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Cognitive variability in psychotic disorders: a cross-diagnostic cluster analysis

Published online by Cambridge University Press:  07 April 2014

K. E. Lewandowski ,
S. H. Sperry ,
B. M. Cohen  and
D. Öngür
K. E. Lewandowski*
Affiliation:
Schizophrenia and Bipolar Disorder Program, McLean Hospital, 115 Mill Street, Belmont, MA 02478, USA Harvard Medical School, Department of Psychiatry, Landmark Center, 401 Park Drive, Boston, MA 02215, USA
S. H. Sperry
Affiliation:
Schizophrenia and Bipolar Disorder Program, McLean Hospital, 115 Mill Street, Belmont, MA 02478, USA
B. M. Cohen
Affiliation:
Schizophrenia and Bipolar Disorder Program, McLean Hospital, 115 Mill Street, Belmont, MA 02478, USA Harvard Medical School, Department of Psychiatry, Landmark Center, 401 Park Drive, Boston, MA 02215, USA
D. Öngür
Affiliation:
Schizophrenia and Bipolar Disorder Program, McLean Hospital, 115 Mill Street, Belmont, MA 02478, USA Harvard Medical School, Department of Psychiatry, Landmark Center, 401 Park Drive, Boston, MA 02215, USA
*
* Address for correspondence: K. E. Lewandowski, Ph.D., AB328, Mail Stop 108, McLean Hospital, 115 Mill Street, Belmont, MA 02478, USA. (Email: [email protected])
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Abstract

Background.

Cognitive dysfunction is a core feature of psychotic disorders; however, substantial variability exists both within and between subjects in terms of cognitive domains of dysfunction, and a clear ‘profile’ of cognitive strengths and weaknesses characteristic of any diagnosis or psychosis as a whole has not emerged. Cluster analysis provides an opportunity to group individuals using a data-driven approach rather than predetermined grouping criteria. While several studies have identified meaningful cognitive clusters in schizophrenia, no study to date has examined cognition in a cross-diagnostic sample of patients with psychotic disorders using a cluster approach. We aimed to examine cognitive variables in a sample of 167 patients with psychosis using cluster methods.

Method.

Subjects with schizophrenia (n = 41), schizo-affective disorder (n = 53) or bipolar disorder with psychosis (n = 73) were assessed using a battery of cognitive and clinical measures. Cognitive data were analysed using Ward's method, followed by a K-means cluster approach. Clusters were then compared on diagnosis and measures of clinical symptoms, demographic variables and community functioning.

Results.

A four-cluster solution was selected, including a ‘neuropsychologically normal’ cluster, a globally and significantly impaired cluster, and two clusters of mixed cognitive profiles. Clusters differed on several clinical variables; diagnoses were distributed amongst all clusters, although not evenly.

Conclusions.

Identification of groups of patients who share similar neurocognitive profiles may help pinpoint relevant neural abnormalities underlying these traits. Such groupings may also hasten the development of individualized treatment approaches, including cognitive remediation tailored to patients' specific cognitive profiles.

Keywords

Bipolarclustercognitionpsychosisschizophrenia
Type
Original Articles
Information
Psychological Medicine , Volume 44 , Issue 15 , November 2014 , pp. 3239 - 3248
Copyright
Copyright © Cambridge University Press 2014 

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