The ICeX framework: Brain age estimation from thalamic nuclei with conformalized and eXplainable random forest regression

Accurate estimation of brain age is essential to identify deviations from typical aging trajectories, which may signal early neurodegenerative, psychiatric, or cognitive dysfunctions. Traditional models often rely on global brain features, potentially overlooking subtle region-specific alterations. This study introduces a novel framework for brain age prediction based on thalamic nuclei volumes-anatomically and functionally distinct structures known to be sensitive to aging. The objective is to enhance interpretability and reliability by integrating feature attribution and uncertainty quantification.

A dataset of 630 healthy young adults from the Human Connectome Project was used to train a Random Forest Regression model. Thalamic nuclei volumes were extracted from structural MRI scans. To enhance transparency, the model was coupled with SHAP-based feature attribution and Conformal Prediction to generate subject-specific prediction intervals. These components were combined into a unified approach called Individual Conformalized Explanations (ICeX), providing insights into how individual features influence both predicted brain age and its associated uncertainty.

The model achieved a mean absolute error of 2.77 years and a 90.77 % coverage, with an average prediction interval width of 11.03 years. Key contributors to prediction accuracy included the left Lateral Geniculate, left Paratenial, and right Ventromedial nuclei. ICeX enabled a detailed understanding of how each feature influenced both prediction and uncertainty at the individual level.

The proposed framework provides reliable and interpretable brain age predictions by combining region-specific biomarkers with robust uncertainty quantification. ICeX offers clinicians and researchers a powerful tool for exploring individual aging trajectories with both precision and transparency, supporting future applications in early detection and personalized intervention strategies for age-related neurological conditions.