Poster No:
1022
Submission Type:
Abstract Submission
Authors:
Akram Shourkeshti1, Subhasri Viswanathan1, Patricia Conrod1
Institutions:
1University of Montreal, Montreal, Quebec
First Author:
Co-Author(s):
Introduction:
Adolescence is a critical period of development marked by significant changes in brain structure and function. Neuroimaging studies have revealed microstructural changes in white matter during this time; however, the age-related trajectories of white matter organization remain incompletely understood. In this study, we applied network science to investigate both local and global features of brain organization.
Methods:
In this study, a subset of 151 participants aged 12 to 18 years was recruited from the Co-Venture cohort (O'Leary-Barrett et al., 2017) and underwent magnetic resonance imaging (MRI) up to three times. Structural MRI preprocessing was performed using QSIPrep (v0.22.1; Cieslak et al., 2021) and FreeSurfer (v7.3; Fischl et al, 2002). White matter structural networks were constructed through probabilistic fiber tractography for 246 regions based on the Brainnetome parcellation (Fan et al., 2016). Network measures-including global efficiency, local efficiency, clustering coefficient, characteristic path length, and rich-club coefficient-were computed using the Brain Connectivity Toolbox(Rubinov & Sporns, 2010). Linear mixed-effects models with random intercepts for individuals were used to examine the association between network measures and age trajectories over time.
Results:
While clustering coefficient (p < 0.05, FWR corrected) followed an increasing linear trajectory with age, global efficiency and characteristic path length did not show a significant change with age. Analysis of rich club coeffiecient (p < 0.05, FWR corrected) revealed distinct developmental trajectories at different conectivity thresholds. Local efficiency (p < 0.05, FWR corrected) revealed distinct developmental trajectories across various brain regions.
Conclusions:
These results support a shift toward increase in more localized and specialized brain organization during adolescence, which may contribute to preparing the brain for upcoming functional and cognitive specialization changes.
Lifespan Development:
Early life, Adolescence, Aging
Normal Brain Development: Fetus to Adolescence 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Diffusion MRI Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity 2
Keywords:
White Matter
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
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.
Other
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?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Diffusion MRI
For human MRI, what field strength scanner do you use?
3.0T
Provide references using APA citation style.
Cieslak, M., Cook, P. A., He, X., Yeh, F. C., Dhollander, T., Adebimpe, A., ... & Satterthwaite, T. D. (2021). QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data. Nature methods, 18(7), 775-778.
Fan L, Li H, Zhuo J, Zhang Y, Wang J, Chen L, Yang Z, Chu C, Xie S, Laird AR, Fox PT, Eickho! SB, Yu C, Jiang T (2016): The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture. Cereb Cortex 26.
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., ... & Dale, A. M. (2002). Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron, 33(3), 341-355.
Lebel, C., Treit, S., & Beaulieu, C. (2019). A review of diffusion MRI of typical white matter development from early childhood to young adulthood. NMR in Biomedicine, 32(4), e3778.
O'Leary-Barrett, M., Mâsse, B., Pihl, R. O., Stewart, S. H., Séguin, J. R., & Conrod, P. J. (2017). A cluster-randomized controlled trial evaluating the effects of delaying onset of adolescent substance abuse on cognitive development and addiction following a selective, personality-targeted intervention programme: the Co-Venture trial. Addiction (Abingdon, England), 112(10), 1871–1881. https://doi.org/10.1111/add.13876
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/J.NEUROIMAGE.2009.10.003
No