Medicine (RMH Academic Centre) - Research Publications

Permanent URI for this collection

http://hdl.handle.net/11343/192919

Filters

2016 - 2017 2
2
Reset filters

Settings
Statistics
Citations
Author: Neill, Erica × Author: Rossell, Susan × Author: van Rheenen, Tamsyn ×

Search Results

Now showing 1 - 2 of 2
  • Item
    Thumbnail Image
    The dopamine D1 receptor gene is associated with negative schizotypy in a non-clinical sample
    Gurvich, C ; Tan, EJ ; Bozaoglu, K ; Neill, E ; Louise, S ; Van Rheenen, TE ; Rossell, SL (ELSEVIER IRELAND LTD, 2016-01-30)
  • Item
    Thumbnail Image
    Characterizing cognitive heterogeneity on the schizophrenia-bipolar disorder spectrum
    Van Rheenen, TE ; Lewandowski, KE ; Tan, EJ ; Ospina, LH ; Ongur, D ; Neill, E ; Gurvich, C ; Pantelis, C ; Malhotra, AK ; Rossell, SL ; Burdick, KE (CAMBRIDGE UNIV PRESS, 2017-07-01)
    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.