Advancing brain age estimation: normative deviation mapping (NDM) for sensitive detection of pathological aging.

Abstract

Brain age is a valuable neuroimaging-based biomarker for assessing brain health, typically estimated using machine learning (ML) models. However, ML approaches suffer from inherent bias, requiring post-hoc correction, and may mask age-related biological variation, limiting their sensitivity to detect subtle biological aging. To overcome these limitations, we proposed a normative deviation mapping (NDM) model as an alternative to conventional ML. We analyzed MRI-derived volumes of 223 brain regions from 10,539 participants (aged 4-98 years). The NDM model assumes a normal distribution for age-specific volumes to calculate regional deviations, which are then aggregated across the brain to determine the final brain age. Compared to standard ML models (e.g., neural networks, extreme gradient boosting), the NDM model effectively eliminated regression bias. Consequently, the NDM model mitigated the underestimation of brain age in older adults, significantly enhancing the detection of pathological changes associated with neurodegenerative diseases, such as Alzheimer's disease. Furthermore, in healthy individuals, the NDM model showed a stronger correlation with cognitive function than chronological age. Our findings indicate that the use of ComBat-GAM for data harmonization could unintentionally mitigate the pathological associations of the brain age gap, suggesting a need for caution to preserve vital biological information. Overall, our model outperforms conventional ML in detecting pathological changes and reflecting biological brain age, while revealing the effects of amyloid accumulation and lifestyle habits on brain health, offering a more robust and biologically meaningful biomarker.