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Record W4296485917 · doi:10.26034/cortica.2022.3137

Sex differences and symptom based gray and white matter densities in schizophrenia

2022· article· en· W4296485917 on OpenAlexafffund
Adham Mancini Marïë

Bibliographic record

VenueCortica · 2022
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsUniversité de Montréal
FundersInstitute of Gender and HealthFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsWhite matterFrontal lobeSuperior frontal gyrusCerebellumVoxelPsychologyGyrusGray (unit)AudiologyParietal lobeAnatomyMedicineMagnetic resonance imagingNeuroscienceNuclear medicineRadiologyCognition

Abstract

fetched live from OpenAlex

We investigated the association between densities in gray matter (GMD) and white matter (WMD) phenotypes and positive (PS) and negative (NS) symptoms in 40 schizophrenia patients (SZ). Cerebral densities were compared with 41 normal controls (NC) matched for age and sex using voxel-based morphometry on T1-3T-MRI. We found decreased GMD in the anterior cingulate-temporal gyri and increased GMD in the posterior cingulate gyrus in SZ relative to NC. WMD reduction was found in the inferior frontal and posterior parietal regions in SZ relative to NC. GMD in the insula/caudate correlated with PS, while GMD in the middle frontal gyrus and cerebellum correlated with NS. WMD in the middle frontal and superior frontal regions correlated with PS and NS respectively. Invers correlations were found between GMD in the parietal lobe and the uvula with PS. An inverse correlation was found between GMD in the cerebellum and NS. Inverse correlation was also found in the WMD of the occipital region and superior frontal regions with PS and NS respectively. Comparison between male groups revealed decreased total GMD in male patients, while no differences were observed between female groups. These correlational findings suggest that symptom profiles in schizophrenia show unique GM/WM phenotypes.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.217

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.288
Teacher spread0.258 · 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.

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

Citations1
Published2022
Admission routes2
Has abstractyes

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