Mapping the psychopathic brain: Divergent neuroimaging findings converge onto a common brain network
Bibliographic record
Abstract
Psychopathy is a personality disorder characterized by a constellation of interpersonal, affective, lifestyle, and antisocial features. Its neural underpinnings remain poorly understood due to the discrepancies in result of functional neuroimaging studies. Here, we tackled this lack of replication by investigating whether heterogeneous peak locations associated with psychopathy could in fact map onto a common functional connectivity network. A coordinate-based meta-analysis of 38 functional neuroimaging studies (40 independent samples) on psychopathy revealed only weak spatial convergence across samples. However, using functional connectomes of 1000 healthy participants, we demonstrated that the heterogeneous findings do indeed map onto a common brain network with a replicability reaching up to 87.5 % across samples. As indicators of convergent validity, we subsequently showed strong associations between this Psychopathy Network and a brain network of 17 lesion sites causally linked to the emergence of antisocial behaviours, as well as psychological processes, neurotransmission systems, and genetic markers that have been previously implicated in the pathophysiology of psychopathy. Taken together, our work highlights the importance of examining the neural correlates of psychopathy from a network perspective, which can be validated using a multilevel approach encompassing psychological, neuropsychological, genetic and neurochemical data. Ultimately, this approach may pave the way for novel and more personalised treatments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".