Exploring Heterogeneity on the Wisconsin Card Sorting Test in Schizophrenia Spectrum Disorders: A Cluster Analytical Investigation

Abstract

Objectives: The Wisconsin Card Sorting Test (WCST) is a complex measure of executive function that is frequently employed to investigate the schizophrenia spectrum. The successful completion of the task requires the interaction of multiple intact executive processes, including attention, inhibition, cognitive flexibility, and concept formation. Considerable cognitive heterogeneity exists among the schizophrenia spectrum population, with substantive evidence to support the existence of distinct cognitive phenotypes. The within-group performance heterogeneity of individuals with schizophrenia spectrum disorder (SSD) on the WCST has yet to be investigated. A data-driven cluster analysis was performed to characterise WCST performance heterogeneity. Methods: Hierarchical cluster analysis with k-means optimisation was employed to identify homogenous subgroups in a sample of 210 schizophrenia spectrum participants. Emergent clusters were then compared to each other and a group of 194 healthy controls (HC) on WCST performance and demographic/clinical variables. Results: Three clusters emerged and were validated via altered design iterations. Clusters were deemed to reflect a relatively intact patient subgroup, a moderately impaired patient subgroup, and a severely impaired patient subgroup. Conclusions: Considerable within-group heterogeneity exists on the WCST. Identification of subgroups of patients who exhibit homogenous performance on measures of executive functioning may assist in optimising cognitive interventions. Previous associations found using the WCST among schizophrenia spectrum participants should be reappraised. (JINS, 2019, 25, 750–760)

Using country-level variables to classify countries according to the number of confirmed COVID-19 cases: An unsupervised machine learning approach

“…We are unaware of other studies that have aimed to classify countries based on simple open-access variables, and that can stratify the countries based on the number of COVID-19 cases. Most of the previous research using unsupervised machine learning clustering algorithms on health research has focused on individuals and diseases [16][17][18][19] . This work complements the available evidence at the individual level with preliminary information on clusters at the country level, with potential relevant applications in the current COVID-19 pandemic.…”

“…This approach is considered a paradigm in unsupervised machine learning, because it assigns the elements into clusters which were unknown at the beginning of the analysis 15 . A few authors have used this methodology in clinical and public health research [16][17][18][19] .…”

Placing Cognitive Rigidity in Interpersonal Context in Psychosis: Relationship With Low Cognitive Reserve and High Self-Certainty

“…Waltz suggests that excessive cognitive rigidity is likely to be characteristic of subgroups of patients with specific disorder profiles (3,12). This idea is supported by empirical studies that have detected subgroups of patients with different performance on the WCST (14)(15)(16). For instance, patients with a general and marked executive functioning impairment showed lower IQ and severe negative symptomatology.…”

Cortical activation abnormalities in bipolar and schizophrenia patients in a combined oddball–incongruence paradigm

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