Objective: The clinical diversity of schizophrenia is reflected by structural brain variability. It remains unclear how this variability manifests across different gray and white matter features. In this meta- and mega-analysis, the authors investigated how brain heterogeneity in schizophrenia is distributed across multimodal structural indicators.

Methods: The authors used the ENIGMA dataset of MRI-based brain measures from 22 international sites with up to 6,037 individuals for a given brain measure. Variability and mean values of cortical thickness, cortical surface area, cortical folding index, subcortical volume, and fractional anisotropy were examined in individuals with schizophrenia and healthy control subjects.

Results: Individuals with schizophrenia showed greater variability in cortical thickness, cortical surface area, subcortical volume, and fractional anisotropy within the frontotemporal and subcortical network. This increased structural variability was mainly associated with psychopathological symptom domains, and the schizophrenia group frequently displayed lower mean values in the respective structural measures. Unexpectedly, folding patterns were more uniform in individuals with schizophrenia, particularly in the right caudal anterior cingulate region. The mean folding values of the right caudal anterior cingulate region did not differ between the schizophrenia and healthy control groups, and folding patterns in this region were not associated with disease-related parameters.

Conclusions: In patients with schizophrenia, uniform folding patterns in the right caudal anterior cingulate region contrasted with the multimodal variability in the frontotemporal and subcortical network. While variability in the frontotemporal and subcortical network was associated with disease-related diversity, uniform folding may indicate a less flexible interplay between genetic and environmental factors during neurodevelopment.