F93. CLUSTER ANALYSIS ON CORTICAL THICKNESS OF DEFAULT MODE NETWORK-ASSOCIATED REGIONS IN PATIENTS WITH SCHIZOPHRENIA AND HEALTHY CONTROLS

Multiple clustering algorithms have been proposed to organize and thereby reduce the clinical, cognitive, neurobiological and functional heterogeneity in the schizophrenia spectrum. Cortical thickness (CT) is a key gene-mediated aspect of brain morphology that may reflect reduced synaptic structure and excessive pruning. It is also an aspect of neurobiology that correlates with the risk of developing schizophrenia. Previous heterogeneity-reducing strategies have included cluster analyses of CT values across the brain (Sugihara et al., 2017). However, cluster analytic approaches have not used regional values associated with the default mode network (DMN). Disruption or overreaction of DMN was found to be associated with psychopathology including schizophrenia (Buckner, Andrews-Hanna, & Schacter, 2008).

CT was measured with a 3T short bore General Electric MRI machine and the obtained scans were processed with FreeSurfer. Cortical parcellations were obtained for 19 DMN-related regions of interest according to Destrieux, Fischl, Dale and Halgren (2010) and the DMN literature. We carried out agglomerative hierarchical cluster analysis on these CT data obtained from 63 healthy controls and 74 patients with a diagnosis of DSM-IV schizophrenia or schizoaffective disorder. Ward’s method with Euclidean distance was applied and the NbClust function in R was used to determine the optimal number of clusters. Demographic, clinical (PANSS) and cognitive (MCCB) measures were also obtained.

Thirteen out of 30 clustering validity indices indicated a 3-cluster solution. The proportion of patients and controls varied significantly across clusters, X2 (2, N = 137) = 12.635, p = 0.002. The first cluster (n1=19) was composed of largely of patients (84%). A large second cluster (n2=92) comprised 50 patients and 42 healthy control participants. In contrast, two-thirds of the third cluster (n3=26) comprised healthy controls. However, there were no significant cluster differences in the distribution of males and females. MANCOVA on all 19 DMN-related regions revealed a main effect of cluster F(38, 230) = 7.467, p<0.001, with age as a significant covariate. Pairwise comparisons showed that mean cortical thinning was greater in cluster 1 than in cluster 2 on 17/19 DMN regions. Cluster 2 demonstrated significantly greater thinning than cluster 3 on 15/19 regions. Comparison of MCCB composite scores with age as a covariate showed significant differences between cluster 1 and 3 and between cluster 2 and 3.

This study showed that a majority of schizophrenia patients cluster with healthy controls in terms of DMN-related regional cortical thickness. This suggests that DMN-specific cortical thinning is unlikely to reflect a disease process common to patients with a schizophrenia spectrum diagnosis and is more likely to represent an illness variant or a subtype. Potentially important but smaller subgroups exist and comprised primarily patients or controls. One largely patient-defined cluster demonstrated extensive thinning in DMN regions along with cognitive impairment, whereas patients were under-represented in a cluster marked by cognitive proficiency and greater thickness values. The findings imply that significant cortical thinning in the DMN is expressed in a minority of schizophrenia patients, especially those with cognitive impairment. These data may help explain the variability in the literature on this aspect of brain morphology and contribute to improved illness definition and reduced heterogeneity.