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Record W4413284207 · doi:10.1093/ijnp/pyaf052.168

165. A NOVEL DIATHESIS-STRESS MODEL OF COMMONLY COMORBID EARLY ONSET PSYCHIATRIC DISORDERS

2025· article· en· W4413284207 on OpenAlexaff
Charlotte Bowers Caswell, Niki Hosseini‐Kamkar, Sylvia M. L. Cox, Muhammad Saad Iqbal, Tomáš Paus, Marco Leyton

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicHealth, Environment, Cognitive Aging
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineRoyal Ottawa Mental Health CentreMcGill University
Fundersnot available
KeywordsDiathesisPsychiatryMedicinePsychology

Abstract

fetched live from OpenAlex

Abstract Background We recently reported that commonly comorbid early onset mental health outcomes were predicted with high accuracy (>90%) and statistical robustness (p = 2.4 x 10-5) by a three-factor model composed of adolescent externalizing (EXT) traits, early life adversity, and midbrain dopamine autoreceptor availability (n = 52). Aims & Objectives Here, we investigated whether this positron emission tomography (PET) based model could be reproduced in a much larger sample using functional magnetic resonance imaging (fMRI) data. Method IMAGEN database members with Childhood Trauma Questionnaire (CTQ) data were selected for analyses (n = 1338). Average adolescent EXT scores were derived from the Strengths and Difficulties Questionnaire collected at ages 14 and 16. fMRI reward anticipation responses (large minus no reward, Lg-No; large minus small reward, Lg-Sm) were assessed using a monetary incentive delay (MID) task at ages 14 and 19. Regions of interest were the ventral striatum (VS), caudate, putamen, anterior cingulate cortex (ACC), ventromedial prefrontal cortex (vmPFC), and orbitofrontal cortex (OFC). DSM-IV diagnoses were made using the Development and Wellbeing Assessment at ages 14, 16, and 19. Binary logistic regression models were constructed to determine whether a combination of EXT traits, childhood trauma, and MID fMRI contrasts at either 14 or 19 years of age identified participants with a DSM-IV disorder by age 19. Additional models tested for moderation of CTQ score effects by fMRI blood-oxygen level-dependent (BOLD) signals, and for mediation of CTQ effects by EXT traits. Results With all possible fMRI contrast parameters, the three-factor models were highly significant (p < 1.0 x 10-21). In each of these models, EXT and CTQ scores were significant individual predictors (p < 0.001). At age 14, reward anticipation responses in the VS (Lg-No & Lg-Sm), caudate (Lg-No & Lg-Sm), putamen (Lg-Sm), and ACC (Lg-Sm) were significant predictors in their respective models (p ≤ 0.05). At age 19, reward anticipation in the VS (Lg-No) was a significant predictor within its model (p = 0.025). The models had an overall accuracy of nearly 75% and accounted for ≥ 11% (Nagelkerke R2) of the variance in psychiatric disorders. The relationship between CTQ scores and diagnoses was partially mediated by EXT scores (indirect path B = 0.0535, 95% CI = 0.0301-0.0835) and moderated by age-14 ACC (p = 0.0038) and putamen (p = 0.0135) BOLD signals: the lower the reward anticipation response, the greater the effect of high CTQ scores on the likelihood of a diagnosis. Discussion & Conclusions The results extend our previous findings in a large sample, increasing confidence in our novel diathesis-stress model of commonly comorbid early onset mental health problems. The results have profound implications for diagnostic classification schemes and pleiotropic views of psychiatric disorder etiology.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.015
GPT teacher head0.304
Teacher spread0.289 · 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 designTheoretical or conceptual
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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