Mapping the Structural Brain Network of Psychopathy: Convergent Evidence from Humans and Chimpanzees
Bibliographic record
Abstract
Psychopathy, a condition marked by profound emotional and interpersonal deficits, has long been hypothesized to arise, in part, from structural abnormalities in the brain. Yet neuroimaging studies have reported widely disparate findings, preventing definitive conclusions. Here, we sought to resolve these discrepancies by testing whether heterogeneous peak locations identified across studies converge on a common brain network. We conducted a systematic literature search of PubMed, Web of Science, EMBASE, and Scopus, identifying 18 studies (20 independent samples) examining whole-brain grey matter volume in psychopathy. Using neuroanatomical data from 1000 healthy participants, we found that grey matter volume at these peak locations varied together, forming a shared network that primarily encompassed paralimbic regions, including the cingulate and insular cortices. We next examined whether psychopathic and personality traits predicted inter-individual variation in grey matter volume within this network in humans (n = 107) and chimpanzees (n = 148). Significant effects were observed in both species, explaining 15-16% and 42-48% of the variance, respectively. Taken together, these findings indicate that psychopathy is best characterized by structural deficits in a distributed neural system rather than isolated regions, providing a unifying account of decades of inconsistent findings and advancing our understanding of its clinical, biological, and evolutionary bases.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".