Early Prediction of Cognitive Deficit in Very Preterm Infants Using Brain Structural Connectome With Transfer Learning Enhanced Deep Convolutional Neural Networks

Frontiers in Neuroscience

Ming Chen¹  Mekbib Altaye²  Lili He²  Nehal A. Parikh²  Weihong Yuan³  Jinghua Wang⁴  Hailong Li⁶ 

[1] Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, OH, United States;Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States;Department of Radiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States;Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States;Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States;The Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States;

关键词: convolutional neural network;    deep learning;    cognitive deficit;    transfer learning;    structural connectome;   

DOI  :  10.3389/fnins.2020.00858

来源: DOAJ

【 摘 要 】

Up to 40% of very preterm infants (≤32 weeks’ gestational age) were identified with a cognitive deficit at 2 years of age. Yet, accurate clinical diagnosis of cognitive deficit cannot be made until early childhood around 3–5 years of age. Recently, brain structural connectome that was constructed by advanced diffusion tensor imaging (DTI) technique has been playing an important role in understanding human cognitive functions. However, available annotated neuroimaging datasets with clinical and outcome information are usually limited and expensive to enlarge in the very preterm infants’ studies. These challenges hinder the development of neonatal prognostic tools for early prediction of cognitive deficit in very preterm infants. In this study, we considered the brain structural connectome as a 2D image and applied established deep convolutional neural networks to learn the spatial and topological information of the brain connectome. Furthermore, the transfer learning technique was utilized to mitigate the issue of insufficient training data. As such, we developed a transfer learning enhanced convolutional neural network (TL-CNN) model for early prediction of cognitive assessment at 2 years of age in very preterm infants using brain structural connectome. A total of 110 very preterm infants were enrolled in this work. Brain structural connectome was constructed using DTI images scanned at term-equivalent age. Bayley III cognitive assessments were conducted at 2 years of corrected age. We applied the proposed model to both cognitive deficit classification and continuous cognitive score prediction tasks. The results demonstrated that TL-CNN achieved improved performance compared to multiple peer models. Finally, we identified the brain regions most discriminative to the cognitive deficit. The results suggest that deep learning models may facilitate early prediction of later neurodevelopmental outcomes in very preterm infants at term-equivalent age.

【 授权许可】

Unknown