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Record W3028215079 · doi:10.1093/schbul/sbaa029.722

T162. THICKER PREFRONTAL CORTEX IS ASSOCIATED WITH SUBCLINICAL NEGATIVE SYMPTOMS IN SCHIZOTYPY - AN ENIGMA CONSORTIUM META-ANALYSIS

2020· article· en· W3028215079 on OpenAlexaff
Matthias Kirschner, Benazir Hodzic-Santor, Tilo Kircher, Igor Nenadić, Alex Fornito, Melissa J. Green, Yann Quidé, Christos Pantelis, Udo Dannlowski, Pamela DeRosse, Raymond Chan, Martin Debbané, Wulf Rössler, И. С. Лебедева, Haeme Park, Jan-Bernard Marsman, James Gilleen, Anne‐Kathrin Fett, Theo G.M. van Erp, Jessica A. Turner, Paul M. Thompson, André Alemán, Gemma Modinos, Stefan Kaiser

Bibliographic record

VenueSchizophrenia Bulletin · 2020
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsSchizotypySubclinical infectionPsychologySchizophrenia (object-oriented programming)PsychosisClinical psychologyPopulationPsychiatryMedicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Negative symptoms can be seen to represent a continuum from subclinical manifestations in the general population to severe symptoms in schizophrenia. Neuroanatomical studies show evidence of fronto-striatal structural abnormalities linked to negative symptoms in patients with schizophrenia (Walton et al. 2018). However, it remains an open question whether these structural associations are also observed in ostensibly healthy individuals reporting subclinical negative symptoms. The present study used structural T1-weighted brain imaging data from the ENIGMA Schizotypy Working Group to investigate the relationship between subclinical negative symptoms and fronto-striatal structural measures. Methods We included 2,235 healthy unmedicated individuals with varying levels of schizotypy from 17 centers around the world. The complete sample had a weighted mean (range) age of 29.2 (15.9–39.6) and 59.4% (51–100) were male. Subclinical negative symptoms were assessed at each site separately using factor scores from self-report schizotypy questionnaires (i.e., the Community Assessment of Psychic Experiences, the Oxford-Liverpool Inventory of Feelings and Experiences, or the Schizotypal Personality Questionnaire). Based on prior studies in schizophrenia, we obtained cortical thickness from 22 frontal regions-of-interest (ROIs) and subcortical volumes from 6 striatal ROIs using FreeSurfer. We performed meta-analyses of effect sizes (standardized regression coefficients) from a model predicting mean cortical thickness by subclinical negative symptom scores, adjusting for age, sex, and site. The same analysis was repeated for subcortical volumes including intracranial volume as additional covariate. Results Meta-analyses revealed significant positive associations between subclinical negative symptoms and cortical thickness of the left frontal pole (βstd=0.091; pFDR=0.009), right medial orbitofrontal cortex (βstd=0.083; pFDR=0.009) and right anterior cingulate cortex (βstd=0.07; pFDR=0.011). Discussion Using a large sample of healthy unmedicated individuals with varying levels of schizotypal personality traits, this ENIGMA meta-analysis showed that subclinical negative symptoms are associated with thicker prefrontal cortex. The present data are contrary to previous findings in schizophrenia, which demonstrates a relationship between negative symptoms and lower prefrontal cortical thickness (Walton et al. 2018). These divergent neural correlates suggest that thicker cortex could be a potential compensatory mechanism preventing individuals with schizotypy from the clinical manifestation of severe negative symptoms. Alternatively, greater prefrontal cortical thickness could also be associated with pathological processes along the negative symptom continuum prior to clinical manifestation.

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)0.008
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.015
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.028
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.074
GPT teacher head0.286
Teacher spread0.211 · 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 designMeta-analysis
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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Citations2
Published2020
Admission routes1
Has abstractyes

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