Somatosensory and auditory deviance detection for outcome prediction during postanoxic coma

Decoding Analyses Show Dynamic Waxing and Waning of Event-Related Potentials in Coma Patients

Background/Objectives: Coma prognosis is challenging, as patient presentation can be misleading or uninformative when using behavioral assessments only. Event-related potentials have been shown to provide valuable information about a patient's chance of survival and emergence from coma. Our prior work revealed that the mismatch negativity (MMN) in particular waxes and wanes across 24 h in some coma patients. This "cycling" aspect of the presence/absence of neurophysiological responses may require fine-grained tools to increase the chances of detecting levels of neural processing in coma. This study implements multivariate pattern analysis (MVPA) to automatically quantify patterns of neural discrimination between duration deviant and standard tones over time at the single-subject level in seventeen healthy controls and in three comatose patients. Methods: One EEG recording, containing up to five blocks of an auditory oddball paradigm, was performed in controls over a 12 h period. For patients, two EEG sessions were conducted 3 days apart for up to 24 h, denoted as day 0 and day 3, respectively. MVPA was performed using a support-vector machine classifier. Results: Healthy controls exhibited reliable discrimination or classification performance during the latency intervals associated with MMN and P3a components. Two patients showed some intervals with significant discrimination around the second half of day 0, and all had significant results on day 3. Conclusions: These findings suggest that decoding analyses can accurately classify neural responses at a single-subject level in healthy controls and provide evidence of small but significant changes in auditory discrimination over time in coma patients. Further research is needed to confirm whether this approach represents an improved technology for assessing cognitive processing in coma.

Temporal dynamics of neural synchrony and complexity of auditory EEG responses in post-hypoxic ischemic coma

The capacity to integrate information across brain regions and sufficient diversity of neural activity is necessary for consciousness. In patients in a post-hypoxic ischemic coma, the integrity of the auditory processing network is indicative of chances of regaining consciousness. However, our understanding of how measures of integration and differentiation of auditory responses manifest across time of coma is limited. We investigated the temporal evolution of neural synchrony of auditory-evoked electroencephalographic (EEG) responses, measured via their phase-locking value (PLV), and of their neural complexity in unconscious post-hypoxic ischemic comatose patients. Our results show that the PLV was predictive of chances to regain consciousness within the first 40 h post-cardiac arrest, while its predictive value diminished over subsequent time after coma onset. This was due to changing trajectories of PLV over time of coma for non-survivors, while survivors had stable PLV. The complexity of EEG responses was not different between patients who regained consciousness and those who did not, but it significantly diminished over time of coma, irrespective of the patient's outcome. Our findings provide novel insights on the optimal temporal window for assessing auditory functions in post-hypoxic ischemic coma. They are of particular importance for guiding the implementation of quantitative techniques for prognostication and contribute to an evolving understanding of neural functions within the acute comatose state.

Decoding cognition in neurodevelopmental, psychiatric and neurological conditions with multivariate pattern analysis of EEG data

Multivariate pattern analysis (MVPA) of electroencephalographic (EEG) data represents a revolutionary approach to investigate how the brain encodes information. By considering complex interactions among spatio-temporal features at the individual level, MVPA overcomes the limitations of univariate techniques, which often fail to account for the significant inter- and intra-individual neural variability. This is particularly relevant when studying clinical populations, and therefore MVPA of EEG data has recently started to be employed as a tool to study cognition in brain disorders. Here, we review the insights offered by this methodology in the study of anomalous patterns of neural activity in conditions such as autism, ADHD, schizophrenia, dyslexia, neurological and neurodegenerative disorders, within different cognitive domains (perception, attention, memory, consciousness). Despite potential drawbacks that should be attentively addressed, these studies reveal a peculiar sensitivity of MVPA in unveiling dysfunctional and compensatory neurocognitive dynamics of information processing, which often remain blind to traditional univariate approaches. Such higher sensitivity in characterizing individual neurocognitive profiles can provide unique opportunities to optimise assessment and promote personalised interventions.

