Inter-individual variability in developmental trajectories of adolescent brain volume
Poster No:
964
Submission Type:
Abstract Submission
Authors:
Chloe Carrick1, William Frans Christiaan Baaré2, Silia Vitoratou1, Kathrine Skak Madsen2, Delia Fuhrmann1
Institutions:
1King's College London, London, United Kingdom, 2Danish Research Centre for Magnetic Resonance, Copenhagen, Denmark
First Author:
Co-Author(s):
William Frans Christiaan Baaré, PhD
Danish Research Centre for Magnetic Resonance
Copenhagen, Denmark
Danish Research Centre for Magnetic Resonance
Copenhagen, Denmark
Introduction:
Adolescence, the developmental phase between late childhood and early adulthood, is a period characterised by protracted structural brain development (Bethlehem et al., 2022). These structural changes include nonlinear reductions in cortical grey matter volume, nonlinear increases in white matter volume, and a varied pattern of volumetric increases and decreases in subcortical structures (Backhausen et al., 2024; Mills et al., 2016). While patterns of adolescent structural brain development have been well-defined at the group-level, a growing body of literature indicates considerable inter-individual variability in developmental trajectories (Bottenhorn et al., 2023; Fuhrmann et al., 2022; Mills et al., 2021). Characterising individual differences in structural brain development could help to identify specific maturational patterns linked to the emergence of psychopathological symptoms later in life (Becht & Mills, 2020; Foulkes & Blakemore, 2018). However, few studies have formally quantified heterogeneity in developmental trajectories of adolescent brain structure. The present study characterises inter-individual variability in trajectories of cortical grey matter, white matter, and subcortical brain volume development, by leveraging novel nonlinear mixed modelling techniques and a single-cohort dataset with up to 12 MRI scans per individual, spanning late childhood to early adulthood.
Methods:
This is a longitudinal study leveraging data from the Danish HUBU cohort, ("Hjernens Udvikling hos Børn og Unge", Brain Maturation in Children and Adolescents; Madsen et al., 2020). The final sample in this study (N = 90; 59% female, 41 % male) were scanned up to 12 times (mean number of scans = 8.28, SD = 3.34) and were aged between 7.58 and 21.56 years old. 745 3T MRI scans, processed using the FreeSurfer 6.0 longitudinal stream, were used for analyses. The relationship between age and volumetric development in cortical, white matter, and subcortical regions (amygdala, hippocampus, putamen, caudate, thalamus, pallidum, accumbens) were modelled using two nonlinear modelling techniques to quantify development at the individual level: 1) Generalised Additive Mixed Models (GAMMs), and 2) Nonlinear Mixed Models (NLMMs). Using the NLMM framework, model fit was compared between two nonlinear models (4-parameter logistic and logarithmic) and a simple linear model for each brain region.
Results:
We observed age-related increases in white matter, hippocampus, amygdala, and pallidum volumes, and decreases in cortical, putamen, caudate, thalamus, and accumbens volumes. NLMMs indicated that the 4-parameter logistic model was the best fitting model of volumetric change in the cortex, white matter, accumbens, and pallidum, indicating that development was s-shaped in these structures across adolescence. The linear model was the best fitting model in the hippocampus, amygdala, thalamus, caudate, and putamen. On average, peak rate of volumetric change occurred at 14.52 years in the cortex, 13.91 years in the white matter, 10.99 years in the pallidum, and 11.44 years in the accumbens, as indexed by the inflection point. The age at which volumetric change occurred most rapidly in structures with s-shaped growth (inflection point) and the rate of change in structures with linear development (slope) varied between individuals (e.g., minimum cortical inflection point = 13.34 years, maximum = 20.83 years).
Conclusions:
These findings extend knowledge on group-level patterns of structural development, providing insight into the shape of volumetric development in cortical, white matter, and subcortical structures across the entire adolescent period. Individual differences in inflection point and slope estimates indicate that adolescent brain maturation is heterogeneous. This study demonstrates that nonlinear mixed modelling techniques can be used to precisely quantify inter-individual variability in trajectories of brain maturation from late childhood to early adulthood.
Lifespan Development:
Early life, Adolescence, Aging 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Normal Development 2
Keywords:
Cortex
Development
Modeling
STRUCTURAL MRI
Sub-Cortical
White Matter
Other - Adolescence
1|2Indicates the priority used for review
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