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使用多变量神经认知模式预测双相情感障碍的易感性：一项初步研究。

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

作者信息

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

机构信息

UT Center of Excellence on Mood Disorder, Department of Psychiatry and Behavioral Sciences, The University of Texas Science Center at Houston, Houston, TX, USA.

Department of Psychiatry & Behavioral Sciences, The University of Texas Health Science Center, 1941 East Road, Houston, TX, 77054, USA.

出版信息

Int J Bipolar Disord. 2017 Sep 1;5(1):32. doi: 10.1186/s40345-017-0101-9.

DOI:10.1186/s40345-017-0101-9
PMID:28861763
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5578943/
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.

摘要

双相情感障碍（BD）是一种在普通人群中复发率较高的常见疾病。拥有客观的生物标志物以在个体层面识别BD患者至关重要。包括情感性Go/No-go任务和剑桥赌博任务在内的神经认知特征显示出区分BD患者与健康对照以及识别BD患者个体亲属的潜力。此外，这些神经认知特征在两个独立队列中显示出可重复性，这表明具有推广的可能性。未来的研究将探讨将神经认知数据与其他生物学数据相结合以开发更准确特征的可能性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c1/5578943/7e648b3cc667/40345_2017_101_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c1/5578943/837cc31cda9e/40345_2017_101_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c1/5578943/d3ff12a49227/40345_2017_101_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c1/5578943/7e648b3cc667/40345_2017_101_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c1/5578943/837cc31cda9e/40345_2017_101_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c1/5578943/00f159bccaca/40345_2017_101_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c1/5578943/d3ff12a49227/40345_2017_101_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7c1/5578943/7e648b3cc667/40345_2017_101_Fig4_HTML.jpg

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本文引用的文献

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Individualized identification of euthymic bipolar disorder using the Cambridge Neuropsychological Test Automated Battery (CANTAB) and machine learning.使用剑桥神经心理测试自动成套系统（CANTAB）和机器学习对双相情感障碍缓解期进行个体化识别。
J Affect Disord. 2016 Mar 1;192:219-25. doi: 10.1016/j.jad.2015.12.053. Epub 2015 Dec 30.
2
Impulsivity: present during euthymia in bipolar disorder? - a systematic review.双相障碍轻躁期存在冲动性吗？——一项系统综述
Int J Bipolar Disord. 2014 Mar 11;2:2. doi: 10.1186/2194-7511-2-2. eCollection 2014.
3
Reduced white matter integrity and verbal fluency impairment in young adults with bipolar disorder: a diffusion tensor imaging study.
基于计算机化CANTAB测试的多发性硬化症复发和恢复期间认知障碍的独特模式
Front Neurol. 2019 Jun 4;10:572. doi: 10.3389/fneur.2019.00572. eCollection 2019.
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The use of component-wise gradient boosting to assess the possible role of cognitive measures as markers of vulnerability to pediatric bipolar disorder.使用逐分量梯度提升法来评估认知测量作为小儿双相情感障碍易感性标志物的可能作用。
Cogn Neuropsychiatry. 2019 Mar;24(2):93-107. doi: 10.1080/13546805.2019.1580190. Epub 2019 Feb 18.
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Erratum to: Prediction of vulnerability to bipolar disorder using multivariate neurocognitive patterns: a pilot study.《使用多变量神经认知模式预测双相情感障碍易感性：一项初步研究》勘误
Int J Bipolar Disord. 2017 Sep 18;5(1):33. doi: 10.1186/s40345-017-0105-5.
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Neuro-cognitive functioning in unaffected siblings of patients with bipolar disorder: Comparison with bipolar patients and healthy controls.双相障碍患者未受影响的兄弟姐妹的神经认知功能：与双相患者和健康对照的比较。
Indian J Psychiatry. 2014 Jul;56(3):283-8. doi: 10.4103/0019-5545.140645.
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Neuropsychological testing of cognitive impairment in euthymic bipolar disorder: an individual patient data meta-analysis.双相情感障碍稳定期认知障碍的神经心理学测试：一项个体患者数据荟萃分析。
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