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.
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.
Authors: Jan Claassen; Angela Velazquez; Emma Meyers; Jens Witsch; M Cristina Falo; Soojin Park; Sachin Agarwal; J Michael Schmidt; Nicholas D Schiff; Jacobo D Sitt; Lionel Naccache; E Sander Connolly; Hans-Peter Frey Journal: Ann Neurol Date: 2016-08-16 Impact factor: 10.422
Authors: Peter B Forgacs; Mary M Conte; Esteban A Fridman; Henning U Voss; Jonathan D Victor; Nicholas D Schiff Journal: Ann Neurol Date: 2014-10-24 Impact factor: 10.422
Authors: Brian L Edlow; Camille Chatelle; Camille A Spencer; Catherine J Chu; Yelena G Bodien; Kathryn L O'Connor; Ronald E Hirschberg; Leigh R Hochberg; Joseph T Giacino; Eric S Rosenthal; Ona Wu Journal: Brain Date: 2017-09-01 Impact factor: 13.501