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Wu MJ, Mwangi B, Passos IC, et al. Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns: a pilot study. Int J Bipolar Disord. 2017;5(1):32doi: 10.1186/s40345-017-0101-9.
Wu, M. J., Mwangi, B., Passos, I. C., Bauer, I. E., Cao, B., Frazier, T. W., Zunta-Soares, G. B., & Soares, J. C. (2017). Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns: a pilot study. International journal of bipolar disorders, 5(1), 32. https://doi.org/10.1186/s40345-017-0101-9
Wu, Mon-Ju, et al. "Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns: a pilot study." International journal of bipolar disorders vol. 5,1 (2017): 32. doi: https://doi.org/10.1186/s40345-017-0101-9
Wu MJ, Mwangi B, Passos IC, Bauer IE, Cao B, Frazier TW, Zunta-Soares GB, Soares JC. Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns: a pilot study. Int J Bipolar Disord. 2017 Sep 01;5(1):32. doi: 10.1186/s40345-017-0101-9. PMID: 28861763; PMCID: PMC5578943.
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Int J Bipolar Disord. 2017 Sep 01;5(1):32. doi: 10.1186/s40345-017-0101-9.

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

International journal of bipolar disorders

Mon-Ju Wu, Benson Mwangi, Ives Cavalcante Passos, Isabelle E Bauer, Bo Cao, Thomas W Frazier, Giovana B Zunta-Soares, Jair C Soares

Affiliations

  1. UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA. [email protected].
  2. Department of Psychiatry & Behavioral Sciences, The University of Texas Health Science Center, 1941 East Road, Houston, TX, 77054, USA. [email protected].
  3. UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA.
  4. Cleveland Clinic Children's Hospital Center for Pediatric Behavioral Health, Cleveland, OH, USA.

PMID: 28861763 PMCID: PMC5578943 DOI: 10.1186/s40345-017-0101-9

Abstract

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.

Keywords: Bipolar disorder; CANTAB; Machine learning; Neurocognition; Vulnerability

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