CoChat
Sign Up
Cited by 148Advanced Neuroimaging Techniques and ApplicationsCognitive Abilities and TestingDevelopmental and Educational Neuropsychology

The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.

White matter maturation profiles through early childhood predict general cognitive ability

Open access
Deoni, Sean C. L., O’Muircheartaigh, Jonathan, Elison, Jed T., Walker, Lindsay, Doernberg, Ellen, Waskiewicz, Nicole, Dirks, Holly, Piryatinsky, Irene, Dean, Doug C., Jumbe, N. L.·2016·Brain Structure and Function
DOI 10.1007/s00429-014-0947-xPMC PMC4771819
Open this paper in CoChat — free accountOpen PDF

Abstract

Infancy and early childhood are periods of rapid brain development, during which brain structure and function mature alongside evolving cognitive ability. An important neurodevelopmental process during this postnatal period is the maturation of the myelinated white matter, which facilitates rapid communication across neural systems and networks. Though prior brain imaging studies in children (4 years of age and above), adolescents, and adults have consistently linked white matter development with cognitive maturation and intelligence, few studies have examined how these processes are related throughout early development (birth to 4 years of age). Here, we show that the profile of white matter myelination across the first 5 years of life is strongly and specifically related to cognitive ability. Using a longitudinal design, coupled with advanced magnetic resonance imaging, we demonstrate that children with above-average ability show differential trajectories of myelin development compared to average and below average ability children, even when controlling for socioeconomic status, gestation, and birth weight. Specifically, higher ability children exhibit slower but more prolonged early development, resulting in overall increased myelin measures by ~3 years of age. These results provide new insight into the early neuroanatomical correlates of cognitive ability, and suggest an early period of prolonged maturation with associated protracted white matter plasticity may result in strengthened neural networks that can better support later development. Further, these results reinforce the necessity of a longitudinal perspective in investigating typical or suspected atypical cognitive maturation.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s00429-014-0947-x

Cites

78 of 78 references verified0 citation errors · 0 not checkable

Statistics

Figures

Fig. 1 Example of modified Gompertz growth model defined by four parameters that reflect an initial period of slow growth (β), initial growth rate (γ), a period of transition from fast to slower growth (α), and a secondary growth rate (η)
Fig. 1 Example of modified Gompertz growth model defined by four parameters that reflect an initial period of slow growth (β), initial growth rate (γ), a period of transition from fast to slower growth (α), and a secondary growth rate (η)CC BY · PubMed Central
Fig. 2 Correlations between ELC and MWF. Across the full cohort we found a significant (p < 0.05 corrected) positive correlation between overall cognitive ability and MWF in the splenium of the corpus callosum. In the individual age groups, we found significant positive correlations were identified in the body and splenium of the corpus callosum; bilateral internal capsule, corticospinal tracts and primary motor and somatosensory cortices, optic radiations, superior longitudinal fasciculus, posterior cingulate, visual and auditory cortex, and cerebellar white matter in toddlers. In young children, significant positive correlations were found in throughout the corpus callosum; left Wernicke’s area and primary somatosensory cortex; and right premotor cortex and anterior cingulate
Fig. 2 Correlations between ELC and MWF. Across the full cohort we found a significant (p < 0.05 corrected) positive correlation between overall cognitive ability and MWF in the splenium of the corpus callosum. In the individual age groups, we found significant positive correlations were identified in the body and splenium of the corpus callosum; bilateral internal capsule, corticospinal tracts and primary motor and somatosensory cortices, optic radiations, superior longitudinal fasciculus, posterior cingulate, visual and auditory cortex, and cerebellar white matter in toddlers. In young children, significant positive correlations were found in throughout the corpus callosum; left Wernicke’s area and primary somatosensory cortex; and right premotor cortex and anterior cingulateCC BY · PubMed Central
Fig. 3 Across the full cohort of children, we also examined age × ELC interaction within our GLM framework. Brain regions exhibiting significant interaction are shown and include frontal, parietal, and temporal lobe WM, as well as splenium and body of the corpus callosum
Fig. 3 Across the full cohort of children, we also examined age × ELC interaction within our GLM framework. Brain regions exhibiting significant interaction are shown and include frontal, parietal, and temporal lobe WM, as well as splenium and body of the corpus callosumCC BY · PubMed Central
Fig. 4 Correlations between MWF and VDQ (red-yellow) and NVDQ (light to dark blue). Significant positive associations between MWF and NVDQ were identified throughout the corpus callosum and left cerebellum in the full cohort. A trend towards a significant relationship between MWF and VDQ was also identified in left Broca’s area (p < 0.09). In the individual age groups, no associations were identified in the infant group. In toddlers, MWF and NVDQ were significantly associated in bilateral corticospinal tracts, cerebellum, and premotor and primary motor cortices; and a trend towards a significant (p < 0.10, corrected) MWF and VDQ association was found in right temporal lobe and superior longitudinal fasciculus. In young children, a significant MWF and NVDQ association was found in bilateral cerebellum, left temporal lobe, left internal capsule and right premotor and primary motor cortices
Fig. 4 Correlations between MWF and VDQ (red-yellow) and NVDQ (light to dark blue). Significant positive associations between MWF and NVDQ were identified throughout the corpus callosum and left cerebellum in the full cohort. A trend towards a significant relationship between MWF and VDQ was also identified in left Broca’s area (p < 0.09). In the individual age groups, no associations were identified in the infant group. In toddlers, MWF and NVDQ were significantly associated in bilateral corticospinal tracts, cerebellum, and premotor and primary motor cortices; and a trend towards a significant (p < 0.10, corrected) MWF and VDQ association was found in right temporal lobe and superior longitudinal fasciculus. In young children, a significant MWF and NVDQ association was found in bilateral cerebellum, left temporal lobe, left internal capsule and right premotor and primary motor corticesCC BY · PubMed Central
Fig. 5 Reconstructed continuous mean Gompertz growth curves for each brain region investigated. In all cases, a developmental trend with above average ELC (blue lines) > average ELC (red lines) > below average ELC (green lines)
Fig. 5 Reconstructed continuous mean Gompertz growth curves for each brain region investigated. In all cases, a developmental trend with above average ELC (blue lines) > average ELC (red lines) > below average ELC (green lines)CC BY · PubMed Central
Fig. 6 Comparison of whole-brain white matter MWF development. Reconstructed continuous growth models for mean whole-brain white matter calculated using the longitudinal data from the above average (blue), average (red), and below average (green) ELC children. Individual data points represent the mean values calculated from each individual child’s data. The results show a trend seen in all other investigated brain regions, with overall development in the above-average children > average > below average. Differences in development are more clearly visualized by examining the ΔMWF curves, which show an early delay in myelination onset and prolonged myelination in the above-average children, resulting in an overall increase in MWF in later childhood
Fig. 6 Comparison of whole-brain white matter MWF development. Reconstructed continuous growth models for mean whole-brain white matter calculated using the longitudinal data from the above average (blue), average (red), and below average (green) ELC children. Individual data points represent the mean values calculated from each individual child’s data. The results show a trend seen in all other investigated brain regions, with overall development in the above-average children > average > below average. Differences in development are more clearly visualized by examining the ΔMWF curves, which show an early delay in myelination onset and prolonged myelination in the above-average children, resulting in an overall increase in MWF in later childhoodCC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 78 references

