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Yu T, Pei W, Xu C, Zhang X, Deng C. Investigation of peripheral inflammatory biomarkers in association with violence in schizophrenia. BMC Psychiatry 2024; 24:542. [PMID: 39085826 PMCID: PMC11293062 DOI: 10.1186/s12888-024-05966-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 07/15/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Violent behavior carried out by patients with schizophrenia (SCZ) is a public health issue of increasing importance that may involve inflammation. Peripheral inflammatory biomarkers, such as the systemic immune inflammation index (SII), the neutrophil lymphocyte ratio (NLR), the platelet-lymphocyte ratio (PLR) and the monocyte lymphocyte ratio (MLR) are objective, easily accessible and cost-effective measures of inflammation. However, there are sparse studies investigating the role of peripheral inflammatory biomarkers in violence of patients with SCZ. METHODS 160 inpatients diagnosed with SCZ between January and December 2022 were recruited into this study. Violent behavior and positive symptoms of all participants were evaluated using Modified Overt Aggression Scale (MOAS) and Positive and Negative Syndrome Scale (PANSS), respectively. The partial correlation analysis was performed to examine the relationship of inflammatory indices and positive symptoms. Based on machine learning (ML) algorithms, these different inflammatory indices between groups were used to develop predictive models for violence in SCZ patients. RESULTS After controlling for age, SII, NLR, MLR and PANSS positive scores were found to be increased in SCZ patients with violence, compared to patients without violence. SII, NLR and MLR were positively related to positive symptoms in all participants. Positive symptoms partially mediated the effects of peripheral inflammatory indices on violent behavior in SCZ. Among seven ML algorithms, penalized discriminant analysis (pda) had the best performance, with its an area under the receiver operator characteristic curve (AUC) being 0.7082. Subsequently, with the use of pda, we developed predictive models using four inflammatory indices, respectively. SII had the best performance and its AUC was 0.6613. CONCLUSIONS These findings suggest that inflammation is involved in violent behavior of SCZ patients and positive symptoms partially mediate this association. The models built by peripheral inflammatory indices have a good median performance in predicting violent behavior in SCZ patients.
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Affiliation(s)
- Tao Yu
- Affiliated Psychological Hospital of Anhui Medical University; Anhui Mental Health Center; Hefei Fourth People's Hospital; Anhui Clinical Research Center for mental disorders, Hefei, Anhui, 230022, China
| | - Wenzhi Pei
- Affiliated Psychological Hospital of Anhui Medical University; Anhui Mental Health Center; Hefei Fourth People's Hospital; Anhui Clinical Research Center for mental disorders, Hefei, Anhui, 230022, China
| | - Chunyuan Xu
- Affiliated Psychological Hospital of Anhui Medical University; Anhui Mental Health Center; Hefei Fourth People's Hospital; Anhui Clinical Research Center for mental disorders, Hefei, Anhui, 230022, China
| | - Xulai Zhang
- Affiliated Psychological Hospital of Anhui Medical University; Anhui Mental Health Center; Hefei Fourth People's Hospital; Anhui Clinical Research Center for mental disorders, Hefei, Anhui, 230022, China.
| | - Chenchen Deng
- Hefei Maternity & Child Health Hospital, Hefei, Anhui, 230022, China.
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Liu W, Zhao J, Ding C, Chen H. The neurofunctional basis of human aggression varies by levels of femininity. Soc Neurosci 2024:1-13. [PMID: 39039838 DOI: 10.1080/17470919.2024.2382768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Revised: 07/12/2024] [Indexed: 07/24/2024]
Abstract
Aggression can be categorized into reactive aggression (RA) and proactive aggression (PA) based on their underlying motivations. However, previous research has rarely identified the relationship between femininity and RA/PA, and there is a lack of understanding regarding the femininity-related neurofunctional basis of these aggressive behaviors. Thus, this study first examined the relationships between femininity and aggression, then explored the aggression-by-femininity interactions on the fractional amplitude of low-frequency fluctuations using resting-state fMRI among 705 university participants (mean age = 19.14 ± 0.99). The behavioral data indicated that femininity was more negatively associated with RA and PA when masculinity was controlled for. Additionally, the neural data revealed that femininity-specific relationships of RA in the left middle occipital gyrus (i.e. individuals with low femininity had positive relationships between RA and the left middle occipital gyrus, whereas those with high femininity had negative relationships) as well as of PA in the left middle frontal gyrus (i.e. individuals with high femininity showed significant negative relationships, whereas those with low femininity did not exhibit significant relationships). These findings reflect that individuals with varying levels of femininity exhibit distinct neural bases when expressing different subtypes of aggression, which are associated with societal expectations of gender.
