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Record W4407417895 · doi:10.1093/ijnp/pyae059.392

ELEVATED INTRINSIC CORTICAL CURVATURE IN TREATMENT-RESISTANT SCHIZOPHRENIA: STRUCTURAL DEFORMATION OF FUNCTIONAL ACTIVITY AREAS

2025· article· en· W4407417895 on OpenAlexaff
Edgardo Torres‐Carmona, Fumihiko Ueno, Yusuke Iwata, Shinichiro Nakajima, Jianmeng Song, Wanna Mar, Ali Abdolizadeh, Sri Mahavir Agarwal, Vincenzo De Luca, Gary Remington, Philip Gerretsen, Ariel Graff‐Guerrero

Bibliographic record

VenueThe International Journal of Neuropsychopharmacology · 2025
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)NeurosciencePsychologyFunctional connectivityCurvatureDeformation (meteorology)MedicineMaterials sciencePsychiatryGeometryMathematicsComposite material

Abstract

fetched live from OpenAlex

Abstract Background Growing research shows abnormal brain structure and connectivity in patients with treatment-resistant schizophrenia (TRS) compared to first-line-responders (TxR) and healthy-controls (HC). While differences are documented as early as first-episode-psychosis (FEP), standard measures of structure and connectivity including thickness, volume, functional connectivity, and diffusion, show susceptibility to antipsychotic treatment and interpatient heterogeneity that limit their clinical and research use for TRS. In this regard, Intrinsic-Cortical-Curvature (ICC), a highly sensitive neurodevelopmental measure of both structure and connectivity, associated with the position and spread of functional activity areas, may provide a novel approach for the assessment and differentiation of TRS. Indeed, preliminary studies have not only linked elevations of ICC with TRS-like traits in FEP, including worsening cognition at follow-ups and reduced response to treatment, but methodology comparison studies have also proven ICC to be especially sensitive to schizophrenia differences compared to more common extrinsic gyrification measures. Despite this evidence, due to the previous complexity and computational demands required for ICC quantification, ICC research in schizophrenia and TRS remains scarce. Aim Our goal was to investigate whether differences in ICC between TRS, TxR and HC exist, their association to symptomology, and susceptibility to antipsychotic treatment, as a possible marker for treatment-resistance. Methods ICC was assessed from brain imaging data acquired using 3T high resolution magnetic resonance imaging (MRI). Regions of interest associated with TRS literature were processed, including the bilateral anterior-cingulate-cortices (ACC), dorsolateral-prefrontal- cortices (DLPFC), temporal-cortices, and parietal-cortices of TxR=38, TRS (clozapine-resistant ClzR- =30, clozapine-responders ClzR+=37), and HC=52. Positive, negative, and cognitive symptom severity was assessed (PANSS, MMSE, EXIT), along with chlorpromazine-equivalence (APT) and nicotine-use (NIC) to assess possible interactions. Results ICC elevations were observed in temporal-cortices of ClzR- and ClzR+ compared to HC (p=0.001), and DLPFC of ClzR- compared to TxR and HC (p<0.001). ICC elevations correlated with reduced cognition (p<0.007) and negative symptomology (p=0.036) in ClzR-. LH- temporal and parietal-cortices correlated with reduced cognition in ClzR+ (p=0.018) and TxR (p=0.008), respectively. No ICC/APT/NIC interactions were found (p>0.092). Conclusion Results show elevations of ICC in patients with TRS, particularly in regions associated with glutamatergic dysregulation. Elevations of ICC were also associated with reduced cognition, primarily in ClzR-. Considering ICC was not associated with APT or NIC, future research may prove ICC a resilient marker associated with TRS abnormalities.

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)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.319
Teacher spread0.292 · 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
Published2025
Admission routes1
Has abstractyes

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