Narrative Review: Quantitative EEG in Disorders of Consciousness

Wutzl, Betty; Golaszewski, Stefan; Leibnitz, Kenji; Langthaler, Patrick B.; Kunz, Alexander; Leis, Stefan; Schwenker, Kerstin; Thomschewski, Aljoscha; Bergmann, Jürgen; Trinka, Eugen

doi:10.3390/brainsci11060697

Cited by 38 publications

(23 citation statements)

References 131 publications

(206 reference statements)

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“…A conspicuous number of predictive parameters in rehabilitation of patients with DoC have been reported both from the clinical world [3]- [5] and the instrumental world [6]- [8]. Multiple electroencephalography (EEG) descriptors as reactivity, alpha waves and an anteroposterior gradient (APG) have been shown to be predictive of a possible consciousness recovery [9]- [12]. Many are also braincomputer interfaces application with embedded EEG-based prognostic models [13]- [15].…”

Section: Introductionmentioning

confidence: 99%

Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction

Liuzzi

Grippo

Campagnini

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Liuzzi et al. Merging clinical and EEG biomarkers in an Elastic-Net regression for disorder of consciousness prognosis prediction Abstract-Patients with Disorder of Consciousness (DoC) entering Intensive Rehabilitation Units after a severe Acquired Brain Injury have a highly variable evolution of the state of consciousness which is a complex aspect to predict. Besides clinical factors, electroencephalography has clearly shown its potential into the identification of prognostic biomarkers of consciousness recovery. In this retrospective study, with a dataset of 271 patients with DoC, we proposed three different Elastic-Net regressors trained on different datasets to predict the Coma Recovery Scale-Revised value at discharge based on data collected at admission. One dataset was completely EEG-based, one solely clinical databased and the last was composed by the union of the two. Each model was optimized, validated and tested with a robust nested cross-validation pipeline. The best models resulted in a median absolute test error of 4.54 [IQR = 4.56], 3.39 [IQR = 4.36], 3.16 [IQR = 4.13] for respectively the EEG, clinical and hybrid model. Furthermore, the hybrid model for what concerns overcoming an unresponsive wakefulness state and exiting a DoC results in an AUC of 0.91 and 0.88 respectively. Small but useful improvements are added by the EEG dataset to the clinical model for what concerns overcoming an unresponsive wakefulness state. Datadriven techniques and namely, machine learning models are hereby shown to be capable of supporting the complex decisionmaking process the practitioners must face.

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Section: Introductionmentioning

confidence: 99%

Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction

Liuzzi

Grippo

Campagnini

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…We focus on four categories of approaches to EEG analysis: (1) conventional EEG measures; (2) quantitative restingstate measures; (3) stimulus-based paradigms; and (4) electroencephalographic sleep features. Multiple other reviews have been published on EEG-based measures in other contexts, [30][31][32] and we direct the reader to these for EEG tools that either have not been demonstrated to have acute prognostic significance or require substantial additional equipment (e.g., transcranial magnetic stimulation [TMS]-EEG). We also direct readers to a dedicated review on sleep electroencephalography contained in this issue.…”

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confidence: 99%

Conventional and Investigational Approaches Leveraging Clinical EEG for Prognosis in Acute Disorders of Consciousness

2022

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Prediction of recovery of consciousness after severe brain injury is difficult and limited by a lack of reliable, standardized biomarkers. Multiple approaches for analysis of clinical electroencephalography (EEG) that shed light on prognosis in acute severe brain injury have emerged in recent years. These approaches fall into two major categories: conventional characterization of EEG background and quantitative measurement of resting state or stimulus-induced EEG activity. Additionally, a small number of studies have associated the presence of electrophysiologic sleep features with prognosis in the acute phase of severe brain injury. In this review, we focus on approaches for the analysis of clinical EEG that have prognostic significance and that could be readily implemented with minimal additional equipment in clinical settings, such as intensive care and intensive rehabilitation units, for patients with acute disorders of consciousness.

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“…Nevertheless, it is necessary for future research to elucidate the effects of neuro-trauma on critical system dynamics to test methods improving not only acute mortality but also long-term patient outcomes. Quantitative EEGs could here provide a useful tool for the continuous assessment of patient neuroelectric status [ 136 ].…”

Section: Reestablishing Critical System Dynamics: a General Strategy ...mentioning

confidence: 99%

Therapeutic Neuromodulation toward a Critical State May Serve as a General Treatment Strategy

2022

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Brain disease has become one of this century’s biggest health challenges, urging the development of novel, more effective treatments. To this end, neuromodulation represents an excellent method to modulate the activity of distinct neuronal regions to alleviate disease. Recently, the medical indications for neuromodulation therapy have expanded through the adoption of the idea that neurological disorders emerge from deficits in systems-level structures, such as brain waves and neural topology. Connections between neuronal regions are thought to fluidly form and dissolve again based on the patterns by which neuronal populations synchronize. Akin to a fire that may spread or die out, the brain’s activity may similarly hyper-synchronize and ignite, such as seizures, or dwindle out and go stale, as in a state of coma. Remarkably, however, the healthy brain remains hedged in between these extremes in a critical state around which neuronal activity maneuvers local and global operational modes. While it has been suggested that perturbations of this criticality could underlie neuropathologies, such as vegetative states, epilepsy, and schizophrenia, a major translational impact is yet to be made. In this hypothesis article, we dissect recent computational findings demonstrating that a neural network’s short- and long-range connections have distinct and tractable roles in sustaining the critical regime. While short-range connections shape the dynamics of neuronal activity, long-range connections determine the scope of the neuronal processes. Thus, to facilitate translational progress, we introduce topological and dynamical system concepts within the framework of criticality and discuss the implications and possibilities for therapeutic neuromodulation guided by topological decompositions.

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Narrative Review: Quantitative EEG in Disorders of Consciousness

Cited by 38 publications

References 131 publications

Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction

Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction

Conventional and Investigational Approaches Leveraging Clinical EEG for Prognosis in Acute Disorders of Consciousness

Therapeutic Neuromodulation toward a Critical State May Serve as a General Treatment Strategy

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