Abstinence reverses EEG-indexed attention bias between drug-related and pleasant stimuli in cocaine-addicted individuals
Bibliographic record
Abstract
BACKGROUND: Increased attention bias toward drug-related cues over non-drug-related intrinsically pleasant reinforcers is a hallmark of drug addiction. In this study we used the late positive potential (LPP) to investigate whether such increased attention bias toward drug-related relative to non-drug-related cues changes over a protracted period of reduced drug use in treatment-seeking individuals with a cocaine use disorder (CUD). METHODS: Treatment-seeking individuals with CUD and matched healthy controls passively viewed a series of pleasant, neutral and drug-related pictures while their event-related potentials were recorded at baseline (≤ 3 weeks after treatment initiation) and at 6-month follow-up (only CUD). RESULTS: We included 19 treatment-seeking individuals with CUD and 18 matched controls in our analyses. The results showed a reversal in attention bias (i.e., LPP amplitude) from baseline (i.e., drug > pleasant) to follow-up (i.e., pleasant > drug) driven by an increased attentional engagement with pleasant pictures; this LPP reversal was paralleled by a concomitant reduction in self-reported wanting and craving for cocaine in the CUD group. Furthermore, reduced attention bias toward drug-related cues (relative to pleasant cues) was correlated with longer duration of abstinence at baseline, and the extent of its longitudinal reversal was correlated with decreased craving at follow-up, providing support for abstinence as a putative mechanism of this bottom-up attentional change. LIMITATIONS: A limited sample size and the use of the same set of pictures at baseline and follow-up were the major limitations of this study. CONCLUSION: Results collectively indicate that, by tracking with drug abstinence, LPP in response to drug-related relative to pleasant cues may serve as an indicator of clinical progress in treatment-seeking individuals with CUD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".