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Record W2001992988 · doi:10.1093/brain/awq046

Evidence for gamma inhibition deficits in the dorsolateral prefrontal cortex of patients with schizophrenia

2010· article· en· W2001992988 on OpenAlexafffund
Faranak Farzan, Mera S. Barr, Andrea Levinson, Robert Chen, Willy Wong, Paul B. Fitzgerald, Zafiris J. Daskalakis

Bibliographic record

VenueBrain · 2010
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsToronto Western HospitalUniversity of TorontoCentre for Addiction and Mental Health
FundersCanadian Institutes of Health Research
KeywordsDorsolateral prefrontal cortexNeuroscienceTranscranial magnetic stimulationSchizophrenia (object-oriented programming)Prefrontal cortexPsychologyStimulationCognitionPsychiatry

Abstract

fetched live from OpenAlex

Previous studies have shown that patients with schizophrenia and bipolar disorder have deficits in cortical inhibition. Through the combination of interleaved transcranial magnetic stimulation and electroencephalography, we have recently reported on methods in which cortical inhibition can be measured from the dorsolateral prefrontal cortex, a cortical region that is more closely associated with the pathophysiology of schizophrenia. Furthermore, it is possible to index cortical inhibition of specific oscillatory frequencies including the gamma band (30-50 Hz) whose modulation has been related to higher order cortical processing. In this study, we show that patients with schizophrenia have significant deficits of cortical inhibition of gamma oscillations in the dorsolateral prefrontal cortex compared to healthy subjects and patients with bipolar disorder, while no deficits are demonstrated in the motor cortex. These results suggest that the lack of inhibition of gamma oscillations in the dorsolateral prefrontal cortex may represent an important frontal neurophysiological deficit, which may be responsible for the spectrum of deficits commonly found in 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 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)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.007
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 head0.038
GPT teacher head0.266
Teacher spread0.229 · 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

Citations166
Published2010
Admission routes2
Has abstractyes

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