Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 26

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Associations between EEG trajectories, family income, and cognitive abilities over the first two years of life

Wilkinson, Carol L., Pierce, Lara J., Sideridis, Georgios, Wade, Mark, Nelson, Charles A.·2023·Developmental Cognitive Neuroscience

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 · 10.1016/j.dcn.2023.101260

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

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

Research works printed but not linked: 44

  1. Anderson, 2018, Developmental change in the resting state electroencephalogram: Insights into cognition and the brain, Brain Cognit., 10.1016/j.bandc.2018.08.001 · DOI
  2. Babadi, 2014, A review of multitaper spectral analysis, IEEE Trans. Biomed. Eng., 61, 1555, 10.1109/TBME.2014.2311996 · DOI · Paper page
  3. Barch, 2022, Early childhood socioeconomic status and cognitive and adaptive outcomes at the transition to adulthood: the mediating role of gray matter development across five scan waves, Biol. Psychiatry.: Cogn. Neurosci. Neuroimaging, 7, 34
  4. Bell, 2001, Brain electrical activity associated with cognitive processing during a looking version of the A-Not-B task, Infancy.: Off. J. Int. Soc. Infant Stud., 2, 311, 10.1207/S15327078IN0203_2 · DOI
  5. Brito, 2016, Associations among family socioeconomic status, EEG power at birth, and cognitive skills during infancy, Dev. Cogn. Neurosci., 19, 144, 10.1016/j.dcn.2016.03.004 · DOI
  6. Brito, 2020, Associations among the home language environment and neural activity during infancy, Dev. Cogn. Neurosci., 43, 10.1016/j.dcn.2020.100780 · DOI
  7. Buzsaki, 2004, Neuronal oscillations in cortical networks, Science, 304, 1926, 10.1126/science.1099745 · DOI · Paper page
  8. Callaghan, 2016, The stress acceleration hypothesis: effects of early-life adversity on emotion circuits and behavior, Curr. Opin. Behav. Sci., 7, 76, 10.1016/j.cobeha.2015.11.018 · DOI · Paper page
  9. Cantiani, 2019, Oscillatory gamma activity mediates the pathway from socioeconomic status to language acquisition in infancy, Infant Behav. Dev., 57, 10.1016/j.infbeh.2019.101384 · DOI · Paper page
  10. Carneiro, 2013, Maternal education, home environments, and the development of children and adolescents, J. Eur. Econ. Assoc., 123, 10.1111/j.1542-4774.2012.01096.x · DOI
  11. Donoghue, 2020, Parameterizing neural power spectra into periodic and aperiodic components, Nat. Neurosci., 23, 1655, 10.1038/s41593-020-00744-x · DOI
  12. Donoghue, 2021, Methodological considerations for studying neural oscillations, Eur. J. Neurosci., 1
  13. Engel, 2001, Temporal binding and the neural correlates of sensory awareness, Trends Cogn. Sci., 5, 16, 10.1016/S1364-6613(00)01568-0 · DOI · Paper page
  14. Farah, 2018, Socioeconomic status and the brain: prospects for neuroscience-informed policy, Nat. Rev. Neurosci., 19, 428, 10.1038/s41583-018-0023-2 · DOI
  15. Gabard-Durnam, 2018, The harvard automated processing pipeline for electroencephalography (HAPPE): standardized processing software for developmental and high-artifact data, Front. Neurosci., 12, 97, 10.3389/fnins.2018.00097 · DOI
  16. Gabard-Durnam, 2019, Longitudinal EEG power in the first postnatal year differentiates autism outcomes, Nat. Commun., 10, 4188, 10.1038/s41467-019-12202-9 · DOI · Paper page
  17. Gao, R., Peterson, E.J., Voytek, B., 2017. Inferring synaptic excitation/inhibition balance from field potentials. https://doi.org/10.1016/j.neuroimage.2017.06.078. · DOI
  18. Gilmore, 2018, Imaging structural and functional brain development in early childhood, Nat. Rev. Neurosci., 19, 123, 10.1038/nrn.2018.1 · DOI · Paper page
  19. Gou, 2011, Resting frontal gamma power at 16, 24 and 36 months predicts individual differences in language and cognition at 4 and 5 years, Behav. Brain Res., 220, 263, 10.1016/j.bbr.2011.01.048 · DOI · Paper page
  20. Harmony, 1990, Correlation between EEG spectral parameters and an educational evaluation, Int. J. Neurosci., 54, 147, 10.3109/00207459008986630 · DOI
  21. Hillman, 2012, Physiology of transition from intrauterine to extrauterine life, Clin. Perinatol., 39, 769, 10.1016/j.clp.2012.09.009 · DOI
  22. Jing, 2010, A longitudinal study of differences in electroencephalographic activity among breastfed, milk formula-fed, and soy formula-fed infants during the first year of life, Early Hum. Dev., 86, 119, 10.1016/j.earlhumdev.2010.02.001 · DOI