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Affiliation(s)
- Weijun Liu
- Faculty of Psychology, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
| | - Jie Zhao
- Faculty of Psychology, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
| | - Cody Ding
- Department of Education Sciences & Professional Programs, University of Missouri-St. Louis,St. Louis, MO, USA
| | - Hong Chen
- Faculty of Psychology, Southwest University, Chongqing, China
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
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Parmigiani G, Meynen G, Mancini T, Ferracuti S. Editorial: Applications of artificial intelligence in forensic mental health: opportunities and challenges. Front Psychiatry 2024; 15:1435219. [PMID: 38932940 PMCID: PMC11199772 DOI: 10.3389/fpsyt.2024.1435219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Accepted: 05/31/2024] [Indexed: 06/28/2024] Open
Affiliation(s)
- Giovanna Parmigiani
- Department of Human Neurosciences, Faculty of Medicine and Dentistry, Sapienza University of Rome, Rome, Italy
| | - Gerben Meynen
- Willem Pompe Institute for Criminal Law and Criminology, School of Law, Faculty of Law, Economics, and Governance, Utrecht University, Utrecht, Netherlands
- Faculty of Humanities, Vrije Universiteit (VU) Amsterdam, Amsterdam, Netherlands
| | - Toni Mancini
- Department of Computer Science, Faculty of Information Engineering, Computer Science and Statistics, Sapienza University of Rome, Rome, Italy
| | - Stefano Ferracuti
- Department of Human Neurosciences, Faculty of Medicine and Dentistry, Sapienza University of Rome, Rome, Italy
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Sonnweber M, Lau S, Kirchebner J. Exploring Characteristics of Homicide Offenders With Schizophrenia Spectrum Disorders Via Machine Learning. INTERNATIONAL JOURNAL OF OFFENDER THERAPY AND COMPARATIVE CRIMINOLOGY 2024; 68:713-732. [PMID: 35730542 DOI: 10.1177/0306624x221102799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The link between schizophrenia and homicide has long been the subject of research with significant impact on mental health policy, clinical practice, and public perception of people with psychiatric disorders. The present study investigates factors contributing to completed homicides committed by offenders diagnosed with schizophrenia referred to a Swiss forensic institution, using machine learning algorithms. Data were collected from 370 inpatients at the Centre for Inpatient Forensic Therapy at the Zurich University Hospital of Psychiatry. A total of 519 variables were explored to differentiate homicidal and other (violent and non-violent) offenders. The dataset was split employing variable filtering, model building, and selection embedded in a nested resampling approach. Ten factors regarding criminal and psychiatric history and clinical factors were identified to be influential in differentiating between homicidal and other offenders. Findings expand the research on influential factors for completed homicide in patients with schizophrenia. Limitations, clinical relevance, and future directions are discussed.
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Parsaei M, Arvin A, Taebi M, Seyedmirzaei H, Cattarinussi G, Sambataro F, Pigoni A, Brambilla P, Delvecchio G. Machine Learning for prediction of violent behaviors in schizophrenia spectrum disorders: a systematic review. Front Psychiatry 2024; 15:1384828. [PMID: 38577400 PMCID: PMC10991827 DOI: 10.3389/fpsyt.2024.1384828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2024] [Accepted: 03/08/2024] [Indexed: 04/06/2024] Open
Abstract
Background Schizophrenia spectrum disorders (SSD) can be associated with an increased risk of violent behavior (VB), which can harm patients, others, and properties. Prediction of VB could help reduce the SSD burden on patients and healthcare systems. Some recent studies have used machine learning (ML) algorithms to identify SSD patients at risk of VB. In this article, we aimed to review studies that used ML to predict VB in SSD patients and discuss the most successful ML methods and predictors of VB. Methods We performed a systematic search in PubMed, Web of Sciences, Embase, and PsycINFO on September 30, 2023, to identify studies on the application of ML in predicting VB in SSD patients. Results We included 18 studies with data from 11,733 patients diagnosed with SSD. Different ML models demonstrated mixed performance with an area under the receiver operating characteristic curve of 0.56-0.95 and an accuracy of 50.27-90.67% in predicting violence among SSD patients. Our comparative analysis demonstrated a superior performance for the gradient boosting model, compared to other ML models in predicting VB among SSD patients. Various sociodemographic, clinical, metabolic, and neuroimaging features were associated with VB, with age and olanzapine equivalent dose at the time of discharge being the most frequently identified factors. Conclusion ML models demonstrated varied VB prediction performance in SSD patients, with gradient boosting outperforming. Further research is warranted for clinical applications of ML methods in this field.
