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Structural cerebellar connectivity in schizophrenia

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Teresa Gomez1, , Sivan Jossinger2, John Kruper1, Adam Richie-Halford3, Michal Ben-Shachar2, Jason Yeatman3, Ariel Rokem1,


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1 Department of Psychology, University of Washington
2 English Department and Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Israel
3 Stanford University Graduate School of Education and Stanford University School of Medicine, Division of Developmental-Behavioral Pediatrics
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Background

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  • Schizophrenia (SZ) is a neurodevelopmental psychiatric disorder that carries significant health burden.
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  • The cognitive dysmetria theory of schizophrenia posits that the core cognitive deficits arise from dysfunctions of cortical-thalamic-cerebellar (CTC) circuits. (1)
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  • Previous research found increased functional connectivity in the cerebello-thalamo-cortical circuits in individuals at clinical high risk for psychosis. (2)
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  • This hyperconnectivity was more pronounced in individuals who converted to psychosis, correlated to the severity of symptoms, and was predictive of the time to conversion.
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+The cerebellum sends its output through the superior cerebellar peduncle (SCP), the contralateral red nucleus (RN), and VA/VL of the thalamus to various cerebral areas… The decussation (d) of the cerebero-thalamo-cortical pathway is indicated by the yellow circle. (3) +

The cerebellum sends its output through the superior cerebellar peduncle (SCP), the contralateral red nucleus (RN), and VA/VL of the thalamus to various cerebral areas… The decussation (d) of the cerebero-thalamo-cortical pathway is indicated by the yellow circle. (3)

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QUESTION: Are the physical properties of the white matter tracts of the CTC different in individuals with SZ?

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Methods

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  • Data: the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study (4), which includes diffusion MRI (dMRI) data from 49 participants with SZ (mean age: 36.2 +/- 8.8 SD; 12 female), 40 participants with ADHD (mean age: 31.95 +/- 10.3 SD; 20 female), 49 participants with bipolar disorder (BD) (mean age: 35.3 +/- 9.0 SD, 21 female), and 123 controls (CTRL) (mean age: 31.6 +/- 8.8 SD; 58 female).
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  • The data were processed using QSIPREP (5) and pyAFQ (6) (7)
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  • The SCPs were identified in each individual using pyAFQ and anatomical criteria that capture the decussation of these bundles. (8)
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  • This resulted in tract profiles of fractional anisotropy (FA) and mean diffusivity (MD) in each white matter pathway (100 nodes each).
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  • We then fit generalized additive models (GAMs) for FA and MD in each bundle as a function of diagnosis (entered as a factor: SZ or CTRL), nodeID (modeled with an adaptive smooth, where degree of the smooth is determined as the k that minimizes the AIC), age, sex (entered as a factor) and data quality (quantified as the neighbor correlation from QSIPREP). (9)
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+The left (dark blue) and right (light blue) SCP bundles visualized in an individual with SZ, with sagittal, coronal and axial anatomical views of the T1-weighted scan of this individual. +
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Results

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  • MD differed significantly in the left superior cerebellar peduncle (SCP) between the SZ and CTRL groups (p<0.05), but not between the ADHD and CTRL groups or BD and CTRL groups.
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  • Individuals with SZ had lower MD in this tract than the matched controls.
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  • This finding held after adding medication as a covariate (haloperidol equivalent dosage).
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  • No other significant differences were found.
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Conclusions

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  • We found decreased MD in the left SCP, a component of the CTC.
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  • Lower MD may indicated increased myelination and therefore increased connectivity.
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  • Increased density and directional coherence (but not axonal diameter) may also have similar effects on MD.
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  • These results appear in line with previous fMRI results that found increased functional connectivity in the CTC in individuals with SZ. (2)
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  • This provides additional support for the cognitive dysmetria theory of SZ.
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Acknowledgements

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NIMH grants: MH121867 (PI: Poldrack), MH121868 (PI: Rokem), EB027585 (PI: Garyfallidis)

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References

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1. N. C. Andreasen, S. Paradiso, D. S. O’Leary, Schizophr. Bull. 24, 203–218 (1998).

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2. H. Cao et al., Nature Communications. 9, 3836 (2018).

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3. F. Palesi et al., Brain Struct. Funct. 220, 3369–3384 (2015).

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4. R. Bilder et al., UCLA consortium for neuropsychiatric phenomics la5c study (2020).

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5. M. Cieslak et al., Nat. Methods. 18, 775–778 (2021).

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6. J. Kruper et al., Apert Neuro. 1 (2021).

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7. J. D. Yeatman, R. F. Dougherty, N. J. Myall, B. A. Wandell, H. M. Feldman, PLoS One. 7, e49790 (2012).

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8. S. Jossinger, M. Yablonski, O. Amir, M. Ben-Shachar, Neurobiol. Lang. (Camb.), 1–40 (2023).

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9. N. M. Muncy, A. Kimbler, A. M. Hedges-Muncy, D. L. McMakin, A. T. Mattfeld, Neuroimage Clin. 33, 102937 (2022).

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