Abstract
The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979, p < 0.001) and 3D (MAE = 1.114, p < 0.001) CNNs. The saliency maps from our method indicated that the anatomical information describing the cortex and ventricles was the primary contributor to brain age prediction. With the application of the proposed method to external MRIs from 21 healthy fetuses, we obtained an MAE of 0.508 weeks. Based on the external MRIs, we found that the stack-wise MAE of the 2D single-channel CNN (0.743 weeks) was significantly lower than those of the 2D multi-channel (1.466 weeks, p < 0.001) and 3D (1.241 weeks, p < 0.001) CNNs. These results demonstrate that our method with multiplanar slices accurately predicts fetal brain age without the need for increased dimensionality or complex MRI preprocessing steps.
Highlights
The human brain exhibits lifelong age-related changes under complex genetic and environmental factors (Rakic, 2004; Kremen et al, 2010; Rando and Chang, 2012; Alexander-Bloch et al, 2020)
We aimed to propose a new method for fetal brain age prediction in an attempt to overcome the limitations of previous approaches
We proposed a new method for predicting fetal brain age using structural fetal brain magnetic resonance imaging (MRI)
Summary
The human brain exhibits lifelong age-related changes under complex genetic and environmental factors (Rakic, 2004; Kremen et al, 2010; Rando and Chang, 2012; Alexander-Bloch et al, 2020). The brain ages were predicted in patients with Alzheimer’s disease (Franke and Gaser, 2012; Gaser et al, 2013), schizophrenia (Koutsouleris et al, 2014; Schnack et al, 2016), epilepsy (Pardoe et al, 2017), Down syndrome (Cole et al, 2017a), traumatic brain injury (Cole et al, 2015), multiple sclerosis (Cole et al, 2020), and preterm birth (Franke et al, 2012) These studies established age prediction methods for healthy brain development or senescence from children to adults and examined individual discrepancies between the predicted and chronological ages [predicted age difference (PAD); chronological age – predicted brain age]. The accurate prediction of brain age can provide clinically relevant information on brain health to predict future risks and detect structural abnormalities associated with brain disorders
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