Poster No:
524
Submission Type:
Abstract Submission
Authors:
Kami Pearson1, Cindy An2, Katrina Aberizk2, Jessica Turner2
Institutions:
1Neuroscience Graduate Program, The Ohio State University, Columbus, OH, 2Wexner Medical Center, The Ohio State University, Columbus, OH
First Author:
Kami Pearson
Neuroscience Graduate Program, The Ohio State University
Columbus, OH
Co-Author(s):
Cindy An
Wexner Medical Center, The Ohio State University
Columbus, OH
Katrina Aberizk
Wexner Medical Center, The Ohio State University
Columbus, OH
Introduction:
Schizophrenia affects 1% of the U.S. population and is defined by the DSM-5 as two or more persistent symptoms, with at least one positive symptom, such as hallucinations or delusions. Negative symptoms (anhedonia, avolition, asociality, blunted affect, and alogia) overlap with other disorders and are not alleviated by antipsychotic medication, complicating diagnosis and treatment (Kahn, 2020; Luvsannyam et al., 2022). Reduced functional connectivity between the orbitofrontal cortex (OFC) and ventral putamen, and between cerebellar subregion crus II and the parietal lobe in first-episode psychosis, is linked to negative symptoms in schizophrenia (Choi et al., 2023). However, the cerebellum's influence on the OFC, particularly in chronic schizophrenia, and how this may relate to negative symptoms requires further review. Using a combined theory-driven and data-driven approach, we aim to determine if schizophrenia patients with more severe negative symptoms show reduced connectivity within the cerebellum and between the cerebellum and the OFC, relative to those with more severe positive symptoms.
Methods:
Resting-state fMRI data was obtained from the Centers of Biomedical Research Excellence (COBRE) Phase 1 dataset, including schizophrenia patients (n=55) and age-matched healthy controls (n=78). 150 whole brain volumes were acquired using a Siemens 3 T TIM Trio scanner (Aine et al., 2017). ENIGMA HALFpipe was used for fMRI preprocessing and quality assessment (Waller et al., 2022). Blood-oxygenation level dependent (BOLD) signal time series were extracted for 400 regions defined by the Schaeffer combined atlas, including the Buckner 17 network cerebellar atlas. A general linear model of cases vs controls identified cerebellar subregions of interest through amplitude of low frequency fluctuations (ALFF) and fractional ALFF (fALFF) analysis using a cerebellar MNI mask (Zou et al., 2008). These coordinates were matched to the Buckner cerebellar atlas and identified three distinct regions, each corresponding with crus I and II (Buckner et al., 2011; Wang et al., 2020). These cerebellar regions, along with OFC and striatal regions , were used to generate connectivity maps using group iterative multiple model estimation (GIMME), which reliably reveals group-level connections among regions of interest and predicts the direction of influence (Weigard et al., 2019). In addition to healthy control (HC) vs. schizophrenia (SZ), three schizophrenia-only models were created based on: (1) a median split of positive scores, (2) a median split of negative scores, and (3) the difference between the two (predominance of positive or negative symptoms) according to the positive and negative syndrome scale (PANSS).
Results:
Effective connectivity maps including autoregressive, contemporaneous, and lagged estimates were generated for each model. When groupings were defined by positive symptom severity alone or positive symptom predominance, unique lagged estimates were identified between crus I and II. This finding persisted regardless of which striatal regions were included in the model. However, in the models of HC vs SZ, and based on negative symptom severity alone, these subgroup-level paths were lost.
Conclusions:
The findings of this study stress the importance of distinguishing schizophrenia subtypes in understanding its neurobiological basis. GIMME revealed considerable heterogeneity between HC and SZ groups, complicating the detection of subgroup-specific connectivity patterns. However, a reduction in connectivity between crus I and II was observed in patients with more severe negative symptoms relative to those with more severe positive symptoms, highlighting the potential role of cerebellar dysfunction in the pathophysiology of negative symptoms. The results suggest that therapeutic strategies targeting cerebellar connectivity may be valuable for treating negative symptoms in schizophrenia.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis
Keywords:
Cerebellum
Cortex
Data analysis
FUNCTIONAL MRI
Modeling
Psychiatric Disorders
Sub-Cortical
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.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Patients
Was this research conducted in the United States?
Yes
Are you Internal Review Board (IRB) certified?
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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Please indicate which methods were used in your research:
Functional MRI
For human MRI, what field strength scanner do you use?
3.0T
Which processing packages did you use for your study?
Other, Please list
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ENIGMA HALFpipe
Provide references using APA citation style.
1. Aine, C. J., et al. (2017). Multimodal Neuroimaging in Schizophrenia: Description and Dissemination. Neuroinformatics, 15(4), 343–364. https://doi.org/10.1007/s12021-017-9338-9
2. Buckner, R. L., et al. (2011). The organization of the human cerebellum estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(5), 2322–2345. https://doi.org/10.1152/jn.00339.2011
3. Choi, S. Y., et al. (2023). Altered intrinsic cerebellar-cerebral functional connectivity is related to negative symptoms in patients with first-episode psychosis. Schizophrenia Research, 252, 56–63. https://doi.org/10.1016/j.schres.2022.12.041
4. Kahn, R. S. (2020). On the Origins of Schizophrenia. American Journal of Psychiatry, 177(4), 291–297. https://doi.org/10.1176/appi.ajp.2020.20020147
5. Luvsannyam, E., et al. (2022). Neurobiology of Schizophrenia: A Comprehensive Review. Cureus. https://doi.org/10.7759/cureus.23959
6. Waller, L., et al. (2022). ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data. Human Brain Mapping, 43(9), 2727-2742. https://doi.org/10.1002/hbm.25829
7. Wang, K., et al. (2020). Association of γ-aminobutyric acid and glutamate/glutamine in the lateral prefrontal cortex with patterns of intrinsic functional connectivity in adults. Brain Structure and Function, 225(7), 1903–1919. https://doi.org/10.1007/s00429-020-02084-9
8. Weigard, A., et al. (2019). Characterizing the role of the pre‐SMA in the control of speed/accuracy trade‐off with directed functional connectivity mapping and multiple solution reduction. Human Brain Mapping, 40(6), 1829–1843. https://doi.org/10.1002/hbm.24493
9. Zou, Q.-H., et al. (2008). An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: Fractional ALFF. Journal of Neuroscience Methods, 172(1), 137–141. https://doi.org/10.1016/j.jneumeth.2008.04.012
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