Neuropsychological Clustering in Bipolar and Major Depressive Disorder. (15th June 2017)

• Record Type: Journal Article
• Authors: Cotrena, Charles; Damiani Branco, Laura; Ponsoni, André; Milman Shansis, Flávio; Paz Fonseca, Rochele
• Is Part Of: Journal of the International Neuropsychological Society. Volume 23:Number 7(2017)
• Page Start: 584
• Page End: 593
• Publication Date: 2017-06-15
• Subjects: Cognition, -- Mood disorders, -- Executive function, -- Cognition disorders, -- Cluster analysis, -- Neuropsychology; Neuropsychology -- Periodicals; 616.8
• DOI: 10.1017/S1355617717000418
• Languages: English
• ISSNs: 1355-6177

Abstract

Abstract: Objectives: Cognitive dysfunction is a key feature of major depressive (MDD) and bipolar (BD) disorders. However, rather than a single cognitive profile corresponding to each diagnostic categories, recent studies have identified significant intra- and cross-diagnostic variability in patterns of cognitive impairment. The goal of this study was to contribute to the literature on cognitive heterogeneity in mood disorders by identifying cognitive subprofiles in a population of patients with MDD, BD type I, BD type II, and healthy adults.Methods: Participants completed a neuropsychological battery; scores were converted into Z -scores using normative data and submitted to hierarchical cluster analysis.Results: Three distinct neuropsychological clusters were identified: (1) a large cluster containing mostly control participants, as well as some patients with BD and MDD, who performed at above-average levels on all neuropsychological domains; (2) a cluster containing some patients from all diagnostic groups, as well as healthy controls, who performed worse than cluster 1 on most tasks, and showed impairments in motor inhibition and verbal fluency; (3) a cluster containing mostly patients with mood disorders with severe impairments in verbal inhibition and cognitive flexibility.Conclusions: These findings revealed multiple cognitive profiles within diagnostic categories, as well as significant cross-diagnostic overlap, highlighting the importance of developing more specific treatment approaches which consider patients' demographic and cognitive profiles in addition to their diagnosis. ( JINS, 2017, 23, 584–593)