0
Original Contributions |

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

Jonathan C. Bardin, BA; Nicholas D. Schiff, MD; Henning U. Voss, PhD
Arch Neurol. 2012;69(2):176-181. doi:10.1001/archneurol.2011.892.
Text Size: A A A
Published online

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.

Figures in this Article

Sign In to Access Full Content

Don't have Access?

Register and get free email Table of Contents alerts, saved searches, PowerPoint downloads, CME quizzes, and more

Subscribe for full-text access to content from 1998 forward and a host of useful features

Activate your current subscription (AMA members and current subscribers)

Purchase Online Access to this article for 24 hours

Figures

Place holder to copy figure label and caption
Grahic Jump Location

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.

Place holder to copy figure label and caption
Grahic Jump Location

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 .

Place holder to copy figure label and caption
Grahic Jump Location

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.

Place holder to copy figure label and caption
Grahic Jump Location

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.

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

References

Correspondence

CME
Accreditation Information
The American Medical Association is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. The AMA designates this journal-based CME activity for a maximum of 1 AMA PRA Category 1 CreditTM per course. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Physicians who complete the CME course and score at least 80% correct on the quiz are eligible for AMA PRA Category 1 CreditTM.
Note: You must get at least of the answers correct to pass this quiz.
You have not filled in all the answers to complete this quiz
The following questions were not answered:
Sorry, you have unsuccessfully completed this CME quiz with a score of
The following questions were not answered correctly:
Commitment to Change (optional):
Indicate what change(s) you will implement in your practice, if any, based on this CME course.
Your quiz results:
The filled radio buttons indicate your responses. The preferred responses are highlighted
For CME Course: A Proposed Model for Initial Assessment and Management of Acute Heart Failure Syndromes
Indicate what changes(s) you will implement in your practice, if any, based on this CME course.
NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s “Cited By” API will populate this tab (http://www.crossref.org/citedby.html).
Submit a Comment

Some tools below are only available to our subscribers or users with an online account.

Sign In to Access Full Content

Related Content

Customize your page view by dragging & repositioning the boxes below.

See Also...
Multimedia Related by Topic

Author Interview

Articles Related By Topic
Related Topics
PubMed Articles
Jobs