To send content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
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.
Hierarchical clustering analyses were conducted using cognitive data from 1541 participants (SZ n = 564, BD n = 402, healthy control n = 575).
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.
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.
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.
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.
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.
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.
Neurocognitive dysfunction in schizophrenia (SZ), bipolar (BD) and related disorders represents a core feature of these illnesses, possibly a marker of underlying pathophysiology. Substantial overlap in domains of neuropsychological deficits has been reported among these disorders after illness onset. However, it is unclear whether deficits follow the same longitudinal pre- and post-morbid course across diagnoses. We examine evidence for neurocognitive dysfunction as a core feature of all idiopathic psychotic illnesses, and trace its evolution from pre-morbid and prodromal states through the emergence of overt psychosis and into chronic illness in patients with SZ, BD and related disorders.
Articles reporting on neuropsychological functioning in patients with SZ, BD and related disorders before and after illness onset were reviewed. Given the vast literature on these topics and the present focus on cross-diagnostic comparisons, priority was given to primary data papers that assessed cross-diagnostic samples and recent meta-analyses.
Patients with SZ exhibit dysfunction preceding the onset of illness, which becomes more pronounced in the prodrome and early years following diagnosis, then settles into a stable pattern. Patients with BD generally exhibit typical cognitive development pre-morbidly, but demonstrate deficits by first episode that are amplified with worsening symptoms and exacerbations.
Neuropsychological deficits represent a core feature of SZ and BD; however, their onset and progression differ between diagnostic groups. A lifetime perspective on the evolution of neurocognitive deficits in SZ and BD reveals distinct patterns, and may provide a useful guide to the examination of the pathophysiological processes underpinning these functions across disorders.
Email your librarian or administrator to recommend adding this to your organisation's collection.