Poster No:
475
Submission Type:
Abstract Submission
Authors:
Natalie Remiszewski1, Maria Stanica2, Saige Rutherford3, Ravi Tripathi1, Gerhard Hellemann2, Amanda Shen4, Scott Sponheim4, Adrienne Lahti2, Junghee Lee2, Andre Marquand3, Nina Kraguljac1
Institutions:
1The Ohio State University, Columbus, OH, 2University of Alabama at Birmingham, Birmingham, AL, 3Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands, 4University of Minnesota, Minneapolis, MN
First Author:
Co-Author(s):
Saige Rutherford
Donders Institute for Brain, Cognition and Behaviour
Nijmegen, Netherlands
Junghee Lee
University of Alabama at Birmingham
Birmingham, AL
Andre Marquand
Donders Institute for Brain, Cognition and Behaviour
Nijmegen, Netherlands
Introduction:
Psychotic and affective disorders have traditionally been characterized categorically following Kraepelin's original dichotomy. However, recent clinical, genetic, and neuroanatomical evidence suggests that schizophrenia (SZ), schizoaffective disorder (SZA), and bipolar disorder with psychotic features (BP) may be dimensionally characterized as a psychotic disorders continuum with increasing severity (Crow 1986, Dines, Kes et al. 2024). Previous studies investigating these conceptualizations at the neurobiological level have largely been performed on raw structural variables, leaving significant influence of age and sex. Normative modeling can better contextualize these structural variables and help parse the significant heterogeneity seen in these disorders (Wolfers, Doan et al. 2018, Remiszewski, Bryant et al. 2022, Rutherford, Fraza et al. 2022). With many initiatives currently focused on studying psychiatric disorders based on their underlying neurobiological measures, investigating brain structure of these disorders using normative modeling of cortical thickness will allow us to better understand the relationship between these disorders and how they are characterized.
Methods:
Structural neuroimaging data was collected at the University of Alabama at Birmingham (n=126) and obtained from the Psychosis Human Connectome Project (n=155). Data were processed in FreeSurfer 7.1.1 to quantify region-level cortical thickness measures using the Destrieux parcellation. We used the Predictive Clinical Neuroscience braincharts toolkit to compute individual-level deviations from the reference norm for cortical thickness in individuals diagnosed with either SZ, SZA, or BP. A z-value cutoff of ±2 was used to define extreme positive and negative deviation. ANOVA and KS tests were run to compare the total number and distribution of positive and negative deviations between the three diagnoses. Inter-individual overlap was mapped to visualize spatial distribution of deviations in each group and across the entire patient sample. Finally, individuals were grouped by structural similarity and a chi-squared test was run to analyze the overlap between the structural groups and the diagnostic groups.
Results:
An omnibus ANOVA only showed a significant difference in total number of positive deviations (p=.03). Post-hoc testing revealed a significant difference in the number of positive deviations between the SZA and BP (p=.04). KS testing showed no significant difference in the distribution of positive and negative deviations between the groups. Inter-individual overlap showed no highly consistent regions of deviation nor evidence of a continuum of severity. Chi-squared tests comparing the patient composition of structural and diagnostic groups showed no significant relationship (p=.16).
Conclusions:
Our results demonstrate that, at a large-scale neurobiological level, there is no significant evidence for either the discrete disorders or continuum conceptualization of the three disorders. All three disorders show similar rates of both positive and negative deviation load and similar spatial distribution of deviation while not increasing in severity in a linear fashion. There is also no over-representation of any diagnosis in any of the structural similarity groups, indicating that there is no one neurobiological phenotype representative of any of these disorders. These results highlight the significant heterogeneity seen in psychotic disorders and emphasize the importance of understanding these disorders at the biological, and not just clinical, level. Future directions will include expansion of the dataset and analysis of the relationship between clinical variables and structural abnormalities between the diagnostic groups.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Bayesian Modeling
Other Methods
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
Affective Disorders
Cortex
Open-Source Software
Psychiatric Disorders
Schizophrenia
Other - Normative Modeling; Psychosis; Nosology; Bipolar Disorder; Schizoaffective Disorder
1|2Indicates the priority used for review
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Please indicate which methods were used in your research:
Structural MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
AFNI
Free Surfer
Other, Please list
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Predictive Clinical Neuroscience
Provide references using APA citation style.
Crow, T. J. (1986). "The continuum of psychosis and its implication for the structure of the gene." British Journal of Psychiatry 149: 419-429.
Dines, M., et al. (2024). "Bipolar disorders and schizophrenia: discrete disorders?" Frontiers in Psychiatry 15.
Remiszewski, N., J. E. Bryant, et al. (2022). "Contrasting Case-Control and Normative Reference Approaches to Capture Clinically Relevant Structural Brain Abnormalities in Patients With First-Episode Psychosis Who Are Antipsychotic Naive." JAMA Psychiatry 79(11): 1133-1138.
Rutherford, S., et al. (2022). "Charting brain growth and aging at high spatial precision." eLife 11: e72904.
Wolfers, T., et al. (2018). "Mapping the Heterogeneous Phenotype of Schizophrenia and Bipolar Disorder Using Normative Models." JAMA Psychiatry 75(11): 1146-1155.
No