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Record W2127830759 · doi:10.1093/brain/awm307

Reduced error-related activation in two anterior cingulate circuits is related to impaired performance in schizophrenia

2007· article· en· W2127830759 on OpenAlexaff
Frida E. Polli, Jason J.S. Barton, Katharine N. Thakkar, Douglas N. Greve, Donald Goff, Scott L. Rauch, Dara S. Manoach

Bibliographic record

VenueBrain · 2007
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsUniversity of British Columbia
FundersNational Center for Research ResourcesNational Institutes of HealthNational Institute of Mental HealthNational Alliance for Research on Schizophrenia and Depression
KeywordsSchizophrenia (object-oriented programming)Anterior cingulate cortexPsychologyNeuroscienceError-related negativityCognitive psychologyPsychiatryCognition

Abstract

fetched live from OpenAlex

To perform well on any challenging task, it is necessary to evaluate your performance so that you can learn from errors. Recent theoretical and experimental work suggests that the neural sequellae of error commission in a dorsal anterior cingulate circuit index a type of contingency- or reinforcement-based learning, while activation in a rostral anterior cingulate circuit reflects appraisal of the affective or motivational significance of errors. Patients with schizophrenia show rigid, perseverative behaviour that is not optimally responsive to outcome. Findings of reduced anterior cingulate cortex (ACC) activity during error commission in schizophrenia suggest that difficulties in evaluating and modifying behaviour in response to errors may contribute to behavioural rigidity. Using event-related functional MRI and an antisaccade paradigm with concurrent monitoring of eye position, the present study examined error-related activation and its relation to task performance in the anatomic components of two ACC circuits that are theorized to make distinct contributions to error processing. Eighteen chronic-medicated schizophrenia patients and 15 healthy controls participated. Compared to controls, patients showed increased antisaccade error rates and decreased error-related activation in the reinforcement learning network--dorsal ACC, striatum and brainstem (possibly substantia nigra)--and also in the affective appraisal network--rostral ACC, insula and amygdala. These reductions remained when the effects of antipsychotic medication dose and error rate were statistically controlled. Activation in these networks was inversely related to error rate in both patient and control groups, but the slope of this relation was shallower in patients (i.e. across participants with schizophrenia, decrements in error rate were associated with smaller decrements in activation). This indicates that the blunted neural response to errors in schizophrenia was not simply a reflection of more frequent errors. Our findings demonstrate a blunted response to error commission that is associated with worse performance in two ACC circuits in schizophrenia. In the dACC circuit, the blunted response may reflect deficient modification of prepotent stimulus-response mappings in response to errors, and in the rACC network it may reflect diminished concern regarding behavioural outcomes. However, despite these deficits and in the absence of external feedback regarding errors, patients corrected their errors as frequently as controls suggesting intact error recognition and ability to institute corrective action. Impairments in evaluating and learning from errors in schizophrenia may contribute to behaviour that is rigid and perseverative rather than optimally guided by outcomes, and may compromise performance across a wide range of tasks.

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 score0.004
Threshold uncertainty score0.007

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 head0.023
GPT teacher head0.303
Teacher spread0.280 · 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

Citations131
Published2007
Admission routes1
Has abstractyes

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