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Record W2983222483 · doi:10.1503/jpn.190030

Genetic and environmental influences on corticostriatal circuits in twins with autism

2020· article· en· W2983222483 on OpenAlexvenueno aff
John P. Hegarty, Laura C. Lazzeroni, Mira M. Raman, Joachim Hallmayer, Sue C. Cleveland, Olga N. Wolke, Jennifer M. Phillips, Allan L. Reiss, Antonio Y. Hardan

Bibliographic record

VenueJournal of Psychiatry and Neuroscience · 2020
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsnot available
FundersNational Institute of Mental Health
KeywordsAutism spectrum disorderPutamenDizygotic twinTwin studyPsychologyAnterior cingulate cortexMonozygotic twinAutismDizygotic twinsNeuroscienceGeneticsBiologyDevelopmental psychologyCognitionMedicineHeritability

Abstract

fetched live from OpenAlex

Background: Corticostriatal circuits (CSC) have been implicated in the presentation of some restricted and repetitive behaviours (RRBs) in children with autism-spectrum disorder (ASD), and preliminary evidence suggests that disruptions in these pathways may be associated with differences in genetic and environmental influences on brain development. The objective of this investigation was to examine the impact of genetic and environmental factors on CSC regions in twins with and without ASD and to evaluate their relationship with the severity of RRBs. Methods: We obtained T1-weighted MRIs from same-sex monozygotic and dizygotic twin pairs, aged 6–15 years. Good-quality data were available from 48 ASD pairs (n = 96 twins; 30 pairs concordant for ASD, 15 monozygotic and 15 dizygotic; 18 pairs discordant for ASD, 4 monozygotic and 14 dizygotic) and 34 typically developing control pairs (n = 68 twins; 20 monozygotic and 14 dizygotic pairs). We generated structural measures of the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), caudate, putamen, pallidum and thalamus using FreeSurfer. Twin pair comparisons included intraclass correlation analyses and ACE modelling (a2 = additive genetics; c2 = common or shared environment; e2 = unique or nonshared environment). We also assessed correlations with RRB severity. Results: Structural variation in CSC regions was predominantly genetically mediated in typically developing twins (a2 = 0.56 to 0.87), except for ACC white matter volume (a2 = 0.42, 95% confidence interval [CI] 0.08 to 0.77). We also observed similar magnitudes of genetic influence in twins with ASD (a2 = 0.65 to 0.97), but the cortical thickness of the ACC (c2 = 0.44, 95% CI 0.22 to 0.66) and OFC (c2 = 0.60, 95% CI 0.25 to 0.95) was primarily associated with environmental factors in only twins with ASD. Twin pair differences in OFC grey matter volume were also correlated with RRB severity and were predominantly environmentally mediated. Limitations: We obtained MRIs on 2 scanners, and analytical approaches could not identify specific genetic and environmental factors. Conclusion: Genetic factors primarily contribute to structural variation in subcortical CSC regions, regardless of ASD, but environmental factors may exert a greater influence on the development of grey matter thickness in the OFC and ACC in children with ASD. The increased vulnerability of OFC grey matter to environmental influences may also mediate some heterogeneity in RRB severity in children with ASD.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.268
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2020
Admission routes1
Has abstractyes

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