EEG functional connectivity contributes to outcome prediction of postanoxic coma

Gómez, Martín Carrasco; Keijzer, Hanneke M.; Ruijter, Barry J.; Bruña, Ricardo; Tjepkema-Cloostermans, Marleen C.; Hofmeijer, Jeannette; Putten, Michel J.A.M. van

doi:10.1016/j.clinph.2021.02.011

Cited by 19 publications

(15 citation statements)

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“…Previous studies have shown that functional connectivity can be used as a reliable biomarker of neurological function ( Wu et al, 2015 ) and to predict the severity and prognosis of comatose patients ( Alnes et al, 2021 ; Carrasco-Gomez et al, 2021 ). Herein, a phase-based approach (PLV) of functional connectivity was performed.…”

Section: Methodsmentioning

confidence: 99%

“…For spectral analysis, two types of features, relative power and the brain symmetry index (BSI), were calculated. Given the nature of inherently coordinated neural synchronization of brain function ( Zanesco et al, 2020 ; Alnes et al, 2021 ; Carrasco-Gomez et al, 2021 ), a phase synchronization-based (phase-locking value, PLV) method ( Lachaux et al, 1999 ) was used to compute the functional connectivity in five canonical frequency bands. On the other hand, we expanded from prior work using static features by exploring brain spatiotemporal dynamics using microstate analysis.…”

Section: Introductionmentioning

confidence: 99%

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EEG Evidence Reveals Zolpidem-Related Alterations and Prognostic Value in Disorders of Consciousness

et al. 2022

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Effective treatment and accurate long-term prognostication of patients with disorders of consciousness (DOC) remain pivotal clinical issues and challenges in neuroscience. Previous studies have shown that zolpidem produces paradoxical recovery and induces similar change patterns in specific electrophysiological features in some DOC (∼6%). However, whether these specific features are neural markers of responders, and how neural features evolve over time remain unclear. Here, we capitalized on static and dynamic EEG analysis techniques to fully uncover zolpidem-induced alterations in eight patients with DOC and constructed machine-learning models to predict long-term outcomes at the single-subject level. We observed consistent patterns of change across all patients in several static features (e.g., decreased relative theta power and weakened alpha-band functional connectivity) after zolpidem administration, albeit none zolpidem responders. Based on the current evidence, previously published electrophysiological features are not neural markers for zolpidem responders. Moreover, we found that the temporal dynamics of the brain slowed down after zolpidem intake. Brain states before and after zolpidem administration could be completely characterized by the EEG features. Furthermore, long-term outcomes were accurately predicted using connectivity features. Our findings suggest that EEG neural signatures have huge potential to assess consciousness states and predict fine-grained outcomes. In summary, our results extend the understanding of the effects of zolpidem on the brain and open avenues for the application prospect of zolpidem and EEG in patients with DOC.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EEG Evidence Reveals Zolpidem-Related Alterations and Prognostic Value in Disorders of Consciousness

et al. 2022

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“…In general, for patients who remain comatose for more than four weeks, the chance of meaningful recovery is low. Therefore, future studies should focus on innovative tools or techniques to better predict outcomes in various types of patients with impairment of consciousness [67,68].…”

Section: Prognosticationmentioning

confidence: 99%

Clinical approach to coma patients: tips and tricks

2021

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The reticular activating system (RAS) is responsible for wakefulness. The RAS projects activation to either side of the hemisphere. The dysfunction of the RAS or insufficiency of its activation results in impairment of consciousness. Physicians classify levels of awareness into four levels, from normal status to most severe unconsciousness, these being alert, drowsy, stupor, and coma, respectively. While the causes of unconsciousness are varied, physicians generally divide them into structural and metabolic etiologies upon the dominant approach. Surgical management is the primary treatment for the structural coma, while the metabolic coma requires predominantly medical treatment. The diagnosis and management of unconscious patients require extensive clinical assessment, consisting of a careful approach to history-taking and general medical and neurological examinations. Following immediate resuscitation by the ABCDE approach and proper management protocols, physicians should look for causes of loss of consciousness through clinical evidence and investigations. Appropriate management will help to avoid secondary complications related to the impairment of consciousness. The prognostication of a coma is also varied and primarily depends on its etiology. The algorithm for prognostication in a coma is helpful for poor outcome determinants.

