Systematic review of ERP and fMRI studies investigating inhibitory control and error processing in people
Bibliographic record
Abstract
BACKGROUND: Several current theories emphasize the role of cognitive control in addiction. The present review evaluates neural deficits in the domains of inhibitory control and error processing in individuals with substance dependence and in those showing excessive addiction-like behaviours. The combined evaluation of event-related potential (ERP) and functional magnetic resonance imaging (fMRI) findings in the present review offers unique information on neural deficits in addicted individuals. METHODS: We selected 19 ERP and 22 fMRI studies using stop-signal, go/no-go or Flanker paradigms based on a search of PubMed and Embase. RESULTS: The most consistent findings in addicted individuals relative to healthy controls were lower N2, error-related negativity and error positivity amplitudes as well as hypoactivation in the anterior cingulate cortex (ACC), inferior frontal gyrus and dorsolateral prefrontal cortex. These neural deficits, however, were not always associated with impaired task performance. With regard to behavioural addictions, some evidence has been found for similar neural deficits; however, studies are scarce and results are not yet conclusive. Differences among the major classes of substances of abuse were identified and involve stronger neural responses to errors in individuals with alcohol dependence versus weaker neural responses to errors in other substance-dependent populations. LIMITATIONS: Task design and analysis techniques vary across studies, thereby reducing comparability among studies and the potential of clinical use of these measures. CONCLUSION: Current addiction theories were supported by identifying consistent abnormalities in prefrontal brain function in individuals with addiction. An integrative model is proposed, suggesting that neural deficits in the dorsal ACC may constitute a hallmark neurocognitive deficit under lying addictive behaviours, such as loss of control.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".