Heterogeneity of morphometric similarity networks in health and schizophrenia

Abstract

Reduced structural network connectivity is proposed as a biomarker for chronic schizophrenia. This study assessed regional morphometric similarity as an indicator of cortical inter-regional connectivity, employing longitudinal normative modeling to evaluate whether decreases are consistent across individuals with schizophrenia. Normative models were trained and validated using data from healthy controls (n = 4310). Individual deviations from these norms were measured at baseline and follow-up, and categorized as infra-normal, normal, or supra-normal. Additionally, we assessed the change over time in the total number of infra- or supra-normal regions for each individual. At baseline, patients exhibited reduced morphometric similarity within the default mode network compared to healthy controls. The proportion of patients with infra- or supra-normal values in any region at both baseline and follow-up was low (<6%) and similar to that of healthy controls. Mean intra-group changes in the number of infra- or supra-normal regions over time were minimal (<1) for both the schizophrenia and control groups, with no significant differences observed between them. Normative modeling with multiple timepoints enables the identification of patients with significant static decreases and dynamic changes of morphometric similarity over time and provides further insight into the pervasiveness of morphometric similarity abnormalities across individuals with chronic schizophrenia.

Fig. 1. Age distributions of datasets.

Eleven datasets were used in the study. Ten datasets were cross-sectional and included healthy participants. For each of these ten datasets, 90% of individuals were included in the training set and 10% were part of the test set. One longitudinal clinical dataset (two timepoints) included healthy controls and individuals with chronic schizophrenia. Of the healthy controls belonging to the longitudinal clinical dataset, 20% were included in the training set and 80% in the test set. All individuals with schizophrenia were included in the test set. The age distribution for each dataset within the training/test samples is shown. The table shows the descriptives per dataset. N, number of subjects.

Fig. 2. Study overview.

A Ten cross-sectional datasets consisting of healthy individuals only and one longitudinal dataset consisting of healthy controls and individuals with chronic schizophrenia (Utrecht) were used in the study. B morphometric similarity matrices were constructed following established protocols using cortical thickness, cortical volume, surface area, mean curvature, and Gaussian curvature extracted for each cortical region. C Example centile plot from normative modeling of regional morphometric similarity of the left hemispheric superior frontal gyrus for assessing individual deviance (Z). Normative modeling was done following established protocols using prediction on unseen test data following training of the model. D Cortical maps depicting percentage of extreme deviance below the norm, i.e., infra-normal deviance, in individuals with schizophrenia and healthy individuals. n number of subjects, CT cortical thickness, SA surface area, CV cortical volume, MC mean curvature, GC Gaussian curvature, MS morphometric similarity.

Fig. 3. Group comparisons of network z-scores.

Violin plots of significant (FDR-corrected) differences in z-scores of morphometric similarity of two functional networks that differed significantly between the group of healthy controls and the group of individuals with schizophrenia at baseline.

Fig. 4. Cortical maps showing the percentage of individuals with infra- and supra-deviance morphometric similarity per region.

A At baseline and B follow-up. C Group differences in the change in z-scores over time, with light red indicating uncorrected group differences and dark red indicating FDR-corrected group differences. No corrected significant group differences were observed.

Fig. 5. ‘Raw’ morphometric similarity.

Violin plots of significant (FDR-corrected) differences in z-scores and ‘raw’ morphometric similarity (i.e., traditional values rather than normative modeling-based z-scores) of two functional networks between the group of healthy controls and the group of individuals with schizophrenia. MS, morphometric similarity.