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“…Where low-density scalp coverage has been examined, 29 no significant association was found between low-density EEG network measures and good outcomes at 3-6 months after injury, although EEG measures of functional connectivity have demonstrated promise in predicting poor outcomes in small cohorts of patients with postanoxic coma. 30 We sought to develop models utilizing resting-state cEEG for predicting emergence from DoC continuously. Individual EEG features, such as alpha power and variability, 31,15 have demonstrated promise for the cross-sectional classification of unresponsive wakefulness syndrome (UWS) versus minimally conscious state (MCS).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Resting-State Electroencephalography for Continuous, Passive Prediction of Coma Recovery After Acute Brain Injury

Zabihi

Rubin

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et al. 2022

Preprint

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Accurately predicting emergence from disorders of consciousness (DoC) after acute brain injury can profoundly influence mortality, acute management, and rehabilitation planning. While recent advances in functional neuroimaging and stimulus-based EEG offer the potential to enrich shared decision-making, their procedural sophistication and expense limit widespread availability or repeated performance. We investigated continuous EEG (cEEG) within a passive, resting-state framework to provide continuously updated predictions of DoC recovery at 24-, 48-, and 72-hour prediction horizons. To develop robust, continuous prediction models from a large population of patients with acute brain injury (ABI), we leveraged a recently described pragmatic approach transforming Glasgow Coma Scale assessment sub-score combinations into frequently assessed DoC diagnoses: coma, vegetative state, minimally conscious state with or without language, and post-injury confusional or recovered states. We retrospectively identified consecutive patients undergoing cEEG following acute traumatic brain injury (TBI), subarachnoid hemorrhage (SAH), or intracerebral hemorrhage (ICH). Models continuously predicting DoC diagnosis for multiple prediction horizons were evaluated utilizing recent clinical assessments with or without cEEG information, which comprised a comprehensive EEG feature set of 288 time, frequency, and time-frequency characteristics computed from consecutive 5-minute EEG epochs, with 6 additional features capturing each EEG feature's temporal dynamics. Features were fed into a predictive model developed with cross-validation; the ordinal DoC diagnosis was discriminated using an ensemble of XGBoost binary classifiers. For 201 ABI patients (46 TBI, 140 SAH, 15 ICH patients comprising 27,280 cEEG-hours with concomitant clinical assessments), cEEG-augmented models accurately predicted the future DoC diagnosis at 24 hours (one-vs-rest AU-ROC, 92.4%; weighted-F1 84.1%), 48 hours (one-vs-rest AU-ROC=88%, weighted-F1=80%) and 72 hours (one-vs-rest AU-ROC=86.3%, weighted-F1=76.6%). Models were robust to utilizing different ordinal cut-points for the DoC prediction target and evaluating additional models derived from specific sub-populations using a confound-isolating cross-validation framework. The most robust features across evaluation configurations included Petrosian fractal dimension, relative power of high to low (gamma-beta to delta-alpha) EEG frequency spectra, energy within the 12-35 Hz frequency band in the short-time Fourier transform domain, and wavelet entropy. The cEEG-augmented model exceeded the performance of models using preceding clinical assessments, continuously predicting future DoC diagnosis with one-vs-rest AU-ROC in the range of 84.3-92.4% while utilizing approaches to limit overfitting. The proposed continuous, resting-state cEEG prediction method represents a promising tool to predict DoC emergence in ABI patients. Enabling these methods prospectively would represent a new paradigm of continuous prognostic monitoring for predicting coma recovery and assessing treatment response.

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EEG functional connectivity contributes to outcome prediction of postanoxic coma

Cited by 19 publications

References 40 publications

EEG Evidence Reveals Zolpidem-Related Alterations and Prognostic Value in Disorders of Consciousness

EEG Evidence Reveals Zolpidem-Related Alterations and Prognostic Value in Disorders of Consciousness

Clinical approach to coma patients: tips and tricks

Resting-State Electroencephalography for Continuous, Passive Prediction of Coma Recovery After Acute Brain Injury

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