Top-down threat bias in pain perception is predicted by higher segregation between resting-state networks

Author:

Pak Veronika12,Hashmi Javeria Ali34ORCID

Affiliation:

1. Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada

2. McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada

3. Department of Anesthesia, Pain Management, and Perioperative Medicine, Nova Scotia Health Authority, Halifax, NS, Canada

4. Dalhousie University, Halifax, NS, Canada

Abstract

Abstract Top-down processes such as expectations have a strong influence on pain perception. Predicted threat of impending pain can affect perceived pain even more than the actual intensity of a noxious event. This type of threat bias in pain perception is associated with fear of pain and low pain tolerance, and hence the extent of bias varies between individuals. Large-scale patterns of functional brain connectivity are important for integrating expectations with sensory data. Greater integration is necessary for sensory integration; therefore, here we investigate the association between system segregation and top-down threat bias in healthy individuals. We show that top-down threat bias is predicted by less functional connectivity between resting-state networks. This effect was significant at a wide range of network thresholds and specifically in predefined parcellations of resting-state networks. Greater system segregation in brain networks also predicted higher anxiety and pain catastrophizing. These findings highlight the role of integration in brain networks in mediating threat bias in pain perception.

Funder

Canadian Institute of Health Research

NSERC Discovery Grant

Nova Scotia Health Research Foundation

Nova Scotia Health Authority (NSHA) Establishment Grant

NSHA Fibromyalgia Research Grant

Canada Research Chairs Program

John R. Evans Leaders and Canada Innovation Funds

Publisher

MIT Press

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,General Neuroscience

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