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Connectome-based prediction of brain age in Rolandic epilepsy: a protocol for a multicenter cross-sectional study
Fuqin Wang1, Yu Yin1, Yang Yang1
1Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China.
Insights
This study uses connectome-based predictive modeling (CPM) to predict brain age in children with Rolandic epilepsy (RE). Findings will aid understanding of RE
Area of Science:
- Neuroscience
- Pediatric Neurology
- Machine Learning in Medicine
Background:
- Rolandic epilepsy (RE) is a common pediatric epilepsy syndrome associated with cognitive impairments.
- Epilepsy can lead to accelerated brain aging, differing from normal brain development.
- Connectome-based predictive modeling (CPM) offers a novel approach to study brain-behavior relationships.
Purpose of the Study:
- To develop and validate a CPM for predicting brain age in pediatric patients with Rolandic epilepsy.
- To investigate age-related neurodevelopmental changes in the brain associated with RE.
Main Methods:
- A multicenter, cross-sectional study involving 100 RE patients and 100 healthy children.
- Neuropsychological testing using the Wechsler Intelligence Scale and magnetic resonance imaging (MRI).
- Application of CPM to predict brain age based on functional brain connectivity.
Conclusions:
- The study aims to enhance understanding of brain developmental changes in children with RE.
- This research could be crucial for developing early interventions for Rolandic epilepsy.
Background:
Rolandic epilepsy (RE) is a common pediatric idiopathic partial epilepsy syndrome. Children with RE display varying degrees of cognitive impairment. In epilepsy, age-related neuroanatomic and cognitive changes differ greatly from those observed in the healthy brain, and may be defined as accelerated brain aging. Connectome-based predictive modeling (CPM) is a recently developed machine learning approach that uses whole-brain connectivity measured with neuroimaging data ("neural fingerprints") to predict brain-behavior relationships. The aim of the study will be to develop and validate a CPM for predicting brain age in patients with RE.
Methods:
A multicenter, cross-sectional study will be conducted in 5 Chinese hospitals. A total of 100 RE patients (including 50 patients receiving anti-epileptic drugs and 50 drug-naïve patients) and 100 healthy children will be recruited to undergo a neuropsychological test using the Wechsler Intelligence Scale. Magnetic resonance images will also be collected. CPM will be applied to predict the brain age of children with RE based on brain functional connectivity.
Discussion:
The findings of the study will facilitate our understanding of developmental changes in the brain in children with RE and could also be an important milestone in the journey toward developing effective early interventions for this disorder.
Trial Registration:
The study has been registered with Chinese Clinical Trial Registry (ChiCTR2000032984).
