Unveiling Normative Trajectories of Lifespan Brain Maturation Using Quantitative MRI

Background: Brain maturation and aging involve significant microstructural changes, resulting in functional and cognitive alterations. Quantitative MRI (qMRI) can measure this evolution, distinguishing the physiological effects of normal aging from pathological deviations. Methods: We conducted a multicentre study using qMRI metrics (R1, R2*, and Quantitative Susceptibility Mapping) to model age trajectories across brain structures, including tractography-based white matter bundles (TWMB), superficial white matter (SWM), and cortical grey matter (CGM). MRI data from 537 healthy subjects, aged 8 to 79 years, were harmonized using two independent methods. We modeled age trajectories and performed regional analyses to capture maturation patterns and aging effects across the lifespan. Findings: Our findings revealed a distinct brain maturation gradient, with early qMRI peak values in TWMB, followed by SWM, and culminating in CGM regions. This gradient was observed as a posterior-to-anterior maturation pattern in the cortex and an inferior-to-superior maturation pattern in white matter tracts. R1 demonstrated the most robust age trajectories, while R2* and susceptibility exhibited greater variability and different patterns. The normative modeling framework confirmed the reliability of our age-modelled trajectories across datasets. Interpretation: Our study highlights the potential of multiparametric qMRI to capture complex, region-specific brain development patterns, addressing the need for comprehensive, age-spanning studies across multiple brain structures. Various harmonization strategies can merge qMRI cohorts, improving the robustness of qMRI-based age models and facilitating the understanding of normal patterns and disease-associated deviations.

💡 Research Summary

This multicenter study provides a comprehensive lifespan characterization of brain microstructural development using quantitative MRI (qMRI) metrics—longitudinal relaxation rate (R1), effective transverse relaxation rate (R2*), and quantitative susceptibility mapping (QSM). A total of 537 healthy participants aged 8–79 years were recruited from three sites (Basel, Nijmegen, and a third Swiss site). Imaging protocols included MP2RAGE for T1 mapping and multi‑echo GRE for R2* and QSM, all acquired on 3 T Siemens scanners. After rigorous preprocessing (B1+ correction, noise removal, phase unwrapping, background field correction), the three qMRI maps were generated for each subject.

Because the data originated from different scanners, acquisition parameters, and age‑biased cohorts, the authors applied two independent harmonization strategies: NeuroCombat (an empirical Bayes method) and Hierarchical Bayesian Regression (HBR). Both approaches successfully removed site‑specific mean and variance differences while preserving biologically relevant variance, as demonstrated by cross‑validation and normative modeling.

The authors modeled age trajectories for three anatomical compartments: tractography‑based white‑matter bundles (TWMB), superficial white matter (SWM), and cortical grey matter (CGM). Using generalized additive models with Bayesian regularization, they captured non‑linear age effects for each qMRI metric across 68 cortical parcels and 20 major white‑matter tracts.

Key findings include:

1. Maturation Gradient – TWMB exhibited the earliest peak in all three metrics, followed by SWM, with CGM reaching its maximum later. This establishes a hierarchical maturation order (deep white‑matter → superficial white‑matter → cortex).
2. Spatial Directionality – Within the cortex, a posterior‑to‑anterior gradient was observed (sensory‑motor regions mature first, association areas later). In white‑matter tracts, an inferior‑to‑superior gradient emerged, mirroring known myelination patterns.
3. Metric‑Specific Sensitivity – R1 showed the most robust, monotonic age trajectories, reflecting its sensitivity to water content, tissue density, and macromolecular exchange (primarily myelin). R2* and QSM displayed greater regional variability because they capture both iron accumulation (paramagnetic) and myelin loss (diamagnetic), leading to divergent patterns across deep grey matter, white‑matter, and cortex.
4. Harmonization Validity – Both NeuroCombat and HBR yielded highly concordant age curves; normative models built on harmonized data reproduced site‑specific trajectories with R² > 0.85, confirming that multi‑site qMRI datasets can be reliably merged.

The study also discusses methodological limitations: the cross‑sectional design precludes individual longitudinal inference; sample distribution is uneven across age bands, reducing power in the youngest and oldest cohorts; QSM reconstruction parameters and R2* orientation dependence were not fully standardized; and potential residual scanner‑specific biases may remain despite harmonization.

In conclusion, the work demonstrates that multiparametric qMRI can delineate nuanced, region‑specific brain maturation and aging trajectories across the entire human lifespan. The identified normative trajectories provide a valuable reference for future studies aiming to detect pathological deviations in neurodegenerative, neurodevelopmental, or neuroinflammatory disorders. Moreover, the successful application of both NeuroCombat and HBR underscores the feasibility of integrating heterogeneous qMRI cohorts, paving the way for large‑scale, lifespan‑spanning neuroimaging consortia.