Neural complexity and the spectral slope characterise auditory processing in wakefulness and sleep

Auditory processing and the complexity of neural activity can both indicate residual consciousness levels and differentiate states of arousal. However, how measures of neural signal complexity manifest in neural activity following environmental stimulation and, more generally, how the electrophysiological characteristics of auditory responses change in states of reduced consciousness remain under-explored. Here, we tested the hypothesis that measures of neural complexity and the spectral slope would discriminate stages of sleep and wakefulness not only in baseline electroencephalography (EEG) activity but also in EEG signals following auditory stimulation. High-density EEG was recorded in 21 participants to determine the spatial relationship between these measures and between EEG recorded pre- and post-auditory stimulation. Results showed that the complexity and the spectral slope in the 2-20 Hz range discriminated between sleep stages and had a high correlation in sleep. In wakefulness, complexity was strongly correlated to the 20-40 Hz spectral slope. Auditory stimulation resulted in reduced complexity in sleep compared to the pre-stimulation EEG activity and modulated the spectral slope in wakefulness. These findings confirm our hypothesis that electrophysiological markers of arousal are sensitive to sleep/wake states in EEG activity during baseline and following auditory stimulation. Our results have direct applications to studies using auditory stimulation to probe neural functions in states of reduced consciousness.

A Novel Approach to Screen for Somatosensory Evoked Potentials in Critical Care

BACKGROUND

Somatosensory evoked potentials (SSEPs) help prognostication, particularly in patients with diffuse brain injury. However, use of SSEP is limited in critical care. We propose a novel, low-cost approach allowing acquisition of screening SSEP using widely available intensive care unit (ICU) equipment, specifically a peripheral "train-of-four" stimulator and standard electroencephalograph.

METHODS

The median nerve was stimulated using a train-of-four stimulator, and a standard 21-channel electroencephalograph was recorded to generate the screening SSEP. Generation of the SSEP was supported by visual inspection, univariate event-related potentials statistics, and a multivariate support vector machine (SVM) decoding algorithm. This approach was validated in 15 healthy volunteers and validated against standard SSEPs in 10 ICU patients. The ability of this approach to predict poor neurological outcome, defined as death, vegetative state, or severe disability at 6 months, was tested in an additional set of 39 ICU patients.

RESULTS

In each of the healthy volunteers, both the univariate and the SVM methods reliably detected SSEP responses. In patients, when compared against the standard SSEP method, the univariate event-related potentials method matched in nine of ten patients (sensitivity = 94%, specificity = 100%), and the SVM had 100% sensitivity and specificity when compared with the standard method. For the 49 ICU patients, we performed both the univariate and the SVM methods: a bilateral absence of short latency responses (n = 8) predicted poor neurological outcome with 0% FPR (sensitivity = 21%, specificity = 100%).

CONCLUSIONS

Somatosensory evoked potentials can reliably be recorded using the proposed approach. Given the very good but slightly lower sensitivity of absent SSEPs in the proposed screening approach, confirmation of absent SSEP responses using standard SSEP recordings is advised.

Auditory stimulation and deep learning predict awakening from coma after cardiac arrest

Assessing the integrity of neural functions in coma after cardiac arrest remains an open challenge. Prognostication of coma outcome relies mainly on visual expert scoring of physiological signals, which is prone to subjectivity and leaves a considerable number of patients in a 'grey zone', with uncertain prognosis. Quantitative analysis of EEG responses to auditory stimuli can provide a window into neural functions in coma and information about patients' chances of awakening. However, responses to standardized auditory stimulation are far from being used in a clinical routine due to heterogeneous and cumbersome protocols. Here, we hypothesize that convolutional neural networks can assist in extracting interpretable patterns of EEG responses to auditory stimuli during the first day of coma that are predictive of patients' chances of awakening and survival at 3 months. We used convolutional neural networks (CNNs) to model single-trial EEG responses to auditory stimuli in the first day of coma, under standardized sedation and targeted temperature management, in a multicentre and multiprotocol patient cohort and predict outcome at 3 months. The use of CNNs resulted in a positive predictive power for predicting awakening of 0.83 ± 0.04 and 0.81 ± 0.06 and an area under the curve in predicting outcome of 0.69 ± 0.05 and 0.70 ± 0.05, for patients undergoing therapeutic hypothermia and normothermia, respectively. These results also persisted in a subset of patients that were in a clinical 'grey zone'. The network's confidence in predicting outcome was based on interpretable features: it strongly correlated to the neural synchrony and complexity of EEG responses and was modulated by independent clinical evaluations, such as the EEG reactivity, background burst-suppression or motor responses. Our results highlight the strong potential of interpretable deep learning algorithms in combination with auditory stimulation to improve prognostication of coma outcome.

