NDPI-predict: A Multi-dimensional Model for Simulating Violent Behavior Risk

Abstract

Violent behavior poses a significant threat to both societal and individual security and has been consistently associated with Callous-Unemotional (CU) traits. These traits, defined by diminished empathy and guilt, are linked to structural and functional brain alterations, including reduced gray matter in the paralimbic cortex, orbitofrontal cortex, and anterior cingulate cortex, alongside decreased connectivity within empathy-related neural networks. Moreover, exposure to childhood trauma and heightened reactivity to violence may intensify CU-related dispositions, thereby elevating the risk of aggression and premeditated harm.
Drawing upon the Neurodevelopment Pathway-Driven Intervention (NPDI) model, this study explores the neural and psychological mechanisms underlying CU traits and their contribution to violent behavior, with specific attention to sex-related variations. To evaluate risk, a machine learning framework was developed incorporating key performance metrics (Accuracy = 0.92; AUC = 0.95) and integrating multimodal data sources, neural biomarkers (gray matter volume, functional connectivity, amygdala reactivity), personality indices, and sex. Results distinctly differentiate high-risk from low-risk groups, demonstrating the model’s robust predictive capability. These findings underscore the interplay between neurobiological and personality dimensions of CU traits and highlight the model’s potential application in forensic risk assessment and early intervention.