Pattern Classification of Volitional Functional Magnetic Resonance Imaging Responses in Patients With Severe Brain Injury
Pattern Classification of Volitional Functional Magnetic Resonance Imaging Responses in Patients With Severe Brain Injury
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DOI：
10.1001/archneurol.2011.892
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发表时间：
2012-02-01
影响因子：
--
通讯作者：
Voss, Henning U.
中科院分区：
文献类型：
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作者：
Bardin, Jonathan C.;Schiff, Nicholas D.;Voss, Henning U.
背景资料：最近的神经影像学研究探索了使用心理意象任务作为明显运动反应的代理，其中患者被要求想象执行一项任务，例如“想象自己在游泳。“目标：检测隐蔽意志脑活动的严重脑损伤患者使用模式分类的血氧水平依赖（BOLD）的反应在精神imageration.Design的单变量功能磁共振成像analysis.Design的结果进行比较：病例对照研究。严重脑损伤的患者构成了一个方便的sample.Main结果Measures：功能磁共振成像数据采集的患者被要求按照命令或回答问题，使用运动想象作为代理response.Results：在控制，反应被准确地分类。在患者组中，5例患者中有3例的反应被正确分类。其余2例患者在标准单变量分析中未显示显著BOLD应答，表明他们未执行任务。此外，我们还展示了在命令遵循数据上训练的分类器可用于评估稍后的通信运行。这种技术被用来成功地消除歧义2个潜在的BOLD响应到一个单一的question.Conclusions：模式分类功能磁共振成像是一个很有前途的技术，为推进理解严重脑损伤患者的意志脑反应，并可能作为一个强大的补充，传统的一般线性模型为基础的单变量分析方法。
Background: Recent neuroimaging investigations have explored the use of mental imagery tasks as proxies for an overt motor response, in which patients are asked to imagine performing a task, such as "Imagine yourself swimming."Objectives: To detect covert volitional brain activity in patients with severe brain injury using pattern classification of the blood oxygenation level-dependent (BOLD) response during mental imagery and to compare these results with those of a univariate functional magnetic resonance imaging analysis.Design: Case-control study.Setting: Academic research.Participants: Experiments were performed in 8 healthy control subjects and in 5 patients with severe brain injury. The patients with severe brain injury constituted a convenience sample.Main Outcome Measures: Functional magnetic resonance imaging data were acquired as the patients were asked to follow commands or to answer questions using motor imagery as a proxy response.Results: In the controls, the responses were accurately classified. In the patient group, the responses of 3 of 5 patients were correctly classified. The remaining 2 patients showed no significant BOLD response in a standard univariate analysis, suggesting that they did not perform the task. In addition, we showed that a classifier trained on command-following data can be used to evaluate a later communication run. This technique was used to successfully disambiguate 2 potential BOLD responses to a single question.Conclusions: Pattern classification in functional magnetic resonance imaging is a promising technique for advancing the understanding of volitional brain responses in patients with severe brain injury and may serve as a powerful complement to traditional general linear model-based univariate analysis methods.