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Record W2600651189 · doi:10.1093/schbul/sbx021.221

163. An Effect Size Profile of Cortical Thickness in Schizophrenia

2017· article· en· W2600651189 on OpenAlexaff
Walter Heinrichs, Farzaneh Mashhadi, Farena Pinnock, Melissa Parlar, Colin Hawco

Bibliographic record

VenueSchizophrenia Bulletin · 2017
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsCentre for Addiction and Mental HealthYork University
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)SulcusSalience (neuroscience)PsychologyWhite matterNeuroscienceTemporal lobeNeuroimagingAudiologyMagnetic resonance imagingCartographyMedicineEpilepsyPsychiatryRadiologyGeography

Abstract

fetched live from OpenAlex

Background: Thinning of the cerebral cortex has been reported in schizophrenia, with reductions in frontotemporal regions a common finding. However, considerable heterogeneity across studies has also been reported with little consensus on a typical profile or signature of thinning in the disorder. In addition, reporting conventions tend to highlight regional findings meeting significance thresholds rather than effect sizes reflecting the magnitude of group difference and distribution overlap. Here we present Cohen’s d and t test statistics for 148 cortical regions in both hemispheres indexing schizophrenia-healthy control group differences in thickness. The aim of this approach was to assess the magnitude of regional differences and with special reference to thickness patterns corresponding to several key networks implicated in schizophrenia. These include the default mode, salience, central executive and social brain networks. Methods: Participants (66 schizophrenia patients and 63 healthy controls) underwent scanning with a 3.0 Tesla whole-body short bore General Electric System MRI scanner with an 8-channel parallel receiver head coil at the Imaging Research Centre, St. Joseph’s Healthcare Hamilton. Cortical thickness was defined as the distance between pial surface to the gray/white matter border across 160 000 vertices in both cerebral hemispheres. Cortical parcellations were obtained for regions of interest (ROIs) using the methods described by Destrieux et al. (2010) in Freesurfer. The Destrieux atlas involves both gyral and sulcal structures for bilateral hemispheric parcellation. Results: The largest group differences were obtained for the superior temporal sulcus bilaterally (Cohen’s d = 0.88–0.91), suggesting that a majority of patients demonstrate thinning in this cortical region. This effect size range corresponds to a group prediction probability of .66 –.67. However, 143 of 148 comparisons yielded smaller effect sizes implying more than 50% distribution overlap between patients and controls and 35 of 148 comparisons were below the conventional value (d = 0.2) for a small effect size. Small-medium range effect sizes (d = 0.3–0.75) were obtained for regions associated with selected brain networks. In terms of statistical testing (P < .05), 25 of 148 comparisons survived conservative correction, primarily in temporal–parietal–occipital and cingulate cortical regions. No regional prefrontal differences in thickness were significant. Conclusion: Multifocal cortical thinning occurs in schizophrenia, but significant group mean differences do not necessarily translate into findings that apply to most patients with the disorder. Cortical thinning in several network-associated regions probably applies to a minority. A possible exception is the bilateral superior temporal sulcus region, which demonstrates thinning in a clear majority of patients with the disorder.

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)0.010
metaresearch head score (Gemma)0.022
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 score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.022
GPT teacher head0.278
Teacher spread0.256 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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