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Affiliation(s)
- Mohammadamin Parsaei
- Maternal, Fetal & Neonatal Research Center, Family Health Research Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Alireza Arvin
- Center for Orthopedic Trans-disciplinary Applied Research (COTAR), Tehran University of Medical Sciences, Tehran, Iran
| | - Morvarid Taebi
- Center for Orthopedic Trans-disciplinary Applied Research (COTAR), Tehran University of Medical Sciences, Tehran, Iran
| | - Homa Seyedmirzaei
- Sports Medicine Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran
| | - Giulia Cattarinussi
- Department of Neuroscience (DNS), Padua Neuroscience Center, University of Padova, Padua, Italy
- Padua Neuroscience Center, University of Padova, Padua, Italy
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, United Kingdom
| | - Fabio Sambataro
- Department of Neuroscience (DNS), Padua Neuroscience Center, University of Padova, Padua, Italy
- Padua Neuroscience Center, University of Padova, Padua, Italy
| | - Alessandro Pigoni
- Social and Affective Neuroscience Group, MoMiLab, Institutions, Markets, Technologies (IMT) School for Advanced Studies Lucca, Lucca, Italy
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Paolo Brambilla
- Social and Affective Neuroscience Group, MoMiLab, Institutions, Markets, Technologies (IMT) School for Advanced Studies Lucca, Lucca, Italy
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
- Department of Neurosciences and Mental Health, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
| | - Giuseppe Delvecchio
- Department of Neurosciences and Mental Health, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy
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Tortora L. Beyond Discrimination: Generative AI Applications and Ethical Challenges in Forensic Psychiatry. Front Psychiatry 2024; 15:1346059. [PMID: 38525252 PMCID: PMC10958425 DOI: 10.3389/fpsyt.2024.1346059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/31/2024] [Indexed: 03/26/2024] Open
Abstract
The advent and growing popularity of generative artificial intelligence (GenAI) holds the potential to revolutionise AI applications in forensic psychiatry and criminal justice, which traditionally relied on discriminative AI algorithms. Generative AI models mark a significant shift from the previously prevailing paradigm through their ability to generate seemingly new realistic data and analyse and integrate a vast amount of unstructured content from different data formats. This potential extends beyond reshaping conventional practices, like risk assessment, diagnostic support, and treatment and rehabilitation plans, to creating new opportunities in previously underexplored areas, such as training and education. This paper examines the transformative impact of generative artificial intelligence on AI applications in forensic psychiatry and criminal justice. First, it introduces generative AI and its prevalent models. Following this, it reviews the current applications of discriminative AI in forensic psychiatry. Subsequently, it presents a thorough exploration of the potential of generative AI to transform established practices and introduce novel applications through multimodal generative models, data generation and data augmentation. Finally, it provides a comprehensive overview of ethical and legal issues associated with deploying generative AI models, focusing on their impact on individuals as well as their broader societal implications. In conclusion, this paper aims to contribute to the ongoing discourse concerning the dynamic challenges of generative AI applications in forensic contexts, highlighting potential opportunities, risks, and challenges. It advocates for interdisciplinary collaboration and emphasises the necessity for thorough, responsible evaluations of generative AI models before widespread adoption into domains where decisions with substantial life-altering consequences are routinely made.
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Affiliation(s)
- Leda Tortora
- School of Nursing and Midwifery, Trinity College Dublin, Dublin, Ireland
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Economou A, Kontos J. Testamentary capacity assessment in dementia using artificial intelligence: prospects and challenges. Front Psychiatry 2023; 14:1137792. [PMID: 37324813 PMCID: PMC10264688 DOI: 10.3389/fpsyt.2023.1137792] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 05/15/2023] [Indexed: 06/17/2023] Open
Abstract
Testamentary capacity (TC), a set of capacities involved in making a valid Will, has become prominent in capacity evaluations due to the demographic increase in older persons and associated increase in cognitive impairment. The assessment of contemporaneous TC follows the criteria derived from the Banks v Goodfellow case, which do not bind capacity solely on the basis of presence of a cognitive disorder. Although effort is being made for establishing more objective criteria for TC judgment, variations in situational complexity call for incorporating the different circumstances of the testator in capacity assessment. Artificial intelligence (AI) technologies such as statistical machine learning have been used in forensic psychiatry mainly for the prediction of aggressive behavior and recidivism but little has been done in the area of capacity assessment. However, the statistical machine learning model responses are difficult to interpret and explain, which presents problems with regard to the new General Data Protection Regulation (GDPR) of the European Union. In this Perspective we present a framework for an AI decision support tool for TC assessment. The framework is based on AI decision support and explainable AI (XAI) technology.
