Rule violation errors are associated with right lateral prefrontal cortex atrophy in neurodegenerative disease
Bibliographic record
Abstract
Good cognitive performance requires adherence to rules specific to the task at hand. Patients with neurological disease often make rule violation (RV) errors, but the anatomical basis for RV during cognitive testing remains debated. The present study examined the neuroanatomical correlates of RV errors made on tests of executive functioning in 166 subjects diagnosed with neurodegenerative disease or as neurologically healthy. Specifically, RV errors were voxel-wisely correlated with gray matter volume derived from high-definition magnetic resonance images using voxel-based morphometry implemented in SPM2. Latent variable analysis showed that RV errors tapped a unitary construct separate from repetition errors. This analysis was used to generate factor scores to represent what is common among RV errors across tests. The extracted RV factor scores correlated with tissue loss in the lateral middle and inferior frontal gyri and the caudate nucleus bilaterally. When a more stringent control for global cognitive functioning was applied using Mini Mental State Exam scores, only the correlations with the right lateral prefrontal cortex (PFC) remained significant. These data underscore the importance of right lateral PFC in behavioral monitoring and highlight the potential of RV error assessment for identifying patients with damage to this region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".