Structural covariance network topology in individuals at clinical high risk for psychosis: the ENIGMA-CHR Study

Abstract

Brain network architecture is anticipated to influence future grey matter loss in individuals at Clinical High Risk (CHR) for psychosis. However, existing studies on grey matter structural network properties in CHR are scarce and constrained by small sample sizes. Here, we examined network topology differences comparing a) CHR versus healthy controls (HC); b) CHR who transitioned to psychosis (CHR-T) versus those who did not (CHR-NT); and c) different subsyndromes. We included structural scans from 1842 CHR individuals and 1417 HC individuals from 31 sites within the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. At the global level, CHR individuals exhibited lower structural covariance (q < 0.001; Cohen's d = 0.164) and less optimal structural network configuration than HC (lower global efficiency and clustering coefficient, d = 0.100,0.087, qs <= 0.027). Though no global difference between CHR-T and CHR-NT, network distinctiveness of the frontal and temporal surface area networks was higher in CHR-T than CHR-NT (d = 0.223,0.237) and HC (d = 0.208,0.219) (qs < 0.001). Network distinctiveness of the frontal cortical thickness network was lower in CHR-T (d = 0.218, q < 0.001) than CHR-NT and HC (d = 0.165, q < 0.001). Importantly, higher network distinctiveness was associated with worse positive symptoms in CHR-NT (frontal surface area, q = 0.008, R² = 0.013) and at trend with worse negative symptoms in CHR-T (frontal thickness, q = 0.063, R² = 0.049). Further, the brief intermittent psychotic syndrome subgroup showed more severe network alterations. Together, brain structural networks inform symptoms and the risk of transition to psychosis in CHR individuals.

Fig. 1. Construction of structural covariance network.

In line with previous work [44], we constructed the structural covariance matrix using cortical thickness, cortical surface area and subcortical volume. For each participant, the cortical thickness and cortical surface area estimates from 34 ROIs [42], and the volumes of 8 subcortical regions, from each hemisphere represented 76 nodes in the covariance matrix after averaging across hemispheres, covariate regression, z-score transformation and covariation calculation based on z-score differences. A high value in the covariance matrix suggests that two nodes differed similarly from the controls (i.e growing or shrinking together). One covariance matrix was obtained for each participant, representing the individualised pattern of covarying brain morphology. ROI region of interest; CHR clinical high risk; L left; R right; M_c mean of the control group; SD_c standard deviation of the control group.

Fig. 2. A less efficient network configuration in CHR.

A mean structural covariance matrix for healthy controls (left) and CHR individuals (right). Rows and columns follow the same order of 76 nodes (top to bottom and left to right). The colours indicate the covariance strength, with the lowest in blue and the highest in red. The white gridlines separate CT, SA and volume nodes. CHR individuals exhibited lower structural covariance than controls. B Bar charts (with standard errors) indicating lower global efficiency, local efficiency, and clustering coefficient in CHR individuals. CHR clinical high risk; CT cortical thickness; SA cortical surface area; Vol subcortical volume.

Fig. 3. Network distinctiveness was higher in frontal and temporal SA communities and lower in a frontal CT community in CHR-T versus CHR-NT.

A Nine identified communities (colours) projected to surface (cortical regions) or structural image (subcortical regions). Among others, SA and CT of occipital and sensorimotor areas, and subcortical brain volumes, were grouped together into the same communities. Separate SA and CT communities were identified for temporal and frontal regions. B Bar charts (with standard errors) visualizing higher frontal and temporal SA system segregation index, and lower frontal CT community segregation index in CHR-T versus CHR-NT (FDR-corrected across 9 communities). Horizontal bar with * indicates significant group difference after FDR correction (q < 0.05). C mean structural covariance matrix for healthy controls (left), CHR-NT (middle) and CHR-T individuals (right). Rows and columns were grouped according to the 9 communities. The colour bars on the left and the bottom of each matrix and the colour boxes along the diagonal of each matrix indicate the community following the same colour scheme in Fig. 3A. Within each matrix, the colours indicate the covariance strength, with the lowest in blue and the highest in red. CHR-T individuals at clinical high risk for psychosis who transited to psychosis later in the follow-up; CHR-NT individuals at clinical high risk for psychosis who did not transit to psychosis later in the follow-up; CT cortical thickness; SA cortical surface area; q FDR corrected p value; FDR false discovery rate.

Fig. 4. Association between network distinctiveness and clinical symptoms.

Residuals, after removing the effects of age, sex and random intercepts of site from the clinical scores estimated using the LMM method, were plotted against the system segregation index of the frontal communities. Each dot represents one participant. The lines indicate the fitted association. The beta and p values were estimated using the LMM method before FDR correction. Upper panel: Higher frontal SA community system segregation index was associated with higher SOPS positive symptom score in CHR-NT (q = 0.008). CHR-T was higher in frontal SA system segregation index and SOPS positive symptom score than CHR-NT. Lower panel: The lower frontal CT community was associated with higher SOPS negative symptom scores in CHR-T (uncorrected p = 0.016, q = 0.063). CHR-T was lower in the frontal CT community system segregation index and higher in SOPS negative symptom scores than CHR-NT. CHR-T individuals at clinical high risk for psychosis who transited to psychosis later in the follow-up; CHR-NT individuals at clinical high risk for psychosis who did not transit to psychosis later in the follow-up; SA cortical surface area; CT cortical thickness; SOPS Scale of Psychosis-risk Symptoms; LMM linear mixed modelling; q FDR corrected p value; FDR false discovery rate; * significant at both uncorrected p < 0.05 and corrected p < 0.05; + significant at uncorrected p < 0.05 but did not survive multiple comparison corrections.

Fig. 5. Schematic representations of network breakdown stages.

A Schematic representations of deficits spreading from stage 1 to 3 of network breakdown. Circles represent nodes in the network. Lines connecting the circles are edges in the network. Two networks are depicted where deficits (orange circle) spread from one network (left) to the other (right). Orange lines indicate the path of spreading from the initial deficit node in stage 1, followed by the spreading to four additional nodes within the network (left) in stage 2, and eventually affecting node of the other network (right) in stage 3. B Within (upper panel) and between (lower panel) network covariance of the frontal CT network. We interpreted the lower frontal CT network distinctiveness in CHR-T than CHR-NT to reflect stage 1 of the frontal CT’s network breakdown, which is driven by the lower within-network covariance (upper panel). C Within (upper panel) and between (lower panel) network covariance of the frontal SA network. We interpreted the higher frontal SA network distinctiveness in CHR-T than CHR-NT to reflect stage 2 of the frontal SA’s network breakdown, which is driven by the higher within-network covariance (upper panel). In B and C, lower between-network covariance in CHR-NT than controls may reflect the lower mean structural covariance in CHR than controls. Horizontal bars indicate significant group differences (q < 0.05). D Schematic representations of within (upper panel) and between (lower panel) network covariance changes across network breakdown stages. We expect that the stage 1 and 2 of network breakdown are dominated by within-network covariance changes. The between-network covariance increase in stage 3 due to deficit spreading across communities. E A schematic summary of the network breakdown stages. * based on the literature [22]. CHR-T individuals at clinical high risk for psychosis who transited to psychosis later in the follow-up; CHR-NT individuals at clinical high risk for psychosis who did not transit to psychosis later in the follow-up; CT cortical thickness; SA cortical surface area; q FDR corrected p value; FDR false discovery rate.