Poster No:
519
Submission Type:
Abstract Submission
Authors:
Melissa Thalhammer1, Julia Schulz2, Mohamed El Mehdi Oubaggi1, Moritz Bonhoeffer3, Viktor Neumaier3, Rebecca Hippen1, Annalisa Lella4, Jessica Turner5, Theo van Erp6, Paul Thompson7, ENIGMA Schizophrenia Working Group8, Felix Brandl9, Christian Sorg10
Institutions:
1Technical University of Munich, Munich, Bavaria, 2Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM), Munich, Bavaria, 3TU Munich, Munich, Bavaria, 4University of Bari Aldo Moro, Bari, Bari, 5Wexner Medical Center, The Ohio State University, Columbus, OH, 6University of California, Irvine,, Irvine, CA, 7University of Southern California, Los Angeles, CA, 8University of California, Irvine, CA, 9Department of Psychiatry, School of Medicine, TUM, Munich, Germany, 10Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München, Munich, Bavaria
First Author:
Co-Author(s):
Julia Schulz
Department of Neuroradiology, School of Medicine, Technical University of Munich (TUM)
Munich, Bavaria
Felix Brandl
Department of Psychiatry, School of Medicine, TUM
Munich, Germany
Christian Sorg
Department of Psychiatry, Klinikum Rechts der Isar, Technische Universität München
Munich, Bavaria
Introduction:
While thalamic abnormalities are considered a hallmark of schizophrenia, traditional case-control studies have largely overlooked both the distinct roles of individual thalamic nuclei and the marked heterogeneity of the disorder. Recent methodological advances now enable detailed investigation of thalamic nuclei (Iglesias et al., 2018), yet our understanding of individual heterogeneity of thalamic nuclei volumes and their cellular underpinnings remains limited. To address this gap, we used normative modeling to characterize individual deviations of thalamic nuclei volumes in schizophrenia and investigated their relationship with cell-specific markers of core and matrix cells (Hawrylycz et al., 2015; Müller et al., 2020).
Methods:
For this, we analyzed T1-weighted MRI scans from a large multi-site dataset comprising 1,195 schizophrenia patients and 4,574 healthy controls (ages 18 - 85 years) gathered from 25 sites. After cortical reconstruction, the thalamus was segmented into 26 nuclei per hemisphere using FreeSurfer (v7.1.1) (Iglesias et al., 2018). A warped Bayesian Linear Regression model (Fraza et al., 2021; Rutherford et al., 2022) was trained on 80 % of the healthy control data to normatively model sex-specific thalamic nuclei development across adulthood. Thereby, age, sex, and estimated total intracranial volume were entered as covariates and random effects of study site were estimated. The model was tested on the remaining 20 % of healthy controls and applied to schizophrenia patients to determine the individual deviation of each thalamic nucleus. We associated the number of infranormal (i.e., below the 5th percentile) regions per patient with IQ scores where available (n = 354) using Spearman's rank correlation. To investigate cellular underpinnings, we integrated our findings with regional gene expression data of core (PVALB) and matrix (CALB1) cell markers of the thalamus (Hawrylycz et al., 2015; Müller et al., 2020). Next, we applied k-means clustering to divide patients into two groups based on their association with the cell marker genes.
Results:
Normative modeling revealed distinct age-related volume trajectories across thalamic nuclei, confirming previous results using a different modeling approach and cohorts (Huang et al., 2024). Only up to 8 % of individuals with schizophrenia shared infranormal (i.e., below the 5th percentile) nuclei volume in any region, preferentially in dorsomedial, lateral, and pulvinar areas. The number of infranormal deviations per subject was significantly associated with IQ (Spearman rho = -0.14, p = 0.008) but not with other clinical symptoms. Analysis of cellular underpinnings demonstrated no preferential association with either core or matrix cell types across the patient group, highlighting substantial biological heterogeneity between patients. However, cluster analysis based on cell-type associations revealed two distinct patient subgroups, with the core cell-associated cluster showing significantly higher negative symptoms measured by PANSS negative scores compared to the matrix cell-associated group.
Conclusions:
Our findings demonstrate remarkable heterogeneity in thalamic abnormalities across schizophrenia patients, challenging the notion of uniform thalamic pathology. The identification of distinct patient subgroups based on cellular associations, coupled with their relationship to specific symptom domains, suggests potential biological subtypes within schizophrenia. These results highlight the importance of considering individual variation in both research and clinical approaches to schizophrenia.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Other Methods 2
Keywords:
ADULTS
Cellular
Data analysis
Modeling
MRI
Psychiatric Disorders
Schizophrenia
STRUCTURAL MRI
Thalamus
Other - normative modeling
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Other
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Structural MRI
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Free Surfer
Other, Please list
-
Python
Provide references using APA citation style.
1. Fraza, C. J., et al., (2021). Warped Bayesian linear regression for normative modelling of big data. NeuroImage, 245, 118715. https://doi.org/10.1016/j.neuroimage.2021.118715
2. Hawrylycz, M., et al., (2015). Canonical genetic signatures of the adult human brain. Nature Neuroscience, 18(12), 1832–1844. https://doi.org/10.1038/nn.4171
3. Huang, A. S., et al., (2024). Lifespan development of thalamic nuclei and characterizing thalamic nuclei abnormalities in schizophrenia using normative modeling. Neuropsychopharmacology, 49(10), 1518–1527. https://doi.org/10.1038/s41386-024-01837-y
4. Iglesias, J. E., et al., (2018). A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology. NeuroImage, 183, 314–326. https://doi.org/10.1016/j.neuroimage.2018.08.012
5. Müller, E. J., et al., (2020). Core and matrix thalamic sub-populations relate to spatio-temporal cortical connectivity gradients. NeuroImage, 222, 117224. https://doi.org/10.1016/j.neuroimage.2020.117224
6. Rutherford, S., et al., (2022). The normative modeling framework for computational psychiatry. Nature Protocols, 17(7), 1711–1734. https://doi.org/10.1038/s41596-022-00696-5
No