Related Experiment Video
Updated: Aug 17, 2025
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Predicting the brain age of children with cerebral palsy using a two-dimensional convolutional neural networks
Chun-Yu Zhang1, Bao-Feng Yan1, Nurehemaiti Mutalifu1
1Cerebral Palsy Center in Neurosurgery, Second Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Insights
Children with cerebral palsy (CP) show accelerated brain aging, with higher brain age gaps observed in males and those with bilateral spastic CP compared to healthy peers. This indicates altered brain development trajectories post-injury.
Area of Science:
- Neuroscience
- Medical Imaging
- Developmental Pediatrics
Background:
- Abnormal brain development is a known characteristic of cerebral palsy (CP).
- Recent data on the brain age of children with CP is limited.
- Understanding brain development in CP is crucial for clinical insights.
Purpose of the Study:
- To investigate brain development patterns in children with cerebral palsy (CP).
- To utilize a brain age prediction model to assess brain development in CP.
- To identify factors influencing brain age in CP patients.
Main Methods:
- Developed a 2D convolutional neural networks (CNNs) model for brain age prediction.
- Trained and tested the model on MRI scans from a public database of healthy individuals.
- Applied the model to predict the brain age of children with CP aged 5-27 years.
Main Results:
- The CNN model achieved high accuracy (test MAE=3.98, r=0.95).
- Patients with CP exhibited a significantly higher brain age gap (BrainAGE) than healthy controls (p < 0.0001).
- Higher BrainAGE was noted in male CP patients versus females (p < 0.05) and in bilateral spastic CP versus unilateral (p < 0.05).
Conclusions:
- A 2D CNN model can predict brain age from T1-weighted MRI without brain matter segmentation.
- Brain aging is evident in CP patients following brain damage.
- Sex and injury laterality influence brain development trajectories in CP, with males and bilateral injuries showing more pronounced effects.
Background:
Abnormal 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.
Objective:
Our objective is to use the brain age prediction model to explore the law of brain development in children with CP.
Methods:
A 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.
Results:
The 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).
Conclusion:
A 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.