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Affiliation(s)
- Alexandra Economou
- Department of Psychology, School of Philosophy, National and Kapodistrian University of Athens, Athens, Greece
| | - John Kontos
- Department of History and Philosophy of Science, National and Kapodistrian University of Athens, Athens, Greece
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Cheng N, Guo M, Yan F, Guo Z, Meng J, Ning K, Zhang Y, Duan Z, Han Y, Wang C. Application of machine learning in predicting aggressive behaviors from hospitalized patients with schizophrenia. Front Psychiatry 2023; 14:1016586. [PMID: 37020730 PMCID: PMC10067917 DOI: 10.3389/fpsyt.2023.1016586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 03/01/2023] [Indexed: 04/07/2023] Open
Abstract
Objective To establish a predictive model of aggressive behaviors from hospitalized patients with schizophrenia through applying multiple machine learning algorithms, to provide a reference for accurately predicting and preventing of the occurrence of aggressive behaviors. Methods The cluster sampling method was used to select patients with schizophrenia who were hospitalized in our hospital from July 2019 to August 2021 as the survey objects, and they were divided into an aggressive behavior group (611 cases) and a non-aggressive behavior group (1,426 cases) according to whether they experienced obvious aggressive behaviors during hospitalization. Self-administered General Condition Questionnaire, Insight and Treatment Attitude Questionnaire (ITAQ), Family APGAR (Adaptation, Partnership, Growth, Affection, Resolve) Questionnaire (APGAR), Social Support Rating Scale Questionnaire (SSRS) and Family Burden Scale of Disease Questionnaire (FBS) were used for the survey. The Multi-layer Perceptron, Lasso, Support Vector Machine and Random Forest algorithms were used to build a predictive model for the occurrence of aggressive behaviors from hospitalized patients with schizophrenia and to evaluate its predictive effect. Nomogram was used to build a clinical application tool. Results The area under the receiver operating characteristic curve (AUC) values of the Multi-Layer Perceptron, Lasso, Support Vector Machine, and Random Forest were 0.904 (95% CI: 0.877-0.926), 0.901 (95% CI: 0.874-0.923), 0.902 (95% CI: 0.876-0.924), and 0.955 (95% CI: 0.935-0.970), where the AUCs of the Random Forest and the remaining three models were statistically different (p < 0.0001), and the remaining three models were not statistically different in pair comparisons (p > 0.5). Conclusion Machine learning models can fairly predict aggressive behaviors in hospitalized patients with schizophrenia, among which Random Forest has the best predictive effect and has some value in clinical application.
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Affiliation(s)
- Nuo Cheng
- Department of Clinical Medicine, Zhengzhou University, Zhengzhou, Henan, China
| | - Meihao Guo
- Department of Infection Prevention and Control, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Fang Yan
- Department of Infection Prevention and Control, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Zhengjun Guo
- Henan Mental Disease Prevention and Control Center, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Jun Meng
- Editorial Department of Journal of Clinical Psychosomatic Diseases, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Kui Ning
- Department of Medical Administration, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Yanping Zhang
- Department of Medicine, Zhengzhou University, Zhengzhou, Henan, China
| | - Zitian Duan
- The Seventh Psychiatric Department, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
| | - Yong Han
- Henan Key Laboratory of Biological Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
- *Correspondence: Han Yong,
| | - Changhong Wang
- Department of Clinical Psychiatry, The Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan, China
- Wang Changhong,
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Yan C, Ding C, Duan G. PMMS: Predicting essential miRNAs based on multi-head self-attention mechanism and sequences. Front Med (Lausanne) 2022; 9:1015278. [DOI: 10.3389/fmed.2022.1015278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 10/25/2022] [Indexed: 11/18/2022] Open
Abstract
Increasing evidence has proved that miRNA plays a significant role in biological progress. In order to understand the etiology and mechanisms of various diseases, it is necessary to identify the essential miRNAs. However, it is time-consuming and expensive to identify essential miRNAs by using traditional biological experiments. It is critical to develop computational methods to predict potential essential miRNAs. In this study, we provided a new computational method (called PMMS) to identify essential miRNAs by using multi-head self-attention and sequences. First, PMMS computes the statistic and structure features and extracts the static feature by concatenating them. Second, PMMS extracts the deep learning original feature (BiLSTM-based feature) by using bi-directional long short-term memory (BiLSTM) and pre-miRNA sequences. In addition, we further obtained the multi-head self-attention feature (MS-based feature) based on BiLSTM-based feature and multi-head self-attention mechanism. By considering the importance of the subsequence of pre-miRNA to the static feature of miRNA, we obtained the deep learning final feature (WA-based feature) based on the weighted attention mechanism. Finally, we concatenated WA-based feature and static feature as an input to the multilayer perceptron) model to predict essential miRNAs. We conducted five-fold cross-validation to evaluate the prediction performance of PMMS. The areas under the ROC curves (AUC), the F1-score, and accuracy (ACC) are used as performance metrics. From the experimental results, PMMS obtained best prediction performances (AUC: 0.9556, F1-score: 0.9030, and ACC: 0.9097). It also outperformed other compared methods. The experimental results also illustrated that PMMS is an effective method to identify essential miRNA.
