Brain Age Estimation: A Multi-Region Approach Using Groupwise Registration and 3D Shape Contexts Derived from Displacement Vector Fields
Bibliographic record
Abstract
Brain age estimation has emerged as a crucial biomarker for quantifying inter-individual variability in brain aging and identifying risks of cognitive decline. This is particularly relevant for the study of neurodegenerative conditions like Alzheimer’s disease, where deviations from normal brain aging patterns can serve as early indicators of pathology. We introduce an interpretable framework for brain age estimation that adopts a multi-region perspective to capture heterogeneous neuroanatomical changes. Groupwise registration was employed to construct sex- and age-stratified templates, enabling the computation of anatomically meaningful displacement vector fields (DVFs) that characterize voxel-wise structural deviations from the population average. From these DVFs, features were extracted in fifteen brain regions using a 3D shape context descriptor to encode displacement direction and magnitude. Validation was performed on 1,956 T1-weighted MRI scans from cognitively normal individuals in the ADNI dataset (ages 60–90). The combined multi-region model achieved a mean absolute error (MAE) of$1.66 \pm 0.15$years ($R^{2} = 0.84 \pm 0.05$) for males and$1.81 \pm 0.12$years ($R^{2} = 0.80 \pm 0.04$) for females, significantly outperforming many contemporary deep learning models. These findings demonstrate that biologically interpretable, registration-derived features—when analyzed from a multi-regional perspective—can yield robust and accurate estimates of brain age. Beyond predictive accuracy, the framework provides insights into the regional specificity of aging processes, thereby offering a foundation for early detection strategies and targeted clinical applications in neurodegenerative disease research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".