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Neuroimaging Evidence for the Deficit Subtype of Schizophrenia

2013· article· en· W2155846146 on OpenAlexafffund
Aristotle N. Voineskos, George Foussias, Jason P. Lerch, Daniel Felsky, Gary Remington, Tarek K. Rajji, Nancy J. Lobaugh, Bruce G. Pollock, Benoit H. Mulsant

Bibliographic record

VenueJAMA Psychiatry · 2013
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsSchizophrenia (object-oriented programming)White matterBrief Psychiatric Rating ScaleNeuroimagingPsychiatryPsychologyPopulationPositive and Negative Syndrome ScalePsychosisMagnetic resonance imagingMedicine

Abstract

fetched live from OpenAlex

IMPORTANCE: A major obstacle to the identification of the neurobiological correlates of schizophrenia is the substantial clinical heterogeneity present in this disorder. Dividing schizophrenia into "deficit" and "nondeficit" subtypes may reduce heterogeneity and facilitate identification of neurobiological markers of disease. OBJECTIVE: To determine whether patients with deficit schizophrenia differ from patients with nondeficit schizophrenia and healthy controls in neuroimaging-based measures of white matter tracts and gray matter morphology. DESIGN: A cross-sectional neuroimaging study of patients with the deficit or nondeficit subtype of schizophrenia and healthy controls. SETTING: University hospital. PARTICIPANTS: Seventy-seven patients with schizophrenia and 79 healthy controls. INTERVENTIONS: All participants were administered the Structured Clinical Interview for DSM-IV-TR Axis I Disorders and the Positive and Negative Syndrome Scale; IQ was measured using the Wechsler Test for Adult Reading; global cognitive impairment was grossly assessed using the Mini-Mental State Examination; comorbid physical illness burden was measured by administration of the Clinical Information Rating Scale-Geriatrics; high-resolution magnetic resonance imaging was performed as part of a multimodal imaging protocol; and deficit status was determined using the proxy scale for the deficit syndrome. MAIN OUTCOME MEASURES: Diffusion-based measures of white matter tracts, cortical thickness, cortical surface area, and volumes of subcortical structures. RESULTS: In both an individually matched approach (18 patients with deficit schizophrenia, 18 patients with nondeficit schizophrenia, and 18 healthy controls) and an unmatched population-based approach (18 patients with deficit schizophrenia, 59 patients with nondeficit schizophrenia, and 79 health controls), the patients with deficit schizophrenia demonstrated disruption of white matter tracts compared with patients with nondeficit schizophrenia and healthy controls at the right inferior longitudinal fasciculus, the right arcuate fasciculus, and the left uncinate fasciculus. These findings were supported in patients with first-episode schizophrenia (n = 20) who had a deficit score that was strongly correlated with disruption at these same tracts. In contrast, patients with schizophrenia of either subtype exhibited cortical thickness reductions compared with healthy controls, in near-identical neuroanatomic patterns. Surface areas and subcortical volumes did not differ significantly among the 3 groups. CONCLUSIONS AND RELEVANCE: The convergence of findings in our individually matched sample, our unmatched overall sample, and our first-episode schizophrenia sample demonstrate (1) white matter tract disruption as a neurobiological feature of the deficit syndrome and (2) reductions in cortical thickness as a common feature of patients with a diagnosis of schizophrenia. When taken with previous results in gray matter, our findings in white matter tracts point to neural circuitry important for socioemotional function as a core neurobiological feature of the deficit subtype of schizophrenia.

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)0.002
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.049
GPT teacher head0.333
Teacher spread0.284 · 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

Citations175
Published2013
Admission routes2
Has abstractyes

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