Cognitive deficits are present in the majority of psychosis patients. They range across various domains, such as working memory, and executive functioning, and are linked to neurobiological changes, including changes in gray matter volume. In this study, we explored data-driven clustering solutions using behavioural, demographic, psychological and structural brain data, but no clinical data, in a cross-diagnostic sample combining affective (N=51) and non-affective (N=111) psychosis patients as well as healthy controls (N=55). The goal of the study was (1) to test classification sensitivity of diagnostic groups using data without diagnostic information, and (2) to explore identification of potential cognitive phenotypes. We used K-means and spectral clustering, investigating two-, three- and four-cluster solutions, and group-membership matching. We then explored cognitive deficits and symptom expression within the four-cluster solution. Our results revealed best group-cluster matching using PCA-pre-selected only non-brain, mostly cognitive, features. The three groups were clustered with medium sensitivity, correctly identifying between 44% and 78% of individuals per group. More importantly, however, clustering using four clusters allowed the identification of cognitive phenotypes, that significantly varied in clinical and cognitive impairment. We identified one cluster expressing the lowest symptom scores and unimpaired cognition, one other cluster with the highest symptom scores, especially negative symptoms, and global cognitive impairments, across all domains, and two intermediate clusters. In conclusion, these results provide evidence for cognitive phenotypes with specific symptom expressions combining individuals with different overall diagnoses. The clear link between the cognitive deficits and symptoms indicates the need for the development of cognition-based interventions.

Clustering cognitive phenotypes in affective and non-affective psychosis

Abstract Background: Psychotic 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. Methods: The 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. Results: SCN-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. Conclusion: Alterations 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.

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

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