Cognitive subtypes in recent onset psychosis: distinct neurobiological fingerprints?

Abstract

In schizophrenia, neurocognitive subtypes can be distinguished based on cognitive performance and they are associated with neuroanatomical alterations. We investigated the existence of cognitive subtypes in shortly medicated recent onset psychosis patients, their underlying gray matter volume patterns and clinical characteristics. We used a K-means algorithm to cluster 108 psychosis patients from the multi-site EU PRONIA (Prognostic tools for early psychosis management) study based on cognitive performance and validated the solution independently (N = 53). Cognitive subgroups and healthy controls (HC; n = 195) were classified based on gray matter volume (GMV) using Support Vector Machine classification. A cognitively spared (N = 67) and impaired (N = 41) subgroup were revealed and partially independently validated (N_spared = 40, N_impaired = 13). Impaired patients showed significantly increased negative symptomatology (p_fdr = 0.003), reduced cognitive performance (p_fdr < 0.001) and general functioning (p_fdr < 0.035) in comparison to spared patients. Neurocognitive deficits of the impaired subgroup persist in both discovery and validation sample across several domains, including verbal memory and processing speed. A GMV pattern (balanced accuracy = 60.1%, p = 0.01) separating impaired patients from HC revealed increases and decreases across several fronto-temporal-parietal brain areas, including basal ganglia and cerebellum. Cognitive and functional disturbances alongside brain morphological changes in the impaired subgroup are consistent with a neurodevelopmental origin of psychosis. Our findings emphasize the relevance of tailored intervention early in the course of psychosis for patients suffering from the likely stronger neurodevelopmental character of the disease.

Fig. 1. Neuropsychological and clinical differences between clusters and HC in the discovery sample.

Differences between the impaired (blue; N = 41) and spared cluster (green; N = 67) and HC (yellow; N = 195) regarding A the neuropsychological PCA components, B the General Assessment of Functioning score (GAF), C the General Functioning score (GF), D the Positive and Negative Syndrom Scale (PANSS) and E Premorbid Verbal Intelligence are shown. A High PCA scores represent high performance. PCA scales for cognitive domains where high PCA scores represent low performance, are inverted. socog social cognition, wm working memory, proc processing speed, exfun executive functioning, att attention, verbmem verbal memory, vismem visual memory, sal salience.

Fig. 2. Reliability of predictive voxels for the impaired vs. HC classification model.

Voxel-wise reliabilities are represented by the cross-validation ratio. Warm colors represent the 10% most reliable voxels predicting impaired ROP status, i.e., areas with increased gray matter (GM) in ROP. Cool colors represent the 10% most reliable voxels predicting HC status, i.e., areas with increased GM in HC. Left and right hemisphere are reversed.