Connectome-based prediction of brain age in Rolandic epilepsy: a protocol for a multicenter cross-sectional study

Abstract

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).

Keywords: MRI; Rolandic epilepsy (RE); brain age; machine learning.

Figure 1

Flowchart of the study protocol. FC, functional connectivity; CA, chronological age; EAA, the most important edges associated with age; LOSOCV, leave-one-site-out cross-validation; RE, Rolandic epilepsy.