Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.
Mengting LiuMinhua LuSharon Y KimHyun Ju LeeBen A DuffyShiyu YuanYaqiong ChaiJames H ColeXiaotong WuArthur W TogaNeda JahanshadDawn GanoAnthony James BarkovichDuan XuHosung KimPublished in: European radiology (2023)
•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.
Keyphrases
- resting state
- convolutional neural network
- white matter
- deep learning
- risk factors
- low birth weight
- functional connectivity
- cerebral ischemia
- computed tomography
- type diabetes
- neural network
- pregnant women
- preterm infants
- artificial intelligence
- blood brain barrier
- adipose tissue
- preterm birth
- subarachnoid hemorrhage
- network analysis