A STUDY ON THE CHARACTERISTICS OF SCHIZOPHRENIA PATIENTS WITH VIOLENT CRIMES BASED ON CLINICAL, PREFRONTAL FUNCTIONAL CONNECTIVITY, AND POLYGENIC RISK SCORES
Authors: Guo *Huijuan , Gu Yu , Huang Ying , Zhou Jiansong , Wang Xiaoping 
Published: February 2025

Abstract

Objectives
The aim of this study is to investigate the clinical characteristics, the characteristics of prefrontal cortex functional connectivity in the resting state prefrontal cortex region and the genetic risk of schizophrenia patients with violent criminal behavior. And to construct a violence classification model for patients with schizophrenia using machine learning methods, based on the data of these factors.

Methods
A case-control study was conducted consist of two groups of subjects, including 134 violent offenders with schizophrenia and 141 non-violent patients with schizophrenia. Based on the multidimensional factors, including clinical characteristics, prefrontal cortex functional connectivity and SCZ-PRS, three machine learning methods were used to establish violence classification models of schizophrenia patients.

Results
1. In the study, we found that compared with the non-violent group, the violent group showed lower educational level, longer untreated period of mental illness and the hospitalization time, and the proportion of previous psychiatric visits was lower (P&lt;0.05). The total score and each factor score of HCR-20 and PCL:SV in the violent group were comparatively higher than those in the non-violent group (P&lt;0.001). 2. In terms of brain functional connectivity characteristics, compared with the non-violent group, the FC between the frontal pole and right pars triangularis Broca's area (PFC-TRI.R) (P=0.041), the right dorsolateral prefrontal cortex and the right pars triangularis Broca's area (DLPFC.R-TRI.R) (P=0.028), the right pars triangularis Broca's area and the right Pre-Motor and Supplementary Motor Cortex (TRI.R-PMSMC.R) (P=0.016) and the right pars triangularis Broca's area and the left Pre-Motor and Supplementary Motor Cortex (TRI.R-PMSMC.L) (P=0.048) were lower in the violent group, but the FC between the bilateral Pre-Motor and Supplementary Motor Cortex (P&lt;0.001) was higher. In addition, the results of correlation analysis showed that in the violent group, FCPMSMC.R- PMSMC.L were positively correlated with the antisocial factors of psychopathic (PCL:SV) (P=0.048). 3.The results of SCZ-PRS correlation analysis showed that in violent group, SCZ-PRS was positively correlated with FCPMSMC.R-PMSMC.L(P=0.002-0.029). However, no significant correlation was found between SCZ-PRS and violence-related clinical indicators. 4. Among the machine learning models constructed based on the data of the multidimensional factors, the random forest model (RF) had achieved the best performance to classify the violent group and the non-violent group. It has an accuracy of 0.942, a sensitivity of 0.985, and a specificity of 0.895.

Conclusions
1. The abnormal pivotal function of the right triangle in the prefrontal lobe, which is responsible for language management, and the abnormal activation of the connections between the premotor and auxiliary motor cortex, which are responsible for complex motor control and cognitive control, may be related to violence in schizophrenia. 2.The polygenic risk score for schizophrenia may affect the individual's impulse control ability by affecting the functional connectivity of the prefrontal cortex emotional control and cognitive decision-making related brain regions. 3.The classification model constructed based on clinical and imaging genetic multidimensional factors can be used to identify schizophrenia patients with violent criminal behavior.