Sex differences and age-related changes of large-scale brain networks - Shujun Zhang; Meiqin Li; Anqiang Chen; Liping Jiang; Hao Yu; Yueqin Chen (BMC Medical Imaging)
Abstract Background Previous studies have identified sex differences in brain structure, function, and connectivity. However, the sex differences and age-related changes at the large-scale brain network level remain unclear. Methods Resting-state functional MRI data from 436 healthy adults were analyzed using independent component analysis to extract large-scale brain networks. Multivariate analysis of covariance was applied to investigate sex differences in inter-network connectivity between these networks, and further performed voxel-level analysis to examine intra-network connectivity differences. Additionally, Pearson correlation analysis was used to explore the relationship between inter-network and intra-network functional connectivity (FC) and age. Results Males exhibited stronger inter-network FC across multiple networks compared to females. In contrast, females demonstrated stronger intra-network FC in several networks, including the sensorimotor, salience, auditory, and executive control networks. Males showed stronger intra-network FC only in specific regions of the posterior default mode and left frontoparietal networks. Additionally, inter-network FC in females appeared more susceptible to age-related changes, whereas males demonstrated relatively stable inter-network connectivity across the lifespan. Intra-network FC generally decreases with age in both males and females, with the exception of the ventral attention network in females and the dorsal sensorimotor network in males. Conclusion Males typically have stronger inter-network connectivity, while females show enhanced intra-network connectivity in key networks. Age-related inter-network connectivity declines are more pronounced in females, both sexes experience a reduction in most intra-network connectivity with age. The study offers valuable insights into how age and sex shape the large-scale brain networks.