Task-free spectral EEG dynamics track and predict patient recovery from severe acquired brain injury

• Main Authors: R.L. van den Brink, S. Nieuwenhuis, G.J.M. van Boxtel, G. van Luijtelaar, H.J. Eilander, V.J.M. Wijnen
• Format: Article
• Language: English
• Published: Elsevier 2018-01-01
• Series: NeuroImage: Clinical
• Online Access: http://www.sciencedirect.com/science/article/pii/S2213158217302449

For some patients, coma is followed by a state of unresponsiveness, while other patients develop signs of awareness. In practice, detecting signs of awareness may be hindered by possible impairments in the patient's motoric, sensory, or cognitive abilities, resulting in a substantial proportion of misdiagnosed disorders of consciousness. Task-free paradigms that are independent of the patient's sensorimotor and neurocognitive abilities may offer a solution to this challenge. A limitation of previous research is that the large majority of studies on the pathophysiological processes underlying disorders of consciousness have been conducted using cross-sectional designs. Here, we present a study in which we acquired a total of 74 longitudinal task-free EEG measurements from 16 patients (aged 6–22years, 12 male) suffering from severe acquired brain injury, and an additional 16 age- and education-matched control participants. We examined changes in amplitude and connectivity metrics of oscillatory brain activity within patients across their recovery. Moreover, we applied multi-class linear discriminant analysis to assess the potential diagnostic and prognostic utility of amplitude and connectivity metrics at the individual-patient level. We found that over the course of their recovery, patients exhibited nonlinear frequency band-specific changes in spectral amplitude and connectivity metrics, changes that aligned well with the metrics' frequency band-specific diagnostic value. Strikingly, connectivity during a single task-free EEG measurement predicted the level of patient recovery approximately 3months later with 75% accuracy. Our findings show that spectral amplitude and connectivity track patient recovery in a longitudinal fashion, and these metrics are robust pathophysiological markers that can be used for the automated diagnosis and prognosis of disorders of consciousness. These metrics can be acquired inexpensively at bedside, and are fully independent of the patient's neurocognitive abilities. Lastly, our findings tentatively suggest that the relative preservation of thalamo-cortico-thalamic interactions may predict the later reemergence of awareness, and could thus shed new light on the pathophysiological processes that underlie disorders of consciousness. Keywords: Disorders of consciousness, Brain injury, EEG, Classification