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Arch Neurol. 2012 Feb;69(2):176-81. doi: 10.1001/archneurol.2011.892.

Pattern classification of volitional functional magnetic resonance imaging responses in patients with severe brain injury.

Author information

1
Department of Neuroscience, Weill Cornell Graduate School of Medical Sciences, New York, NY 10021, USA.

Abstract

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.

PMID:
22332186
DOI:
10.1001/archneurol.2011.892
[Indexed for MEDLINE]
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