White matter functional connectivity predicts brain maturity in youth
Poster No:
977
Submission Type:
Abstract Submission
Authors:
Shaoling Zhao1, Haoshu Xu1, Longzhou Xu1, Shen Zhang2, Zaixu Cui1
Institutions:
1Chinese Institute for Brain Research, Beijing, Beijing, China, 2Xuanwu Hospital Capital Medical University, Beijing, China
First Author:
Co-Author(s):
Introduction:
While functional MRI studies have predominantly focused on gray matter, recent studies have consistently shown that blood-oxygenation-level-dependent (BOLD) signals can also be reliably detected in white matter(Ding et al., 2018; Huang et al., 2023; Li et al., 2021; Li et al., 2019), and the functional connectivity is organized into distributed networks in white matter(Peer et al., 2017). However, few studies have examined how this white matter functional connectivity relates to neurodevelopment in youth. Here, we investigate the hypothesis that the functional connectivity in white matter is refined with age and encodes brain maturity throughout childhood and adolescence.
Methods:
We performed the analyses in a large sample of youths, the Human Connectome Project: Development (n = 633, ages 11–18 years)(Somerville et al., 2018). We used resting-state functional MRI data and the JHU ICBM-DTI-81 atlas to extract white matter BOLD signal(Mori et al., 2008), and characterized the functional connectivity matrix by signal synchronization between white mater tracts. A multivariate approach with nested two-fold cross validation (2F-CV) was utilized to evaluate whether white matter functional connectivity could be used to predict unseen individuals' chronological age, with the outer 2F-CV evaluated the model's generalizability, and the inner 2F-CV identified the optimal parameter, consistent with previous studies (Cui et al., 2020; Cui et al., 2022; Zhao et al., 2024). Prediction accuracy was measured by Pearson's correlation between the predicted and actual age, averaged across the two subsets.
Results:
Using ridge regression with nested 2F-CV, we found that functional connectivity in white matter significantly predicted unseen individuals' chronological age. The median correlation between the actual and predicted age was r = 0.55 across 101 repetitions (Figure 1A). A permutation test (1,000 iterations) indicated that this prediction accuracy was significantly higher than that expected by chance (Pperm < 0.001). The median mean absolute error between the actual and predicted age was 2.6 years. To visualize the correlation between the actual and predicted age, we displayed a scatter plot of the 2F-CV with the median correlation r across the 101 repetitions (Figure 1B). These results suggest that the white matter functional connectivity refines with age and can accurately predict brain maturity in unseen individuals. To understand the developmental effects underlying these results, we evaluated model prediction weights. In ridge regression model, each functional connectivity received a feature weight. After averaging the feature weight maps from the repeated (i.e., 101 runs) random 2F-CV and summing the positive and negative weights separately within each white matter tract, we found that internal capsule, cerebellar peduncles, and corona radiata contributed most to the multivariate prediction (Figure 1C).
Conclusions:
Overall, our results emphasize the importance of considering white matter functional connectivity during brain maturation, providing new insights in how whiter matter function supports neurodevelopment.
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Keywords:
FUNCTIONAL MRI
White Matter
1|2Indicates the priority used for review
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