  23. Kozak, 2021, Paid maternal leave is associated with better language and socioemotional outcomes during toddlerhood, Infancy, 26, 536, 10.1111/infa.12399 · DOI
  24. Kraybill, 2013, Infancy predictors of preschool and post-kindergarten executive function, Dev. Psychobiol., 55, 530, 10.1002/dev.21057 · DOI
  25. Levin, 2018, BEAPP: the batch electroencephalography automated processing platform, Front. Neurosci., 12, 513, 10.3389/fnins.2018.00513 · DOI
  26. Lopes da Silva, F, 1991, Neural mechanisms underlying brain waves: from neural membranes to networks, Electroencephalogr. Clin. Neurophysiol., 79, 81, 10.1016/0013-4694(91)90044-5 · DOI · Paper page
  27. Marshall, 2004, A comparison of the electroencephalogram between institutionalized and community children in Romania, J. Cogn. Neurosci., 16, 1327, 10.1162/0898929042304723 · DOI · Paper page
  28. Mathalon, 2015, Neural oscillations and synchrony in brain dysfunction and neuropsychiatric disorders: it’s about time, JAMA Psychiatry, 72, 840, 10.1001/jamapsychiatry.2015.0483 · DOI
  29. Matousek, M., Petersen, I., 1973. Frequency analysis of the electro encephalogram in normal children and adolescents. Kellaway, Peter And Ingemar Petersen (Ed) Automation Of Clinical Electroencephalography Proceedings Of A Conference Viii+318p Illus Raven Press, Publishers, 75–102.
  30. Matousek, 1971, [Frequency analysis of EEG registrations in normal children 1-16 years old], Nord. Med., 85, 637
  31. Nelson, 2020, Early adversity and critical periods: neurodevelopmental consequences of violating the expectable environment, Trends Neurosci., 43, 133, 10.1016/j.tins.2020.01.002 · DOI
  32. Pierce, 2019, Association of perceived maternal stress during the perinatal period with electroencephalography patterns in 2-month-old infants, JAMA Pediatr., 173, 561, 10.1001/jamapediatrics.2019.0492 · DOI · Paper page
  33. Pierce, 2021, Associations between maternal stress, early language behaviors, and infant electroencephalography during the first year of life, J. Child Lang., 48, 737, 10.1017/S0305000920000501 · DOI
  34. Rowe, 2018, Understanding socioeconomic differences in parents’ speech to children, Child Dev. Perspect., 12, 122, 10.1111/cdep.12271 · DOI
  35. Shonkoff, 2009, Neuroscience, molecular biology, and the childhood roots of health disparities: building a new framework for health promotion and disease prevention, JAMA, 301, 2252, 10.1001/jama.2009.754 · DOI · Paper page
  36. Smit, 2012, Individual differences in EEG spectral power reflect genetic variance in gray and white matter volumes, Twin Res. Hum. Genet.: Off. J. Int. Soc. Twin Stud., 15, 384, 10.1017/thg.2012.6 · DOI
  37. Tierney, 2012, Developmental trajectories of resting eeg power: an endophenotype of autism spectrum disorder, PLoS ONE, 7, 10.1371/journal.pone.0039127 · DOI · Paper page
  38. Tomalski, 2013, Socioeconomic status and functional brain development—Associations in early infancy, Dev. Sci., 16, 676, 10.1111/desc.12079 · DOI · Paper page
  39. Tröndle, 2022, Decomposing the role of alpha oscillations during brain maturation, ELife, 11, 10.7554/eLife.77571 · DOI
  40. Voytek, 2015, Dynamic network communication as a unifying neural basis for cognition, development, aging, and disease, Biol. Psychiatry, 77, 1089, 10.1016/j.biopsych.2015.04.016 · DOI · Paper page
  41. Voytek, 2015, Age-related changes in 1/f neural electrophysiological noise, J. Neurosci., 35, 13257, 10.1523/JNEUROSCI.2332-14.2015 · DOI
  42. Wang, 2010, Neurophysiological and computational principles of cortical rhythms in cognition, Physiol. Rev., 90, 1195, 10.1152/physrev.00035.2008 · DOI · Paper page
  43. Winkler, 2011, Automatic classification of artifactual ICA-Components for artifact removal in EEG signals, Behav. Brain Funct., 7, 30, 10.1186/1744-9081-7-30 · DOI
  44. Winkler, I., Debener, S., Muller, K.R., & Tangermann, M. (2015). On the influence of high-pass filtering on ICA-based artifact reduction in EEG-ERP. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2015-Novem, 4101–4105. https://doi.org/10.1109/EMBC.2015.7319296. · DOI

Cited by

See all 26

Metadata

Authors
Wilkinson, C. L., Pierce, L. J., Sideridis, G., Wade, M., & Nelson, C. A.
Venue
Developmental Cognitive Neuroscience
Publisher
Elsevier BV
Published
2023-06
Volume
61
Pages
101260
ISSN
1878-9293
Cited by
26
References
14
Language
en
Funders
  • National Institutes of Health (1T32MH112510, K23DC07983, R01-DC010290)
  • Harvard University

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

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.