The Deficits of Individual Morphological Covariance Network Architecture in Schizophrenia Patients With and Without Violence

Abstract

Background: Schizophrenia is associated with a significant increase in the risk of violence, which constitutes a public health concern and contributes to stigma associated with mental illness. Although previous studies revealed structural and functional abnormalities in individuals with violent schizophrenia (VSZ), the neural basis of psychotic violence remains controversial.Methods: In this study, high-resolution structural magnetic resonance imaging (MRI) data were acquired from 18 individuals with VSZ, 23 individuals with non-VSZ (NSZ), and 22 age- and sex-matched healthy controls (HCs). Whole-brain voxel-based morphology and individual morphological covariance networks were analysed to reveal differences in gray matter volume (GMV) and individual morphological covariance network topology. Relationships among abnormal GMV, network topology, and clinical assessments were examined using correlation analyses.Results: GMV in the hypothalamus gradually decreased from HCs and NSZ to VSZ and showed significant differences between all pairs of groups. Graph theory analyses revealed that morphological covariance networks of HCs, NSZ, and VSZ exhibited small worldness. Significant differences in network topology measures, including global efficiency, shortest path length, and nodal degree, were found. Furthermore, changes in GMV and network topology were closely related to clinical performance in the NSZ and VSZ groups.Conclusions: These findings revealed the important role of local structural abnormalities of the hypothalamus and global network topological impairments in the neuropathology of NSZ and VSZ, providing new insight into the neural basis of and markers for VSZ and NSZ to facilitate future accurate clinical diagnosis and targeted treatment.

Brain structural alterations associated with impulsiveness in male violent patients with schizophrenia

“…We recruited 45 patients with schizophrenia who had engaged in serious violent behaviors and referred to the forensic psychiatry department of the Second Xiangya Hospital (China) for examination. According to previous study [ 16 ], the present research defined violence as behaviors causing severe physical injuries to others, including homicide and other serious assaults. What’s more, for better quantitative description, we also required that the scores of the Modified Overt Aggression Scale (MOAS) to be more than 4 in the group of VSC [ 8 , 34 ].…”

“…Volume alterations in the limbic system, especially in the hippocampus and amygdala, are also reported in some studies [ 14 , 15 ]. A study reported decreased GMV of the hypothalamus in patients with VSC, compared to NSC and HC [ 16 ], and the GMV of the hypothalamus was found negatively correlated with the scores of PANSS and MOAS. Furthermore, reduced bilateral hippocampal GMV was also found in patients with SCZ who committed homicide, as compared to those with NSC and HC [ 17 ].…”

“…The disparities and even contradictory results might be attributed to the relatively small sample size, the inconsistent definitions of violence, and the heterogeneity of the studied patient groups [ 8 , 27 ]. For instance, in some studies, violence was defined as physical harm to others, with or without homicide, while in some other studies, violence was defined only using the score of some scales [ 12 , 13 , 16 , 17 ]. However, the examination of neurobiological underpinnings of violence that has caused serious consequences can be more helpful.…”

Aberrant individual structure covariance network in patients with mesial temporal lobe epilepsy

“…Recently, the method of constructing an individual specialized SCN was introduced to depict subject-specific covariance variability in healthy people ( Zhao et al, 2021 ) and to outline covariance alterations in neuropsychiatric disease patients ( Liu et al, 2021 ; Shen et al, 2021 ; Han et al, 2022 ). The individualized SCN is measured according to the correlation of multiple morphometric features ( Li et al, 2017 ; Seidlitz et al, 2018 ; Morgan et al, 2019 ; Zhao et al, 2021 ), regional probability density distribution ( Kong et al, 2014 ; Wang et al, 2016 ) or network perturbation approaches ( Kim et al, 2016 ; Wolfers et al, 2018 ).…”