Comatose Patients After Cardiopulmonary Resuscitation: An Analysis Based on Quantitative Methods of EEG Reactivity

Objective

Every year, approximately 50–110/1,00,000 people worldwide suffer from cardiac arrest, followed by hypoxic-ischemic encephalopathy after cardiopulmonary resuscitation (CPR), and approximately 40–66% of patients do not recover. The purpose of this study was to identify the brain network parameters and key brain regions associated with awakening by comparing the reactivity characteristics of the brain networks between the awakening and unawakening groups of CPR patients after coma, thereby providing a basis for further awakening interventions.

Method

This study involved a prospective cohort study. Using a 64-electrode electroencephalography (EEG) wireless 64A system, EEG signals were recorded from 16 comatose patients after CPR in the acute phase (<1 month) from 2019 to 2020. MATLAB (2017b) was used to quantitatively analyze the reactivity (power spectrum and entropy) and brain network characteristics (coherence and phase lag index) after pain stimulation. The patients were divided into an awakening group and an unawakening group based on their ability to execute commands or engage in repeated and continuous purposeful behavior after 3 months. The above parameters were compared to determine whether there were differences between the two groups.

Results

(1) Power spectrum: the awakening group had higher gamma, beta and alpha spectral power after pain stimulation in the frontal and parietal lobes, and lower delta and theta spectral power in the bilateral temporal and occipital lobes than the unawakening group. (2) Entropy: after pain stimulation, the awakening group had higher entropy in the frontal and parietal lobes and lower entropy in the temporal occipital lobes than the unawakening group. (3) Connectivity: after pain stimulation, the awakening group had stronger gamma and beta connectivity in nearly the whole brain, but weaker theta and delta connectivity in some brain regions (e.g., the frontal-occipital lobe and parietal-occipital lobe) than the unawakening group.

Conclusion

After CPR, comatose patients were more likely to awaken if there was a higher stimulation of fast-frequency band spectral power, higher entropy, stronger whole-brain connectivity and better retention of frontal-parietal lobe function after pain stimulation.

Evoked and Event-Related Potentials as Biomarkers of Consciousness State and Recovery

SUMMARY

The definition of consciousness has been the subject of great interest for many scientists and philosophers. To better understand how evoked potentials may be identified as biomarkers of consciousness and recovery, the different theoretical models sustaining neural correlates of consciousness are reviewed. A multimodal approach can help to better predict clinical outcome in patients presenting with disorders of consciousness. Evoked potentials are inexpensive and easy-to-implement bedside examination techniques. Evoked potentials are an integral part of prognostic evaluation, particularly in cases of cognitive motor dissociation. Prognostic criteria are well established in postanoxic disorders of consciousness, especially postcardiac arrest but are less well determined in other etiologies. In the early examination, bilateral absence of N20 in disorder of consciousness patients is strongly associated with unfavorable outcome (i.e., death or unresponsive wakefulness syndrome) especially in postanoxic etiologies. This predictive value is lower in other etiologies and probably also in children. Both N20 and mismatch negativity are proven outcome predictors for acute coma. Many studies have shown that mismatch negativity and P3a are characterized by a high prognostic value for awakening, but some patients presenting unresponsive wakefulness syndrome also process a P3a. The presence of long-latency event-related potential components in response to stimuli is indicative of a better recovery. All neurophysiological data must be integrated within a multimodal approach combining repeated clinical evaluation, neuroimaging, functional imaging, biology, and neurophysiology combining passive and active paradigms.

