Population Atlas Analysis of Emerging Brain Structural Connections in the Human Fetus.
complex network analysis
fetal brain
in utero imaging
structural connectome
Journal
Journal of magnetic resonance imaging : JMRI
ISSN: 1522-2586
Titre abrégé: J Magn Reson Imaging
Pays: United States
ID NLM: 9105850
Informations de publication
Date de publication:
16 Oct 2023
16 Oct 2023
Historique:
revised:
27
09
2023
received:
14
08
2023
accepted:
28
09
2023
medline:
16
10
2023
pubmed:
16
10
2023
entrez:
16
10
2023
Statut:
aheadofprint
Résumé
A lack of in utero imaging data hampers our understanding of the connections in the human fetal brain. Generalizing observations from postmortem subjects and premature newborns is inaccurate due to technical and biological differences. To evaluate changes in fetal brain structural connectivity between 23 and 35 weeks postconceptional age using a spatiotemporal atlas of diffusion tensor imaging (DTI). Retrospective. Publicly available diffusion atlases, based on 60 healthy women (age 18-45 years) with normal prenatal care, from 23 and 35 weeks of gestation. 3.0 Tesla/DTI acquired with diffusion-weighted echo planar imaging (EPI). We performed whole-brain fiber tractography from DTI images. The cortical plate of each diffusion atlas was segmented and parcellated into 78 regions derived from the Edinburgh Neonatal Atlas (ENA33). Connectivity matrices were computed, representing normalized fiber connections between nodes. We examined the relationship between global efficiency (GE), local efficiency (LE), small-worldness (SW), nodal efficiency (NE), and betweenness centrality (BC) with gestational age (GA) and with laterality. Linear regression was used to analyze changes in GE, LE, NE, and BC throughout gestation, and to assess changes in laterality. The t-tests were used to assess SW. P-values were corrected using Holm-Bonferroni method. A corrected P-value <0.05 was considered statistically significant. Network analysis revealed a significant weekly increase in GE (5.83%/week, 95% CI 4.32-7.37), LE (5.43%/week, 95% CI 3.63-7.25), and presence of SW across GA. No significant hemisphere differences were found in GE (P = 0.971) or LE (P = 0.458). Increasing GA was significantly associated with increasing NE in 41 nodes, increasing BC in 3 nodes, and decreasing BC in 2 nodes. Extensive network development and refinement occur in the second and third trimesters, marked by a rapid increase in global integration and local segregation. 3 TECHNICAL EFFICACY: Stage 2.
Sections du résumé
BACKGROUND
BACKGROUND
A lack of in utero imaging data hampers our understanding of the connections in the human fetal brain. Generalizing observations from postmortem subjects and premature newborns is inaccurate due to technical and biological differences.
PURPOSE
OBJECTIVE
To evaluate changes in fetal brain structural connectivity between 23 and 35 weeks postconceptional age using a spatiotemporal atlas of diffusion tensor imaging (DTI).
STUDY TYPE
METHODS
Retrospective.
POPULATION
METHODS
Publicly available diffusion atlases, based on 60 healthy women (age 18-45 years) with normal prenatal care, from 23 and 35 weeks of gestation.
FIELD STRENGTH/SEQUENCE
UNASSIGNED
3.0 Tesla/DTI acquired with diffusion-weighted echo planar imaging (EPI).
ASSESSMENT
RESULTS
We performed whole-brain fiber tractography from DTI images. The cortical plate of each diffusion atlas was segmented and parcellated into 78 regions derived from the Edinburgh Neonatal Atlas (ENA33). Connectivity matrices were computed, representing normalized fiber connections between nodes. We examined the relationship between global efficiency (GE), local efficiency (LE), small-worldness (SW), nodal efficiency (NE), and betweenness centrality (BC) with gestational age (GA) and with laterality.
STATISTICAL TESTS
METHODS
Linear regression was used to analyze changes in GE, LE, NE, and BC throughout gestation, and to assess changes in laterality. The t-tests were used to assess SW. P-values were corrected using Holm-Bonferroni method. A corrected P-value <0.05 was considered statistically significant.
RESULTS
RESULTS
Network analysis revealed a significant weekly increase in GE (5.83%/week, 95% CI 4.32-7.37), LE (5.43%/week, 95% CI 3.63-7.25), and presence of SW across GA. No significant hemisphere differences were found in GE (P = 0.971) or LE (P = 0.458). Increasing GA was significantly associated with increasing NE in 41 nodes, increasing BC in 3 nodes, and decreasing BC in 2 nodes.
DATA CONCLUSION
CONCLUSIONS
Extensive network development and refinement occur in the second and third trimesters, marked by a rapid increase in global integration and local segregation.
LEVEL OF EVIDENCE
METHODS
3 TECHNICAL EFFICACY: Stage 2.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NICHD NIH HHS
ID : R01 HD109395
Pays : United States
Organisme : NICHD NIH HHS
ID : R01 HD110772
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB032366
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB018988
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB019483
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01 EB031849
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS106030
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS121657
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS128281
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM013608
Pays : United States
Informations de copyright
© 2023 International Society for Magnetic Resonance in Medicine.
Références
Machado-Rivas F, Gandhi J, Choi JJ, et al. Normal growth, sexual dimorphism, and lateral asymmetries at fetal brain MRI. Radiology 2022;303:162-170.
