The origins of evil: From lesions to the functional architecture of the antisocial brain
Bibliographic record
Abstract
In the past decades, a growing body of evidence has suggested that some individuals may exhibit antisocial behaviors following brain lesions. Recently, some authors have shown that lesions underpinning antisocial behaviors may disrupt a particular brain network during resting-state. However, it remains unknown whether these brain lesions may alter specific mental processes during tasks. Therefore, we conducted meta-analytic co-activation analyses on lesion masks of 17 individuals who acquired antisocial behaviors following their brain lesions. Each lesion mask was used as a seed of interest to examine their aberrant co-activation network using a database of 143 whole-brain neuroimaging studies on antisocial behaviors (n= 5,913 subjects). We aimed to map the lesion brain network that shows deficient activity in antisocial population against a null distribution derived from 655 control lesions. We further characterized the lesion-based meta-analytic network using term-based decoding (Neurosynth) as well as receptor/transporter density maps (JuSpace). We found that the lesion meta-analytic network included the amygdala, orbitofrontal cortex, ventro- and dorso-medial prefrontal cortex, fusiform face area, and supplementary motor area (SMA), which correlated mainly with emotional face processing and serotoninergic system (5-HT1Aand 5-HTT). We also investigated the heterogeneity in co-activation networks through data-driven methods and found that lesions could be grouped in four main networks, encompassing emotional face processing, general emotion processing, and reward processing. Our study shows that the heterogeneous brain lesions underpinning antisocial behaviors may disrupt specific mental processes, which further increases the risk for distinct antisocial symptoms. It also highlights the importance and complexity of studying brain lesions in relationship with antisocial behaviors.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".