Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning

JOURNAL OF AFFECTIVE DISORDERS

卷:192

Article

Wu, Mon-Ju¹  Passos, Ives Cavalcante^1,2,3  Bauer, Isabelle E.¹  Lavagnino, Luca¹  Cao, Bo¹  Zunta-Soares, Giovana B.¹  Kapczinski, Flavio^1,2,3  Mwangi, Benson¹  Soares, Jair C.¹ 

[1] Univ Texas Hlth Sci Ctr Houston, Dept Psychiat & Behav Sci, UT Ctr Excellence Mood Disorder, Houston, TX 77030 USA

[2] Univ Fed Rio Grande do Sul, Bipolar Disorder Program, Porto Alegre, RS, Brazil

[3] Univ Fed Rio Grande do Sul, Lab Mol Psychiat, Porto Alegre, RS, Brazil

关键词: Bipolar disorder;    Neurocognition;    Rapid cycling;    CANTAB;    Machine learning;   

DOI  :  10.1016/j.jad.2015.12.053

来源: Elsevier

【 摘 要 】

Background: Previous studies have reported that patients with bipolar disorder (BD) present with cognitive impairments during mood episodes as well as euthymic phase. However, it is still unknown whether reported neurocognitive abnormalities can objectively identify individual BD patients from healthy controls (HC). Methods: A total of 21 euthymic BD patients and 21 demographically matched HC were included in the current study. Participants performed the computerized Cambridge Neurocognitive Test Automated Battery (CANTAB) to assess cognitive performance. The least absolute shrinkage selection operator (LASSO) machine learning algorithm was implemented to identify neurocognitive signatures to distinguish individual BD patients from HC. Results: The LASSO machine learning algorithm identified individual BD patients from HC with an accuracy of 71%, area under receiver operating characteristic curve of 0.7143 and significant at p=0.0053. The LASSO algorithm assigned individual subjects with a probability score (0-healthy, 1-patient). Patients with rapid cycling (RC) were assigned increased probability scores as compared to patients without RC. A multivariate pattern of neurocognitive abnormalities comprising of affective Go/No-go and the Cambridge gambling task was relevant in distinguishing individual patients from HC. Limitations: Our study sample was small as we only considered euthymic BD patients and demographically matched HC. Conclusion: Neurocognitive abnormalities can distinguish individual euthymic BD patients from HC with relatively high accuracy. In addition, patients with RC had more cognitive impairments compared to patients without RC. The predictive neurocognitive signature identified in the current study can potentially be used to provide individualized clinical inferences on BD patients. (C) 2015 Elsevier B.V. All rights reserved.

【 授权许可】

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