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Record W4380488541 · doi:10.7554/elife.88117.1

Gene-Environment Pathways to Cognitive Development and Psychotic-Like Experiences in Children

2023· preprint· en· W4380488541 on OpenAlexaff
Junghoon Park, Eunji Lee, Gyeongcheol Cho, Heungsun Hwang, Bo‐Gyeom Kim, Gakyung Kim, Yoonjung Yoonie Joo, Jiook Cha

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsMcGill University
Fundersnot available
KeywordsCognitionPsychologySchizophrenia (object-oriented programming)Developmental psychologyNeurosciencePsychiatry

Abstract

fetched live from OpenAlex

In children, psychotic-like experiences (PLEs) are related to risk of psychosis, schizophrenia, and other mental disorders. Maladaptive cognitive functioning is a well-known risk factor and early marker for psychosis, schizophrenia, and other mental disorders. Since cognitive functioning is linked to various genetic and environmental factors during development, we hypothesize that it mediates the effects of those factors on childhood PLEs. Using large, representative, longitudinal data, we tested the relationships of genetic and environmental factors (such as familial and neighborhood environment) with cognitive intelligence and their relationships with current and future PLEs in children.To estimate unbiased associations against potential confounding variables, we leveraged large-scale, representative, multimodal data of 6,602 children (aged 9-10 years old; 47.15% females; 5,211 European-ancestry) from the Adolescent Brain and Cognitive Development Study. Linear mixed model and a novel structural equation modeling (SEM) method that allows unbiased estimation of both components and factors were used to estimate the joint effects of cognitive capacity polygenic scores (PGSs), familial and neighborhood socioeconomic status (SES), and supportive environment on NIH Toolbox cognitive intelligence and PLEs. We adjusted for ethnicity (genetically defined), schizophrenia PGS, and additionally unobserved confounders (using computational confound modeling).We identified that lower cognitive intelligence and higher PLEs correlated significantly with several genetic and environmental variables: i.e., lower PGSs for cognitive capacity, lower familial SES, lower neighborhood SES, lower supportive parenting behavior, and lower positive school environment. In SEM, lower cognitive intelligence significantly mediated the genetic and environmental influences on higher PLEs (Indirect effects of PGS: β range=-0.0355∼ -0.0274; Family SES: β range=-0.0429∼ -0.0331; Neighborhood SES: β range=0.0126∼ 0.0164; Positive Environment: β range=-0.0039∼ -0.003). Supportive parenting and a positive school environment had the largest total impact on PLEs (β range=-0.152∼ -0.1316) than genetic or environmental factors.Our results reveal the role of genetic and environmental factors on children’s risk for psychosis via its negative impact on cognitive intelligence. Our findings have policy implications in that improving the school and family environment and promoting local economic development might be a way to enhance cognitive and mental health in children.While existing research shows the association between cognitive decline and the onset of psychosis, the genetic and environmental pathways to cognitive intelligence and psychotic risk in children remain unclear.We identified the significant role of genetic and environmental factors (family, neighborhood, and school) on children’s risk for psychosis via a negative impact on cognitive intelligence.Obtaining unbiased estimation by leveraging large, representative samples with multimodal data and advanced computational modeling for confounders, our results underscore the importance of incorporating socioeconomic policies into children’s cognitive and mental health programs.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.077
GPT teacher head0.309
Teacher spread0.232 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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