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Sex differences in brain modular organization in chronic pain
Camille Fauchon1,2, David Meunier3, Anton Rogachov1,2
1Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, ON, Canada.
Pain
|October 12, 2020
Summary
Chronic pain alters functional brain networks, with distinct sex-specific changes observed in network organization. These sex differences in brain connectivity could inform personalized pain treatments.
Area of Science:
- Neuroscience
- Medical Imaging
- Pain Research
Background:
- Sex differences in pain sensitivity and chronic pain prevalence are well-documented.
- Evidence suggests sex-based variations in brain structure, but large-scale functional network organization in chronic pain remains unclear.
Purpose of the Study:
- To investigate sex differences in the organization of large-scale functional brain networks in individuals with chronic pain.
- To explore how chronic pain affects network topology and identify sex-specific alterations.
Main Methods:
- Utilized resting-state functional magnetic resonance imaging (rs-fMRI) data from 220 participants (155 healthy controls, 65 with chronic low back pain due to ankylosing spondylitis).
- Applied graph theory, modular analysis, and machine learning to analyze brain network organization and identify sex-specific topological characteristics.
- Examined cross-network connectivity and nodal graph properties, including hub disruption.
Main Results:
- Found significant overlap between brain network partitions and intrinsic systems (default mode, central, visual, sensorimotor).
- Observed sex-specific network topological characteristics in both healthy individuals and those with chronic pain.
- Identified higher cross-network connectivity in chronic pain patients, with sex-specific nodal property changes (e.g., hub disruption) linked to pain severity.
- Females showed increased functional segregation in specific brain regions and altered connectivity with certain modules, while males exhibited stronger sensorimotor module connectivity.
- Machine learning models accurately classified sex and chronic pain status (77%-92% accuracy) based on nodal graph metrics.
Conclusions:
- Chronic pain is associated with organizational abnormalities in resting-state functional brain networks.
- Significant sex-specific differences exist in brain network organization, both in health and chronic pain conditions.
- These findings provide a foundation for developing sex-specific therapeutic strategies for chronic pain management.
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