Grey matter morphometric biomarkers for classifying early schizophrenia and PD psychosis: a multicentre study

Abstract

BackgroundPsychotic symptoms occur in a majority of schizophrenia patients, and in approximately 50% of all Parkinson’s disease (PD) patients. Altered grey matter (GM) structure within several brain areas and networks may contribute to their pathogenesis. Little, however, is known about transdiagnostic similarities when psychotic symptoms occur in different disorders, such as schizophrenia and PD.MethodsThe present study investigated a large, multicenter sample containing 722 participants: 146 patients with first episode psychosis, FEP; 106 individuals at-risk mental state for developing psychosis, ARMS; 145 healthy controls matching FEP and ARMS, Con-Psy; 92 PD patients with psychotic symptoms, PDP; 145 PD patients without psychotic symptoms, PDN; 88 healthy controls matching PDN and PDP, Con-PD. We applied source-based morphometry in association with receiver operating curves (ROC) analyses to identify common GM structural covariance networks (SCN) and investigated their accuracy in identifying the different patient groups. We assessed group-specific homogeneity and variability across the different networks and potential associations with clinical symptoms.ResultsSCN-extracted GM values differed significantly between FEP and Con-Psy, PDP and Con-PD as well as PDN and Con-PD, indicating significant overall grey matter reductions in PD and early schizophrenia. ROC analyses showed that SCN-based classification algorithms allow good classification (AUC∼0.80) of FEP and Con-Psy, and fair performance (AUC∼0.72) when differentiating PDP from Con-PD. Importantly, best performance was found in partly overlapping networks including the precuneus. Finally, reduced GM volume in SCN with increased variability was linked to increased psychotic symptoms in both FEP and PDP.ConclusionAlterations within selected SCNs seem to be related to the presence of psychotic symptoms in both early schizophrenia and PD psychosis, indicating some commonality of underlying mechanisms. Furthermore, results provide first evidence that GM volume within specific SCNs may serve as a biomarker for identifying FEP and PDP.

Cognitive subgroups of affective and non-affective psychosis show differences in medication and cortico-subcortical brain networks

“…Incorporating structural brain data into the clustering analysis of behavioural, psychological and demographic data did not improve the performance significantly. This is interesting as alterations in grey matter volume have been associated with an increased risk for psychosis and disease development [66–68], and provided the basis for good classification in a recent multicohort-study [69], as well as in earlier studies [70, 71], although classification results are inconsistent [72] and these previous studies contained different patient groups. Future research should investigate whether functional brain data, either resting state or task, or white matter connectivity data may increase the specificity of the clustering.…”

“…Especially, anticholinergic medication has been associated with a high cognitive burden [78], which is being supported by our results. Changes in gray matter volume have been associated with an increased risk for psychosis and disease development [80–82], and provided the basis for good classification in a recent multicohort-study [83], as well as in earlier studies [84, 85], although classification results are inconsistent [86]. Our results show that the cluster with the strongest cognitive deficits has increased grey matter volume in three brain networks spanning fronto-parietal and sub-cortical areas.…”