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Updated: Jul 18, 2025
Cortical Source Analysis of High-Density EEG Recordings in Children
Published on: June 30, 2014
MRI-based brain age prediction model for children under 3 years old using deep residual network
Lianting Hu1,2,3, Qirong Wan4, Li Huang5
1Guangzhou Women and Children's Medical Center, Guangzhou, 510623, Guangdong, China.
Insights
This study developed a deep learning model to accurately predict brain age in children under 3 using MRI scans. The model shows potential for early detection of abnormal brain development in conditions like ADHD and low birth weight.
Area of Science:
- Neuroscience
- Pediatric Imaging
- Artificial Intelligence in Medicine
Background:
- Early identification of abnormal brain development in children under 3 is crucial.
- Existing brain age prediction models are primarily designed for adults, limiting their use in pediatric populations.
- Brain biological age, predicted from neuroimaging, correlates with health and cognitive abilities.
Purpose of the Study:
- To develop and validate an accurate brain age prediction model for children aged 0-3 years using deep learning.
- To assess the model's performance in capturing age-related brain changes in early childhood.
- To investigate the utility of the model in identifying developmental differences in specific pediatric conditions.
Main Methods:
- Collected 658 T1-weighted MRI scans from healthy children aged 0-3 years.
- Developed a deep learning model, specifically a convolutional neural network (CNN), for brain age prediction.
- Validated the model's accuracy by comparing predicted brain age with chronological age, achieving a 91% correlation.
Main Results:
- The deep learning model achieved high accuracy in predicting chronological age from T1-weighted MRI scans in young children.
- The model's performance was comparable to Support Vector Regression (SVR) methods.
- Abnormal brain age predictions were observed in children with extremely low birth weight (delayed) and Attention-Deficit/Hyperactivity Disorder (ADHD) (accelerated).
Conclusions:
- A child-specific deep learning model accurately predicts brain age using minimally processed MRI data.
- This model serves as a valuable quantitative tool for detecting abnormal brain development in early childhood.
- The findings support the early identification and intervention of neurodevelopmental disorders through advanced neuroimaging analysis.
Abstract:
Early identification and intervention of abnormal brain development individual subjects are of great significance, especially during the earliest and most active stage of brain development in children aged under 3. Neuroimage-based brain's biological age has been associated with health, ability, and remaining life. However, the existing brain age prediction models based on neuroimage are predominantly adult-oriented. Here, we collected 658 T1-weighted MRI scans from 0 to 3 years old healthy controls and developed an accurate brain age prediction model for young children using deep learning techniques with high accuracy in capturing age-related changes. The performance of the deep learning-based model is comparable to that of the SVR-based model, showcasing remarkable precision and yielding a noteworthy correlation of 91% between the predicted brain age and the chronological age. Our results demonstrate the accuracy of convolutional neural network (CNN) brain-predicted age using raw T1-weighted MRI data with minimum preprocessing necessary. We also applied our model to children with low birth weight, premature delivery history, autism, and ADHD, and discovered that the brain age was delayed in children with extremely low birth weight (less than 1000 g) while ADHD may cause accelerated aging of the brain. Our child-specific brain age prediction model can be a valuable quantitative tool to detect abnormal brain development and can be helpful in the early identification and intervention of age-related brain disorders.
