Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns: a pilot study

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作者
Mon-Ju Wu
Benson Mwangi
Ives Cavalcante Passos
Isabelle E. Bauer
Bo Cao
Thomas W. Frazier
Giovana B. Zunta-Soares
Jair C. Soares
机构
[1] The University of Texas Science Center at Houston,UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences
[2] Cleveland Clinic Children’s Hospital Center for Pediatric Behavioral Health,Department of Psychiatry & Behavioral Sciences
[3] The University of Texas Health Science Center,undefined
关键词
Bipolar disorder; Neurocognition; Vulnerability; CANTAB; Machine learning;
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摘要
Bipolar disorder (BD) is a common disorder with high reoccurrence rate in general population. It is critical to have objective biomarkers to identify BD patients at an individual level. Neurocognitive signatures including affective Go/No-go task and Cambridge Gambling task showed the potential to distinguish BD patients from health controls as well as identify individual siblings of BD patients. Moreover, these neurocognitive signatures showed the ability to be replicated at two independent cohorts which indicates the possibility for generalization. Future studies will examine the possibility of combining neurocognitive data with other biological data to develop more accurate signatures.
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