Investigating systematic bias in brain age estimation with application to post‐traumatic stress disorders. Issue 11 (28th March 2019) - Liang, Hualou
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.