Junghee Lee1, Shemra Rizzo2, Lori Altshuler3, David C Glahn4, David J Miklowitz3, Catherine A Sugar5, Jonathan K Wynn6, Michael F Green7. 1. Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States; Veterans Affairs Desert Pacific Mental Illness Research, Education and Clinical Center, Los Angeles, CA, United States. Electronic address: jungheelee@ucla.edu. 2. Department of Biostatistics at UCLA, Los Angeles, CA, United States. 3. Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States. 4. Department of Psychiatry, Yale University School of Medicine, New Haven, CT, United States; Olin Neuropsychiatry Research Center, Institute of Living, Hartford Hospital, Hartford, CT, United States. 5. Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States; Department of Biostatistics at UCLA, Los Angeles, CA, United States. 6. Veterans Affairs Desert Pacific Mental Illness Research, Education and Clinical Center, Los Angeles, CA, United States; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States. 7. Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, United States; Veterans Affairs Desert Pacific Mental Illness Research, Education and Clinical Center, Los Angeles, CA, United States.
Abstract
BACKGROUND: Bipolar disorder (BD) and schizophrenia (SZ) show substantial overlap. It has been suggested that a subgroup of patients might contribute to these overlapping features. This study employed a cross-diagnostic cluster analysis to identify subgroups of individuals with shared cognitive phenotypes. METHOD: 143 participants (68 BD patients, 39 SZ patients and 36 healthy controls) completed a battery of EEG and performance assessments on perception, nonsocial cognition and social cognition. A K-means cluster analysis was conducted with all participants across diagnostic groups. Clinical symptoms, functional capacity, and functional outcome were assessed in patients. RESULTS: A two-cluster solution across 3 groups was the most stable. One cluster including 44 BD patients, 31 controls and 5 SZ patients showed better cognition (High cluster) than the other cluster with 24 BD patients, 35 SZ patients and 5 controls (Low cluster). BD patients in the High cluster performed better than BD patients in the Low cluster across cognitive domains. Within each cluster, participants with different clinical diagnoses showed different profiles across cognitive domains. LIMITATIONS: All patients are in the chronic phase and out of mood episode at the time of assessment and most of the assessment were behavioral measures. CONCLUSIONS: This study identified two clusters with shared cognitive phenotype profiles that were not proxies for clinical diagnoses. The finding of better social cognitive performance of BD patients than SZ patients in the Lowe cluster suggest that relatively preserved social cognition may be important to identify disease process distinct to each disorder.
BACKGROUND:Bipolar disorder (BD) and schizophrenia (SZ) show substantial overlap. It has been suggested that a subgroup of patients might contribute to these overlapping features. This study employed a cross-diagnostic cluster analysis to identify subgroups of individuals with shared cognitive phenotypes. METHOD: 143 participants (68 BDpatients, 39 SZ patients and 36 healthy controls) completed a battery of EEG and performance assessments on perception, nonsocial cognition and social cognition. A K-means cluster analysis was conducted with all participants across diagnostic groups. Clinical symptoms, functional capacity, and functional outcome were assessed in patients. RESULTS: A two-cluster solution across 3 groups was the most stable. One cluster including 44 BDpatients, 31 controls and 5 SZ patients showed better cognition (High cluster) than the other cluster with 24 BDpatients, 35 SZ patients and 5 controls (Low cluster). BDpatients in the High cluster performed better than BDpatients in the Low cluster across cognitive domains. Within each cluster, participants with different clinical diagnoses showed different profiles across cognitive domains. LIMITATIONS: All patients are in the chronic phase and out of mood episode at the time of assessment and most of the assessment were behavioral measures. CONCLUSIONS: This study identified two clusters with shared cognitive phenotype profiles that were not proxies for clinical diagnoses. The finding of better social cognitive performance of BDpatients than SZ patients in the Lowe cluster suggest that relatively preserved social cognition may be important to identify disease process distinct to each disorder.
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