<p>Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns</p>

Mon-Ju Wu; Benson Mwangi; Ives Passos; Isabelle Bauer; Cao Bo; Thomas Frazier; Giovana Zunta-Soares; Jair Soares

doi:10.7490/f1000research.1111942.1

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Wu MJ, Mwangi B, Passos I et al. Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns [version 1; not peer reviewed]. F1000Research 2016, 5:957 (poster) (https://doi.org/10.7490/f1000research.1111942.1)

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Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns

Mon-Ju Wu¹, Benson Mwangi, Ives Passos, Isabelle Bauer, Cao Bo, Thomas Frazier, Giovana Zunta-Soares, Jair Soares

Published 23 May 2016 (https://doi.org/10.7490/f1000research.1111942.1)

Author Affiliations

¹ UT Houston, USA

Copyright © 2016 Wu MJ et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Published 23 May 2016

Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns
[version 1; not peer reviewed]

Mon-Ju Wu¹, Benson Mwangi, Ives Passos, Isabelle Bauer, Cao Bo, Thomas Frazier, Giovana Zunta-Soares, Jair Soares

Author Affiliations

¹ UT Houston, USA

Presented at

71st Society of Biological Psychiatry Annual Meeting 2016

Abstract

Competing Interests

This research was funded by NIH R01 MH 085667, the Dunn Foundation and Pat Rutherford, Jr. Chair in Psychiatry at UTHealth to Jair C. Soares.

Keywords

Bipolar disorder, CANTAB, Machine learning, Vulnerability, At-risk

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