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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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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. |
format | Online Article Text |
id | pubmed-8304646 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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