Poster No:
986
Submission Type:
Abstract Submission
Authors:
Subhasri Viswanathan1, Jeremy Watts1, Sean Spinney2, Patricia Conrod1
Institutions:
1University of Montreal, Montreal, Quebec, 2CHU Sainte-Justine Research Center, Montreal, Quebec
First Author:
Co-Author(s):
Sean Spinney
CHU Sainte-Justine Research Center
Montreal, Quebec
Introduction:
Adolescence is a critical period of brain network maturation supporting cognitive and behavioral development ( Dahl et al., 2018). The sensorimotor-association (S-A) axis, a principal gradient of cortical variation, provides a framework for understanding hierarchical patterns of functional connectivity, ranking regions from sensorimotor to transmodal association cortices (Sydnor et al., 2023). Prior studies report mixed findings of increasing and decreasing brain network connectivity with age (Fair et al., 2009; Sanders et al, 2023), and the integration of graph theory metrics with the S-A axis remains underexplored.
This study examined age-related changes in graph theory metrics and their alignment with the S-A axis. We first analyzed within-network and between-network connectivity to replicate Luo et al. (2024). Next, we explored node-level measures (Degree Centrality, Clustering Coefficient, Closeness Centrality, Betweenness Centrality, Nodal Efficiency) and network-level metrics (System Segregation and Participation Coefficient).
Methods:
We analyzed longitudinal resting-state fMRI data from 150 adolescents across three timepoints (baseline, 24 months, and 48 months). Functional connectivity was computed using the Gordon atlas (333 regions, 13 networks) following preprocessing with fmriprep, confound regression, and distance censoring to exclude connections <30 mm geodesic distance (Gordon et al., 2018). Connectivity changes were quantified using Generalized Additive Models (GAMs), comparing a full model (with a smooth age term) to a null model. Delta R², the difference in explained variance (R²) between models, was calculated to isolate the unique contribution of age. Covariates included gender, alcohol use, cannabis use, and framewise displacement.Each brain parcel was assigned an SA rank based on its position along the S-A axis (Sydnor et al., 2023). To explore hierarchical alignment, Spearman correlations between Delta R² and SA ranks were computed. Statistical significance was assessed using spin permutation tests (n = 10,000), implemented via a rotate parcellation function that preserves spatial contiguity and hemispheric symmetry.
Results:
Age-related changes in brain connectivity demonstrated significant alignment with the S-A axis, particularly in higher-order association regions. At the node level, Clustering Coefficient exhibited the strongest negative correlation (r = -0.41, p < 0.0001), followed by Degree Centrality (r = -0.22, p = 0.0001), Nodal Efficiency (r = -0.22, p = 0.0001), and Closeness Centrality (r = -0.20, p = 0.0002). Betweenness Centrality showed a weaker negative correlation (r = -0.14, p = 0.0139)
At the network level, between-network connectivity showed the strongest alignment with the S-A axis (r = -0.63, p = 0.0245), followed by within-network connectivity ( r = -0.51, p = 0.0759). In contrast, system segregation ( r = -0.08, p = 0.81) and participation coefficient ( r = -0.02, p =0.91) failed to show significant correlations. Permutation tests (n = 10,000) confirmed the robustness of these findings.
Conclusions:
Our results reveal significant hierarchical refinement of brain connectivity during adolescence, with local connectivity measures (e.g., Clustering Coefficient, Degree Centrality) aligning strongly with the S-A axis. Between-network connectivity also showed pronounced alignment, indicating greater developmental changes in cross-network interactions in association regions. In contrast, system segregation and participation metrics failed to align, suggesting that these measures capture network properties orthogonal to the hierarchical organization defined by the S-A axis. Together, these findings underscore the dynamic interplay between local specialization and global network reorganization, providing novel insights into the hierarchical maturation of brain networks during adolescence.
Higher Cognitive Functions:
Imagery
Lifespan Development:
Early life, Adolescence, Aging 1
Normal Brain Development: Fetus to Adolescence
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Keywords:
Development
FUNCTIONAL MRI
Other - Graph Theory, Adolescence,
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
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Not applicable
Please indicate which methods were used in your research:
Functional MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
-
fmriprep
Provide references using APA citation style.
Dahl, R. E., Allen, N. B., Wilbrecht, L., & Suleiman, A. B. (2018). Importance of investing in adolescence from a developmental science perspective. Nature, 554(7693), 441–450.
Fair, D. A., Cohen, A. L., Power, J. D., Dosenbach, N. U., Church, J. A., Miezin, F. M., … & Petersen, S. E. (2009). Functional brain networks develop from a “local to distributed” organization. PLoS Computational Biology, 5(5), e1000381.
Sanders, Z. B., Salehi, M., van den Heuvel, M. P., & Bassett, D. S. (2023). Brain network reconfiguration during adolescence: Insights from graph theory. Cerebral Cortex, 33(4), 1972–1988.
Luo, A. C., Sydnor, V. J., Pines, A., Larsen, B., Alexander-Bloch, A. F., Cieslak, M., Covitz, S., Chen, A. A., Esper, N. B., Feczko, E., Franco, A. R., Gur, R. E., Gur, R. C., Houghton, A., Hu, F., Keller, A. S., Kiar, G., Mehta, K., Salum, G. A., … Satterthwaite, T. D. (2024). Functional connectivity development along the sensorimotor-association axis enhances the cortical hierarchy. Nature Communications, 15(1), 3511.
Sydnor, V. J., Larsen, B., Bassett, D. S., Alexander-Bloch, A., Fair, D. A., Liston, C., Mackey, A. P., Milham, M. P., Pines, A., Roalf, D. R., Seidlitz, J., Xu, T., Raznahan, A., & Satterthwaite, T. D. (2021). Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology. Neuron, 109(18), 2820–2846. https://doi.org/10.1016/j.neuron.2021.06.016
Gordon, E.M., Lynch, C.J., Gratton, C., Laumann, T.O., Gilmore, A.W., Greene, D.J., Ortega, M., Nguyen, A.L., Schlaggar, B.L., Petersen, S.E., et al. (2018). Three distinct sets of connector hubs integrate human brain function. Cell Rep. 24, 1687–1695.e4. https://doi.org/10.1016/j.celrep. 2018.07.050.
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