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Record W2794458364 · doi:10.1093/schbul/sby017.709

F178. NEUROANATOMICAL PROFILES OF TREATMENT-RESISTANCE IN PATIENTS WITH SCHIZOPHRENIA

2018· article· en· W2794458364 on OpenAlexaff
Eric Plitman, Yusuke Iwata, Shinichiro Nakajima, Jun Ku Chung, Raihaan Patel, Fernando Caravaggio, Julia Kim, Vincenzo De Luca, Sofia Chavez, Gary Remington, M. Mallar Chakravarty, Philip Gerretsen, Ariel Graff‐Guerrero

Bibliographic record

VenueSchizophrenia Bulletin · 2018
Typearticle
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)ClozapineAntipsychoticGlobus pallidusStriatumBasal gangliaMedicinePutamenThalamusInternal medicineAmygdalaPsychologyNeurosciencePsychiatryDopamineCentral nervous system

Abstract

fetched live from OpenAlex

About 20 to 35% of patients with schizophrenia show partial or no response to standard first-line antipsychotic treatment and are thus believed to have treatment-resistant schizophrenia (TRS). Notably, the antipsychotic clozapine (CLZ) has been reported to have superior efficacy compared to other agents for the treatment of TRS. However, a subset of patients still do not respond to CLZ treatment and are thus considered to have ultra-treatment-resistant schizophrenia (UTRS). Overall, the pathophysiology associated with UTRS appears to be different than TRS, yet both remain elusive. In light of the unknown factors underlying UTRS and TRS, along with the widely reported structural alterations that exist in patients with schizophrenia, our study aimed to examine subcortical structure volumes and cortical thickness in patients with UTRS, patients with TRS responding to CLZ (henceforth, TRS), patients responding to a first-line antipsychotic (treatment non-resistant schizophrenia (TnRS)), and healthy controls (HC). We hypothesized that deficits in subcortical structure volumes and cortical thickness would exist within the UTRS group compared to other groups. As of December 2017, the sample consisted of a total of 94 participants, including 24 patients with UTRS, 24 patients with TRS, 21 patients with TnRS, and 25 HCs. Participants underwent a 3-dimensional T1-weighted scan in a 3T MRI machine. The MAGeT-Brain segmentation algorithm was used to acquire volumes of the thalamus, striatum, globus pallidus, hippocampus, and amygdala. Cortical thickness was estimated using the CIVET processing pipeline. Total brain volume was obtained using the BEaST method. Group comparisons were performed using analyses of covariance and post-hoc comparisons. Group volumetric differences were identified bilaterally within the thalamus, striatum, and globus pallidus (p<0.01). Post-hoc investigations revealed that bilateral thalamic volumes were smaller in the UTRS group compared to the HC group (p<0.01), bilateral striatal volumes were larger in the TnRS group compared to the UTRS and HC groups (p<0.01), and bilateral globus pallidus volumes were larger in the TnRS group compared to the HC group (p<0.01). No differences in hippocampal, amygdala, or total brain volume were observed. At a 5% false discovery rate, widespread cortical thinning was identified in both the UTRS and TRS groups compared to the TnRS and HC groups; this effect was stronger and more diffuse in the UTRS group. Our findings suggest that thalamic volume deficits might be a distinct feature of UTRS. Contrastingly, striatal and globus pallidus volume enlargement may be associated with first-line antipsychotic response or treatment. Cortical thinning is apparent in both the UTRS and TRS groups. In many cases, structural compromise appears to follow a continuum of response, whereby deficits are most severe in UTRS patients, followed by TRS patients, who are followed by TnRS patients and HCs.

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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.009
GPT teacher head0.248
Teacher spread0.239 · 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
Published2018
Admission routes1
Has abstractyes

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