Normal Brain Response to Propofol in Advance of Recovery from Unresponsive Wakefulness Syndrome

Blain-Moraes, Stefanie; Boshra, Rober; Kan, Heung; Mah, Richard; Ruiter, Kyle I.; Avidan, Michael S.; Connolly, John F.; Mashour, George A.

doi:10.3389/fnhum.2016.00248

Cited by 17 publications

(11 citation statements)

References 25 publications

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“…Both parameters of the aperiodic component were estimated from 1-45 Hz using the 'Fitting oscillations and one over f' (FOOOF) algorithm (16) (see Methods). Oscillatory power in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45) bandwidth was calculated before and after the removal of the aperiodic component. The detection of oscillatory peak frequency was performed using the FOOOF algorithm.…”

Section: Resultsmentioning

confidence: 99%

Aperiodic brain activity and response to anesthesia vary in disorders of consciousness

Maschke

Duclos²,

Owen

et al. 2022

Preprint

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In the human electroencephalogram (EEG), oscillatory power peaks co-exist with non-oscillatory, aperiodic activity. Although EEG analysis has traditionally focused exclusively on oscillatory power, recent investigations have shown that the aperiodic EEG component can distinguish conscious wakefulness from sleep and anesthetic-induced unconsciousness (Lendner et al. 2020). This study investigates the aperiodic EEG component of individuals in a disorder of consciousness (DOC), and how it changes in response to exposure to anesthesia. High-density EEG was recorded from 43 individuals in a DOC. To measure the brain’s reaction to global perturbation, a subset of n = 16 were also exposed to a targeted infusion of propofol anesthesia. The aperiodic component was defined by the spectral slope and offset in the 1-45 Hz and 30-45 Hz range of the power spectral density. Brain network criticality and complexity were estimated using the pair correlation function (PCF) and Lempel-Ziv complexity, respectively. The level of responsiveness of all individuals in DOC was assessed using the Coma Recovery Scale-Revised (CRS-R). Recovery of consciousness was assessed three months post-EEG. At baseline, the EEG aperiodic component was more strongly correlated to the participants’ level of consciousness than the oscillatory component. Anesthesia caused a steepening of the spectral slope across participants. Importantly, the change in spectral slope positively correlated with the individual participant’s level of responsiveness. The spectral slope during exposure to anesthesia contained prognostic value for individuals with DOC. The anesthetic-induced change in aperiodic EEG was accompanied by loss of information-richness and a reduction in network criticality. The aperiodic EEG component in individuals with DOC has been historically neglected; this research highlights the importance of considering this measure for future research investigating brain mechanisms underlying consciousness.Significance StatementThe analysis of human EEG has traditionally focused on oscillatory power, which is characterized by peaks above a broadband, aperiodic component in the EEG power spectral density. This study is the first to demonstrate the value of the aperiodic EEG component and specifically, its reaction to propofol anesthesia for assessing individual’s level of, and capacity for, consciousness. Our results demonstrates that the pharmacological induced change in the aperiodic component accompanies the brain’s loss of network criticality and complexity. Most importantly, the magnitude of brain response to propofol anesthesia relies on an individual’s pre-anesthetic level of consciousness. Whereas the aperiodic EEG component has been historically neglected; this research highlights the necessity of considering this measure for future research that seeks to understand the neurophysiological underpinnings of consciousness.

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Section: Resultsmentioning

confidence: 99%

Aperiodic brain activity and response to anesthesia vary in disorders of consciousness

Maschke

Duclos²,

Owen

et al. 2022

Preprint

Self Cite

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“…1-month following the study, the patient had clinically recovered consciousness (Glasgow Coma Scale = 14; Coma Recovery Scale-Revised = 23) and was capable of responding to commands as well as verbally respond in conversation. The authors note that data from this participant has previously been published in Blain-Moraes et al 2016 [69].…”

