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).
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).
Authors: Riya Paul; Till F M Andlauer; Darina Czamara; David Hoehn; Susanne Lucae; Benno Pütz; Cathryn M Lewis; Rudolf Uher; Bertram Müller-Myhsok; Marcus Ising; Philipp G Sämann Journal: Transl Psychiatry Date: 2019-08-05 Impact factor: 6.222
Authors: Jacob J Crouse; Kate M Chitty; Frank Iorfino; Joanne S Carpenter; Django White; Alissa Nichles; Natalia Zmicerevska; Ashleigh M Tickell; Rico S C Lee; Sharon L Naismith; Elizabeth M Scott; Jan Scott; Daniel F Hermens; Ian B Hickie Journal: BJPsych Open Date: 2020-03-19
Authors: Muriel Vicent-Gil; Maria J Portella; Maria Serra-Blasco; Guillem Navarra-Ventura; Sara Crivillés; Eva Aguilar; Diego Palao; Narcís Cardoner Journal: Psychol Med Date: 2020-06-01 Impact factor: 7.723