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Functional Connectivity-Derived Optimal Gestational-Age Cut Points for Fetal Brain Network Maturity

The architecture of the human connectome changes with brain maturation. Pivotal to understanding these changes is defining developmental periods when transitions in network topology occur. Here, using 110 resting-state functional connectivity data sets from healthy fetuses between 19 and 40 gestatio...

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Autores principales: De Asis-Cruz, Josepheen, Barnett, Scott Douglas, Kim, Jung-Hoon, Limperopoulos, Catherine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8304646/
https://www.ncbi.nlm.nih.gov/pubmed/34356155
http://dx.doi.org/10.3390/brainsci11070921
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author De Asis-Cruz, Josepheen
Barnett, Scott Douglas
Kim, Jung-Hoon
Limperopoulos, Catherine
author_facet De Asis-Cruz, Josepheen
Barnett, Scott Douglas
Kim, Jung-Hoon
Limperopoulos, Catherine
author_sort De Asis-Cruz, Josepheen
collection PubMed
description The architecture of the human connectome changes with brain maturation. Pivotal to understanding these changes is defining developmental periods when transitions in network topology occur. Here, using 110 resting-state functional connectivity data sets from healthy fetuses between 19 and 40 gestational weeks, we estimated optimal gestational-age (GA) cut points for dichotomizing fetuses into ‘young’ and ‘old’ groups based on global network features. We computed the small-world index, normalized clustering and path length, global and local efficiency, and modularity from connectivity matrices comprised 200 regions and their corresponding pairwise connectivity. We modeled the effect of GA at scan on each metric using separate repeated-measures generalized estimating equations. Our modeling strategy involved stratifying fetuses into ‘young’ and ‘old’ based on the scan occurring before or after a selected GA (i.e., 28 to 33). We then used the quasi-likelihood independence criterion statistic to compare model fit between ‘old’ and ‘young’ cohorts and determine optimal cut points for each graph metric. Trends for all metrics, except for global efficiency, decreased with increasing gestational age. Optimal cut points fell within 30–31 weeks for all metrics coinciding with developmental events that include a shift from endogenous neuronal activity to sensory-driven cortical patterns.
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spelling pubmed-83046462021-07-25 Functional Connectivity-Derived Optimal Gestational-Age Cut Points for Fetal Brain Network Maturity De Asis-Cruz, Josepheen Barnett, Scott Douglas Kim, Jung-Hoon Limperopoulos, Catherine Brain Sci Article The architecture of the human connectome changes with brain maturation. Pivotal to understanding these changes is defining developmental periods when transitions in network topology occur. Here, using 110 resting-state functional connectivity data sets from healthy fetuses between 19 and 40 gestational weeks, we estimated optimal gestational-age (GA) cut points for dichotomizing fetuses into ‘young’ and ‘old’ groups based on global network features. We computed the small-world index, normalized clustering and path length, global and local efficiency, and modularity from connectivity matrices comprised 200 regions and their corresponding pairwise connectivity. We modeled the effect of GA at scan on each metric using separate repeated-measures generalized estimating equations. Our modeling strategy involved stratifying fetuses into ‘young’ and ‘old’ based on the scan occurring before or after a selected GA (i.e., 28 to 33). We then used the quasi-likelihood independence criterion statistic to compare model fit between ‘old’ and ‘young’ cohorts and determine optimal cut points for each graph metric. Trends for all metrics, except for global efficiency, decreased with increasing gestational age. Optimal cut points fell within 30–31 weeks for all metrics coinciding with developmental events that include a shift from endogenous neuronal activity to sensory-driven cortical patterns. MDPI 2021-07-12 /pmc/articles/PMC8304646/ /pubmed/34356155 http://dx.doi.org/10.3390/brainsci11070921 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
De Asis-Cruz, Josepheen
Barnett, Scott Douglas
Kim, Jung-Hoon
Limperopoulos, Catherine
Functional Connectivity-Derived Optimal Gestational-Age Cut Points for Fetal Brain Network Maturity
title Functional Connectivity-Derived Optimal Gestational-Age Cut Points for Fetal Brain Network Maturity
title_full Functional Connectivity-Derived Optimal Gestational-Age Cut Points for Fetal Brain Network Maturity
title_fullStr Functional Connectivity-Derived Optimal Gestational-Age Cut Points for Fetal Brain Network Maturity
title_full_unstemmed Functional Connectivity-Derived Optimal Gestational-Age Cut Points for Fetal Brain Network Maturity
title_short Functional Connectivity-Derived Optimal Gestational-Age Cut Points for Fetal Brain Network Maturity
title_sort functional connectivity-derived optimal gestational-age cut points for fetal brain network maturity
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8304646/
https://www.ncbi.nlm.nih.gov/pubmed/34356155
http://dx.doi.org/10.3390/brainsci11070921
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