Volumetric Abnormalities in Violent Schizophrenia Patients on the General Psychiatric Ward

Liu, FengJu; Shao, Yi; Li, Xin; Liu, Li; Zhao, Rong; Xie, Bin; Qiao, Yi

doi:10.3389/fpsyt.2020.00788

Cited by 9 publications

(13 citation statements)

References 50 publications

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“…In addition to the STG and TP, we also found significantly decreased GMV in the FFG in vSZ rather than nvSZ. This result is accord with other studies, which supported the malfunction of the FFG in the etiology of violence in patients with SZ ( 8 , 17 , 47 ). For instance, Liu ( 47 ) and Del Bene ( 17 ) found reduced GMV in the right FFG in vSZ compared to HCs, as well as reduced cortical thickness of this region compared to those with nvSZ.…”

Section: Discussionsupporting

confidence: 92%

“…This result is accord with other studies, which supported the malfunction of the FFG in the etiology of violence in patients with SZ ( 8 , 17 , 47 ). For instance, Liu ( 47 ) and Del Bene ( 17 ) found reduced GMV in the right FFG in vSZ compared to HCs, as well as reduced cortical thickness of this region compared to those with nvSZ. However, no correlation was reported between the aberrance in the FFG and any clinical characteristics.…”

Section: Discussionsupporting

confidence: 92%

“…Extensive research has elucidated the involvement of the temporal gyrus in violence, not only in patients with SZ ( 10 , 44 ), but also in non-psychotic criminals ( 45 , 46 ). For instance, Kuroki et al ( 11 ) found great losses in the volume of the STG and TP, Liu et al ( 47 ) also found significantly reduced GMVs in widespread frontal, temporal, and limbic regions in vSZ compared to HCs. A recent work demonstrated that violence in male violent patients with SZ is related to reduced cortical thickness in the temporal region and FFG ( 48 ).…”

Section: Discussionmentioning

confidence: 99%

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Structural Deficits in the Frontotemporal Network Associated With Psychopathic Traits in Violent Offenders With Schizophrenia

Gou

Zhang

et al. 2022

Front. Psychiatry

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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 without violent behavior (nvSZ), and 43 healthy controls (HC), completed the T1-weighted magnetic resonance imaging (MRI) scan and were analyzed using voxel-based morphometry approach. The psychopathic traits were assessed using the Psychopathy Checklist: Screening Version (PCL:SV). The results showed decreased gray matter volume (GMV) in the vSZ group in the right temporal lobe and bilateral inferior frontal gyri compared to HCs; while reduced GMV in the inferior parietal lobe, parahippocampal and orbital frontal gyri was found in the nvSZ group compared with HCs. Correlation analyses showed that psychopathic traits were negatively associated with the GMV in the right superior temporal and left fusiform gyri in the vSZ group, indicating that psychopathic traits, as reflected by the score of antisocial factor, might be related to structural deficits in the temporal lobe, which led to a propensity to violent behavior in patients with SZ. Our findings suggest that violent behavior in patients with SZ might have a personality background associated with the frontotemporal network aberrance. In future studies, we need to take a closer look at psychopathic traits for better understanding of the mechanism of interpersonal violence in patients with SZ and to explore whether the imaging findings from this study can serve as a biomarker to predict future violent behaviors and community living.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Structural Deficits in the Frontotemporal Network Associated With Psychopathic Traits in Violent Offenders With Schizophrenia

Gou

Zhang

et al. 2022

Front. Psychiatry

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“…Previous studies have reported that the abnormal activity in the superior frontal cortex might be related to excessive selfreferential processing and impairment in emotional cognitive control processing in patients with MDD (58). A large number of studies found that the structure of the superior frontal cortex had an important association with depressive symptoms among different populations (59)(60)(61)(62). One study reported that the GMV alterations in superior frontal cortex was associated with the severity of depression in patients with MDD (63).…”

Section: Brain Structurementioning

confidence: 99%

Common and Distinct Alterations of Cognitive Function and Brain Structure in Schizophrenia and Major Depressive Disorder: A Pilot Study

Zhang

Xiao

et al. 2021

Front. Psychiatry

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Objective: Numerous studies indicate that schizophrenia (SCZ) and major depressive disorder (MDD) share pathophysiological characteristics. Investigating the neurobiological features of psychiatric-affective disorders may facilitate the diagnosis of psychiatric disorders. Hence, we aimed to explore whether patients with SCZ and patients with MDD had the similar or distinct cognitive impairments and GMV alterations to further understand their underlying pathophysiological mechanisms.Methods: We recruited a total of 52 MDD patients, 64 SCZ patients, and 65 healthy controls (HCs). The Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery was used to assess cognitive functions. In addition, voxel-based morphometry (VBM) analysis was used to evaluate the gray matter volume (GMV) by using MRI scanning. One-way ANOVA and post-hoc tests were used to find the differences among the MDD, SCZ, and HCs. Finally, we explored the correlation between structural alterations and cognitive functions.Results: Compared with that of HCs, processing speed was impaired in both patients with SCZ and patients with MDD (F = 49.505, p < 0.001). SCZ patients displayed impaired cognitive performance in all dimensions of cognitive functions compared with HCs (p < 0.001, except social cognition, p = 0.043, Bonferroni corrected). Whole-brain VBM analysis showed that both SCZ and MDD groups had reductions of GMV in the medial superior frontal cortex (cluster-level FWE p < 0.05). Patients with SCZ exhibited declining GMV in the anterior cingulate cortex and right middle frontal cortex (MFC) compared with HCs and MDD patients (cluster-level FWE p < 0.05). The mean values of GMV in the right MFC had a positive correlation with the attention/vigilance function in patients with MDD (p = 0.014, partial. r = 0.349, without Bonferroni correction).Conclusions: In total, our study found that MDD and SCZ groups had common cognitive impairments and brain structural alterations, but the SCZ group exhibited more severe impairment than the MDD group in both fields. The above findings may provide a potential support for recognizing the convergent and divergent brain neural pathophysiological mechanisms between MDD and SCZ.

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“…Structural magnetic resonance imaging (sMRI) as a 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 con rmed signi cant alterations in multiple cerebral regions, in particular frontal and temporal lobes, in SCZ patients with violence [12][13][14][15]. 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 [16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of violence in schizophrenia using sMRI, based on machine learning algorithms

Pei

et al. 2022

Preprint

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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, different sMRI features between groups were integrated into the prediction models for identification of SCZ patients with violence. 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.

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Volumetric Abnormalities in Violent Schizophrenia Patients on the General Psychiatric Ward

Cited by 9 publications

References 50 publications

Structural Deficits in the Frontotemporal Network Associated With Psychopathic Traits in Violent Offenders With Schizophrenia

Structural Deficits in the Frontotemporal Network Associated With Psychopathic Traits in Violent Offenders With Schizophrenia

Common and Distinct Alterations of Cognitive Function and Brain Structure in Schizophrenia and Major Depressive Disorder: A Pilot Study

Prediction of violence in schizophrenia using sMRI, based on machine learning algorithms

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