Social Cognitive and Affective NeuroscienceNeurosciencePeer-reviewed journal article
Inter-subject phase synchronization differentiates neural networks underlying physical pain empathy
Lei XuiD, Taylor BoltiD, Jason S. NomiiD, Jia‐Lin LiiD, Xiaoxiao ZhengiD, Meina FuiD, Keith Maurice KendrickiD, Benjamin BeckeriD, Lucina Q. UddiniD
Abstract
Recent approaches for understanding the neural basis of pain empathy emphasize the dynamic construction of neural networks underlying this multifaceted social cognitive process. Inter-subject phase synchronization (ISPS) is an approach for exploratory analysis of task-based fMRI data that reveals brain networks dynamically synchronized to task-features across participants. We applied ISPS to task-fMRI data assessing vicarious pain empathy in a large sample of healthy participants (n=238). The task employed physical (limb) and affective (faces) painful and corresponding non-painful visual stimuli. ISPS revealed two distinct networks synchronized during physical pain observation, one encompassing anterior insula and midcingulate regions strongly engaged in (vicarious) pain, and another encompassing parietal and inferior frontal regions associated with social cognitive processes which may further modulate and support the physical pain empathic response. No robust network synchronization was observed while processing affective pain, possibly reflecting high inter-individual variation in response to socially transmitted pain experiences. ISPS also revealed networks related to task onset or general processing of physical (limb) or affective (face) stimuli which encompassed networks engaged in object manipulation or face processing, respectively. Together, the ISPS approach permits segregation of networks engaged in different psychological processes, providing additional insight into shared neural mechanisms of empathy for physical pain, but not affective pain, across individuals.
- organism: human
- assay: multiome
- research focus: cell type annotation
- research focus: clustering
- research focus: spatial domain detection
- approach: statistical model
- Citations
- 20
- FWCI
- 3.55
- Field percentile
- top 8%
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