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

Abstract

Schizophrenia is characterised by cognitive impairments that are already present during early stages, including in the clinical high-risk for psychosis (CHR-P) state and first-episode psychosis (FEP). Moreover, data suggest the presence of distinct cognitive subtypes during early-stage psychosis, with evidence for spared vs. impaired cognitive profiles that may be differentially associated with symptomatic and functional outcomes. Using cluster analysis, we sought to determine whether cognitive subgroups were associated with clinical and functional outcomes in CHR-P individuals. Data were available for 146 CHR-P participants of whom 122 completed a 6- and/or 12-month follow-up; 15 FEP participants; 47 participants not fulfilling CHR-P criteria (CHR-Ns); and 53 healthy controls (HCs). We performed hierarchical cluster analysis on principal components derived from neurocognitive and social cognitive measures. Within the CHR-P group, clusters were compared on clinical and functional variables and examined for associations with global functioning, persistent attenuated psychotic symptoms and transition to psychosis. Two discrete cognitive subgroups emerged across all participants: 45.9% of CHR-P individuals were cognitively impaired compared to 93.3% of FEP, 29.8% of CHR-N and 30.2% of HC participants. Cognitively impaired CHR-P participants also had significantly poorer functioning at baseline and follow-up than their cognitively spared counterparts. Specifically, cluster membership predicted functional but not clinical outcome. Our findings support the existence of distinct cognitive subgroups in CHR-P individuals that are associated with functional outcomes, with implications for early intervention and the understanding of underlying developmental processes.

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

“…Interestingly, patients of the severely impaired cluster also showed lower general functioning scores at a trend at 6- and 12-month follow-up. Similar results were reported by Haining and colleagues [74]. Tan and colleagues [75] on the other hand did not find associations between the three cognitive clusters and symptom expression, but found that the cognitively impaired subgroup already showed worse academic performance at the level of childhood, early and late adolescence.…”

“…Tan and colleagues [75] on the other hand did not find associations between the three cognitive clusters and symptom expression, but found that the cognitively impaired subgroup already showed worse academic performance at the level of childhood, early and late adolescence. These findings confirm the critical relevance of cognitive deficits for early detection and functional prediction [26–28, 74]. This often replicated distribution of a severely impaired cluster, indicating that early interventions based on such cluster analysis would be suitable too.…”

“…Psychosis dataset and data-driven clustering on standardized cognitive, perceptual and emotional task and score data, but no clinical data; to explore (2) di↵erences in and (3) associations between cognition, symptom expression, medication and grey matter volume. Using a data driven parameter selection and clustering approach, we were able to identify three clusters that significantly di↵ered in across cognitive deficits forming a cognitively intact cluster, an intermediately a↵ected cluster and an cognitively a↵ected cluster, and potentially characterise subgroups previously described in the literature [36,50,[70][71][72][73][74][75]. Importantly, our results extend those findings, showing that patients within those clusters also di↵er in medication dosage, in specific grey matter brain networks and in clinical symptoms.…”

Identifying individuals with intellectual disability who access mental health support and are at high risk for adverse clinical outcomes: cohort study

“… 14 Applying machine learning techniques to large clinical data-sets provides an opportunity to explore data in novel ways and generate new insights. 15 One such technique is cluster analysis, which is the method of delineating distinct subgroups in a data-set such that the features of one group are more similar to each other than the features of another group. 16 Clustering has been used to identify clinically meaningful groupings of patients within heterogeneous populations, including people in intensive care, 17 people with diabetes, 18 people with psychosis 5 and people with autism.…”

Multivariate associations between neuroanatomy and cognition in unmedicated and medicated individuals with schizophrenia

“…Impaired cognitive functioning is a major symptom of schizophrenia and is associated with poor prognosis and quality of life in affected individuals 1 , 2 . Considerable variability in cognition has been reported in individuals with schizophrenia across different illness courses and in individuals at high risk of psychosis 3 – 7 , which poses challenges for developing effective interventions. Additionally, previous research 8 , 9 indicates that antipsychotics may impact the cognitive function of individuals with schizophrenia, emphasizing the complexity of discovering biomarkers for cognition-related therapy.…”