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Record W2607398829 · doi:10.1038/tp.2017.28

Infant frontal EEG asymmetry in relation with postnatal maternal depression and parenting behavior

2017· article· en· W2607398829 on OpenAlexaff
Daniel J. Wen, Ni Ni Soe, Lit Wee Sim, Shamini Sanmugam, Kenneth Kwek, Yap Seng Chong, P. D. Gluckman, Michael J. Meaney, Anne Rifkin‐Graboi, Anqi Qiu

Bibliographic record

VenueTranslational Psychiatry · 2017
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersMedical Research CouncilNational University Health SystemJPB FoundationMinistry of Education, IndiaNational Medical Research CouncilHarvard University
KeywordsElectroencephalographyPsychologyDepression (economics)MoodMaternal sensitivityAudiologyBrain asymmetryDevelopmental psychologyMedicinePsychiatryLateralization of brain functionNeuroscience

Abstract

fetched live from OpenAlex

Right frontal electroencephalogram (EEG) asymmetry associates with negative affect and depressed mood, which, among children, are predicted by maternal depression and poor parenting. This study examined associations of maternal depression and maternal sensitivity with infant frontal EEG asymmetry based on 111 mother-6-month-infant dyads. There were no significant effects of postnatal maternal depression or maternal sensitivity, or their interaction, on infant EEG frontal asymmetry. However, in a subsample for which the infant spent at least 50% of his/her day time hours with his/her mother, both lower maternal sensitivity and higher maternal depression predicted greater relative right frontal EEG asymmetry. Our study further showed that greater relative right frontal EEG asymmetry of 6-month-old infants predicted their greater negative emotionality at 12 months of age. Our study suggested that among infants with sufficient postnatal maternal exposure, both maternal sensitivity and mental health are important influences on early brain development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.396

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.301
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations37
Published2017
Admission routes1
Has abstractyes

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