Section: Case Studymentioning

confidence: 68%

Eliciting and Recording Event Related Potentials (ERPs) in Behaviourally Unresponsive Populations: A Retrospective Commentary on Critical Factors

et al. 2021

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A consistent limitation when designing event-related potential paradigms and interpreting results is a lack of consideration of the multivariate factors that affect their elicitation and detection in behaviorally unresponsive individuals. This paper provides a retrospective commentary on three factors that influence the presence and morphology of long-latency event-related potentials—the P3b and N400. We analyze event-related potentials derived from electroencephalographic (EEG) data collected from small groups of healthy youth and healthy elderly to illustrate the effect of paradigm strength and subject age; we analyze ERPs collected from an individual with severe traumatic brain injury to illustrate the effect of stimulus presentation speed. Based on these critical factors, we support that: (1) the strongest paradigms should be used to elicit event-related potentials in unresponsive populations; (2) interpretation of event-related potential results should account for participant age; and (3) speed of stimulus presentation should be slower in unresponsive individuals. The application of these practices when eliciting and recording event-related potentials in unresponsive individuals will help to minimize result interpretation ambiguity, increase confidence in conclusions, and advance the understanding of the relationship between long-latency event-related potentials and states of consciousness.

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“…Although it could be stated that this control group may not be optimal, we argue that for the purposes of the present study—to develop a clinical tool capable of predicting a patient’s outcome—a comparison with a typical healthy, conscious response is the most appropriate. In a basic research study examining the nature of consciousness, comparison of anaesthetised patients with those in a comatose state would be appropriate (see ref 42); however, this is not the objective of the present project.…”

Section: Methodsmentioning

confidence: 99%

“…Candidate features include power spectral density and fractal dimension,44 45 along with other features relating to brain connectivity. These include phase-lag index, symbolic transfer entropy, directed phase-lag index and Granger causality 42 46–49. Note that candidate features are computed from uniformly sized blocks of RS EEG or from averages of stimulus-locked trials within a block of ERP data.…”

Section: Methodsmentioning

confidence: 99%

Development of a point of care system for automated coma prognosis: a prospective cohort study protocol

Connolly

Reilly²,

Fox-Robichaud

et al. 2019

BMJ Open

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IntroductionComa is a deep state of unconsciousness that can be caused by a variety of clinical conditions. Traditional tests for coma outcome prediction are based mainly on a set of clinical observations. Recently, certain event-related potentials (ERPs), which are transient electroencephalogram (EEG) responses to auditory, visual or tactile stimuli, have been introduced as useful predictors of a positive coma outcome (ie, emergence). However, such tests require the skills of clinical neurophysiologists, who are not commonly available in many clinical settings. Additionally, none of the current standard clinical approaches have sufficient predictive accuracies to provide definitive prognoses.ObjectiveThe objective of this study is to develop improved machine learning procedures based on EEG/ERP for determining emergence from coma.Methods and analysisData will be collected from 50 participants in coma. EEG/ERP data will be recorded for 24 consecutive hours at a maximum of five time points spanning 30 days from the date of recruitment to track participants’ progression. The study employs paradigms designed to elicit brainstem potentials, middle-latency responses, N100, mismatch negativity, P300 and N400. In the case of patient emergence, data are recorded on that occasion to form an additional basis for comparison. A relevant data set will be developed from the testing of 20 healthy controls, each spanning a 15-hour recording period in order to formulate a baseline. Collected data will be used to develop an automated procedure for analysis and detection of various ERP components that are salient to prognosis. Salient features extracted from the ERP and resting-state EEG will be identified and combined to give an accurate indicator of prognosis.Ethics and disseminationThis study is approved by the Hamilton Integrated Research Ethics Board (project number 4840). Results will be disseminated through peer-reviewed journal articles and presentations at scientific conferences.Trial registration numberNCT03826407.

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Normal Brain Response to Propofol in Advance of Recovery from Unresponsive Wakefulness Syndrome

Cited by 17 publications

References 25 publications

Aperiodic brain activity and response to anesthesia vary in disorders of consciousness

Aperiodic brain activity and response to anesthesia vary in disorders of consciousness

Eliciting and Recording Event Related Potentials (ERPs) in Behaviourally Unresponsive Populations: A Retrospective Commentary on Critical Factors

Development of a point of care system for automated coma prognosis: a prospective cohort study protocol

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