Unveiling common and distinct parabrachial pathways involved in pain and emotion with 7T fMRI
Poster No:
1416
Submission Type:
Abstract Submission
Authors:
Byeol Kim Lux1, Philip Kragel2, Jordan Theriault3, Danlei Chen4, Ajay Satpute3, Lawrence Wald5, Marta Čeko6, Martin Lindquist7, Lisa Feldman Barrett3, Tor Wager8
Institutions:
1Dartmouth College, Hanover, NH, 2Emory University, Atlanta, GA, 3Northeastern University, Boston, MA, 4MIT, Cambridge, MA, 5Harvard Medical School, Boston, MA, 6University of Colorado, Boulder, CO, 7Johns Hopkins University, Baltimore, MD, 8Department of Psychological and Brain Sciences, Hanover, NH
First Author:
Co-Author(s):
Introduction:
The parabrachial nucleus (PBN), located in the pons, processes pain and modulates breathing and aversion by relaying sensory information to the forebrain through multiple circuits (1,2,3). The lateral PBN (LPB) and medial PBN (MPB) are cytoarchitecturally distinct subpopulations (4,5). While PBN pathways are well-studied in animals, their role in humans remains largely unexplored. This study used 7T fMRI to examine the functional connectivity (FC) of the LPB and MPB during pain and aversive emotion tasks via the conventional FC and MPathI methods (6). The findings revealed shared and condition-specific connectivity patterns of PBN.
Methods:
In 7T fMRI experiments, participants performed an avoidance learning task under a pain (N = 23) or emotion condition (N = 24) (7), each comprising 120 negative or neutral trials. Negative trials involved high-pressure pain (5kg/cm²) or negatively valenced IAPS images based on norm ratings, while Neutral trials featured non-painful pressure (3kg/cm²) or neutral images.
Regions of interest were selected based on prior studies of PBN projections and included LPB and MPB (8), central nucleus of the amygdala (CeA), ventral posteromedial and posterolateral thalamus(VPL), preoptic area of the hypothalamus (POA), bed nucleus of the stria terminalis (BNST), ventrolateral (vlPAG), lateral (lPAG), and dorsomedial periaqueductal gray (dmPAG), anterior insular cortex (aIns), posterior insular cortex (pIns), ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), and primary somatosensory cortex (S1).
Conventional FC analysis estimated pairwise FC between regions using the Pearson correlation coefficient between timeseries. MPathI is a multivariate pattern analysis to identify functional pathways between regions and their latent timeseries based on maximized covarying activity patterns using partial least squares. It computes connectivity as the Pearson correlation between timeseries within subjects with leave-one-run-out cross-validation. Lastly, trial-wise MpathI connectivity, obtained based on the timeseries for each subject, was regressed onto trial type (Negative vs. Neutral) to get beta estimates.
Regions of interest were selected based on prior studies of PBN projections and included LPB and MPB (8), central nucleus of the amygdala (CeA), ventral posteromedial and posterolateral thalamus(VPL), preoptic area of the hypothalamus (POA), bed nucleus of the stria terminalis (BNST), ventrolateral (vlPAG), lateral (lPAG), and dorsomedial periaqueductal gray (dmPAG), anterior insular cortex (aIns), posterior insular cortex (pIns), ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), and primary somatosensory cortex (S1).
Conventional FC analysis estimated pairwise FC between regions using the Pearson correlation coefficient between timeseries. MPathI is a multivariate pattern analysis to identify functional pathways between regions and their latent timeseries based on maximized covarying activity patterns using partial least squares. It computes connectivity as the Pearson correlation between timeseries within subjects with leave-one-run-out cross-validation. Lastly, trial-wise MpathI connectivity, obtained based on the timeseries for each subject, was regressed onto trial type (Negative vs. Neutral) to get beta estimates.
Results:
Conventional FC revealed significant connectivity between PBN and target regions (one-sample t-test, FDR q < 0.05) in both conditions and significantly higher connectivity between PBN to VPL, aIns, vmPFC, and ACC in pain compared to emotion condition (independent t-test, p < 0.05). LPB showed significantly stronger connectivity than MPB in both conditions, except with dmPAG in emotion condition (Fig.1).
MPathI analysis identified significant connectivity for all region pairs in both conditions, showing patterns largely consistent with the conventional FC results, including high connectivity for LPB-vlPAG, LPB-aIns, and LPB-ACC (Fig.2). LPB also showed stronger connectivity than MPB across all regions in both conditions, similar to FC results, while condition contrasts revealed significantly higher MPB-vmPFC connectivity in pain compared to emotion.
Regressing MpathI connectivity onto trial type revealed condition-specific patterns. Higher LPB-CeA connectivity predicted negative versus neutral trials in emotion condition (mean β = 0.015, one-sample t-test, p < 0.05, t = 2.54, df = 23) but not in pain condition. Conversely, lower MPB-ACC connectivity predicted negative trials in pain condition (mean β = -0.013, p < 0.05, t = -2.27, df = 22) but not in emotion condition.
MPathI analysis identified significant connectivity for all region pairs in both conditions, showing patterns largely consistent with the conventional FC results, including high connectivity for LPB-vlPAG, LPB-aIns, and LPB-ACC (Fig.2). LPB also showed stronger connectivity than MPB across all regions in both conditions, similar to FC results, while condition contrasts revealed significantly higher MPB-vmPFC connectivity in pain compared to emotion.
Regressing MpathI connectivity onto trial type revealed condition-specific patterns. Higher LPB-CeA connectivity predicted negative versus neutral trials in emotion condition (mean β = 0.015, one-sample t-test, p < 0.05, t = 2.54, df = 23) but not in pain condition. Conversely, lower MPB-ACC connectivity predicted negative trials in pain condition (mean β = -0.013, p < 0.05, t = -2.27, df = 22) but not in emotion condition.
·Figure 1
·Figure 2
Conclusions:
Using conventional FC and MPathI, we found stronger LPB connectivity with brainstem and cortical areas compared to MPB. While the condition contrasts differed between two approaches, MPathI provided additional insights by predicting trial types within conditions based on temporal changes in connectivity, uncovering condition-specific relationships between connectivity and trial valence. These findings extend PBN circuits research to humans and lay the groundwork for future studies on its role in affective and sensory processing.
Emotion, Motivation and Social Neuroscience:
Emotional Learning 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 1
Multivariate Approaches
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Computational Neuroscience
Emotions
Limbic Systems
Pain
1|2Indicates the priority used for review
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