Nonlinear age trajectories of subcortical microstructural and morphometric change in the ABCD study
Poster No:
954
Submission Type:
Abstract Submission
Authors:
Alison Rigby1, Diana Smith1, Diliana Pecheva1, Pravesh Parekh2, Donald Hagler Jr1, Ashley Becker1, Robert Loughnan1, Carolina Makowski1, Terry Jernigan1, Anders Dale1
Institutions:
1University of California San Diego, San Diego, CA, 2University of Oslo, Oslo, Oslo
First Author:
Co-Author(s):
Introduction:
During adolescence, the brain undergoes dynamic changes, entailing modifications that differ in timing and spatial distribution [1]. Prior work points towards age effects beyond white matter tracts, including basal ganglia, and nuclei of the thalamus and brain stem [2,3]. However, few studies have described the trajectories of both microstructural and morphometric change in subcortical gray matter in adolescence. In this study, we aim to extend this research in a large adolescent sample by (1) investigating linear and non-linear associations of microstructure and morphometry with age, and (2) exploring sex differences between these associations.
Methods:
We used diffusion-weighted imaging data from adolescents in the Adolescent Brain Cognitive Development (ABCD) Study (data release 6.0). This included 22,209 observations across baseline (n=9033, 9-10 years), 2-year follow-up (n=5714, 11-14 years), 4-year follow-up (n=4507, 13-16 years), and 6-year follow-up (n=2955, 14-18 years). We used the restriction spectrum imaging (RSI) model to assess tissue microstructure. The RSI model separately estimates the restricted (mostly intracellular), hindered (mostly extracellular) and free water compartments of diffusivity in each voxel. We characterized morphometric changes using the Jacobian determinant (JA), a value reflecting local volume change at each voxel relative to an atlas.
We used voxel-wise sex-stratified GAMMs to measure the associations between age and three metrics-the restricted (RNT) and hindered (HNT) normalized total signal fractions, both derived from RSI, and JA. Using the Fast and Efficient Mixed Effects Algorithm (FEMA) [4], we modeled smooth natural cubic spline functions of age, as well as sociodemographics (household income and parental education), the first ten genetic principal components, scanner, and software version as covariates of no interest, while accounting for random effects of subject and genetic family relatedness.
We used voxel-wise sex-stratified GAMMs to measure the associations between age and three metrics-the restricted (RNT) and hindered (HNT) normalized total signal fractions, both derived from RSI, and JA. Using the Fast and Efficient Mixed Effects Algorithm (FEMA) [4], we modeled smooth natural cubic spline functions of age, as well as sociodemographics (household income and parental education), the first ten genetic principal components, scanner, and software version as covariates of no interest, while accounting for random effects of subject and genetic family relatedness.
Results:
We observed a nonlinear relationship between age and RNT, HNT, and JA across multiple regions. The Wald test confirmed highly significant effects (p < 0.001) in bilateral pallidum, nucleus accumbens, caudate, putamen, thalamic nuclei, substantia nigra, and subregions of the amygdala and hippocampus for all three metrics. The main patterns observed in these regions across age included (1) monotonic nonlinear increases in RNT, (2) monotonic nonlinear decreases in HNT, and (3) monotonic nonlinear increases or decreases in JA, depending on the brain region. Patterns differed by sex: (1) males tended to have higher mean RNT and lower mean HNT compared to females across subcortical structures. (2) Females tended to have a higher mean JA compared to males. (3) Male trajectories appeared phase-shifted relative to females, who exhibited earlier, subtle asymptotes (age ~13-15) in amygdala, pallidum, thalamus, and substantia nigra for RNT and HNT, and in nucleus accumbens and putamen for JA.
Conclusions:
This work leverages a large longitudinal adolescent sample with observations of participants between 9 and 18 years of age, showcasing distinct changes in microstructure and morphology, particularly in subcortical structures. Further, our application of GAMMs revealed the importance of capturing non-linear associations that may have been missed with traditional general linear models. Our RSI and morphological results suggest cellular-level alterations that could include cell morphology change, myelination, dendritic sprouting, glial and microglial activation, or protracted neuron migration/differentiation [3, 7]. Concurrent developmental processes in this age range, such as puberty and increases in body mass, can be explored in future studies. Integrating multiple neuroimaging modalities provides deeper insight into neurobiological dynamics during adolescent brain development, offering a more comprehensive understanding of brain maturation.
Lifespan Development:
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Univariate Modeling 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development
Subcortical Structures
Keywords:
Development
Modeling
Morphometrics
MRI
Statistical Methods
Sub-Cortical
Univariate
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
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.
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.
Please indicate which methods were used in your research:
Provide references using APA citation style.
2. Herting, M. M., Johnson, C., Mills, K. L., Vijayakumar, N., Dennison, M., Liu, C., Goddings, A.-L., Dahl, R. E., Sowell, E. R., Whittle, S., Allen, N. B., & Tamnes, C. K. (2018). Development of subcortical volumes across adolescence in males and females: A multisample study of longitudinal changes. NeuroImage, 172, 194–205. https://doi.org/10.1016/j.neuroimage.2018.01.020
3. Palmer, C. E., Pecheva, D., Iversen, J. R., Hagler, D. J., Sugrue, L., Nedelec, P., Fan, C. C., Thompson, W. K., Jernigan, T. L., & Dale, A. M. (2022). Microstructural development from 9 to 14 years: Evidence from the ABCD Study. Developmental Cognitive Neuroscience, 53, 101044. https://doi.org/10.1016/j.dcn.2021.101044
4. Parekh, P., Fan, C. C., Frei, O., Palmer, C. E., Smith, D. M., Makowski, C., Iversen, J. R., Pecheva, D., Holland, D., Loughnan, R., Nedelec, P., Thompson, W. K., Hagler Jr, D. J., Andreassen, O. A., Jernigan, T. L., Nichols, T. E., & Dale, A. M. (2024). FEMA: Fast and efficient mixed-effects algorithm for large sample whole-brain imaging data. Human Brain Mapping, 45(2), e26579. https://doi.org/10.1002/hbm.26579
5. Pecheva, D., Iversen, J. R., Palmer, C. E., Watts, R., Jernigan, T. L., Hagler, D. J., & Dale, A. M. (2022). Multimodal Image Normalisation Tool (MINT) for the Adolescent Brain and Cognitive Development study: The MINT ABCD Atlas [Preprint]. Neuroscience. https://doi.org/10.1101/2022.08.09.503395
6. Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., Montillo, A., Makris, N., Rosen, B., & Dale, A. M. (2002). Whole Brain Segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain. Neuron, 33(3), 341–355. https://doi.org/10.1016/S0896-6273(02)00569-X
7. Sorrells, S. F. (2024). Which neurodevelopmental processes continue in humans after birth? Frontiers in Neuroscience, 18. https://doi.org/10.3389/fnins.2024.1434508