Neuropsychological Clustering in Bipolar and Major Depressive Disorder
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 cogn...
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Published in | Journal of the International Neuropsychological Society Vol. 23; no. 7; pp. 584 - 593 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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New York, USA
Cambridge University Press
01.08.2017
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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) |
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AbstractList | 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) 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. Participants completed a neuropsychological battery; scores were converted into Z-scores using normative data and submitted to hierarchical cluster analysis. 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. 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). 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.OBJECTIVESCognitive 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.Participants completed a neuropsychological battery; scores were converted into Z-scores using normative data and submitted to hierarchical cluster analysis.METHODSParticipants completed a neuropsychological battery; scores were converted into Z-scores using normative data and submitted to hierarchical cluster analysis.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.RESULTSThree 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.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).CONCLUSIONSThese 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). |
Author | Paz Fonseca, Rochele Damiani Branco, Laura Milman Shansis, Flávio Ponsoni, André Cotrena, Charles |
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BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28762320$$D View this record in MEDLINE/PubMed |
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Keywords | Cluster analysis Mood disorders Executive function Cognition Cognition disorders Neuropsychology |
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Snippet | Objectives: Cognitive dysfunction is a key feature of major depressive (MDD) and bipolar (BD) disorders. However, rather than a single cognitive profile... Objectives: Cognitive dysfunction is a key feature of major depressive (MDD) and bipolar (BD) disorders. However, rather than a single cognitive profile... Cognitive dysfunction is a key feature of major depressive (MDD) and bipolar (BD) disorders. However, rather than a single cognitive profile corresponding to... |
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SubjectTerms | Adult Adults Behavior disorders Bipolar disorder Bipolar Disorder - complications Bipolar Disorder - physiopathology Cluster Analysis Clustering Cognition & reasoning Cognitive ability Cognitive Dysfunction - classification Cognitive Dysfunction - etiology Cognitive Dysfunction - physiopathology Dementia Depressive Disorder, Major - complications Depressive Disorder, Major - physiopathology Female Humans Male Mental depression Middle Aged Mood Motor task performance Neuropsychology Psychiatry Psychosis Schizophrenia Studies |
Title | Neuropsychological Clustering in Bipolar and Major Depressive Disorder |
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