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Abstract Background Evidence for age related brain white matter (WM) abnormalities in schizophrenia (SZ) has been observed using MRI, and interpreted by various studies as reflecting either developmental, maturational and/or degenerative pathology. Such conflicting findings, mostly due to lack of longitudinal data and statistical power, have hindered consensus on patterns and trajectories of brain dysfunction in SZ. ‘Big Data’ provides a new and powerful means to identify subtle abnormalities across the course of SZ. We have accumulated and processed, what we believe the biggest, to-date, sample of thoughtfully harmonized diffusion MRI (dMRI) cross sectional data, and performed the study aimed at comprehensively characterizing age-related WM changes (trajectories) through the course of SZ. Methods Our dMRI data comprises a total of 1092 participants, aged between 14 and 65. This includes 600 individuals with SZ at different illness stages (383 males, 217 females, age: 31.3+/- 12) and 492 healthy controls-HC (275 males, 217 females, age: 29.8+/-13), from 13 different sites. Preprocessing and dMRI data harmonization based on the rotation invariant spherical harmonics were used to remove the nonlinear scanner and sequence differences across sites. All harmonized data was registered to a common template. Fractional Anisotropy (FA) for Whole Brain (WB) and 14 individual WM regions of interest (ROIs) were computed using a probabilistic tractography atlas. We modeled FA changes over age by quadratic curves (the best fitted model: highest adjusted r^2) and fitted separately to SZ and HC. Peak age and upper and lower bounds of the model were estimated after 5000 bootstraps. FA% differences were modeled at each age between SZ and HC for WB and each ROI, while sex was treated as a confound. The effect sizes (Cohen’s d) between SZ and HC were also computed at each age. Finally, WM pathologies (i.e. based on between-group differences) were clustered into three groups according to d occurring along trajectory using kmeans. Results In WB, FA was lower in SZ comparing to HC at each age, but the percentage differences as well as d varied significantly by age (range of %FA change = [1.5 7], d = [.5 1.8]). Also, WB peaks of FA differed between groups, observed at the age of 33 in HC, shifted to the age of 27 in SZ. Three groups emerged from examining the degree of WM pathology across the age trajectories in ROIs, characterized by: 1) .3Discussion This work provides an initial benchmark for regionally-specific trajectories of WM abnormalities in SZ. Our findings accord with a developmental perspective, suggesting that widely distributed WM deficits emerge early or display perturbed maturation. In addition, it appears that the callosal and long-range association fibers undergo accelerated aging processes. This regional diversity could explain the heterogeneity encountered across previous dMRI studies and suggests that WM pathology in SZ dynamically interacts with maturation and aging processes and manifests itself in regionally-specific brain areas at different ages and disease stages.


Journal article


Schizophrenia bulletin

Publication Date





S178 - S179


Brigham & Women’s Hospital, Harvard Medical School