The Impact of MRI Data Harmonization on Brain Age Prediction

Machine learning (ML) techniques are increasingly used in brain age prediction to assess brain health and detect neurological and psychiatric disorders. The availability of large, publicly accessible imaging datasets has accelerated the adoption of ML-based methods. However, multi-site MRI datasets present challenges due to site effects, which can introduce biases and affect the accuracy of brain age prediction. In this study, we examined the influence of MRI data harmonization on brain age prediction by comparing models trained with and without harmonization across a large-scale, multi-site dataset of 10,938 healthy individuals aged 5 to 95 years. Using automated ML approaches, we trained various models and computed SHapley Additive exPlanations (SHAP) values to identify the key features driving brain age predictions. Our results showed that while a weighted ensemble method achieved high prediction accuracy (MAE = 7.013; R = 0.860), data harmonization reduced prediction performance, indicating that site-related variability contains valuable information influencing model predictions. SHAP analysis also revealed substantial site-specific biases impacting the predictions. These findings suggest the need to account for site-specific factors in multi-site MRI studies. Understanding the impact of site harmonization is crucial for developing robust and generalizable brain age prediction models that can be applied across diverse populations and imaging settings.