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Cited by 130Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsNeonatal and fetal brain pathology

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Intrinsic Functional Brain Architecture Derived from Graph Theoretical Analysis in the Human Fetus

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Thomason, Moriah E., Brown, Jesse A., Dassanayake, Maya T., Shastri, Rupal, Marusak, Hilary A., Hernandez-Andrade, Edgar, Yeo, Lami, Mody, Swati, Berman, Susan, Hassan, Sonia S., Romero, Roberto·2014·PLoS ONE
DOI 10.1371/journal.pone.0094423PMC PMC4006774
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Abstract

The human brain undergoes dramatic maturational changes during late stages of fetal and early postnatal life. The importance of this period to the establishment of healthy neural connectivity is apparent in the high incidence of neural injury in preterm infants, in whom untimely exposure to ex-uterine factors interrupts neural connectivity. Though the relevance of this period to human neuroscience is apparent, little is known about functional neural networks in human fetal life. Here, we apply graph theoretical analysis to examine human fetal brain connectivity. Utilizing resting state functional magnetic resonance imaging (fMRI) data from 33 healthy human fetuses, 19 to 39 weeks gestational age (GA), our analyses reveal that the human fetal brain has modular organization and modules overlap functional systems observed postnatally. Age-related differences between younger (GA <31 weeks) and older (GA≥31 weeks) fetuses demonstrate that brain modularity decreases, and connectivity of the posterior cingulate to other brain networks becomes more negative, with advancing GA. By mimicking functional principles observed postnatally, these results support early emerging capacity for information processing in the human fetal brain. Current technical limitations, as well as the potential for fetal fMRI to one day produce major discoveries about fetal origins or antecedents of neural injury or disease are discussed.

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Figure 1 Fetal fMRI graph processing pipeline. 33 preprocessed fetal fMRI datasets (A) were divided into 149 distinct regions of interest (B), from which regional blood oxygen level dependent (BOLD) timeseries was extracted (C). After removing principal components of signals from cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM), as well as the translational and rotational movement parameters (D), timeseries were correlated with one another in pairwise fashion to derive a 149×149 correlation matrix (E), which was used to assess graph metrics (e.g., modularity) and to derive graph visualizations of human fetal brain networks.
Figure 1 Fetal fMRI graph processing pipeline. 33 preprocessed fetal fMRI datasets (A) were divided into 149 distinct regions of interest (B), from which regional blood oxygen level dependent (BOLD) timeseries was extracted (C). After removing principal components of signals from cerebrospinal fluid (CSF), gray matter (GM), and white matter (WM), as well as the translational and rotational movement parameters (D), timeseries were correlated with one another in pairwise fashion to derive a 149×149 correlation matrix (E), which was used to assess graph metrics (e.g., modularity) and to derive graph visualizations of human fetal brain networks.CC BY · PubMed Central
Figure 2 Macro-scale functional brain network modules for younger ( = 27.6 weeks; n = 17) and older ( = 34.4 weeks; n = 16) fetuses. Nodes are represented as colored spheres and edges are represented as the connections between nodes. Common colors are assigned to maximally similar modules in the two groups, but color assignments are arbitrary.
Figure 2 Macro-scale functional brain network modules for younger ( = 27.6 weeks; n = 17) and older ( = 34.4 weeks; n = 16) fetuses. Nodes are represented as colored spheres and edges are represented as the connections between nodes. Common colors are assigned to maximally similar modules in the two groups, but color assignments are arbitrary.CC BY · PubMed Central
Figure 3 Specific modular changes with development in younger versus older fetuses. Graph visualizations provided in Figure 2 are depicted for the right and left lateral brain perspective and overlaid on a semi-transparent anatomical reference scan. Modules that show significant reconfiguration in older fetuses are highlighted. In older fetuses, sensorimotor cortices (red nodes, red circles) were more functionally connected to regions of the cerebellum and inferior temporal cortices. Prefrontal areas (green nodes, green circles) were more functionally connected to regions of the posterior parietal cortex in older fetuses. A ventral frontal temporal cortex module (teal nodes, teal circles) was present in both groups, but this module became more left lateralized and functionally integrated with inferior frontal and parietal regions in older fetuses. The occipital module (magenta nodes, magenta circles) expanded superiorly to encompass primary and secondary visual cortices in older fetuses.
Figure 3 Specific modular changes with development in younger versus older fetuses. Graph visualizations provided in Figure 2 are depicted for the right and left lateral brain perspective and overlaid on a semi-transparent anatomical reference scan. Modules that show significant reconfiguration in older fetuses are highlighted. In older fetuses, sensorimotor cortices (red nodes, red circles) were more functionally connected to regions of the cerebellum and inferior temporal cortices. Prefrontal areas (green nodes, green circles) were more functionally connected to regions of the posterior parietal cortex in older fetuses. A ventral frontal temporal cortex module (teal nodes, teal circles) was present in both groups, but this module became more left lateralized and functionally integrated with inferior frontal and parietal regions in older fetuses. The occipital module (magenta nodes, magenta circles) expanded superiorly to encompass primary and secondary visual cortices in older fetuses.CC BY · PubMed Central
Figure 4 Range and frequency of functional correlations (r) in younger and older fetuses. A bar plot of younger fetuses’ correlation values subtracted from older fetuses’ correlation values is shown. The majority of correlation values measured in older fetuses occurred between -.1 and.5. In contrast, a wider range of correlation values were measured in younger fetuses. Differences in the full range of correlation values between young and old groups showed a trend, r = −0.32, p = 0.07.
Figure 4 Range and frequency of functional correlations (r) in younger and older fetuses. A bar plot of younger fetuses’ correlation values subtracted from older fetuses’ correlation values is shown. The majority of correlation values measured in older fetuses occurred between -.1 and.5. In contrast, a wider range of correlation values were measured in younger fetuses. Differences in the full range of correlation values between young and old groups showed a trend, r = −0.32, p = 0.07.CC BY · PubMed Central
Figure 5 Negative connectivity in the posterior cingulate cortex (PCC). With advancing gestational age, intramodular anticorrelations of the PCC (in yellow, A) significantly increase (B), p<.05, FDR corrected. The PCC thus becomes more anticorrelated with other brain modules as fetuses mature.
Figure 5 Negative connectivity in the posterior cingulate cortex (PCC). With advancing gestational age, intramodular anticorrelations of the PCC (in yellow, A) significantly increase (B), p<.05, FDR corrected. The PCC thus becomes more anticorrelated with other brain modules as fetuses mature.CC BY · PubMed Central

