[Paper Review] Charting the velocity of brain growth and development

Bayer, Johanna M. M., Augustijn A. A. de Boer+31 more|Research Portal (King's College London)|Jan 12, 2026

Dementia and Cognitive Impairment Research1 citations

TL;DR

The paper presents velocity centiles to model longitudinal brain-change rates from large multi-site imaging data, enabling detection of individual deviations and improved prediction of cognitive decline trajectories.

ABSTRACT

Brain charts have emerged as a highly useful approach for understanding brain development and aging on the basis of brain imaging and have shown substantial utility in describing typical and atypical brain development with respect to a given reference model. However, all existing models are fundamentally cross-sectional and cannot capture change over time at the individual level. We address this using velocity centiles, which directly map change over time and can be overlaid onto cross-sectionally derived population centiles. We demonstrate this by modelling rates of change for 24062 scans from 10795 healthy individuals with up to 8 longitudinal measurements across the lifespan. We provide a method to detect individual deviations from a stable trajectory, generalising the notion of thrive lines, which are used in pediatric medicine to declare failure to thrive. Using this approach, we predict transition from mild cognitive impairment to dementia more accurately than by using either time point alone, replicated across two datasets. Last, by taking into account multiple time points, we improve the sensitivity of velocity models for predicting the future trajectory of brain change. This highlights the value of predicting change over time and makes a fundamental step towards precision medicine.

Motivation & Objective

• Motivate the need for lead indicators of change over time in brain development beyond cross-sectional models.
• Propose velocity centiles to map change over time and overlay them on cross-sectional population centiles.
• Demonstrate the approach on a large, longitudinal imaging dataset to model rates of brain change across the lifespan.
• Show how velocity modeling can detect deviations from stable trajectories and improve predictive accuracy for clinical transitions.

Proposed method

• Develop velocity centile models that quantify rates of brain change over time.
• Integrate longitudinal measurements (up to 8 per subject) with cross-sectional centiles.
• Generalize thrive-line concepts to detect individual deviations from stable trajectories.
• Apply the method to predict transition from mild cognitive impairment to dementia.
• Validate predictive performance across two independent datasets.

Experimental results

Research questions

• RQ1 Can velocity centiles accurately map individual brain-change rates over time compared to cross-sectional centiles?
• RQ2 Do velocity-based deviations from stable trajectories improve detection of atypical development or decline?
• RQ3 Can the approach predict transitions such as mild cognitive impairment to dementia more accurately than single time-point models?
• RQ4 Does incorporating multiple time points enhance the sensitivity of brain-change trajectory forecasts?

Key findings

• Modeling rates of change for 24,062 scans from 10,795 healthy individuals with up to eight longitudinal measurements yields a trajectory-driven view of brain development.
• Velocity centiles can be overlaid onto cross-sectionally derived population centiles to map change over time.
• The method can detect individual deviations from stable trajectories (thriving vs. failure to thrive concept extension).
• Velocity-based prediction of progression from mild cognitive impairment to dementia is more accurate than time-point–only approaches, replicated across two datasets.
• Using multiple time points improves sensitivity for forecasting future brain-change trajectories.