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Yu T, Pei W, Xu C, Zhang X, Deng C. Prediction of violence in male schizophrenia using sMRI, based on machine learning algorithms. BMC Psychiatry 2022; 22:676. [PMID: 36320010 PMCID: PMC9628088 DOI: 10.1186/s12888-022-04331-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
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.
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Affiliation(s)
- Tao Yu
- grid.452190.b0000 0004 1782 5367Anhui Mental Health Center; Affiliated Psychological Hospital of Anhui Medical University; Hefei Fourth People’s Hospital; Anhui Clinical Research Center for Mental Disorders, Hefei, 230022 Anhui China
| | - Wenzhi Pei
- grid.452190.b0000 0004 1782 5367Anhui Mental Health Center; Affiliated Psychological Hospital of Anhui Medical University; Hefei Fourth People’s Hospital; Anhui Clinical Research Center for Mental Disorders, Hefei, 230022 Anhui China
| | - Chunyuan Xu
- grid.452190.b0000 0004 1782 5367Anhui Mental Health Center; Affiliated Psychological Hospital of Anhui Medical University; Hefei Fourth People’s Hospital; Anhui Clinical Research Center for Mental Disorders, Hefei, 230022 Anhui China
| | - Xulai Zhang
- Anhui Mental Health Center; Affiliated Psychological Hospital of Anhui Medical University; Hefei Fourth People's Hospital; Anhui Clinical Research Center for Mental Disorders, Hefei, 230022, Anhui, China.
| | - Chenchen Deng
- Anhui Province Maternity & Child Health Hospital, Hefei, 230022, Anhui, China.
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Tesli N, Bell C, Hjell G, Fischer-Vieler T, I Maximov I, Richard G, Tesli M, Melle I, Andreassen OA, Agartz I, Westlye LT, Friestad C, Haukvik UK, Rokicki J. The age of violence: Mapping brain age in psychosis and psychopathy. Neuroimage Clin 2022; 36:103181. [PMID: 36088844 PMCID: PMC9474919 DOI: 10.1016/j.nicl.2022.103181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Revised: 07/31/2022] [Accepted: 08/30/2022] [Indexed: 12/14/2022]
Abstract
Young chronological age is one of the strongest predictors for antisocial behaviour in the general population and for violent offending in individuals with psychotic disorders. An individual's age can be predicted with high accuracy using neuroimaging and machine-learning. The deviation between predicted and chronological age, i.e., brain age gap (BAG) has been suggested to reflect brain health, likely relating partly to neurodevelopmental and aging-related processes and specific disease mechanisms. Higher BAG has been demonstrated in patients with psychotic disorders. However, little is known about the brain-age in violent offenders with psychosis and the possible associations with psychopathy traits. We estimated brain-age in 782 male individuals using T1-weighted MRI scans. Three machine learning models (random forest, extreme gradient boosting with and without hyper parameter tuning) were first trained and tested on healthy controls (HC, n = 586). The obtained BAGs were compared between HC and age matched violent offenders with psychosis (PSY-V, n = 38), violent offenders without psychosis (NPV, n = 20) and non-violent psychosis patients (PSY-NV, n = 138). We ran additional comparisons between BAG of PSY-V and PSY-NV and associations with Positive and Negative Syndrome Scale (PANSS) total score as a measure of psychosis symptoms. Psychopathy traits in the violence groups were assessed with Psychopathy Checklist-revised (PCL-R) and investigated for associations with BAG. We found significantly higher BAG in PSY-V compared with HC (4.9 years, Cohen'sd = 0.87) and in PSY-NV compared with HC (2.7 years, d = 0.41). Total PCL-R scores were negatively associated with BAG in the violence groups (d = 1.17, p < 0.05). Additionally, there was a positive association between psychosis symptoms and BAG in the psychosis groups (d = 1.12, p < 0.05). While the significant BAG differences related to psychosis and not violence suggest larger BAG for psychosis, the negative associations between BAG and psychopathy suggest a complex interplay with psychopathy traits. This proof-of-concept application of brain age prediction in severe mental disorders with a history of violence and psychopathy traits should be tested and replicated in larger samples.
