Visualisations of the core assumptions of normative modelling: (A) The parameters of the fitted normative model are independent of the time of sampling. (B) People of the same age are comparable irrespective of their year of birth (datasets sampled at different times can be combined)

Probability of detecting a true disruption Δ for various values of autocorrelation ρ (θ = 0.05)

The overview of the analytical pipeline for our schizophrenia patients: First, data are preprocessed using Freesurfer’s longitudinal pipeline. Subsequently, the pre-trained models are adjusted to a local sample of healthy controls. The site-specific measurement noise variance in healthy subjects is estimated using held-out controls, and finally, the z-diff score is computed.

Clinical description of the dataset after quality control

The effect of preprocessing across all subjects and IDPs: (A) Cross-sectional preprocessing: Heatmap of the difference of the original z-scores (z(2) − z(1)) on held-out controls. (B) Longitudinal preprocessing: Heatmap of the difference of the original z-scores (z(2) −z(1)) on held-out controls. (C) Histogram of the average (z(2) −z(1)) across all IDPs stratified by health status and preprocessing. (D) Histogram of the average (z(2) − z(1)) of each subject stratified by health status and preprocessing.

Cross-sectional results for each visit separately: p-values of Mann-Whitney U test between patients and held-out controls surviving Benjamini-Hochberg correction. The sign indicates the direction of change (negative means lower thickness in patients).

Regions significantly changed between the visits: Map of regions significantly changed between the two visits (centre). Each region is described using a scatterplot of z-scores across all patients for both visits (the x-axis describes age, and the y-axis depicts the z-score. Blue dots represent the first and pink dots represent the second visit). The grey dashed line highlights z=0. Histograms in the golden circles depict the distribution of the z-diff score.

Results of the PCA analysis: (A) Scree plot of the explained variance of PCA components. (B) Scatterplot of change in the GAF scale vs. the change in the PANSS scale (C Left) Scatter plot of the first PCA component and difference in the GAF scale. (C Right) Heatmap of PCA loadings for the first component. (D Left) Scatter plot of the second PCA component and difference in the PANSS scale. (D Right) A Heatmap of PCA loadings for the second component. (E) Average z-diff score.

Quality of fit as measured by Rho for the first and the second visit.

Regions significantly changed between the visits (longitudinal preprocessing): Map of regions significantly changed between the two visits (centre). Each region is described using a scatterplot of z-diff across all patients for both visits (the x-axis describes age, and the y-axis depicts the z-diff. Blue dots represent individual patients and the pink line shows a trend of z-diff change). The Grey dashed line highlights z=0. Histograms in the golden circles depict the distribution of the z-diff score.

Regions significantly changed between the visits (cross-sectional preprocessing): Map of regions significantly changed between the two visits (centre). Each region is described using a scatterplot of z-diff scores across all patients for both visits (the x-axis describes age, and the y-axis depicts the z-diff score. The grey dashed line highlights z=0. Histograms in the golden circles depict the distribution of the z-diff score.

Raw changes in grey matter thickness: Each significantly changed region is presented twice, once as a scatter plot containing the original grey matter thickness for both visits (left); females are plotted in pink, males in blue. The figure on the right depicts visit 2 minus visit 1 in raw thicknesses (separately for females – pink, and males – blue).