Research works printed but not linked: 2

  1. Campbell J, Stikov N, RF D, Pike GB (2014) Combined NODDI and qMT for full-brain g-ratio mapping with complex subvoxel microstructure. In: Proc. Intl. Soc. Mag. Reson. Med., Milan, p 393
  2. Hurley SA, Mossahebi PM, Samsonov AA (2010) Multicomponent Relaxometry (mcDESPOT) in the Shaking Pup Model of Dysmyelination. In: Proceedings of ISMRM, Stockholm, p 4516

Cited by

Sorted by citations

Items 101–148 of 148

Related

See all

Metadata

Authors
Deoni, S. C. L., O’Muircheartaigh, J., Elison, J. T., Walker, L., Doernberg, E., Waskiewicz, N., Dirks, H., Piryatinsky, I., Dean, D. C., & Jumbe, N. L.
Affiliations
  • Brown University (US)
  • King's College London (GB)
  • University of Minnesota (US)
  • Gates Foundation (US)
Corresponding author
Deoni, Sean C. L.
ORCID
Venue
Brain Structure and Function
Publisher
Springer Science and Business Media LLC
Published
2014-11-29
Volume
221
Issue
2
Pages
1189-1203
ISSN
  • 1863-2653 (ISSN-L)
  • 1863-2661
DOI
10.1007/s00429-014-0947-x
PMC
PMC4771819
OpenAlex
W2000113085
Cited by
147
References
78
Citations by year
2026: 10 · 2025: 11 · 2024: 10 · 2023: 22 · 2022: 17 · 2021: 19 · 2020: 16 · 2019: 11 · 2018: 14 · 2017: 5 · 2016: 5 · 2015: 6
FWCI
4.17
Type
journal article
Language
en
Open access
Licence
Locations
  • Brain Structure and Function (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research Portal (King's College London) (publishedVersion, cc-by)
Funders
  • Bill and Melinda Gates Foundation
  • Wellcome Trust (096195)
  • National Institute of Mental Health (R01MH087510)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
  • Social Sciences › Psychology › Neuropsychology and Physiological Psychology › Developmental and Educational Neuropsychology
Keywords
white matter maturation, myelination, cognitive ability, early childhood, infancy, brain development, white matter plasticity, intelligence, longitudinal brain development
MeSH terms
Brain, Brain Mapping, Child Development, Child, Preschool, Cognition, Female, Forecasting, Humans, Infant, Intelligence, Magnetic Resonance Imaging, Male, Myelin Sheath, Nerve Fibers, Myelinated, Case-Control Studies, White Matter

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review