Friday, Jun 27: 3:45 PM - 5:00 PM
1269
Symposium
Brisbane Convention & Exhibition Centre
Room: M3 (Mezzanine Level)
Violence and aggression committed by individuals with mental disorders are major challenges to treatment outcome, and societal safety, and lead to stigmatization of large patient groups. A key to prevent such events is to understand the complex biopsychosocial underpinnings. Neuroimaging research addressing brain anatomical and functional correlates of antisocial behavior and mental disorders, has over the years demonstrated alterations in distinct brain regions that are also implicated in fear processing, reward systems, empathy, and the formation of psychosis symptoms such as hallucinations and delusions. However, the results vary and distinct neural underpinnings remain unclear. This not only limits the translation of findings to clinical and forensic practice, but also calls for novel and timely approaches.
In this symposium, we will present how the application of state-of-the art methodologies in neuroimaging can provide novel insights about the neurobiology of antisocial behaviour across the lifespan and diagnostic boundaries. We will combine research using extensive datasets from international databases and collaborative mega-analyses conducted by research centers worldwide which enable us to explore global behavioral patterns and the associated genetic factors on a large scale, with research studying individual profiles in clinical settings.
Specifically, we will present three novel approaches addressing antisocial behaviour at different levels of target groups from forensic psychiatry via general psychiatry to the general population; that is first how normative modelling may shed light on individual brain heterogeneity among individuals in forensic inpatients and prison inmates, secondly how an unsupervised machine learning tool discovers links between aggression, the social exposome, and brain anatomy in early-stage psychosis and depression, and third how applying an advanced imaging genetics tool to large datasets from the population may reveal novel mechanistic associations driving anti-social behaviour, substance abuse, and brain morphology.
Learning outcomes:
After the symposium the attendees should have an overview over the key challenges and opportunities for using novel neuroimaging tools to disentangle the neural basis of antisocial behaviour and targets for translation to clinical and population cohorts.
1. Understand how normative modelling methods can be applied to reveal regional brain abnormality patterns at the individual level among persons from forensic psychiatric wards and prisons.
2. Learn how unsupervised machine learning is able to capture complex neurobiological and clinical signatures of elusive phenotypes such as antisociality, elucidating the dynamic interplay between brain, behavior, and environment.
3. Recognize how imaging genetics tools can be applied to identify phenotypic overlaps across mental disorders and understand the potential of imaging genetics in advancing precision medicine by linking genetic variations to common phenotypic traits and brain regions.
The target audience for this symposium comprises all scientists interested in antisocial behaviour ranging from clinicians to behavioural cognitive scientists, as well as computational neuroscientists interested in the practical application of novel methodological tools and translation from neural underpinnings to behaviour.
Presentations
Neuroimaging research has revealed brain morphological abnormalities associated with violence and psychosis, however individual differences are substantial and results are not consistent across studies. Recently developed normative modeling of brain MRI features provides a possibility to parse this heterogeneity by mapping inter-individual brain characteristics, whose potential has not yet been explored in forensic psychiatry.
We explored brain heterogeneity in individuals with a history of severe violence with or without a schizophrenia spectrum disorder, non-violent patients with schizophrenia spectrum disorders, and healthy non-violent controls. We utilized lifetime normative trajectories of cortical thickness, surface area, subcortical volumes, and cerebellar volumes. We applied two large-scale publicly available normative models: Destrieux and subcortical atlas-derived regions of interest from 58,836 individuals, and cerebellum normative models based on 27,000 individuals without diagnostic conditions across the lifespan (ages 2-100).
Across groups, we found an overall heterogeneous pattern of individual-level deviations, with a significantly higher frequency of extreme negative deviations in persons with a history of severe violence with or without a schizophrenia spectrum disorder (p = .020, Cohen’s d = .31) and non-violent patients with schizophrenia spectrum disorders (p = .019, d =. 48). Differences were mostly present in subcortical volumes and cortical area, but not thickness, with significant regional group-level differences within the subcallosal and insular cortices, and the cerebellum. Specifically, we found decreased grey matter volumes in the posterior cerebellar hemispheres and the vermal regions in persons with schizophrenia with or without violence history, but with a different subregional pattern.
By applying normative modeling and novel analytical tools, this proof-of-concept study demonstrates the heterogeneous pattern of brain morphometry deviations associated with violence and psychosis. The converging results from group-comparisons and normative modeling analyses illustrate different patterns of cerebellar subregion volume reductions associated with violence in individuals with or without schizophrenia spectrum disorders. These complementary methodological approaches may contribute to improved understanding of the complex underpinnings of violence in forensic psychiatry and warrant further replication.
