Neuroimaging Metrics of Drug and Food Processing in Cocaine-Dependence, as a Function of Psychopathic Traits and Substance Use Severity
Bibliographic record
Abstract
Previous studies suggest that psychopathic traits commonly present as comorbid with substance use disorders. Moreover, neuroimaging and psychometric findings suggest that psychopathic traits may predispose individuals to a sensitized reward response to drugs. Given that substance use disorders are characterized by a neurocognitive bias toward drug-reward relative to non-drug reward, it is possible that heightened psychopathic characteristics may further predispose to this processing bias. To evaluate this possibility, we assessed psychopathic traits (measured using the PCL-R; Hare, 2003) in 105 probationers/parolees and evaluated the relationship between PCL-R scores, lifetime duration of drug use, and biases in neural response to drug- compared to food-related videos. Psychopathic traits (potentially driven by interpersonal/affective traits) were positively correlated with drug > food reactivity within the right insula and left amygdala. In addition, psychopathic traits modulated the relationship between drug use and drug > food reactivity within the left dorsomedial prefrontal cortex, right insula, and left caudate nucleus. Specifically, lifetime duration of drug use correlated positively with drug > food reactivity in participants with lower levels of psychopathic traits and correlated negatively with drug > food reactivity in individuals with higher levels of psychopathic traits. These results help reconcile prior studies on psychopathy and drug-stimulus processing and provide neurocognitive support for the notion that psychopathic traits serve as an underlying risk factor for substance use disorders. These results suggest that different treatment regimens for substance abuse for individuals with higher or lower levels of psychopathy may be beneficial and suggest that reduction of neurocognitive biases to drug-related stimuli may offer useful targets for future treatment protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".