MétaCan
Menu
Back to cohort
Record W2003370843 · doi:10.1093/brain/awn099

Response monitoring, repetitive behaviour and anterior cingulate abnormalities in autism spectrum disorders (ASD)

2008· article· en· W2003370843 on OpenAlexaff
Katharine N. Thakkar, Frida E. Polli, Robert M. Joseph, David S. Tuch, Nouchine Hadjikhani, Jason J.S. Barton, Dara S. Manoach

Bibliographic record

VenueBrain · 2008
Typearticle
Languageen
FieldMedicine
TopicAdvanced Neuroimaging Techniques and Applications
Canadian institutionsUniversity of British Columbia
FundersNational Center for Research ResourcesNational Institute of Mental HealthU.S. Department of Energy
KeywordsAnterior cingulate cortexAutismFractional anisotropyPsychologyDiffusion MRINeuroscienceAutism spectrum disorderAudiologyWhite matterCognitionDevelopmental psychologyMedicineMagnetic resonance imaging

Abstract

fetched live from OpenAlex

Autism spectrum disorders (ASD) are characterized by inflexible and repetitive behaviour. Response monitoring involves evaluating the consequences of behaviour and making adjustments to optimize outcomes. Deficiencies in this function, and abnormalities in the anterior cingulate cortex (ACC) on which it relies, have been reported as contributing factors to autistic disorders. We investigated whether ACC structure and function during response monitoring were associated with repetitive behaviour in ASD. We compared ACC activation to correct and erroneous antisaccades using rapid presentation event-related functional MRI in 14 control and ten ASD participants. Because response monitoring is the product of coordinated activity in ACC networks, we also examined the microstructural integrity of the white matter (WM) underlying this brain region using diffusion tensor imaging (DTI) measures of fractional anisotropy (FA) in 12 control and 12 adult ASD participants. ACC activation and FA were examined in relation to Autism Diagnostic Interview-Revised ratings of restricted and repetitive behaviour. Relative to controls, ASD participants: (i) made more antisaccade errors and responded more quickly on correct trials; (ii) showed reduced discrimination between error and correct responses in rostral ACC (rACC), which was primarily due to (iii) abnormally increased activation on correct trials and (iv) showed reduced FA in WM underlying ACC. Finally, in ASD (v) increased activation on correct trials and reduced FA in rACC WM were related to higher ratings of repetitive behaviour. These findings demonstrate functional and structural abnormalities of the ACC in ASD that may contribute to repetitive behaviour. rACC activity following errors is thought to reflect affective appraisal of the error. Thus, the hyperactive rACC response to correct trials can be interpreted as a misleading affective signal that something is awry, which may trigger repetitive attempts at correction. Another possible consequence of reduced affective discrimination between error and correct responses is that it might interfere with the reinforcement of responses that optimize outcomes. Furthermore, dysconnection of the ACC, as suggested by reduced FA, to regions involved in behavioural control might impair on-line modulations of response speed to optimize performance (i.e. speed-accuracy trade-off) and increase error likelihood. These findings suggest that in ASD, structural and functional abnormalities of the ACC compromise response monitoring and thereby contribute to behaviour that is rigid and repetitive rather than flexible and responsive to contingencies. Illuminating the mechanisms and clinical significance of abnormal response monitoring in ASD represents a fruitful avenue for further research.

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 score0.008

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 head0.033
GPT teacher head0.327
Teacher spread0.295 · 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

Citations364
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueBrainSame topicAdvanced Neuroimaging Techniques and ApplicationsFrench-language works237,207