Elsevier

Clinical Neurophysiology

Volume 122, Issue 11, November 2011, Pages 2157-2168
Clinical Neurophysiology

Determination of awareness in patients with severe brain injury using EEG power spectral analysis

https://doi.org/10.1016/j.clinph.2011.03.022Get rights and content

Abstract

Objective

To determine whether EEG spectral analysis could be used to demonstrate awareness in patients with severe brain injury.

Methods

We recorded EEG from healthy controls and three patients with severe brain injury, ranging from minimally conscious state (MCS) to locked-in-state (LIS), while they were asked to imagine motor and spatial navigation tasks. We assessed EEG spectral differences from 4 to 24 Hz with univariate comparisons (individual frequencies) and multivariate comparisons (patterns across the frequency range).

Results

In controls, EEG spectral power differed at multiple frequency bands and channels during performance of both tasks compared to a resting baseline. As patterns of signal change were inconsistent between controls, we defined a positive response in patient subjects as consistent spectral changes across task performances. One patient in MCS and one in LIS showed evidence of motor imagery task performance, though with patterns of spectral change different from the controls.

Conclusions

EEG power spectral analysis demonstrates evidence for performance of mental imagery tasks in healthy controls and patients with severe brain injury.

Significance

EEG power spectral analysis can be used as a flexible bedside tool to demonstrate awareness in brain-injured patients who are otherwise unable to communicate.

Highlights

► Motor and spatial imagery change EEG power spectra over a wide range of channels and frequencies. ► Patterns of spectral change vary between healthy subjects performing the same task. ► Brain injured subjects can demonstrate command following through changes in EEG power spectra.

Introduction

Recent studies using functional MRI (fMRI) and event-related potentials (ERP) demonstrate that some severely brain-injured patients retain a range of cognitive capacities despite minimal or no behavioral evidence of awareness (Kotchoubey et al., 2005, Owen et al., 2006, Perrin et al., 2006, Schnakers et al., 2008, Monti et al., 2010, Bardin et al., 2011). Importantly, Monti et al. (2010) used fMRI detection of motor and spatial navigation imagery to establish communication with a patient who had no overt behavioral ability to communicate.

These results, while compelling, raise an important ethical obligation to seek out patients who may retain significant cognitive abilities not evidenced by behavioral testing as in principle such patients may have a desire and capacity to participate in their own decision-making (Fins and Schiff, 2010). Currently available methods are limited in the types of patients they can assess and in the paradigms available for determination of awareness. For example, fMRI cannot be used in patients who are unable to be transported to the scanner, have implanted ferromagnetic material or make frequent head movements. The need to bring patients to the scanner also makes repeated assessments difficult, and can overlook evidence of awareness in patients whose arousal levels fluctuate through the day (Bardin et al., 2011). ERPs, meanwhile, require exact and consistent timing of subject performance. This limits the range of applicable behavioral paradigms and risks false negative results in patients with delayed or variable response times.

An alternative is a quantitative approach to EEG using power spectral analysis. Unlike fMRI, EEG can be recorded at the bedside, allowing for multiple testing sessions across different states of arousal. EEG measurements can be carried out in patients with ferromagnetic implants, and the EEG signal can be parsed with a precise temporal resolution, allowing for removal of transient movement artifacts. Unlike ERP-based analysis methods, power spectral analysis of EEG allows for detection of responses that are delayed or not tightly synchronized to a stimulus. Finally, EEG power spectral analysis has already been used as a communication tool in patients with stroke and motor neuron disease (Bai et al., 2008) and therefore can in principle serve both as a diagnostic method and basis for development of a communication device.

With this motivation, we investigated whether spatially- and spectrally-localized changes in EEG power spectra can identify behaviorally covert responses to commands in healthy subjects and patients with severe brain injury.

Section snippets

Subjects

Five healthy control (HC) volunteers with no history of neurological disease (three males; mean age 34 years, range 25–52 years) participated in the study. The three patient subjects (PSs) chosen for this study were drawn from a convenience sample enrolled in a multi-modal imaging and behavioral study of the natural history of recovery from severe brain injury. Clinical profiles of the PSs are in Table 1, Fig. 1 and Supplementary Appendix A. The five HCs and three PSs in this study demonstrated

Healthy controls

We begin by stepping through the detailed analysis of two HCs performing the motor imagery task. We then summarize the findings across all HCs on both the motor and the navigation imagery tasks. In all comparisons shown below, we describe increases or decreases in power of the EEG spectrum as task relative to a rest (stop imagining) condition.

Motor imagery task (imagination of swimming)

Results from HC 1 on the motor imagery task are shown in Fig. 3. We begin with the univariate (frequency-by-frequency) analysis on a single run. Fig. 3A

Discussion

In this study we have shown that EEG power spectral analysis can be used to denote performance of a mental imagery task in healthy controls. In addition, we used outcome measures developed from the HC studies to demonstrate evidence of motor imagery task performance in patients with severe brain injury. In the HCs, imagination of either swimming or walking around their house changed EEG spectral power at multiple channels and frequency bands as indicated by the TGT. Based on the HC results, we

Acknowledgements

We thank Hemant Bokil for assistance with the use of the Chronux toolbox and for many helpful conversations on digital signal processing. We thank Jennifer Hersh for assistance with the data collection. This work was supported by NIH-NICHD 51912, the James S McDonnell Foundation, and Weill-Cornell CTSC UL 1RR02499. The funding sources had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for

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