10.1038/s41467-022-31039-3
Woolrich, M. W., Behrens, T., & Beckmann, C. F. (2004). Multilevel linear modelling for FMRI group analysis using Bayesian inference. NeuroImage, 21(4), 1732–1747. https://doi.org/10.1016/j.neuroimage.2003.12.023
Woolrich, M. W., Ripley, B. D., Brady, M., & Smith, S. M. (2001). Temporal autocorrelation in univariate linear modeling of FMRI data. NeuroImage, 14(6), 1370–1386. https://doi.org/10.1006/nimg.2001.0931
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