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Affiliation(s)
- Natalia Tesli
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Christina Bell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Gabriela Hjell
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatry, Østfold Hospital Trust, Graalum, Norway
| | - Thomas Fischer-Vieler
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Vestre Viken Hospital Trust, Drammen, Norway
| | - Ivan I Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Genevieve Richard
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Martin Tesli
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway; Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Ingrid Melle
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Adult Psychiatry, Institute of Clinical Medicine, University of Oslo, Norway
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway; Centre for Psychiatry Research, Department of Clinical Neuroscience, Karolinska Institutet & Stockholm Health Care Services, Stockholm County Council, Stockholm, Sweden
| | - Lars T Westlye
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Christine Friestad
- Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway; University College of Norwegian Correctional Service, Oslo, Norway
| | - Unn K Haukvik
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Psychiatry, Oslo University Hospital, Oslo, Norway; Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway
| | - Jaroslav Rokicki
- Norwegian Centre for Mental Disorders Research (NORMENT), Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Centre of Research and Education in Forensic Psychiatry, Oslo University Hospital, Oslo, Norway.
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Parmigiani G, Barchielli B, Casale S, Mancini T, Ferracuti S. The impact of machine learning in predicting risk of violence: A systematic review. Front Psychiatry 2022; 13:1015914. [PMID: 36532168 PMCID: PMC9751313 DOI: 10.3389/fpsyt.2022.1015914] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 11/07/2022] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Inpatient violence in clinical and forensic settings is still an ongoing challenge to organizations and practitioners. Existing risk assessment instruments show only moderate benefits in clinical practice, are time consuming, and seem to scarcely generalize across different populations. In the last years, machine learning (ML) models have been applied in the study of risk factors for aggressive episodes. The objective of this systematic review is to investigate the potential of ML for identifying risk of violence in clinical and forensic populations. METHODS Following Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, a systematic review on the use of ML techniques in predicting risk of violence of psychiatric patients in clinical and forensic settings was performed. A systematic search was conducted on Medline/Pubmed, CINAHL, PsycINFO, Web of Science, and Scopus. Risk of bias and applicability assessment was performed using Prediction model Risk Of Bias ASsessment Tool (PROBAST). RESULTS We identified 182 potentially eligible studies from 2,259 records, and 8 papers were included in this systematic review. A wide variability in the experimental settings and characteristics of the enrolled samples emerged across studies, which probably represented the major cause for the absence of shared common predictors of violence found by the models learned. Nonetheless, a general trend toward a better performance of ML methods compared to structured violence risk assessment instruments in predicting risk of violent episodes emerged, with three out of eight studies with an AUC above 0.80. However, because of the varied experimental protocols, and heterogeneity in study populations, caution is needed when trying to quantitatively compare (e.g., in terms of AUC) and derive general conclusions from these approaches. Another limitation is represented by the overall quality of the included studies that suffer from objective limitations, difficult to overcome, such as the common use of retrospective data. CONCLUSION Despite these limitations, ML models represent a promising approach in shedding light on predictive factors of violent episodes in clinical and forensic settings. Further research and more investments are required, preferably in large and prospective groups, to boost the application of ML models in clinical practice. SYSTEMATIC REVIEW REGISTRATION [www.crd.york.ac.uk/prospero/], identifier [CRD42022310410].
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Affiliation(s)
| | - Benedetta Barchielli
- Department of Dynamic and Clinical Psychology, and Health Studies, Sapienza University of Rome, Rome, Italy
| | - Simona Casale
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Toni Mancini
- Department of Computer Science, Sapienza University of Rome, Rome, Italy
| | - Stefano Ferracuti
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
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