Journal Article

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates

Yuan, Shiyu, Wu, Xiaotong, Chai, Yaqiong, Kim, Hosung, Duffy, Ben A., Toga, Arthur W., Lu, Minhua, Jahanshad, Neda, Xu, Duan, Liu, Mengting, Cole, James H., Kim, Sharon Y., Lee, Hyun Ju, Gano, Dawn, Barkovich, Anthony James

2024

Overview

Objectives Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. Methods In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. Results Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. Conclusions Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. Clinical relevance statement Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. Key Points •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V
• Subject: Abnormalities / Age / Artificial neural networks / Birth weight / Brain / Brain - diagnostic imaging / Brain - growth & development / Brain injury / Deep learning / Diagnostic Radiology / Female / Gestational Age / Graph neural networks / Head injuries / Humans / Imaging / Imaging Informatics and Artificial Intelligence / Infant, Newborn / Infant, Premature - growth & development / Injuries / Internal Medicine / Interventional Radiology / Machine learning / Magnetic Resonance Imaging - methods / Male / Medicine / Medicine & Public Health / Morphology / Neonates / Neural networks / Neural Networks, Computer / Neurodevelopmental disorders / Neurodevelopmental Disorders - diagnostic imaging / Neuroradiology / Newborn babies / Premature birth / Quality of life / Radiology / Risk factors / Traumatic brain injury / Ultrasound