Hostname: page-component-8448b6f56d-tj2md Total loading time: 0 Render date: 2024-04-23T20:22:11.768Z Has data issue: false hasContentIssue false
  • English
  • Français

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: klewandowski@mclean.harvard.edu)
Get access Rights & Permissions [Opens in a new window]

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Allen, DN, Goldstein, G, Warnick, E (2003). A consideration of neuropsychologically normal schizophrenia. Journal of the International Neuropsychological Society 9, 56–63.Google Scholar
Baldessarini, RJ (2012). Chemotherapy in Psychiatry. Springer Verlag: New York.Google Scholar
Barch, DM (2009). Neuropsychological abnormalities in schizophrenia and major mood disorders: similarities and differences. Current Psychiatry Reports 11, 313–319.Google Scholar
Barker, S, Barron, N, McFarland, B, Bigelow, D (1994). Multnomah Community Ability Scale: User's Manual. Western Mental Health Research Center, Oregon Health Services: Portland, OR.Google Scholar
Benedict, RH (1997). Brief Visuospatial Memory Test – Revised. Psychological Assessment Resources, Inc.: Odessa, FL.Google Scholar
Benedict, RHB, Schretlen, D, Groninger, L, Brandt, J (1998). Hopkins Verbal Learning Test–Revised: normative data and analysis of inter-form and test–retest reliability. Clinical Neuropsychologist 12, 43–55.CrossRefGoogle Scholar
Dawes, SE, Jeste, DV, Palmer, BW (2011). Cognitive profiles in persons with chronic schizoprhrenia. Journal of Clinical and Experimental Neuropsychology 33, 929–936.CrossRefGoogle Scholar
First, MB, Spitzer, RL, Gibbon, M, Williams, JBW (1996). Structured Clinical Interview for DSM-IV Axis I Disorders, Clinician Version (SCID-CV). American Psychiatric Press, Inc.: Washington, DC.Google Scholar
Gladsjo, JA, Schuman, CC, Evans, JD, Peavy, GM, Miller, SW, Heaton, RK (1999). Norms for letter and category fluency: demographic corrections for age, education, and ethnicity. Assessment 6, 147–178.Google Scholar
Golden, CJ (1978). Stroop Color and Word Test. Stoelting Company: Chicago, IL.Google Scholar
Goldstein, G (1990). Neuropsychological heterogeneity in schizophrenia: a consideration of abstraction and problem-solving abilities. Archives of Clinical Neuropsychology 5, 251–264.Google Scholar
Goldstein, G, Allen, DN, Seaton, BE (1998). A comparison of clustering solutions for cognitive heterogeneity in schizophrenia. Journal of the International Neuropsychological Society 4, 353–362.CrossRefGoogle ScholarPubMed
Goldstein, G, Beers, SR, Shemansky, WJ (1996). Neuropsychological differences between schizophrenic patients with heterogeneous Wisconsin Card Sorting Test performance. Schizophrenia Research 21, 13–18.Google Scholar
Goldstein, G, Shemansky, WJ (1995). Influences on cognitive heterogeneity in schizophrenia. Schizophrenia Research 18, 59–69.Google Scholar
Goldstein, G, Shemansky, WJ, Allen, DN (2005). Cognitive function in schizoaffective disorder and clinical subtypes of schizophrenia. Archives of Clinical Neuropsychology 20, 153–159.Google Scholar
Green, MF (2006). Cognitive impairment and functional outcome in schizophrenia and bipolar disorder. Journal of Clinical Psychiatry 67, e12.Google Scholar
Hall, MH, Smoller, JW, Cook, NR, Schulze, K, Hyoun Lee, P, Taylor, G, Bramon, E, Coleman, MJ, Murray, RM, Salisbury, DF, Levy, DL (2012). Patterns of deficits in brain function in bipolar disorder and schizophrenia: a cluster analytic study. Psychiatry Research 200, 272–280.Google Scholar
Heinrichs, RW, Awad, AG (1993). Neurocognitive subtypes of chronic schizophrenia. Schizophrenia Research 9, 49–58.Google Scholar
Heinrichs, RW, Ruttan, L, Zakzanis, KK, Case, D (1997). Parsing schizophrenia with neurocognitive tests: evidence of stability and validity. Brain and Cognition 35, 207–224.Google Scholar
Hill, SK, Ragland, JD, Gur, RC, Gur, RE (2002). Neuropsychological profiles delineate distinct profiles of schizophrenia, an interaction between memory and executive function, and uneven distribution of clinical subtypes. Journal of Clinical and Experimental Neuropsychology 24, 765–780.Google Scholar
Kay, SR, Fiszbein, A, Opler, LA (1987). The Positive and Negative Syndrome Scale (PANSS) for schizophrenia. Schizophrenia Bulletin 13, 261–276.Google Scholar
Lewandowski, KE, Cohen, BM, Keshavan, MS, Öngür, D (2011). Relationship of neurocognitive deficits to diagnosis and symptoms across affective and non-affective psychoses. Schizophrenia Research 133, 212–217.Google Scholar
Lewandowski, KE, Cohen, BM, Keshavan, MS, Sperry, SH, Ongür, D (2013). Neuropsychological functioning predicts community outcomes in affective and non-affective psychoses: a 6-month follow-up. Schizophrenia Research 148, 34–37.Google Scholar
Montgomery, S, Asberg, M (1979). A new depression scale designed to be sensitive to change. British Journal of Psychiatry 134, 382–389.Google Scholar
Ongür, D, Lin, L, Cohen, BM (2009). Clinical characteristics influencing age at onset in psychotic disorders. Comprehensive Psychiatry 50, 13–19.Google Scholar
Palmer, BW, Heaton, RK, Paulsen, JS, Kuck, J, Braff, D, Harris, MJ, Zisook, S, Jeste, DV (1997). Is it possible to be schizophrenic yet neuropsychologically normal? Neuropsychology 11, 437–446.Google ScholarPubMed
Seaton, BE, Allen, DN, Goldstein, G, Kelley, ME, van Kammen, DP (1999). Relations between cognitive and symptom profile heterogeneity in schizophrenia. Journal of Nervous and Mental Disease 187, 414–419.Google Scholar
Seaton, BE, Goldstein, G, Allen, DN (2001). Sources of heterogeneity in schizophrenia: the role of neuropsychological functioning. Neuropsychology Review 11, 45–67.Google Scholar
Selnes, OA, Jacobson, L, Machado, AM, Becker, JT, Wesch, J, Miller, EN, Visscher, B, McArthur, JC (1991). Normative data for a brief neuropsychological screening battery. Multicenter AIDS Cohort Study. Perceptual and Motor Skills 73, 539–550.Google Scholar
Young, RC, Biggs, JT, Ziegler, VE, Meyer, DA (1978). A rating scale for mania: reliability, validity and sensitivity. British Journal of Psychiatry 133, 429–435.Google Scholar