Adolescent brain age modelling: A feasibility study using short-interval longitudinal data
Poster No:
1742
Submission Type:
Abstract Submission
Authors:
Amanda Boyes1, Laura Han2, Paul Schwenn1, Daniel Hermens1
Institutions:
1University of the Sunshine Coast, Birtinya, QLD, 2Amsterdam UMC, Amsterdam, Noord-Holland
First Author:
Co-Author(s):
Introduction:
Both accelerated and attenuated development of adolescent brain structure have been reported depending on experiences of different early life stressors (Beck et al., 2025). 'Brainage' models have been proposed as measures of maturation and mental health problems, however, results to date have been mixed in adolescent studies (Dehestani et al., 2023; Whitmore et al., 2023). The 'brain age gap', calculated as the difference between an individual's chronological age and the age of their brain (based on a range of structural features), may be useful in understanding mental ill-health (Han et al., 2021). However, research to date has included cross-sectional or cross-sequential data only, and thus it remains unclear whether: (i) incorporating longitudinal data at short intervals is useful in 'brainage' models; and (ii) profiles incorporating subclinical measures of mental health, such as psychological distress in adolescence, exhibit different 'brain age gaps'.
Methods:
The current study utilised n=610 MRI, self-report and demographic datasets collected between July 2018-June 2023 from N=116 adolescents enrolled in the Longitudinal Adolescent Brain Study (LABS) across ages 12-17. T1-weighted images were collected on the same 3-T Siemens scanner at the Thompson Institute, Australia, and processed using the FreeSurfer (7.4.0) longitudinal recon-all pipeline. Each participant had a minimum of 2 scans (up to a maximum of 13; 5.3 average). The CentileBrain 'brainage' prediction model was trained on 35,683 healthy individuals (53.59% female, 5–90 years), and performs well for adolescent datasets (Yu et al., 2024). The model utilises 150 morphometrics features including 68 cortical thickness, 68 cortical surface area and 14 subcortical volumes (Yu et al., 2024), and these were extracted for each of the scans in the current sample to calculate: (i) 'brainage' predictions and (ii) the difference between predicted brain age and chronological age (i.e., 'brain age gap'), separately for males and females. Generalised Estimating Equations were used to examine (i) 'brainage' development over time and (ii) longitudinal differences in 'brain age gap' between those who experienced moderate-high psychological distress (i.e., K10 scores >15) at any timepoint over the 5 years (n=86, with 468 datasets) and those who only experienced mild psychological distress (i.e., K10 scores ≤15) at all timepoints (n=30, 142 datasets). Age (centred at 12 years), sex and euler number were included in all models as covariates.
Results:
(i) As expected, 'brainage' (adjusted) significantly increased over time with increasing chronological age, with a large effect size β = 1.4, p <.001. 'Brainage' predictions over time showed large variation at the whole-group level (Figure 1). However, adolescents who experienced moderate-high psychological distress at any timepoint were significantly more likely to have a younger 'brainage' (p<.05), although the odds ratio was close to 1 (.997). Figure 2 also suggests there may be different developmental trajectories in 'brain age' and more variability in those who experienced moderate-high psychological distress between 12-17 years. (ii) 'Brain age gap' was not significantly associated with chronological age. However, group-level findings indicate a small but significant difference between groups, on average, across all timepoints (p<.05), whereby the moderate-high distress group had less 'brain age gap' on average (odds ratio .997).
·Adjusted brain age data across timepoints 1-15 for N-116 (610 scans).
·Adjusted brain age data by group. The ‘high distress’ group had K10 scores >15 at any timepoint (n=86); the ‘low distress’ group had K10 scores ≤15 at all timepoints (n=30).
Conclusions:
This exploratory study suggests that 'brainage' models may benefit from additional data at short intervals, from general population studies, to gain an increased understanding of neurodevelopment across adolescence. Further, datasets with frequent longitudinal timepoints may capture an individual's emerging mental health symptoms and neurobiological data more accurately, which can be combined to develop distinct profiles and track differential associations over time.
Lifespan Development:
Early life, Adolescence, Aging 2
Modeling and Analysis Methods:
Other Methods
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development 1
Keywords:
Modeling
Morphometrics
STRUCTURAL MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Dehestani, N., Whittle, S., Vijayakumar, N., & Silk, T. J. (2023). Developmental brain changes during puberty and associations with mental health problems. Developmental Cognitive Neuroscience, 60, 101227. https://doi.org/10.1016/j.dcn.2023.101227
Han, L. K. M., Dinga, R., Hahn, T., Ching, C. R. K., Eyler, L. T., Aftanas, L., Aghajani, M., Aleman, A., Baune, B. T., Berger, K., Brak, I., Filho, G. B., Carballedo, A., Connolly, C. G., Couvy-Duchesne, B., Cullen, K. R., Dannlowski, U., Davey, C. G., Dima, D., . . . Schmaal, L. (2021). Brain aging in major depressive disorder: results from the ENIGMA major depressive disorder working group. Molecular Psychiatry, 26(9), 5124-5139. https://doi.org/10.1038/s41380-020-0754-0
Whitmore, L. B., Weston, S. J., & Mills, K. L. (2023). BrainAGE as a measure of maturation during early adolescence. Imaging Neuroscience, 1, 1-21. https://doi.org/10.1162/imag_a_00037
Yu, Y., Cui, H.-Q., Haas, S. S., New, F., Sanford, N., Yu, K., Zhan, D., Yang, G., Gao, J.-H., Wei, D., Qiu, J., Banaj, N., Boomsma, D. I., Breier, A., Brodaty, H., Buckner, R. L., Buitelaar, J. K., Cannon, D. M., Caseras, X., . . . Group, E. N.-L. W. (2024). Brain-age prediction: Systematic evaluation of site effects, and sample age range and size. Human Brain Mapping, 45(10), e26768. https://doi.org/https://doi.org/10.1002/hbm.26768