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Original Contributions
Feb 2012

Pattern Classification of Volitional Functional Magnetic Resonance Imaging Responses in Patients With Severe Brain Injury

Author Affiliations

Author Affiliations: Department of Neuroscience, Weill Cornell Graduate School of Medical Sciences (Mr Bardin), and Departments of Neurology and Neuroscience (Dr Schiff) and Radiology and Citigroup Biomedical Imaging Center (Dr Voss), Weill Cornell Medical College, Cornell University, New York, New York.

Arch Neurol. 2012;69(2):176-181. doi:10.1001/archneurol.2011.892

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.