Coma is a state of unconsciousness due to severe brain injury, in which patients are rendered unresponsive to external stimuli. Due to the limitations of current clinical tests in identifying a specific injury or causes associated with coma, devising treatment strategies for coma patients is a persistent clinical challenge. A signature feature of coma is severe disruption of the brain's electrical activity. Thus, the electroencephalogram (EEG), which measures the brain's electrical activity patterns, is routinely used in the neurology and neurosurgery intensive care unit (NNICU) to monitor patients in coma. However, the utility of EEG for diagnosing coma is largely limited to clinicians reading electrical activity in `raw' form as waveform tracings on a monitor. The primary goal of the proposed research is to develop and evaluate new algorithms, derived from engineering theory that will extract information about coma from the EEG that might not be apparent when reading the activity with the naked eye. Consequently, these new methods will enable the automatic EEG-based classification of coma etiology, gradation of injury severity, and prediction of clinical outcome. Eventually, these techniques could potentially be used to help tailor clinical treatment strategies for patients in coma. In this project, we will record EEG data from patients diagnosed with a range of coma etiologies. These data will be assimilated into a biological mathematical model for how the brain produces electrical activity, i.e., the neural dynamics. Enabled by these models, we will use a new type of analysis, called network reachability analysis, which characterizes the different types of electrical activity patterns that the models can produce. As an analogy, an airplane in flight might seem relatively stationary, but the plane's dynamics are actually complex since it could execute many different maneuvers at any time. Our analysis will describe how many `maneuvers' the brain is capable of making, thus providing a dynamical, quantitative characterization of the brain's lability. Our hypothesis is that different types of coma will exhibit different lability. To test this hypothesis, and to explore its clinical utility, we will apply network reachability analysis to the recordings we will obtain from patients with coma. Through this analysis, we will construct quantitative biomarkers that could be integrated into a new type of EEG monitor tailored for coma and other related disorders. Thus, the outcomes of this project will have significant and immediate impact on neurocritical care by facilitating more precise quantitative analysis of the neural dynamics of coma. More generally, the development of these techniques might shed new light on the mechanisms that underlie pathological states of unconsciousness, as well as normal sleep and wakefulness.

Public Health Relevance

(Public Health Relevancy Statement): The proposed research will facilitate the design of new diagnostic tools for determining the cause and prognosis of coma. Moreover, it will help clinicians better interpret patients' brain activity, toward designing and optimizing treatments for coma recovery. The techniques enabled by the proposed research could be eventually implemented with simple clinical technologies, leading to widespread dissemination and enhanced neurocritical care.

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Exploratory/Developmental Grants (R21)
Project #
Application #
Study Section
Neuroscience and Ophthalmic Imaging Technologies Study Section (NOIT)
Program Officer
Conwit, Robin
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Washington University
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
Saint Louis
United States
Zip Code
Khanmohammadi, Sina; Laurido-Soto, Osvaldo; Eisenman, Lawrence N et al. (2018) Intrinsic network reactivity differentiates levels of consciousness in comatose patients. Clin Neurophysiol 129:2296-2305
Kafashan, MohammadMehdi; Palanca, Ben Julian A; Ching, ShiNung (2018) Dimensionality reduction impedes the extraction of dynamic functional connectivity states from fMRI recordings of resting wakefulness. J Neurosci Methods 293:151-161