Mapping Inter-Individual Heterogeneity in Psychosis and Severe Violence
Poster No:
528
Submission Type:
Abstract Submission
Authors:
Unn Kristin Haukvik1, Thomas Fischer-Vieler1, Thomas Wolfers2, Milin Kim3, Andreas Ringen4, Ole Andreassen1, Christine Friestad3, Jaroslav Rokicki3
Institutions:
1University of Oslo, Oslo, Oslo, 2University of Tübingen, Tübingen, Baden-Württemberg, 3Oslo University Hospital, Oslo, Oslo, 4Oslo University Hospital, Oslo, Norway
First Author:
Co-Author(s):
Introduction:
Neuroimaging research has revealed brain morphological abnormalities associated with violence and psychosis, however individual differences are substantial and results are not consistent across studies (Fjellvang et al., 2018). 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 (Kim et al., 2024; Rutherford et al., 2022, 2023). In this study, we investigate the deviation patterns in the whole brain, with a particular focus on the cerebellum, which has been linked both to schizophrenia and aggressive behavior (Klaus et al., 2024), to disentangle the putative underlying mechanisms of violence and psychosis.
Methods:
We explored brain heterogeneity in individuals with a history of severe violence with (n=38) or without (n=20) a schizophrenia spectrum disorder, non-violent patients with schizophrenia spectrum disorders (n=138), and healthy non-violent controls (n=196). 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 (Rutherford et al., 2022), and cerebellum normative models based on 27,000 individuals (Kim et al., 2024) without diagnostic conditions across the lifespan (ages 2-100). We investigated group differences, relative frequencies of extreme deviations (|Z|>2), and associations between brain deviations and psychopathy traits (PCL-R scores).
Results:
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), than healthy non-violent participants. Group 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 SSD-V and SSD-NV, but with a different subregional pattern. SSD-V showed abnormalities in the Crus I (p = .025, d = .62) and Vermis IX (p < .001, d = .81) in group-wise comparisons. Individual-level extreme negative deviations were found in the Vermis IX region in 11% of SSD-V, and in subregions of the posterior cerebellum and the vermis in > 10% of nonSSD-V participants. There were no significant associations to psychopathy traits.
Conclusions:
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.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Cerebellum
Computational Neuroscience
Cortex
DISORDERS
Modeling
Schizophrenia
Social Interactions
STRUCTURAL MRI
Sub-Cortical
1|2Indicates the priority used for review
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Provide references using APA citation style.
Kim, M., Leonardsen, E., Rutherford, S., Selbæk, G., Persson, K., Steen, N. E., Smeland, O. B., Ueland, T., Richard, G., Beckmann, C. F., Marquand, A. F., Andreassen, O. A., Westlye, L. T., Wolfers, T., & Moberget, T. (2024). Mapping cerebellar anatomical heterogeneity in mental and neurological illnesses. Nature Mental Health, 2(10), 1196–1207. https://doi.org/10.1038/s44220-024-00297-z
Klaus, J., Wolfs, E. M., & Schutter, D. J. (2024). Cerebellar roots of aggression in violent psychopathic offenders: Evidence from structural neuroimaging studies. Current Opinion in Behavioral Sciences, 55, 101333. https://doi.org/10.1016/j.cobeha.2023.101333
Rutherford, S., Barkema, P., Tso, I. F., Sripada, C., Beckmann, C. F., Ruhe, H. G., & Marquand, A. F. (2023). Evidence for embracing normative modeling. ELife, 12, e85082. https://doi.org/10.7554/eLife.85082
Rutherford, S., Fraza, C., Dinga, R., Kia, S. M., Wolfers, T., Zabihi, M., Berthet, P., Worker, A., Verdi, S., Andrews, D., Han, L. K., Bayer, J. M., Dazzan, P., McGuire, P., Mocking, R. T., Schene, A., Sripada, C., Tso, I. F., Duval, E. R., … Marquand, A. F. (2022). Charting brain growth and aging at high spatial precision. ELife, 11, e72904. https://doi.org/10.7554/eLife.72904