The Dark Sides of the Brain: A Systematic Review and Meta-Analysis of Functional Neuroimaging Studies on Aggression
Bibliographic record
Abstract
Background. Aggression is a worldwide issue that has significant consequences for both the victims and societies. However, aggression is an umbrella terms with subcomponents such as motivation (i.e., Reactive versus Proactive) and forms (i.e., Physical versus Verbal) which aggressive behaviors may occur. Yet, brain circuits differentiating these subcomponents remains largely unknown.Method. A systematic search strategy was conducted up to May 1st 2023, using PubMed, Google Scholar, and Web of Science, to identify relevant studies. Coordinate-based meta-analysis was conducted on General Aggression, Reactive Aggression, Proactive Aggression, Physical Aggression and Verbal Aggression using spatial convergence (ALE) and effect-size (SDM-PSI) approaches.Results. Sixty-seven studies met the inclusion criteria. Meta-analysis revealed similar yet distinct neural correlates for General Aggression (i.e., Amygdala, Precuneus, Intraparietal Sulcus, Angular and Middle Temporal Gyri), Reactive Aggression (i.e., Amygdala, Periaqueductal Grey, Posterior Insula, & Central Opercular Cortex), Proactive Aggression (i.e., Septal Area, & Amygdala), Physical Aggression (i.e., Dorsal Premotor Cortex, Dorsal Caudate, & Dorsal Anterior Cingulate Cortex), and Verbal (i.e., Dorsal Anterior Cingulate Cortex). Exploratory analyses revealed the importance of affective, cognitive and social cognition processes as well as serotoninergic, dopaminergic, and cholinergic systems in the neural underpinnings of aggressive behaviors.Conclusion. Our findings highlight the importance of examining the subcomponents of aggression (i.e., motivation and forms) within a transdiagnostic framework. Hence, characterizing the neurobiological substrates of aggression may expand our search for targeted neuromodulation and pharmacological 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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".