4.6 Article

Predicting the brain age of children with cerebral palsy using a two-dimensional convolutional neural networks prediction model without gray and white matter segmentation

期刊

FRONTIERS IN NEUROLOGY
卷 13, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fneur.2022.1040087

关键词

cerebral palsy; brain age; deep learning; convolutional neural networks; brain age gap estimation

资金

  1. Tomorrow Plan for Surgical Rehabilitation of Disabled Orphans of the Ministry of Civil Affairs of the People's Republic of China
  2. Rehabilitation Program for disabled Children of the Autonomous region disabled Persons' Federation
  3. Regional Project of the National Natural Science Foundation of China
  4. [81660202]

向作者/读者索取更多资源

In this study, a two-dimensional convolutional neural networks brain age prediction model was used to explore the law of brain development in children with cerebral palsy (CP). The results showed that the brain age of children with CP was generally higher than that of healthy peers, and male patients and those with bilateral spastic CP had higher brain ages.
BackgroundAbnormal brain development is common in children with cerebral palsy (CP), but there are no recent reports on the actual brain age of children with CP. ObjectiveOur objective is to use the brain age prediction model to explore the law of brain development in children with CP. MethodsA two-dimensional convolutional neural networks brain age prediction model was designed without segmenting the white and gray matter. Training and testing brain age prediction model using magnetic resonance images of healthy people in a public database. The brain age of children with CP aged 5-27 years old was predicted. ResultsThe training dataset mean absolute error (MAE) = 1.85, r = 0.99; test dataset MAE = 3.98, r = 0.95. The brain age gap estimation (BrainAGE) of the 5- to 27-year-old patients with CP was generally higher than that of healthy peers (p < 0.0001). The BrainAGE of male patients with CP was higher than that of female patients (p < 0.05). The BrainAGE of patients with bilateral spastic CP was higher than those with unilateral spastic CP (p < 0.05). ConclusionA two-dimensional convolutional neural networks brain age prediction model allows for brain age prediction using routine hospital T1-weighted head MRI without segmenting the white and gray matter of the brain. At the same time, these findings suggest that brain aging occurs in patients with CP after brain damage. Female patients with CP are more likely to return to their original brain development trajectory than male patients after brain injury. In patients with spastic CP, brain aging is more serious in those with bilateral cerebral hemisphere injury than in those with unilateral cerebral hemisphere injury.

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