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Psychol Med. 2017 Jul;47(10):1848-1864. doi: 10.1017/S0033291717000307. Epub 2017 Feb 28.

Characterizing cognitive heterogeneity on the schizophrenia-bipolar disorder spectrum.

Author information

1
Melbourne Neuropsychiatry Centre,Department of Psychiatry,University of Melbourne and Melbourne Health,Carlton,VIC,Australia.
2
Schizophrenia and Bipolar Disorder Program,McLean Hospital,Belmont, MA,USA.
3
Brain and Psychological Sciences Research Centre,Faculty of Health, Arts and Design,School of Health Sciences, Swinburne University,Hawthorn,VIC,Australia.
4
Icahn School of Medicine,Mount Sinai, NY,USA.
5
Cognitive Neuropsychiatry Laboratory,Monash Alfred Psychiatry Research Centre, The Alfred Hospital and Central Clinical School, Monash University,Melbourne,VIC,Australia.
6
Hofstra Northwell School of Medicine,Hempstead, NY,USA.

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:

Clustering; cognition; heterogeneity; neuropsychology; psychosis spectrum

PMID:
28241891
DOI:
10.1017/S0033291717000307
[Indexed for MEDLINE]

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