Heterogeneity of morphometric similarity networks in health and schizophrenia

Abstract

Introduction: Morphometric similarity is a recently developed neuroimaging phenotype of inter-regional connectivity by quantifying the similarity of a region to other regions based on multiple MRI parameters. Altered average morphometric similarity has been reported in psychotic disorders at the group level, with considerable heterogeneity across individuals. We used normative modeling to address cross-sectional and longitudinal inter-individual heterogeneity of morphometric similarity in health and schizophrenia.

Methods: Morphometric similarity for 62 cortical regions was obtained from baseline and follow-up T1-weighted scans of healthy individuals and patients with chronic schizophrenia. Cortical regions were classified into seven predefined brain functional networks. Using Bayesian Linear Regression and taking into account age, sex, image quality and scanner, we trained and validated normative models in healthy controls from eleven datasets (n = 4310). Individual deviations from the norm (z-scores) in morphometric similarity were computed for each participant for each network and region at both timepoints. A z-score ≧ than 1.96 was considered supra-normal and a z-score ≦ -1.96 infra-normal. As a longitudinal metric, we calculated the change over time of the total number of infra- or supra-normal regions per participant.

Results: At baseline, patients with schizophrenia had decreased morphometric similarity of the default mode network and increased morphometric similarity of the somatomotor network when compared with healthy controls. The percentage of patients with infra- or supra-normal values for any region at baseline and follow-up was low (<6%) and did not differ from healthy controls. Mean intra-group changes over time in the total number of infra- or supra-normal regions were small in schizophrenia and healthy control groups (<1) and there were no significant between-group differences.

Conclusions: In a case-control setting, a decrease of morphometric similarity within the default mode network may be a robust finding implicated in schizophrenia. However, normative modeling suggests that significant reductions and changes over time of regional morphometric similarity are evident only in a minority of patients.

Figure A.

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.

Figure B.

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 Guassian curvature extracted for each cortical region (Seidlitz et al., 2018). 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 (Rutherford et al., 2022) using prediction on unseen test data following training of the model. D. Normative models of each cortical region were clustered based on their typical age-dependency. E. Cortical maps depicting percentage longitudinal change 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.

Figure C.

Clustering of brain regions based on the typical age-dependency of regional morphometric similarity. A. The median normative morphometric similarity of the normative model of each cortical region was entered into a Principal Component Analysis (PCA). The first two components explaining 98.6% of the variance were selected. B. The Euclidean distance matrix was extracted from the PCA output and used for hierarchical clustering. A two cluster solution was determined optimal. C. Each plot shows the median normative fits for a cluster with the remaining normative fits in gray. The average fit across all fits belonging to a cluster is added in black. The cluster solution was also visualized on the cortex displaying the distribution of the two clusters across the cortex. L, left hemisphere, R, right hemisphere. The upper row are lateral views, the bottom row are medial views.

Figure D.

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.

Figure E.

Cortical maps showing the percentage of individuals with infra- and supra-deviance morphometric similarity per region, A) at baseline, B) at follow-up, and C) the change in percentage during follow-up (i.e., percentage at follow-up - percentage at baseline). A negative percentage means that the regional percentage of individuals with infra- or supra-normal deviance decreased during follow-up.