Literature DB >> 25523617

Sex differences in normal age trajectories of functional brain networks.

Dustin Scheinost1, Emily S Finn, Fuyuze Tokoglu, Xilin Shen, Xenophon Papademetris, Michelle Hampson, R Todd Constable.   

Abstract

Resting-state functional magnetic resonance image (rs-fMRI) is increasingly used to study functional brain networks. Nevertheless, variability in these networks due to factors such as sex and aging is not fully understood. This study explored sex differences in normal age trajectories of resting-state networks (RSNs) using a novel voxel-wise measure of functional connectivity, the intrinsic connectivity distribution (ICD). Males and females showed differential patterns of changing connectivity in large-scale RSNs during normal aging from early adulthood to late middle-age. In some networks, such as the default-mode network, males and females both showed decreases in connectivity with age, albeit at different rates. In other networks, such as the fronto-parietal network, males and females showed divergent connectivity trajectories with age. Main effects of sex and age were found in many of the same regions showing sex-related differences in aging. Finally, these sex differences in aging trajectories were robust to choice of preprocessing strategy, such as global signal regression. Our findings resolve some discrepancies in the literature, especially with respect to the trajectory of connectivity in the default mode, which can be explained by our observed interactions between sex and aging. Overall, results indicate that RSNs show different aging trajectories for males and females. Characterizing effects of sex and age on RSNs are critical first steps in understanding the functional organization of the human brain.
© 2014 Wiley Periodicals, Inc.

Entities:  

Keywords:  aging; brain networks; functional connectivity; resting state; sex differences

Mesh:

Year:  2014        PMID: 25523617      PMCID: PMC5522589          DOI: 10.1002/hbm.22720

Source DB:  PubMed          Journal:  Hum Brain Mapp        ISSN: 1065-9471            Impact factor:   5.038


  42 in total

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