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Prediction of Communication Impairment in Children With Bilateral Cerebral Palsy Using Multivariate Lesion- and Connectome-Based Approaches: Protocol for a Multicenter Prospective Cohort Study

BACKGROUND: Bilateral cerebral palsy (BCP) is the most common type of CP in children and is often accompanied by different degrees of communication impairment. Several studies have attempted to identify children at high risk for communication impairment. However, most prediction factors are qualitat...

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Autores principales: Hu, Jie, Zhang, Jingjing, Yang, Yanli, Liang, Ting, Huang, Tingting, He, Cheng, Wang, Fuqin, Liu, Heng, Zhang, Tijiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841608/
https://www.ncbi.nlm.nih.gov/pubmed/35173593
http://dx.doi.org/10.3389/fnhum.2022.788037
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author Hu, Jie
Zhang, Jingjing
Yang, Yanli
Liang, Ting
Huang, Tingting
He, Cheng
Wang, Fuqin
Liu, Heng
Zhang, Tijiang
author_facet Hu, Jie
Zhang, Jingjing
Yang, Yanli
Liang, Ting
Huang, Tingting
He, Cheng
Wang, Fuqin
Liu, Heng
Zhang, Tijiang
author_sort Hu, Jie
collection PubMed
description BACKGROUND: Bilateral cerebral palsy (BCP) is the most common type of CP in children and is often accompanied by different degrees of communication impairment. Several studies have attempted to identify children at high risk for communication impairment. However, most prediction factors are qualitative and subjective and may be influenced by rater bias. Individualized objective diagnostic and/or prediction methods are still lacking, and an effective method is urgently needed to guide clinical diagnosis and treatment. The aim of this study is to develop and validate an objective, individual-based model for the prediction of communication impairment in children with BCP by the time they enter school. METHODS: A multicenter prospective cohort study will be conducted in four Chinese hospitals. A total of 178 children with BCP will undergo advanced brain magnetic resonance imaging (MRI) at baseline (corrected age, before the age of 2 years). At school entry, communication performance will be assessed by a communication function classification system (CFCS). Three-quarters of children with BCP will be allocated as a training cohort, whereas the remaining children will be allocated as a test cohort. Multivariate lesion- and connectome-based approaches, which have shown good predictive ability of language performance in stroke patients, will be applied to extract features from MR images for each child with BCP. Multiple machine learning models using extracted features to predict communication impairment for each child with BCP will be constructed using data from the training cohort and externally validated using data from the test cohort. Prediction accuracy across models in the test cohort will be statistically compared. DISCUSSION: The findings of the study may lead to the development of several translational tools that can individually predict communication impairment in children newly diagnosed with BCP to ensure that these children receive early, targeted therapeutic intervention before they begin school. TRIAL REGISTRATION: The study has been registered with the Chinese Clinical Trial Registry (ChiCTR2100049497).
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spelling pubmed-88416082022-02-15 Prediction of Communication Impairment in Children With Bilateral Cerebral Palsy Using Multivariate Lesion- and Connectome-Based Approaches: Protocol for a Multicenter Prospective Cohort Study Hu, Jie Zhang, Jingjing Yang, Yanli Liang, Ting Huang, Tingting He, Cheng Wang, Fuqin Liu, Heng Zhang, Tijiang Front Hum Neurosci Human Neuroscience BACKGROUND: Bilateral cerebral palsy (BCP) is the most common type of CP in children and is often accompanied by different degrees of communication impairment. Several studies have attempted to identify children at high risk for communication impairment. However, most prediction factors are qualitative and subjective and may be influenced by rater bias. Individualized objective diagnostic and/or prediction methods are still lacking, and an effective method is urgently needed to guide clinical diagnosis and treatment. The aim of this study is to develop and validate an objective, individual-based model for the prediction of communication impairment in children with BCP by the time they enter school. METHODS: A multicenter prospective cohort study will be conducted in four Chinese hospitals. A total of 178 children with BCP will undergo advanced brain magnetic resonance imaging (MRI) at baseline (corrected age, before the age of 2 years). At school entry, communication performance will be assessed by a communication function classification system (CFCS). Three-quarters of children with BCP will be allocated as a training cohort, whereas the remaining children will be allocated as a test cohort. Multivariate lesion- and connectome-based approaches, which have shown good predictive ability of language performance in stroke patients, will be applied to extract features from MR images for each child with BCP. Multiple machine learning models using extracted features to predict communication impairment for each child with BCP will be constructed using data from the training cohort and externally validated using data from the test cohort. Prediction accuracy across models in the test cohort will be statistically compared. DISCUSSION: The findings of the study may lead to the development of several translational tools that can individually predict communication impairment in children newly diagnosed with BCP to ensure that these children receive early, targeted therapeutic intervention before they begin school. TRIAL REGISTRATION: The study has been registered with the Chinese Clinical Trial Registry (ChiCTR2100049497). Frontiers Media S.A. 2022-01-31 /pmc/articles/PMC8841608/ /pubmed/35173593 http://dx.doi.org/10.3389/fnhum.2022.788037 Text en Copyright © 2022 Hu, Zhang, Yang, Liang, Huang, He, Wang, Liu and Zhang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Human Neuroscience
Hu, Jie
Zhang, Jingjing
Yang, Yanli
Liang, Ting
Huang, Tingting
He, Cheng
Wang, Fuqin
Liu, Heng
Zhang, Tijiang
Prediction of Communication Impairment in Children With Bilateral Cerebral Palsy Using Multivariate Lesion- and Connectome-Based Approaches: Protocol for a Multicenter Prospective Cohort Study
title Prediction of Communication Impairment in Children With Bilateral Cerebral Palsy Using Multivariate Lesion- and Connectome-Based Approaches: Protocol for a Multicenter Prospective Cohort Study
title_full Prediction of Communication Impairment in Children With Bilateral Cerebral Palsy Using Multivariate Lesion- and Connectome-Based Approaches: Protocol for a Multicenter Prospective Cohort Study
title_fullStr Prediction of Communication Impairment in Children With Bilateral Cerebral Palsy Using Multivariate Lesion- and Connectome-Based Approaches: Protocol for a Multicenter Prospective Cohort Study
title_full_unstemmed Prediction of Communication Impairment in Children With Bilateral Cerebral Palsy Using Multivariate Lesion- and Connectome-Based Approaches: Protocol for a Multicenter Prospective Cohort Study
title_short Prediction of Communication Impairment in Children With Bilateral Cerebral Palsy Using Multivariate Lesion- and Connectome-Based Approaches: Protocol for a Multicenter Prospective Cohort Study
title_sort prediction of communication impairment in children with bilateral cerebral palsy using multivariate lesion- and connectome-based approaches: protocol for a multicenter prospective cohort study
topic Human Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8841608/
https://www.ncbi.nlm.nih.gov/pubmed/35173593
http://dx.doi.org/10.3389/fnhum.2022.788037
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