MétaCan
Menu
Back to cohort
Record W4413160663 · doi:10.1016/j.bpsgos.2025.100593

Glutamate, Contextual Insensitivity, and Disorganized Speech in First-Episode Schizophrenia: A 7T Magnetic Resonance Spectroscopy Study

2025· article· en· W4413160663 on OpenAlexafffund
Yingqi Laetitia Wang, Victoria Sharpe, Michael Mackinley, Gina R. Kuperberg, Kaustubh Supekar, Jean Théberge, Lena Palaniyappan

Bibliographic record

VenueBiological Psychiatry Global Open Science · 2025
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsRobarts Clinical TrialsMcGill UniversityDouglas Mental Health University InstituteSt Joseph's Health CareLawson Health Research InstituteLondon Health Sciences CentreWestern University
FundersJanssen CanadaFonds de Recherche du Québec - SantéNatural Sciences and Engineering Research Council of CanadaNational Institute of Child Health and Human DevelopmentCanadian Institutes of Health ResearchAlliance de recherche numérique du CanadaCanada First Research Excellence FundWestern UniversityAcademic Medical Organization of Southwestern OntarioMcGill UniversityNational Institute on AgingChrysalis
KeywordsSchizophrenia (object-oriented programming)Nuclear magnetic resonancePsychologySpectroscopyMagnetic resonance imagingFunctional magnetic resonance imagingNuclear magnetic resonance spectroscopyAudiologyPhysicsNeuroscienceMedicinePsychiatryAstronomyRadiology

Abstract

fetched live from OpenAlex

In people with schizophrenia, formal thought disorder is a core symptom that emerges early and persists into chronic stages despite treatments. It manifests as disorganized speech and is often associated with poor long-term outcomes. A key feature of this disorganization is an impairment in the buildup and use of context provided by preceding words when choosing upcoming words. Recent work has shown that spoken words are less predictable based on global linguistic context in schizophrenia, but the neural basis of this remains unknown. Glutamate dysfunction in the anterior cingulate cortex has long been implicated in schizophrenia, but its connection to behavioral impairments remains unclear. In this study, we investigated the relationship between linguistic contextual sensitivity and glutamate level in the dorsal anterior cingulate cortex in 39 patients with first-episode psychosis (33 men) and 33 sociodemographically matched healthy control participants (22 men). Contextual sensitivity was measured using a large language model (GPT-3), and glutamate levels were measured using 7T magnetic resonance spectroscopy. We found a significant interaction between diagnosis and glutamate level in predicting contextual sensitivity: Patients with lower glutamate levels had poor contextual sensitivity, a relationship not seen in healthy control participants. Glutamate variation was specifically explained by contextual sensitivity after controlling for other clinical and language variables, underscoring the robustness and specificity of this association. These results highlight a potential glutamatergic basis for disorganized speech in schizophrenia and suggest that contextual sensitivity in speech could reflect anterior cingulate glutamate variations in early psychosis. Disorganized speech is a common and disabling symptom of schizophrenia, but its biological roots remain unclear. In this study, we found that in untreated first-episode psychosis, lower levels of glutamate—the brain’s major excitatory neurotransmitter—in the anterior cingulate cortex were linked to greater difficulty processing context in speech. These findings suggest that speech disruptions in schizophrenia may stem from specific brain chemistry changes early in the illness.

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)0.000
metaresearch head score (Gemma)0.001
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.349
Teacher spread0.324 · 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

Citations2
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueBiological Psychiatry Global Open ScienceSame topicSchizophrenia research and treatmentFrench-language works237,207