Abnormal interhemispheric connectivity in male psychopathic offenders
Bibliographic record
Abstract
BACKGROUND: Psychopathic offenders inevitably violate interpersonal norms and frequently resort to aggressive and criminal behaviour. The affective and cognitive deficits underlying these behaviours have been linked to abnormalities in functional interhemispheric connectivity. However, direct neurophysiological evidence for dysfunctional connectivity in psychopathic offenders is lacking. METHODS: We used transcranial magnetic stimulation combined with electroencephalography to examine interhemispheric connectivity in the dorsolateral and motor cortex in a sample of psychopathic offenders and healthy controls. We also measured intracortical inhibition and facilitation over the left and right motor cortex to investigate the effects of local cortical processes on interhemispheric connectivity. RESULTS: We enrolled 17 psychopathic offenders and 14 controls in our study. Global abnormalities in right to left functional connectivity were observed in psychopathic offenders compared with controls. Furthermore, in contrast to controls, psychopathic offenders showed increased intracortical inhibition in the right, but not the left, hemisphere. LIMITATIONS: The relatively small sample size limited the sensitivity to show that the abnormalities in interhemispheric connectivity were specifically related to the dorsolateral prefrontal cortex in psychopathic offenders. CONCLUSION: To our knowledge, this study provides the first neurophysiological evidence for abnormal interhemispheric connectivity in psychopathic offenders and may further our understanding of the disruptive antisocial behaviour of these offenders.
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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 | 0.001 | 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) | 0.003 | 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".