Investigating systematic bias in brain age estimation with application to post-traumatic stress disorders

Abstract

Brain age prediction using machine-learning techniques has recently attracted growing attention, as it has the potential to serve as a biomarker for characterizing the typical brain development and neuropsychiatric disorders. Yet one long-standing problem is that the predicted brain age is overestimated in younger subjects and underestimated in older. There is a plethora of claims as to the bias origins, both methodologically and in data itself. With a large neuroanatomical dataset (N = 2,026; 6–89 years of age) from multiple shared datasets, we show this bias is neither data-dependent nor specific to particular method including deep neural network. We present an alternative account that offers a statistical explanation for the bias and describe a simple, yet efficient, method using general linear model to adjust the bias. We demonstrate the effectiveness of bias adjustment with a large multi-modal neuroimaging data (N = 804; 8–21 years of age) for both healthy controls and post-traumatic stress disorders patients obtained from the Philadelphia Neurodevelopmental Cohort.