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Characterizing cognitive heterogeneity on the schizophrenia–bipolar disorder spectrum

Published online by Cambridge University Press:  28 February 2017

T. E. Van Rheenen ,
K. E. Lewandowski ,
E. J. Tan ,
L. H. Ospina ,
D. Ongur ,
E. Neill ,
C. Gurvich ,
C. Pantelis ,
A. K. Malhotra  and
S. L. Rossell
...Show all authors
T. E. Van Rheenen*
Affiliation:
Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton, VIC, Australia Brain and Psychological Sciences Research Centre, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Hawthorn, VIC, Australia Cognitive Neuropsychiatry Laboratory, Monash Alfred Psychiatry Research Centre, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, VIC, Australia
K. E. Lewandowski
Affiliation:
Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA
E. J. Tan
Affiliation:
Brain and Psychological Sciences Research Centre, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Hawthorn, VIC, Australia Cognitive Neuropsychiatry Laboratory, Monash Alfred Psychiatry Research Centre, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, VIC, Australia
L. H. Ospina
Affiliation:
Icahn School of Medicine, Mount Sinai, NY, USA
D. Ongur
Affiliation:
Schizophrenia and Bipolar Disorder Program, McLean Hospital, Belmont, MA, USA Department of Psychiatry, Harvard Medical School, Boston, MA, USA
E. Neill
Affiliation:
Brain and Psychological Sciences Research Centre, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Hawthorn, VIC, Australia Department of Psychiatry, St Vincent's Hospital, Melbourne, VIC, Australia
C. Gurvich
Affiliation:
Cognitive Neuropsychiatry Laboratory, Monash Alfred Psychiatry Research Centre, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, VIC, Australia
C. Pantelis
Affiliation:
Melbourne Neuropsychiatry Centre, Department of Psychiatry, University of Melbourne and Melbourne Health, Carlton, VIC, Australia Florey Institute for Neuroscience and Mental Health, Parkville, VIC, Australia Centre for Neural Engineering (CfNE), Department of Electrical and Electronic Engineering, University of Melbourne, Parkville, VIC, Australia
A. K. Malhotra
Affiliation:
Hofstra Northwell School of Medicine, Hempstead, NY, USA
S. L. Rossell
Affiliation:
Brain and Psychological Sciences Research Centre, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Hawthorn, VIC, Australia Cognitive Neuropsychiatry Laboratory, Monash Alfred Psychiatry Research Centre, The Alfred Hospital and Central Clinical School, Monash University, Melbourne, VIC, Australia Department of Psychiatry, St Vincent's Hospital, Melbourne, VIC, Australia
K. E. Burdick
Affiliation:
Icahn School of Medicine, Mount Sinai, NY, USA James J Peters VA Hospital, NY, USA
*
*Address for correspondence: T. Van Rheenen, Ph.D., Melbourne Neuropsychiatry Centre, Level 3, Alan Gilbert Building, 161 Barry Street, Carlton, VIC 3053, Australia. (Email: [email protected])
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Abstract

Background

Current group-average analysis suggests quantitative but not qualitative cognitive differences between schizophrenia (SZ) and bipolar disorder (BD). There is increasing recognition that cognitive within-group heterogeneity exists in both disorders, but it remains unclear as to whether between-group comparisons of performance in cognitive subgroups emerging from within each of these nosological categories uphold group-average findings. We addressed this by identifying cognitive subgroups in large samples of SZ and BD patients independently, and comparing their cognitive profiles. The utility of a cross-diagnostic clustering approach to understanding cognitive heterogeneity in these patients was also explored.

Method

Hierarchical clustering analyses were conducted using cognitive data from 1541 participants (SZ n = 564, BD n = 402, healthy control n = 575).

Results

Three qualitatively and quantitatively similar clusters emerged within each clinical group: a severely impaired cluster, a mild-moderately impaired cluster and a relatively intact cognitive cluster. A cross-diagnostic clustering solution also resulted in three subgroups and was superior in reducing cognitive heterogeneity compared with disorder clustering independently.

Conclusions

Quantitative SZ–BD cognitive differences commonly seen using group averages did not hold when cognitive heterogeneity was factored into our sample. Members of each corresponding subgroup, irrespective of diagnosis, might be manifesting the outcome of differences in shared cognitive risk factors.

Keywords

Clusteringcognitionheterogeneityneuropsychologypsychosis spectrum
Type
Original Articles
Information
Psychological Medicine , Volume 47 , Issue 10 , July 2017 , pp. 1848 - 1864
Copyright
Copyright © Cambridge University Press 2017 

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