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

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.

Figure 1

Classification of command-following data in healthy control subjects. Statistical comparisons between whole-brain and region-of-interest (ROI) analyses were performed using 2-tailed t test (*P < .05 was considered significant.) Horizontal line indicates chance level.

Figure 2

Univariate analysis of command-following data. A, In a representative control subject. B–E, In patients 1, 2, and 3 (D and E show responses of patient 3 at test 1 and test 2, respectively). Numerals on the color bar indicate t scores. Univariate analysis methods are given in the eAppendix (http://www.archneurol.com).

Figure 3

Classification of command-following data in patients with severe brain injury. Statistical comparisons between whole-brain and region-of-interest (ROI) analyses were performed using 2-tailed t tests (asterisks) *P < .05 was considered significant. Patient No. 3 (1) indicates 3 (test 1); 3(2), 3 (test 2). Mean POS indicates the mean calculation for subjects who had a positive result in the univariate analysis. Horizontal line indicates chance level.

Figure 4

Univariate analysis for the multiple-choice card-guessing paradigm. A, Classification of communication face card data in patient 1. Top: Shown is the performance of a classifier trained on the patient’s command-following data and tested on the face card data. Statistical significance was determined first using 1-way analysis of variance, followed by application of Scheffé test (P < .05, corrected for multiple comparisons). Bottom: The results of a univariate general linear model (GLM) analysis are shown for each face card. The symbol for the correct card is outlined in white. B, Same as in A for the suit card data. C, Same as in B for a representative control subject. *Statistically significant differences using the statistical test described earlier.