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

Rudy van den Brink, Sander Nieuwenhuis, G.J.M. van Boxtel, Gilles van Luijtelaar, Henk Eilander, V.J.M. Wijnen

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Abstract

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 6–22 years, 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 3 months 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.
Original languageEnglish
Pages (from-to)43-52
JournalNeuroImage: Clinical
Volume17
DOIs
Publication statusPublished - 2018

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Electroencephalography
Discriminant Analysis
Diagnostic Errors
Education

Keywords

  • Brain injury
  • CORTICAL CORRELATION STRUCTURE
  • Classification
  • Disorders of consciousness
  • EARLY INTENSIVE NEUROREHABILITATION
  • EEG
  • EFFECTIVE CONNECTIVITY
  • MINIMALLY CONSCIOUS STATE
  • MISMATCH NEGATIVITY
  • PERSISTENT VEGETATIVE STATE
  • PROLONGED UNCONSCIOUS STATE
  • SEVERE DISORDERS
  • SPONTANEOUS OSCILLATORY ACTIVITY
  • UNRESPONSIVE WAKEFULNESS SYNDROME

Cite this

van den Brink, Rudy ; Nieuwenhuis, Sander ; van Boxtel, G.J.M. ; van Luijtelaar, Gilles ; Eilander, Henk ; Wijnen, V.J.M. / Task-free spectral EEG dynamics track and predict patient recovery from severe acquired brain injury. In: NeuroImage: Clinical. 2018 ; Vol. 17. pp. 43-52.
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Task-free spectral EEG dynamics track and predict patient recovery from severe acquired brain injury. / van den Brink, Rudy; Nieuwenhuis, Sander; van Boxtel, G.J.M.; van Luijtelaar, Gilles; Eilander, Henk; Wijnen, V.J.M.

In: NeuroImage: Clinical, Vol. 17, 2018, p. 43-52.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

T1 - Task-free spectral EEG dynamics track and predict patient recovery from severe acquired brain injury

AU - van den Brink, Rudy

AU - Nieuwenhuis, Sander

AU - van Boxtel, G.J.M.

AU - van Luijtelaar, Gilles

AU - Eilander, Henk

AU - Wijnen, V.J.M.

PY - 2018

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N2 - 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 6–22 years, 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 3 months 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.

AB - 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 6–22 years, 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 3 months 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.

KW - Brain injury

KW - CORTICAL CORRELATION STRUCTURE

KW - Classification

KW - Disorders of consciousness

KW - EARLY INTENSIVE NEUROREHABILITATION

KW - EEG

KW - EFFECTIVE CONNECTIVITY

KW - MINIMALLY CONSCIOUS STATE

KW - MISMATCH NEGATIVITY

KW - PERSISTENT VEGETATIVE STATE

KW - PROLONGED UNCONSCIOUS STATE

KW - SEVERE DISORDERS

KW - SPONTANEOUS OSCILLATORY ACTIVITY

KW - UNRESPONSIVE WAKEFULNESS SYNDROME

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DO - 10.1016/j.nicl.2017.10.003

M3 - Article

VL - 17

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EP - 52

JO - NeuroImage: Clinical

JF - NeuroImage: Clinical

SN - 2213-1582

ER -