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Metadata

Authors
Thomason, M. E., Brown, J. A., Dassanayake, M. T., Shastri, R., Marusak, H. A., Hernandez-Andrade, E., Yeo, L., Mody, S., Berman, S., Hassan, S. S., & Romero, R.
Affiliations
  • United States Department of Health and Human Services (US)
  • University of California, San Francisco (US)
  • Wayne State University (US)
Corresponding author
Thomason, Moriah E.
ORCID
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2014-05-01
Volume
9
Issue
5
Pages
e94423
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0094423
PMC
PMC4006774
OpenAlex
W2030993584
Cited by
130
References
61
Citations by year
2026: 7 · 2025: 13 · 2024: 12 · 2023: 9 · 2022: 13 · 2021: 19 · 2020: 9 · 2019: 12 · 2018: 9 · 2017: 6 · 2016: 9 · 2015: 7 · 2014: 5
FWCI
5.02
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • U.S. Department of Health and Human Services
  • Wayne State University
  • W.K. Kellogg Foundation (P3018205)
  • National Institutes of Health (P3018205)
  • National Institute of Mental Health (R01MH110793)
  • National Institute of Environmental Health Sciences (R21ES026022)
  • National Institute of Child Health and Human Development
  • Eunice Kennedy Shriver National Institute of Child Health and Human Development (HD-2-3342, N01 HD23342, N01-HD-)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Neonatal and fetal brain pathology
Keywords
fetal brain, functional brain networks, brain modularity, resting-state functional connectivity, posterior cingulate cortex, gestational age, brain maturation, preterm infants, nerve injury, graph theory
MeSH terms
Adolescent, Adult, Brain, Brain Mapping, Female, Fetus, Gestational Age, Gyrus Cinguli, Humans, Magnetic Resonance Imaging, Pregnancy, Young Adult, Connectome

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