Connectome-based prediction of brain age in Rolandic epilepsy: a protocol for a multicenter cross-sectional study.
MRI
Rolandic epilepsy (RE)
brain age
machine learning
Journal
Annals of translational medicine
ISSN: 2305-5839
Titre abrégé: Ann Transl Med
Pays: China
ID NLM: 101617978
Informations de publication
Date de publication:
Mar 2021
Mar 2021
Historique:
entrez:
14
4
2021
pubmed:
15
4
2021
medline:
15
4
2021
Statut:
ppublish
Résumé
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. 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. 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. The study has been registered with Chinese Clinical Trial Registry (ChiCTR2000032984).
Sections du résumé
BACKGROUND
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
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
CONCLUSIONS
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
BACKGROUND
The study has been registered with Chinese Clinical Trial Registry (ChiCTR2000032984).
Identifiants
pubmed: 33850908
doi: 10.21037/atm-21-574
pii: atm-09-06-511
pmc: PMC8039653
doi:
Types de publication
Journal Article
Langues
eng
Pagination
511Informations de copyright
2021 Annals of Translational Medicine. All rights reserved.
Déclaration de conflit d'intérêts
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-21-574). The authors have no conflicts of interest to declare.
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