Judged by your neighbors: A novel framework for personalized assessment of brain structural aging effects in diverse populations

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Abstract
Background: Despite established consensus about the individuality of brain physiology and structural changes, current neuroimaging biomarker paradigms heavily rely on population averages. Here, we define normativity not as a single reference point but as a landscape of permissible configurations arising from diverse physiological manifestations. Methods: We introduce the Nearest Neighbor Normativity (N 3 ) framework, designed to parse the natural heterogeneity in brain structural measurements into a diversity-aware biomarker of brain structural decline. Through repeated comparisons within different demographic subgroups, N 3 contextualizes an individual's measurements from several viewpoints along the aging continuum. Relying on local density estimation, it accommodates multiple, shared normative configurations within and across age groups. We use T1-weighted MRI data from 29,883 individuals for training and 7,013 individuals for validation, and benchmark our framework against Brain Age models and Normative Modeling. We evaluate each biomarker's ability to detect neurodegenerative diseases, using them as representative models of atypical brain structural decline. Results: In group-level analyses, the N 3 biomarker shows the largest effect sizes in the detection of Mild Cognitive Impairment, Alzheimer's Disease, and Frontotemporal Dementia. Similarly, the N 3 biomarker achieves the highest predictive performance in the classification of neurodegenerative diseases on a single subject level. Notably, the N 3 estimates remain stable across age and robust to compositional changes in the reference sample. Finally, correlation analyses indicate that it captures distinct and complementary aspects of brain structural variation compared to existing approaches. Conclusions: Our findings underscore that rethinking normativity assessments to explicitly accommodate natural inter-individual variability can enhance the efficacy and translational value of neuroimaging biomarkers and advance our collective efforts toward truly personalized patient care.

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