Presenter
Jaroslav Rokicki, Oslo University Hospital
Centre for Research and Education in Forensic Psychiatry (SIFER), Oslo University Hospital, Norway
Oslo, Oslo
Norway
Antisocial behavior is a multifaceted phenomenon influenced by the interplay of brain structure and social environmental factors across diagnostic boundaries. Investigating these interactions is crucial for understanding their role in clinical trajectories, necessitating advanced mathematical modeling. Therefore, we present a novel multivariate machine learning approach to elucidate the clinical and neuroanatomical complexity of antisocial behavior in a transdiagnostic adolescent and young adult cohort.
We used data from the prospective, multicentric European Personalized Prognostic Tools for Early Psychosis Management (PRONIA) project, including individuals with recent-onset depression or psychosis and clinical high-risk states for psychosis. To detect parsimonious associations between antisocial behavior (PANSS items P4, P7, G4, G8, G14), social environmental factorsexposome (CTQ, EDS, BS, LEE, MSPSS, RSA) and brain structurevolume (GMV, WMV, CSF), the multi-block sparse partial least squares algorithm was employed within a nested cross-validation framework.
The analysis yielded two significant signatures. The first (P = 0.003, Frobenius norm = 2.32) captured an ageing-related GMV pruning pattern of GMV reduction in frontal and parietal regions. The second (P = 0.01, Frobenius norm = 2.22) linked higher levels of hostility (P7) and excitement (P14) and experience of past and present discrimination and childhood trauma (sexual and physical abuse) to GMV reductions in predominantly frontal areas and enlargement of the third ventricle. This finding was particularly pronounced in younger individuals with recent-onset psychosis.
This study presents the first application of the multi-block sparse partial least squares approach to integrate multiple clinical and neurobiological domains to study antisocial behavior in early-stage mental disorders. The multi-level signature linking antisocial traits, frontal GMV reductions, and social adversities in younger individuals with recent-onset psychosis highlights the complex multi-factor etiology of antisocial behavior in first-episode psychosis and emphasizes the need for early assessment, intervention and long-term risk management specifically identified clinical subpopulations.
Presenter
Clara Weyer, LMU Munich
Department of Psychiatry and Psychotherapy
Munich, Bavaria
Germany
Individuals with antisocial behaviour (ASB) exhibit increased risk of substance use disorders, likely linked to shared genetic vulnerabilities and overlapping neural pathways involved in reward processing and impulse control. This susceptibility is further accentuated by the comorbidity of ASB with various psychiatric disorders, suggesting common genetic underpinnings. Despite this there remains a paucity of work examining the shared genetic etiology of ASB, related neuropsychiatric traits and relevant brain regions.
Here we present how combining GWAS of ASB (n=50,252), surface area and thickness of cortical frontal regions, volumes of the amygdala nuclei, psychiatric and substance use phenotypes, and Linkage Disequilibrium Score Regression (LDSC) can be used to assess the genetic correlation between these phenotypes. We then leveraged genetic overlap to boost discovery of genomic loci associated with ASB, and to identify specific shared loci associated with both ASB and each phenotype, using the conditional/conjunctional false discovery rate (cond/conjFDR) approach.
We identified significant genetic correlations between ASPD and drinks per week (rg= 0.28; p=1.33⨉10-9), cannabis (rg= 0.37; p= 1.91⨉10-8), bipolar disorder (BD, rg= 0.20; p=1.95⨉10-5), major depressive disorder (MDD, rg= 0.53; p=1.09⨉10-15) and marginally significant correlations with caudal anterior cingulate surface area (rg=-0.17; p=0.04), superior frontal surface area (rg=0.18; p=0.03), and global measures of cortical surface area (rg=-0.15 ; p=0.02) and thickness (rg=-0.13 ; p=004) and ICV (rg=-0.2 ; p=0.01). A total of 54 loci, representing 127 independent SNPs, became significant after conditioning on related psychiatric disorders, substance use traits and the corpus callosum. These SNPs have been associated with schizophrenia, disruptive behaviour, risk raking behaviours, substance use disorders and cortical surface area in prior GWAS. Two biological processes were implicated: ethanol oxidation and metabolism.
Our results reveal a complex genetic relationship between substance use, psychiatric traits, and ASB. We provide strong evidence for existence of distinct genetic loci exhibiting pleiotropic effects in both ASB and related traits. The findings provide convergent evidence to suggest that substance use traits and psychiatric disorders have shared genetic underpinnings with ASB.
Presenter
Megan Campbell, University of Cape Town Cape Town, Cape Town
South Africa