Automatic Sensory Predictions: A Review of Predictive Mechanisms in the Brain and Their Link to Conscious Processing

The human brain has the astonishing capacity of integrating streams of sensory information from the environment and forming predictions about future events in an automatic way. Despite being initially developed for visual processing, the bulk of predictive coding research has subsequently focused on auditory processing, with the famous mismatch negativity signal as possibly the most studied signature of a surprise or prediction error (PE) signal. Auditory PEs are present during various consciousness states. Intriguingly, their presence and characteristics have been linked with residual levels of consciousness and return of awareness. In this review we first give an overview of the neural substrates of predictive processes in the auditory modality and their relation to consciousness. Then, we focus on different states of consciousness - wakefulness, sleep, anesthesia, coma, meditation, and hypnosis - and on what mysteries predictive processing has been able to disclose about brain functioning in such states. We review studies investigating how the neural signatures of auditory predictions are modulated by states of reduced or lacking consciousness. As a future outlook, we propose the combination of electrophysiological and computational techniques that will allow investigation of which facets of sensory predictive processes are maintained when consciousness fades away.

Brain functional connectivity during the first day of coma reflects long-term outcome

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Coma patients show different connectivity patterns depending on long-term outcome.

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Time-variance of functional connectivity is an early prognostic marker for coma patients.

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Connectivity patterns observed in chronic patients may develop early after coma onset.

Objective

In patients with disorders of consciousness (DOC), properties of functional brain networks at rest are informative of the degree of consciousness impairment and of long-term outcome. Here we investigate whether connectivity differences between patients with favorable and unfavorable outcome are already present within 24 h of coma onset.

Methods

We prospectively recorded 63-channel electroencephalography (EEG) at rest during the first day of coma after cardiac arrest. We analyzed 98 adults, of whom 57 survived beyond unresponsive wakefulness. Functional connectivity was estimated by computing the ‘debiased weighted phase lag index’ over epochs of five seconds duration. We evaluated the network’s topological features, including clustering coefficient, path length, modularity and participation coefficient and computed their variance over time. Finally, we estimated the predictive value of these topological features for patients’ outcomes by splitting the patient sample in training and test datasets.

Results

Group-level analysis revealed lower clustering coefficient, higher modularity and path length variance in patients with favorable compared to those with unfavorable outcomes (p < 0.01). Within all features, the path length variance in the network provided the best positive predictive value (PPV) for favorable outcome and specificity for unfavorable outcome in the test dataset (PPV: 0.83, p < 0.01; specificity: 0.86, p < 0.01) with above-chance negative predictive value and accuracy. Of note, the exclusion of patients with epileptiform activity (20 in total) eliminates all false positive predictions (n = 6) for path length variance.

Interpretation

Topological features of functional connectivity differ as a function of long-term outcome in patients on the first day of coma. These differences are not interpretable in terms of consciousness levels as all patients were in a deep unconscious state. The time variance of path length is informative of comatose patients’ outcome, as patients with favorable outcome exhibit a richer repertoire of path length than those with unfavorable outcomes.

EEG-based outcome prediction after cardiac arrest with convolutional neural networks: Performance and visualization of discriminative features

Prognostication for comatose patients after cardiac arrest is a difficult but essential task. Currently, visual interpretation of electroencephalogram (EEG) is one of the main modality used in outcome prediction. There is a growing interest in computer-assisted EEG interpretation, either to overcome the possible subjectivity of visual interpretation, or to identify complex features of the EEG signal. We used a one-dimensional convolutional neural network (CNN) to predict functional outcome based on 19-channel-EEG recorded from 267 adult comatose patients during targeted temperature management after CA. The area under the receiver operating characteristic curve (AUC) on the test set was 0.885. Interestingly, model architecture and fine-tuning only played a marginal role in classification performance. We then used gradient-weighted class activation mapping (Grad-CAM) as visualization technique to identify which EEG features were used by the network to classify an EEG epoch as favorable or unfavorable outcome, and also to understand failures of the network. Grad-CAM showed that the network relied on similar features than classical visual analysis for predicting unfavorable outcome (suppressed background, epileptiform transients). This study confirms that CNNs are promising models for EEG-based prognostication in comatose patients, and that Grad-CAM can provide explanation for the models' decision-making, which is of utmost importance for future use of deep learning models in a clinical setting.