Age and Sex Influence Functional Brain Network Organization Across Adulthood
Poster No:
919
Submission Type:
Abstract Submission
Authors:
Abhijot Singh Sidhu1, Kaue Duarte1, Talal Shahid1, M Louis Lauzon1, Cheryl McCreary1, Andrea Protzner1, Bradley Goodyear2, Richard Frayne1
Institutions:
1University of Calgary, Calgary, Alberta, 2University of Calgary, Calgary, alberta
First Author:
Co-Author(s):
Introduction:
Functional brain networks reorganize with age to support physiological changes and mitigate cognitive decline (Edde, 2021). Resting-state functional magnetic resonance imaging (rs-fMRI) studies suggest that networks become less specialized with age, a process reflected by decreased network segregation (Chan, 2014). However, segregation captures only one aspect of brain organization. Graph theory metrics, such as local efficiency, modularity, and participation coefficient, provide complementary insights into localized processing efficiency, within-network cohesiveness, and between-network integration, respectively (Rubinov, 2010). Sex differences may further influence these properties, yet they remain relatively understudied. Using rs-fMRI, we investigated how age and sex interact to modify network processing efficiency across adulthood.
Methods:
rs-fMRI and 3D T1-weighted MR data of 357 cognitively intact adults (range: 18.2-91.8 years; mean age: 49.9±17.1 years; mean MoCA score = 27.7±1.7; 203 (56%) females) from the Calgary Normative Study (McCreary, 2020) were used for this study. Subject-specific functional connectivity matrices were generated using standard processing. These matrices were thresholded to retain the top 5% of positive connections and binarized. The brain connectivity toolbox (Rubinov, 2010) was used to compute modularity, local efficiency, and participation coefficient for the visual (VIS), sensorimotor (SMN), ventral attention (VAN), dorsal attention (DAN), frontoparietal (FPN), and default mode (DMN) networks from the matrices. Linear models were used to evaluate the effects of age, sex, and age×sex, on network specific local efficiency, modularity, and participation coefficient metrics. Results for the same metric were adjusted across networks using Holm-Bonferroni multiple comparisons correction (Holm, 1979).
Results:
Local efficiency decreased with increasing age for the SMN (pcor. < .001), VAN (pcor. < .001) and DMN (pcor. = .015), and modularity decreased with increasing age for the SMN (pcor. = .024), VAN (pcor. = .019), and DMN (pcor. = .046). In contrast, modularity increased with increasing age for the DAN (pcor. = .005), Figure 1. Participation coefficient also increased with increasing age for the VIS (pcor. < .001) and SMN (pcor. = .002), Figure 2. Females had significantly greater DMN local efficiency (pcor. = .003), as well as greater modularity for the VAN (pcor. = .032) and DMN (pcor. = .005) than males, all independent of age. No other significant effects were observed.
Conclusions:
These findings demonstrate distinct and network specific patterns of reorganization across the adult lifespan. Age-related decreases in local efficiency and modularity in the SMN, VAN, and DMN suggest declines in localized processing efficiency and network cohesiveness, likely reflecting functional dedifferentiation (Chan, 2014). The increase in SMN participation coefficient with age may represent a compensatory mechanism, where between-network integration is enhanced to mitigate these localized declines. The stability of the VIS network suggests it preserves functional integrity across adulthood and, in turn, may support other networks experiencing age-related decline through increased between-network integration (Goh, 2009). Similarly, both the FPN and DAN may be less susceptible to age-related dedifferentiation processes (Grady, 2016), as no significant age effects were observed in FPN metrics, while DAN modularity increased with age. Inherent sex differences were also observed in associative networks (VAN, DMN), with females exhibiting greater efficiency and modularity than males across adulthood. This suggests that female may be more resilient to aging effects in cognitively intact populations (Hicks, 2023). Future research should explore how these age and sex related patterns relate to cognitive performance and assess potential alterations during pathological aging, such as in neurodegenerative conditions.
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Keywords:
ADULTS
Aging
Cognition
Computational Neuroscience
Sexual Dimorphism
Other - resting-state fMRI; Resting State Functional Networks; Graph Theory; Sex Differences; Modularity; Participation Coefficient, Local Efficiency
1|2Indicates the priority used for review
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Edde, M. (2021). Functional brain connectivity changes across the human life span: From fetal development to old age. Journal of Neuroscience Research, 99(1), 236-262.
Goh, J.O. (2009). Neuroplasticity and cognitive aging: The scaffolding theory of aging and cognition. Restorative Neurology and Neuroscience, 27(5), 391-403.
Grady, C. (2016). Age differences in the functional interactions among the default, frontoparietal control, and dorsal attention networks. Neurobiology of Aging, 41(1), 156-172.
Hicks, T.H. (2023). Network segregation in aging females and the evaluation of the impact of sex hormones. Frontiers in Human Neuroscience, 17(1).
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65-70.
McCreary, C. (2020). Calgary normative study: Design of a prospective longitudinal study to characterise potential quantitative MR biomarkers of neurodegeneration over the adult lifespan. BMJ Open, 10(8).
Rubinov, M. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059-1069.