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 (Nspared = 40, Nimpaired = 13). Impaired patients showed significantly increased negative symptomatology (pfdr = 0.003), reduced cognitive performance (pfdr < 0.001) and general functioning (pfdr < 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.

Characterising cognitive heterogeneity in individuals at clinical high-risk for psychosis: a cluster analysis with clinical and functional outcome prediction

“…The emergence of a two-cluster solution is in agreement with previous studies involving schizophrenia-spectrum disorders [ 14 , 20 , 28 , 63 , 64 ]. However, three- or four-cluster solutions are more typically reported in mixed samples of FEP and HC participants [ 26 , 27 ].…”

“…In contrast, positive symptom severity did not significantly differ between cognitive subgroups. Cluster analyses have produced mixed findings, reporting either no significant differences across cognitive subgroups in the schizophrenia-spectrum [ 18 , 25 , 28 , 63 ] or greater positive symptom severity in the most cognitively impaired cluster [ 12 , 15 , 26 , 27 ]. Furthermore, the proportion of CHR-P participants meeting CAARMS criteria, SPI-A criteria or both did not differ between the cognitive subgroups, contrasting with previous reports of lesser cognitive deficits in individuals meeting basic symptom, as opposed to UHR, criteria [ 70 ].…”

“…There is preliminary evidence for similar profiles of cognitive impairment in FEP patients, with little consensus on the number of emergent clusters [25][26][27][28]. Wenzel et al [28] and Reser et al [25] identified two and four cognitive subgroups in FEP patients, respectively, with high negative symptom severity and low premorbid IQ characteristic of the most cognitively impaired subgroup.…”

“…There is preliminary evidence for similar profiles of cognitive impairment in FEP patients, with little consensus on the number of emergent clusters [25][26][27][28]. Wenzel et al [28] and Reser et al [25] identified two and four cognitive subgroups in FEP patients, respectively, with high negative symptom severity and low premorbid IQ characteristic of the most cognitively impaired subgroup. Interestingly, Uren et al [27] and Sauvé et al [26] both obtained a three-cluster solution and found that 28% and 54% of FEP participants, respectively, aggregated with HCs in the cognitively spared subgroup, supporting the existence of an FEP subgroup with intact cognitive functioning.…”

Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There?

“…A common approach has been the use of SVM to classify subjects at ultra-high risk for psychosis from healthy controls ( 48 , 49 , 50 ). Due to its capability to deal with high-dimensional data, it has also been applied to classify between recent-onset depression and recent-onset psychosis using both neuroanatomical information and clinical data ( 51 ), to find neurocognitive subtypes based on cognitive performance and neurocognitive alterations in recent onset psychosis ( 52 , 53 ), to identify schizophrenia patients based on subcortical regions ( 54 ) or functional network connectivity data ( 55 ). A multimodal approach combining structural MRI, diffusion tensor imaging, and resting-state functional MRI data was tested to classify patients with chronic schizophrenia vs. patients with FEP comparing different algorithms such as Random Forest (RF), LR, Linear Discriminant Analysis (LDA), and K-Nearest Neighbor classification (KNN), and SVM, resulting in the latter as the best performing one ( 56 ).…”