4.8 Article

Cognitive deficits, clinical variables, and white matter microstructure in schizophrenia: a multisite harmonization study

Journal

MOLECULAR PSYCHIATRY
Volume 27, Issue 9, Pages 3719-3730

Publisher

SPRINGERNATURE
DOI: 10.1038/s41380-022-01731-3

Keywords

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Funding

  1. National Institutes of Health (NIH) [R01MH102377, K24MH110807, R01MH119222, R03 MH110745, K01 MH115247-01A1]
  2. VA Merit Award [U01 MH109977, R01MH108574]
  3. MRC [G0500092, R01MH076995, P50MH080173, 1R01 MH102318-01A1, R01MH092440, MH078113, MH077851, MH077945, MH077862, 1R01MH102324-01A1]
  4. Swiss National Science Foundation (SNF) [152619]
  5. National Research Foundation of Korea (NRF) [NRF-2012R1A1A1006514]
  6. University of Cincinnati Schizophrenia Research Fund (JAS)
  7. BWH Program for Interdisciplinary Neuroscience
  8. Harvard Medical School Livingston Fellowship Award
  9. BBRF NARSAD Young Investigator
  10. Brain & Behavior Research Foundation

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Cognitive deficits are highly prevalent in individuals with schizophrenia and are associated with positive symptoms and medication dosage. White matter microstructure plays a mediating role in the association between schizophrenia and cognitive deficits.
Cognitive deficits are among the best predictors of real-world functioning in schizophrenia. However, our understanding of how cognitive deficits relate to neuropathology and clinical presentation over the disease lifespan is limited. Here, we combine multi-site, harmonized cognitive, imaging, demographic, and clinical data from over 900 individuals to characterize a) cognitive deficits across the schizophrenia lifespan and b) the association between cognitive deficits, clinical presentation, and white matter (WM) microstructure. Multimodal harmonization was accomplished using T-scores for cognitive data, previously reported standardization methods for demographic and clinical data, and an established harmonization method for imaging data. We applied t-tests and correlation analysis to describe cognitive deficits in individuals with schizophrenia. We then calculated whole-brain WM fractional anisotropy (FA) and utilized regression-mediation analyses to model the association between diagnosis, FA, and cognitive deficits. We observed pronounced cognitive deficits in individuals with schizophrenia (p < 0.006), associated with more positive symptoms and medication dosage. Regression-mediation analyses showed that WM microstructure mediated the association between schizophrenia and language/processing speed/working memory/non-verbal memory. In addition, processing speed mediated the influence of diagnosis and WM microstructure on the other cognitive domains. Our study highlights the critical role of cognitive deficits in schizophrenia. We further show that WM is crucial when trying to understand the role of cognitive deficits, given that it explains the association between schizophrenia and cognitive deficits (directly and via processing speed).

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