Medicine (RMH Academic Centre) - Research Publications
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ItemThe dopamine D1 receptor gene is associated with negative schizotypy in a non-clinical sample(ELSEVIER IRELAND LTD, 2016-01-30)
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ItemCurrent Treatment Options for Cognitive Impairment in Bipolar Disorder: a Review(Springer Science and Business Media LLC, 2016-12-01)
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ItemFacial Emotion Recognition Impairments in Bipolar Disorder. A Cognitive Problem?(CAMBRIDGE UNIV PRESS, 2016-07-01)
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ItemDoes cognitive performance map to categorical diagnoses of schizophrenia, schizoaffective disorder and bipolar disorder? A discriminant functions analysis(ELSEVIER, 2016-03-01)OBJECTIVES: Despite known overlaps in the pattern of cognitive impairments in individuals with bipolar disorder (BD), schizophrenia (SZ) and schizoaffective disorder (SZA), few studies have examined the extent to which cognitive performance validates traditional diagnostic boundaries in these groups. METHOD: Individuals with SZ (n=49), schizoaffective disorder (n=33) and BD (n=35) completed a battery of cognitive tests measuring the domains of processing speed, immediate memory, semantic memory, learning, working memory, executive function and sustained attention. RESULTS: A discriminant functions analysis revealed a significant function comprising semantic memory, immediate memory and processing speed that maximally separated patients with SZ from those with BD. Initial classification scores on the basis of this function showed modest diagnostic accuracy, owing in part to the misclassification of SZA patients as having SZ. When SZA patients were removed from the model, a second cross-validated classifier yielded slightly improved diagnostic accuracy and a single function solution, of which semantic memory loaded most heavily. CONCLUSIONS: A cluster of non-executive cognitive processes appears to have some validity in mapping onto traditional nosological boundaries. However, since semantic memory performance was the primary driver of the discrimination between BD and SZ, it is possible that performance differences between the disorders in this cognitive domain in particular, index separate underlying aetiologies.
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ItemConfigural and Featural Face Processing Influences on Emotion Recognition in Schizophrenia and Bipolar Disorder(CAMBRIDGE UNIV PRESS, 2017-03-01)OBJECTIVES: Emotion recognition impairments have been demonstrated in schizophrenia (Sz), but are less consistent and lesser in magnitude in bipolar disorder (BD). This may be related to the extent to which different face processing strategies are engaged during emotion recognition in each of these disorders. We recently showed that Sz patients had impairments in the use of both featural and configural face processing strategies, whereas BD patients were impaired only in the use of the latter. Here we examine the influence that these impairments have on facial emotion recognition in these cohorts. METHODS: Twenty-eight individuals with Sz, 28 individuals with BD, and 28 healthy controls completed a facial emotion labeling task with two conditions designed to separate the use of featural and configural face processing strategies; part-based and whole-face emotion recognition. RESULTS: Sz patients performed worse than controls on both conditions, and worse than BD patients on the whole-face condition. BD patients performed worse than controls on the whole-face condition only. CONCLUSIONS: Configural processing deficits appear to influence the recognition of facial emotions in BD, whereas both configural and featural processing abnormalities impair emotion recognition in Sz. This may explain discrepancies in the profiles of emotion recognition between the disorders. (JINS, 2017, 23, 287-291).
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ItemTaking It at "Face Value": The Use of Face Processing Strategies in Bipolar Disorder and Schizophrenia(CAMBRIDGE UNIV PRESS, 2016-07-01)OBJECTIVES: Use of appropriate face processing strategies is important for facial emotion recognition, which is known to be impaired in schizophrenia (SZ) and bipolar disorder (BD). There is preliminary evidence of abnormalities in the use of face processing strategies in the former, but there has been no explicit attempt to assess face processing in patients with BD. METHODS: Twenty-eight BD I, 28 SZ, and 28 healthy control participants completed tasks assessing featural and configural face processing. The facial inversion effect was used as a proxy of second order configural face processing and compared to featural face processing performance (which is known to be relatively less affected by facial inversion). RESULTS: Controls demonstrated the usual second-order inversion pattern. In the BD group, the absence of a second-order configural inversion effect in the presence of a disproportionate bias toward a featural inversion effect was evident. Despite reduced accuracy performance in the SZ group compared to controls, this group unexpectedly showed a normal second-order configural accuracy inversion pattern. This was in the context of a reverse inversion effect for response latency, suggesting a speed-versus-accuracy trade-off. CONCLUSIONS: To our knowledge, this is the first study to explicitly examine and contrast face processing in BD and SZ. Our findings indicate a generalized impairment on face processing tasks in SZ, and the presence of a second-order configural face processing impairment in BD. It is possible that these face processing impairments represent a catalyst for the facial emotion recognition deficits that are commonly reported in the literature. (JINS, 2016, 22, 652-661).
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ItemCharacterizing cognitive heterogeneity on the schizophrenia-bipolar disorder spectrum(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.