Exploring Regional Nonlinear Maturation Patterns in the Aging Brain
Poster No:
910
Submission Type:
Abstract Submission
Authors:
Gal Kepler1, Yaniv Assaf2
Institutions:
1Tel-Aviv University, Tel-Aviv, Tel-Aviv, 2Tel Aviv University, Tel Aviv, Outside the U.S. & Canada
First Author:
Co-Author:
Introduction:
While brain development stabilizes by late adolescence, microstructural changes in adulthood remain poorly understood. Mean Diffusivity (MD), derived from diffusion MRI (dMRI), reflects tissue microstructure and has been shown to decrease during childhood and adolescence as brain organization increases (Lebel & Beaulieu, 2011; Tamnes et al., 2013).
Previous research has largely focused on MD changes in white matter or volumetric changes in gray matter, leaving the trajectory of MD within gray matter in adulthood-and its regional variability-underexplored. Importantly, brain regions supporting basic sensory and motor functions mature earlier than higher-order cognitive regions, following a hierarchical developmental gradient (Gogtay et al., 2004; Casey et al., 2005). Whether this gradient persists into adulthood through non-linear MD trajectories remains unclear.
Here, we investigated region-specific non-linear relationships between MD and age in a large adult cohort (ages 18–88). Using quadratic regression, we identified the age of minimum MD as a marker of structural stabilization across brain regions.
Previous research has largely focused on MD changes in white matter or volumetric changes in gray matter, leaving the trajectory of MD within gray matter in adulthood-and its regional variability-underexplored. Importantly, brain regions supporting basic sensory and motor functions mature earlier than higher-order cognitive regions, following a hierarchical developmental gradient (Gogtay et al., 2004; Casey et al., 2005). Whether this gradient persists into adulthood through non-linear MD trajectories remains unclear.
Here, we investigated region-specific non-linear relationships between MD and age in a large adult cohort (ages 18–88). Using quadratic regression, we identified the age of minimum MD as a marker of structural stabilization across brain regions.
Methods:
Participants
A total of 1,800 participants (mean age = 31.55 years, SD = 10.44, range = 18–88; females = 747, 41.66%) were included in the brain-age association analysis. Participants were drawn from multiple imaging studies conducted between 2018 and 2025 as part of the Strauss Neuroplasticity Brain Bank (SNBB) (Kepler et al., in prep.). All participants provided informed consent prior to participation. Exclusion criteria included neurological or psychiatric disorders, major head trauma, or significant motion artifacts.
Preprocessing was performed using the KePrep pipeline, including motion and eddy current correction, brain masking, and tensor fitting to derive Mean Diffusivity (MD) maps.
Gray Matter Segmentation
Mean MD was extracted from 400 cortical regions (Schaefer et al., 2018) and subcortical regions (Tian et al., 2020) after aligning gray matter masks to dMRI space.
Statistical Analysis
Weighted Least Squares (WLS) regression model with Inverse Probability Weights (IPW) was applied to account for the imbalance of age in the dataset. For each region, MD was modeled as a linear or quadratic function of age (MD ~ age or MD ~ age + age2 accordingly).
The age of minimum MD was calculated as -β1/2β2.
A total of 1,800 participants (mean age = 31.55 years, SD = 10.44, range = 18–88; females = 747, 41.66%) were included in the brain-age association analysis. Participants were drawn from multiple imaging studies conducted between 2018 and 2025 as part of the Strauss Neuroplasticity Brain Bank (SNBB) (Kepler et al., in prep.). All participants provided informed consent prior to participation. Exclusion criteria included neurological or psychiatric disorders, major head trauma, or significant motion artifacts.
Preprocessing was performed using the KePrep pipeline, including motion and eddy current correction, brain masking, and tensor fitting to derive Mean Diffusivity (MD) maps.
Gray Matter Segmentation
Mean MD was extracted from 400 cortical regions (Schaefer et al., 2018) and subcortical regions (Tian et al., 2020) after aligning gray matter masks to dMRI space.
Statistical Analysis
Weighted Least Squares (WLS) regression model with Inverse Probability Weights (IPW) was applied to account for the imbalance of age in the dataset. For each region, MD was modeled as a linear or quadratic function of age (MD ~ age or MD ~ age + age2 accordingly).
The age of minimum MD was calculated as -β1/2β2.
Results:
The association between regional MD and age was highly significant across nearly all regions, with extremely low p-values for both linear and quadratic models.
In many regions, the quadratic model significantly outperformed the linear fit; however, this pattern was not consistent across all regions. Figure 1 shows the explained variance in MD by the quadratic model and highlights three regions illustrating differences in brain-age associations.
As shown in Figure 2, the estimated age of minimum MD approached the lower age limit of the sample in primary regions, indicating a predominantly linear relationship within the available age range. In contrast, in higher-order cognitive regions, the estimated age of minimum MD varied widely, ranging from 20 to 50 years.
These findings reveal distinct regional patterns of MD trajectories, with earlier stabilization in primary regions and prolonged structural refinement in higher-order association areas.
In many regions, the quadratic model significantly outperformed the linear fit; however, this pattern was not consistent across all regions. Figure 1 shows the explained variance in MD by the quadratic model and highlights three regions illustrating differences in brain-age associations.
As shown in Figure 2, the estimated age of minimum MD approached the lower age limit of the sample in primary regions, indicating a predominantly linear relationship within the available age range. In contrast, in higher-order cognitive regions, the estimated age of minimum MD varied widely, ranging from 20 to 50 years.
These findings reveal distinct regional patterns of MD trajectories, with earlier stabilization in primary regions and prolonged structural refinement in higher-order association areas.
·Figure 1. Quadratic relationship between regional MD and age.
·Figure 2. Age of structural stabilization.
Conclusions:
Our study reveals regional differences in Mean Diffusivity (MD) trajectories across adulthood, highlighting distinct patterns of brain maturation. In primary regions, MD exhibited a predominantly linear relationship with age, suggesting early stabilization. In contrast, higher-order cognitive regions showed non-linear trajectories, with MD minima occurring later (20–50 years), reflecting prolonged structural refinement. These findings provide novel insights into the brain's extended maturation and plasticity in adulthood, emphasizing the ongoing reorganization of association areas critical for complex cognitive functions.
Higher Cognitive Functions:
Higher Cognitive Functions Other
Learning and Memory:
Neural Plasticity and Recovery of Function
Lifespan Development:
Aging 1
Lifespan Development Other
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Keywords:
Aging
Computational Neuroscience
Machine Learning
Modeling
Plasticity
Statistical Methods
1|2Indicates the priority used for review
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Provide references using APA citation style.
Gogtay, N. (2004). Dynamic mapping of human cortical development during childhood through early adulthood. Proceedings of the National Academy of Sciences, 101(21), 8174–8179.
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Lebel, C., & Beaulieu, C. (2011). Longitudinal Development of Human Brain Wiring Continues from Childhood into Adulthood. Journal of Neuroscience, 31(30), 10937–10947.
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Schaefer, A. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex, 28(9), 3095–3114.
Tamnes, C. K. (2013). Brain development and aging: Overlapping and unique patterns of change. NeuroImage, 68, 63–74.
Tian, Y. (2020). Topographic organization of the human subcortex unveiled with functional connectivity gradients. Nature Neuroscience, 23(11), 1421–1432.