Age and Sex Differences in Gray Matter Microstructure Underlying Functional Brain Networks
Poster No:
927
Submission Type:
Abstract Submission
Authors:
Abhijot Singh Sidhu1, Talal Shahid1, Kaue Duarte1, M Louis Lauzon1, Cheryl McCreary1, Richard Frayne1
Institutions:
1University of Calgary, Calgary, Alberta
First Author:
Co-Author(s):
Introduction:
The human brain is organized into canonical resting-state networks (RSNs) that support cognitive and sensory functions (Edde, 2021). Cortical thickness and volume measures from T1-weighted (T1-w) images are widely used as proxies for the structural integrity of gray matter (GM) regions underlying these networks (Chastelaine, 2023). The microstructural properties of these regions, however, remains infrequently measured. Metrics derived from diffusion imaging (DI), including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD), may provide additional valuable insights into GM microstructure (Jacobs, 2013). This study investigated age- and sex-related differences in average FA, MD, RD, and AD across GM regions underlying several RSNs in cognitive aging.
Methods:
DI and 3D T1-w MR data were obtained from 258 cognitively healthy participants (18.2-91.8 years; mean age: 47.70 ± 18.05 years; MoCA ≥ 24; 153 (~59%) females) enrolled in the Calgary Normative Study (McCreary, 2020). DI pre-processing included denoising, Gibbs artifact removal, skull stripping, eddy current and motion corrections, and diffusion tensor estimation using MRtrix3 (Tournier, 2019). T1-w data were processed using the FreeSurfer recon-all pipeline (Fischl, 2012). GM regions were parcellated using the Yeo functional atlas (Yeo, 2011), which includes the visual, sensorimotor, dorsal attention, ventral attention, limbic, frontoparietal, and default mode networks. Processed T1-w images were rigidly registered (Greve, 2010) to DI images (6 degrees of freedom), and average FA, MD, RD, and AD were computed for GM regions underlying each of the seven networks. Age and sex effects on DI metrics were analyzed using a quadratic linear model (Age + Age² + Sex + Age×Sex). The Holm-Bonferroni method corrected for multiple comparisons (Holm, 1979).
Results:
Significant Age and Age² effects (pcor < 0.05) were observed for FA in the ventral attention and frontoparietal networks that followed a U-shaped trajectory, with increases in older adults (Figure 1A). Significant Age and Age² effects (pcor < 0.05) were also observed for MD, RD, and AD across all RSNs, with more pronounced age-related increases noted in sensory networks (visual and sensorimotor) compared to associative networks (Figures 1B, 1C, and 1D, respectively). Significant Age×Sex interactions (pcor < 0.05) were observed for MD and AD in the limbic and default mode networks, as well as RD in the limbic network. Specifically, females exhibited attenuated, or less pronounced, age-related increases in these metrics relative to males. No additional significant effects were observed (pcor > 0.05).
Conclusions:
Our findings reveal that the microstructural properties of GM regions underlying RSNs follow nonlinear aging trajectories, with all networks showing significant reductions in integrity across adulthood. Notably, visual and sensorimotor networks exhibited greater declines, suggesting that GM regions supporting sensory functions may be more vulnerable to degradation in older adulthood compared to associative regions. This pattern aligns with the "developmental-sensory" model of aging, which posits reductions in associative GM regions earlier in adulthood and greater vulnerability in sensory regions later in life (McGinnis, 2011). Females also displayed attenuated age-related increases in diffusivity metrics for GM regions within the limbic and default mode networks. This slower rate of degradation suggests that females may be less susceptible to aging effects in the GM regions supporting these networks, which aligns with well-known sex differences in RSN functional connectivity (Ballard, 2023).This study highlights the complementary value of DI metrics to T1-weighted measures in capturing GM microstructural changes across adulthood and underscores the need for further research on the interplay of age and sex in shaping brain structure, function, and cognition.
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Diffusion MRI Modeling and Analysis 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Cortical Anatomy and Brain Mapping
Keywords:
ADULTS
Aging
Cognition
Cortex
Neuron
Sexual Dimorphism
STRUCTURAL MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
Other - Gray Matter Microstructure; Cortical Regions; Resting State Functional Networks; Fractional Anisotropy; Mean Diffusivity; Radial Diffusivity; Axial Diffusivity; Structural Degradation
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.
Chastelaine, M. (2023). Cortical thickness, gray matter volume, and cognitive performance: a crosssectional study of the moderating effects of age on their interrelationships. Cerebral Cortex, 33(10), 6474-6485.
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Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65-70.
Jacobs, H. (2013). Decreased gray matter diffusivity: A potential early Alzheimer’s disease biomarker?. Alzheimer's & Dementia, 9(1), 93-97.
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).
McGinnis, S. (2011). Age-Related Changes in the Thickness of Cortical Zones in Humans. Brain topography, 24(3).
Tournier, J.D. (2019). MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. NeuroImage, 202(1), 116-137.
Yeo, T. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125-1165.