Impaired attentional and socio-affective networks in subjects with antisocial behaviors: a meta-analysis of resting-state functional connectivity studies
Bibliographic record
Abstract
Abstract In the past decade, there has been a growing interest in examining resting-state functional connectivity deficits in subjects with conduct and antisocial personality disorder. Through meta-analyses and literature reviews, extensive work has been done to characterize their abnormalities in brain activation during a wide range of functional magnetic resonance imaging (fMRI) tasks. However, there is currently no meta-analytical evidence regarding neural connectivity patterns during resting-state fMRI. Therefore, we conducted a coordinate-based meta-analysis of resting-state fMRI studies on individuals exhibiting antisocial behaviors. Of the retrieved studies, 18 used a seed-based connectivity approach (513 cases v. 488 controls), 20 employed a non-seed-based approach (453 cases v. 460 controls) and 20 included a correlational analysis between the severity of antisocial behaviors and connectivity patterns (3462 subjects). Meta-analysis on seed-based studies revealed significant connectivity deficits in the amygdala, middle cingulate cortex, ventral posterior cingulate cortex-precuneus, ventromedial and dorsomedial prefrontal cortex, premotor cortex, and superior parietal lobule. Additionally, non-seed-based meta-analysis showed increased connectivity in the ventral posterior cingulate cortex and decreased connectivity in the parietal operculum, calcarine cortex, and cuneus. Finally, we found meta-analytical evidence for negative relationship between the severity of antisocial behaviors and connectivity with the ventromedial prefrontal cortex. Functional characterization and meta-analytical connectivity modeling indicated that these findings overlapped with socio-affective and attentional processes. This further underscores the importance of these functions in the pathophysiology of conduct and antisocial personality disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".