Kostović I, Sedmak G, Judaš M. Neural histology and neurogenesis of the human fetal and infant brain. Neuroimage 2019;188:743-773.
Carroll L, Braeutigam S, Dawes JM, et al. Autism Spectrum disorders: Multiple routes to, and multiple consequences of, abnormal synaptic function and connectivity. Neuroscientist 2021;27:10-29.
Bastiani M, Andersson JLR, Cordero-Grande L, et al. Automated processing pipeline for neonatal diffusion MRI in the developing human connectome project. Neuroimage 2019;185:750-763.
Ratnarajah N, Rifkin-Graboi A, Fortier MV, et al. Structural connectivity asymmetry in the neonatal brain. Neuroimage 2013;75:187-194.
Ramirez A, Peyvandi S, Cox S, et al. Neonatal brain injury influences structural connectivity and childhood functional outcomes. PloS One 2022;17:e0262310.
Marami B, Mohseni Salehi SS, Afacan O, et al. Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis. Neuroimage 2017;156:475-488.
Marami B, Scherrer B, Afacan O, Erem B, Warfield SK, Gholipour A. Motion-robust diffusion-weighted brain MRI reconstruction through slice-level registration-based motion tracking. IEEE Trans Med Imaging 2016;35:2258-2269.
Khan S, Vasung L, Marami B, et al. Fetal brain growth portrayed by a spatiotemporal diffusion tensor MRI atlas computed from in utero images. Neuroimage 2019;185:593-608.
Xu X, Sun C, Sun J, et al. Spatiotemporal atlas of the fetal brain depicts cortical developmental gradient. J Neurosci 2022;42:9435-9449.
Rubinov M, Sporns O. Complex network measures of brain connectivity: Uses and interpretations. Neuroimage 2010;52:1059-1069.
Papo D, Buldú JM, Boccaletti S, Bullmore ET. Complex network theory and the brain. Philos Trans R Soc Lond B Biol Sci 2014;369:20130520.
Feldmann M, Guo T, Miller SP, et al. Delayed maturation of the structural brain connectome in neonates with congenital heart disease. Brain Commun 2020;2:fcaa209.
Song L, Mishra V, Ouyang M, et al. Human fetal brain connectome: Structural network development from middle fetal stage to birth. Front Neurosci 2017;11:561.
Jakab A, Tuura R, Kellenberger C, Scheer I. In utero diffusion tensor imaging of the fetal brain: A reproducibility study. Neuroimage Clin 2017;15:601-612.
Blesa M, Serag A, Wilkinson AG, et al. Parcellation of the healthy neonatal brain into 107 regions using atlas propagation through intermediate time points in childhood. Front Neurosci 2016;10:10.
Gholipour A, Rollins CK, Velasco-Annis C, et al. A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Sci Rep 2017;7:476.
Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 2008;12:26-41.
Peters JM, Sahin M, Vogel-Farley VK, et al. Loss of white matter microstructural integrity is associated with adverse neurological outcome in tuberous sclerosis complex. Acad Radiol 2012;19:17-25.
Suarez RO, Commowick O, Prabhu SP, Warfield SK. Automated delineation of white matter fiber tracts with a multiple region-of-interest approach. Neuroimage 2012;59:3690-3700.
Lewis WW, Sahin M, Scherrer B, et al. Impaired language pathways in tuberous sclerosis complex patients with autism spectrum disorders. Cereb Cortex 2013;23:1526-1532.
Tournier J-D, Smith R, Raffelt D, et al. MRtrix3: A fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 2019;202:116137.
Hagmann P, Cammoun L, Gigandet X, et al. Mapping the structural core of human cerebral cortex. PLoS Biol 2008;6:e159.
Wang J, Wang X, Xia M, Liao X, Evans A, He Y. GRETNA: A graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 2015;9:386.
Xia M, Wang J, He Y. BrainNet viewer: A network visualization tool for human brain Connectomics. PloS One 2013;8:e68910.
R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2023.
Wickham H. Ggplot2: Elegant graphics for data analysis. New York: Springer-Verlag; 2016.
Yeh F-C, Panesar S, Fernandes D, et al. Population-averaged atlas of the macroscale human structural connectome and its network topology. Neuroimage 2018;178:57-68.
Baum GL, Roalf DR, Cook PA, et al. The impact of in-scanner head motion on structural connectivity derived from diffusion MRI. Neuroimage 2018;173:275-286.
Kostovic I, Rakic P. Developmental history of the transient subplate zone in the visual and somatosensory cortex of the macaque monkey and human brain. J Comp Neurol 1990;297:441-470.
Ball G, Pazderova L, Chew A, et al. Thalamocortical connectivity predicts cognition in children born preterm. Cereb Cortex 2015;25:4310-4318.
Griffiths PD, Bradburn M, Campbell MJ, et al. Use of MRI in the diagnosis of fetal brain abnormalities in utero (MERIDIAN): A multicentre, prospective cohort study. Lancet 2017;389:538-546.
Jaimes C, Machado-Rivas F, Afacan O, et al. In vivo characterization of emerging white matter microstructure in the fetal brain in the third trimester. Hum Brain Mapp 2020;41:3177-3185.
Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett 2001;87:198701.
Buckner RL, Sepulcre J, Talukdar T, et al. Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer's disease. J Neurosci 2009;29:1860-1873.
Latora V, Marchiori M. Economic small-world behavior in weighted networks. Eur Phys J B 2003;32:249-263.