History of withdrawal modulates drug- and food-cue reactivity in cocaine dependent participants
Bibliographic record
Abstract
While the centrality of withdrawal in the diagnosis of addiction has been decreasing with each successive edition of the Diagnostic and Statistical Manual of Mental Disorders, psychometric and neurobiological evidence provides withdrawal a central role in the development and maintenance of addiction. The current study offers insight into these conflicting positions by using secondary analyses to assess how a history of DSM-assessed withdrawal influences the magnitude of bias in neural reactivity to drug- and/or food-related reward cues. To this end, we separated an existing sample of cocaine-dependent participants (Denomme et al., 2018) into those with (WD) and without (N-WD) a history of withdrawal, and compared food- and drug-cue reactivity between these groups, and to a non-dependent control group (ND). Analyses indicated that biases in neural reactivity towards drug- versus food-related cues only occurred among the WD participants (within: left dorsomedial prefrontal cortex, left anterior cingulate cortex, left orbitofrontal cortex, left caudate nucleus, and right ventrolateral prefrontal cortex). Thus, withdrawal status may be an important factor to consider when interpreting dependence-related biases in neural reactivity following reward-related cues. Interestingly, while N-WD participants did not show these broad biases in neural reactivity, the magnitude of their bias correlated positively with years of lifetime substance use history, particularly when psychopathic traits were low. It may be that for individuals who's addiction has not yet reached a compulsive state (see Wise and Koob, 2014), the magnitude of their drug > food bias could serve as a valuable biomarker of addiction severity.
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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 | 0.002 |
| 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) | 0.002 | 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".