Altered power spectra in antisocial males during rest as a function of cocaine dependence: A network analysis
Bibliographic record
Abstract
Abnormalities in the spectral power of offenders' neural oscillations have been noted within select Resting-State Networks (RSNs); however, no study has yet evaluated the influence of cocaine dependence, drug use severity, and psychopathic traits on these abnormalities. To this end, the present study compared rest-related power spectral characteristics between two groups of offenders (with and without a DSM-IV-TR cocaine-dependence diagnosis) and a non-offender control group. Results indicated that both offender groups presented with lower low frequency power ratio (LFPR) scores (i.e. across all RSNs) than non-offenders. These differences in LFPR scores were due to both higher high-frequency power (0.15-0.25 Hz; within seven (in non-dependent offenders) and five (in cocaine-dependent offenders) of eight investigated networks) and decreased low-frequency power (0.01-0.10 Hz; within six (in non-dependent offenders) and one (in cocaine-dependent offenders) of eight investigated networks) compared to non-offenders. Thus, both cocaine-dependent and non-dependent offenders displayed abnormal neural oscillations, suggesting that these oscillatory abnormalities could exist as neurobiological features associated with offender status. Offenders' LFPR levels correlated with lifetime years of cocaine use, but not with the level of psychopathic traits. These findings supplement our knowledge regarding the influence of substance use on resting-state activity in offenders; moreover, they provide further indication of the importance of evaluating shared/unique variance associated with drug use and pyschopathic personality traits.
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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 | 0.000 |
| 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 | 0.000 |
| 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".