Poster No:
2051
Submission Type:
Abstract Submission
Authors:
Sonia Medina1, Sam Hughes1, Sophie Clarke2
Institutions:
1University of Exeter, Exeter, Devon, 2University of Plymouth, Plymouth, Devon
First Author:
Co-Author(s):
Introduction:
Conditioned pain modulation (CPM) is a behavioural paradigm widely used to assess descending pain modulation in humans. Previous findings reveal that CPM responses can switch greatly between pain inhibition and facilitation within subjects, suggesting that CPM reflects descending pain control 'states' rather than 'traits'.[1] Identifying the brain function patterns underlying CPM state tendencies is crucial for understanding mechanisms of descending pain modulation. However, existing studies focus on single-session neural correlates and overlook intersession variability. To address this gap, we applied spectral dynamic causal modelling (spDCM)[2] to examine how effective connectivity patterns during tonic pain predict CPM responses across two sessions in healthy individuals, accounting for intersession response variability and consistency.
Methods:
Twenty-nine healthy participants (mean age(SD)=29(10)) underwent conditioned pain modulation (CPM) assessments across two sessions approximately one week apart using computer-controlled cuff-algometry[3]. Test and conditioning stimuli were applied to the right and left calves, respectively, to determine pain detection thresholds (PDT) during baseline and conditioning phases. CPM effects were calculated as the change in PDT between these phases, with positive values indicating facilitation and negative values indicating inhibition. CPM volatility was evaluated using the Normalised Session Change Index (NSCI), which measures relative CPM change between sessions normalised by standard deviation of session 1. Participants were assigned CPM status based on thresholds defined by two standard errors of measurement (± 2SEM), with scores ranging from -4 (strong inhibition in both sessions) to 4 (strong facilitation in both sessions). In a separate session, participants underwent 6 minutes of multi-echo resting-state fMRI during tonic cold pain. Preprocessing included motion and fieldmap correction (FSL)[4], denoising with TEDANA[5], regression of white matter and cerebrospinal fluid signals, high-pass filtering (0.005 Hz), normalisation to standard space, and smoothing with a 5mm FWHM Gaussian kernel. A fully connected spDCM model incorporating the dorsal anterior cingulate cortex (dACC), anterior insula (AI), thalamus, and periaqueductal grey (PAG) was constructed and fitted in SPM12[6]. A parametric empirical Bayes (PEB)[7] design matrix, including CPM status and NSCI as regressors, was estimated. The connection with probability of being present (pp) >0.99 and the highest connectivity weight was identified and included in Leave-one-out cross-validation (LOOCV).
Results:
CPM responses were stratified as follows: facilitatory response (>+2SEM), inhibitory response (<-2SEM) and non-response (within ± 2SEM)[8], yielding 8 inhibitors, 1 facilitator and 20 non-responders in session 1, and 11 inhibitors, 1 facilitator and 17 non-responders in Session 2. CPM measures showed poor reliability (ICC = 0.34). NSCI ranged from -3 to 2, indicating CPM volatility between sessions did not present on a particular direction. PEB results showed that intersubject variability in CPM status corresponded to patterns of top-down inhibitory connections and bottom-up excitatory connections from the PAG, with pp>0.99. PAG -> right AI effective connectivity successfully predicted CPM status following LOOCV (r = 0.42, p = 0.01), and Pearson's correlation indicated that higher excitatory PAG-right AI weights corresponded to stronger facilitation status (r = 0.40, p = 0.035) and vice versa.
Conclusions:
We present a novel approach to understand the mechanisms underlying descending pain control state transitions via the implementation of Bayesian predictive modelling methodologies. Our results demonstrate that intersession variability in CPM responses is closely associated with PAG-mediated excitatory and inhibitory pathways, providing new insights into the neural dynamics of descending pain modulation in awake humans.
Modeling and Analysis Methods:
Bayesian Modeling 2
Perception, Attention and Motor Behavior:
Perception: Pain and Visceral 1
Keywords:
Other - spDCM, CPM, reliability, fMRI, multi-echo
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.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
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.
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
Behavior
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
SPM
FSL
Other, Please list
-
TEDANA
Provide references using APA citation style.
1. Kennedy, D.L., et al., Reliability of conditioned pain modulation: a systematic review. Pain, 2016. 157(11): p. 2410-2419.
2. Novelli, L., K. Friston, and A. Razi, Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity. Network Neuroscience, 2024. 8(1): p. 178-202.
3. Graven‐Nielsen, T., et al., User‐independent assessment of conditioning pain modulation by cuff pressure algometry. European Journal of Pain, 2017. 21(3): p. 552-561.
4. Jenkinson, M., et al., Fsl. Neuroimage, 2012. 62(2): p. 782-790.
5. DuPre, E., et al., TE-dependent analysis of multi-echo fMRI with* tedana. Journal of Open Source Software, 2021. 6(66): p. 3669.
6. Ashburner, J., et al., SPM12 manual. Wellcome Trust Centre for Neuroimaging, London, UK, 2014. 2464(4).
7. Zeidman, P., et al., A guide to group effective connectivity analysis, part 2: Second level analysis with PEB. Neuroimage, 2019. 200: p. 12 25.
8. Kennedy, D.L., et al., Determining real change in conditioned pain modulation: a repeated measures study in healthy volunteers. The Journal of Pain, 2020. 21(5-6): p. 708-721.
No