Abstract:People with schizophrenia (SZ) are at increased risk of violence compared to the general population. However, the neural mechanisms of violent behavior in patients with SZ are still unclear due to the heterogeneity of the diseased population. In this study, we aimed to examine the neural correlates of violent behavior in SZ and to determine whether the structural deficits were related to psychopathic traits. A total of 113 participants, including 31 SZ patients with violent behavior (vSZ), 39 SZ patients witho… Show more
“…Structural magnetic resonance imaging (sMRI) as an easy access, high resolution, and non-invasive imaging technique has been widely used to understand the neurobiology of violence in SCZ. Recent sMRI studies have confirmed significant alterations in multiple cerebral regions, in particular frontal and temporal lobes, in SCZ patients with violence [ 14 – 17 ]. For example, a study of cortical morphology showed reduced cortical thickness within the precentral, parietal, temporal, and fusiform cortex in SCZ patients with a history of violence [ 18 ].…”
Background
Violent behavior in patients with schizophrenia (SCZ) is a major social problem. The early identification of SCZ patients with violence can facilitate implementation of targeted intervention.
Methods
A total of 57 male SCZ patients were recruited into this study. The general linear model was utilized to compare differences in structural magnetic resonance imaging (sMRI) including gray matter volume, cortical surface area, and cortical thickness between 30 SCZ patients who had exhibited violence and 27 SCZ patients without a history of violence. Based on machine learning algorithms, the different sMRI features between groups were integrated into the models for prediction of violence in SCZ patients.
Results
After controlling for the whole brain volume and age, the general linear model showed significant reductions in right bankssts thickness, inferior parietal thickness as well as left frontal pole volume in the patients with SCZ and violence relative to those without violence. Among seven machine learning algorithms, Support Vector Machine (SVM) have better performance in differentiating patients with violence from those without violence, with its balanced accuracy and area under curve (AUC) reaching 0.8231 and 0.841, respectively.
Conclusions
Patients with SCZ who had a history of violence displayed reduced cortical thickness and volume in several brain regions. Based on machine learning algorithms, structural MRI features are useful to improve predictive ability of SCZ patients at particular risk of violence.
“…Structural magnetic resonance imaging (sMRI) as an easy access, high resolution, and non-invasive imaging technique has been widely used to understand the neurobiology of violence in SCZ. Recent sMRI studies have confirmed significant alterations in multiple cerebral regions, in particular frontal and temporal lobes, in SCZ patients with violence [ 14 – 17 ]. For example, a study of cortical morphology showed reduced cortical thickness within the precentral, parietal, temporal, and fusiform cortex in SCZ patients with a history of violence [ 18 ].…”
Background
Violent behavior in patients with schizophrenia (SCZ) is a major social problem. The early identification of SCZ patients with violence can facilitate implementation of targeted intervention.
Methods
A total of 57 male SCZ patients were recruited into this study. The general linear model was utilized to compare differences in structural magnetic resonance imaging (sMRI) including gray matter volume, cortical surface area, and cortical thickness between 30 SCZ patients who had exhibited violence and 27 SCZ patients without a history of violence. Based on machine learning algorithms, the different sMRI features between groups were integrated into the models for prediction of violence in SCZ patients.
Results
After controlling for the whole brain volume and age, the general linear model showed significant reductions in right bankssts thickness, inferior parietal thickness as well as left frontal pole volume in the patients with SCZ and violence relative to those without violence. Among seven machine learning algorithms, Support Vector Machine (SVM) have better performance in differentiating patients with violence from those without violence, with its balanced accuracy and area under curve (AUC) reaching 0.8231 and 0.841, respectively.
Conclusions
Patients with SCZ who had a history of violence displayed reduced cortical thickness and volume in several brain regions. Based on machine learning algorithms, structural MRI features are useful to improve predictive ability of SCZ patients at particular risk of violence.
“…The volume alteration in regions of VSC appeared to be associated with impulsiveness, indicating that poor impulse control might play an important role in the neurobiological basis of violence in SCZ. Compared to prior studies, the whole-brain approach was superior to the analysis of specific isolated regions in the identification of regions associated with violence, as the analysis of isolated regions could be insufficient to reveal the neurobiological underpinnings due to the complex phenotypes of violence [ 7 , 41 ]. In addition, all the participants with VSC in the present study were free of substance abuse and personality disorder, thus, it is reasonable to infer that the brain structural alterations of these participants might be related to violence [ 13 ].…”
Section: Discussionmentioning
confidence: 99%
“…Some other researchers found that patients with VSC had reduced GMV in the bilateral cerebellum, BA 39/40 [ 11 ], putamen, left cuneus/precuneus and parietal cortex [ 18 ], as well as decreased cortical thickness in sensorimotor regions [ 19 ], compared to those with NSC. However, some neuroimaging studies did not find any significant difference in the brain structure between the VSC and NSC [ 7 , 15 , 20 , 21 ]. Due to the disparities in current findings, the structural changes associated with violence in SCZ are still unclear, nor is its association with clinical risk factors in SCZ, which indicated the necessity to explore the neurobiological underpinning of violent behavior in individuals with SCZ.…”
Background
Violence in schizophrenia (SCZ) is a phenomenon associated with neurobiological factors. However, the neural mechanisms of violence in patients with SCZ are not yet sufficiently understood. Thus, this study aimed to explore the structural changes associated with the high risk of violence and its association with impulsiveness in patients with SCZ to reveal the possible neurobiological basis.
Method
The voxel-based morphometry approach and whole-brain analyses were used to measure the alteration of gray matter volume (GMV) for 45 schizophrenia patients with violence (VSC), 45 schizophrenia patients without violence (NSC), and 53 healthy controls (HC). Correlation analyses were used to examine the association of impulsiveness and brain regions associated with violence.
Results
The results demonstrated reduced GMV in the right insula within the VSC group compared with the NSC group, and decreased GMV in the right temporal pole and left orbital part of superior frontal gyrus only in the VSC group compared to the HC group. Spearman correlation analyses further revealed a positive correlation between impulsiveness and GMV of the left superior temporal gyrus, bilateral insula and left medial orbital part of the superior frontal gyrus in the VSC group.
Conclusion
Our findings have provided further evidence for structural alterations in patients with SCZ who had engaged in severe violence, as well as the relationship between the specific brain alterations and impulsiveness. This work provides neural biomarkers and improves our insight into the neural underpinnings of violence in patients with SCZ.
“…Patienten, die an Schizophrenie leiden, haben ein erhöhtes Risiko, Gewalt gegen andere auszuüben und sind für etwa 10 % aller Tötungsdelikte verantwortlich 5 .…”
Section: Substanzmissbrauch Oder Krankheit?unclassified
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