Dataset Information

Somatosensory and auditory deviance detection for outcome prediction during postanoxic coma.

ABSTRACT:

Objective

Prominent research in patients with disorders of consciousness investigated the electrophysiological correlates of auditory deviance detection as a marker of consciousness recovery. Here, we extend previous studies by investigating whether somatosensory deviance detection provides an added value for outcome prediction in postanoxic comatose patients.

Methods

Electroencephalography responses to frequent and rare stimuli were obtained from 66 patients on the first and second day after coma onset.

Results

Multivariate decoding analysis revealed an above chance-level auditory discrimination in 25 patients on the first day and in 31 patients on the second day. Tactile discrimination was significant in 16 patients on the first day and in 23 patients on the second day. Single-day sensory discrimination was unrelated to patients' outcome in both modalities. However, improvement of auditory discrimination from first to the second day was predictive of good outcome with a positive predictive power (PPV) of 0.73 (CI = 0.52-0.88). Analyses considering the improvement of tactile, auditory and tactile, or either auditory or tactile discrimination showed no significant prediction of good outcome (PPVs = 0.58-0.68).

Interpretation

Our results show that in the acute phase of coma deviance detection is largely preserved for both auditory and tactile modalities. However, we found no evidence for an added value of somatosensory to auditory deviance detection function for coma-outcome prediction.

SUBMITTER: Pfeiffer C

PROVIDER: S-EPMC6144443 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Somatosensory and auditory deviance detection for outcome prediction during postanoxic coma.

Pfeiffer Christian C Nguissi Nathalie Ata Nguepnjo NAN Chytiris Magali M Bidlingmeyer Phanie P Haenggi Matthias M Kurmann Rebekka R Zubler Frédéric F Accolla Ettore E Viceic Dragana D Rusca Marco M Oddo Mauro M Rossetti Andrea O AO De Lucia Marzia M

Annals of clinical and translational neurology 20180726 9

<h4>Objective</h4>Prominent research in patients with disorders of consciousness investigated the electrophysiological correlates of auditory deviance detection as a marker of consciousness recovery. Here, we extend previous studies by investigating whether somatosensory deviance detection provides an added value for outcome prediction in postanoxic comatose patients.<h4>Methods</h4>Electroencephalography responses to frequent and rare stimuli were obtained from 66 patients on the first and seco ...[more]

PMID: 30250859

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Project description:AimCurrent multimodal approaches leave approximately half of the comatose patients after cardiac arrest with an indeterminate prognosis. Here we investigated whether early MRI markers of brain network integrity can distinguish between comatose patients with a good versus poor neurological outcome six months later.MethodsWe performed a prospective cohort study in 48 patients after cardiac arrest submitted in a comatose state to the Intensive Care Unit of two Dutch hospitals. MRI was performed at three days after cardiac arrest, including resting state functional MRI and diffusion-tensor imaging (DTI). Resting state fMRI was used to quantify functional connectivity within ten resting-state networks, and DTI to assess mean diffusivity (MD) in these same networks. We contrasted two groups of patients, those with good (n = 29, cerebral performance category 1-2) versus poor (n = 19, cerebral performance category 3-5) outcome at six months. Mutual associations between functional connectivity, MD, and clinical outcome were studied.ResultsPatients with good outcome show higher within-network functional connectivity (fMRI) and higher MD (DTI) than patients with poor outcome across 8/10 networks, most prominent in the default mode network, salience network, and visual network. While the anatomical distribution of outcome-related changes was similar for functional connectivity and MD, the pattern of inter-individual differences was very different: functional connectivity showed larger inter-individual variability in good versus poor outcome, while the opposite was observed for MD. Exploratory analyses suggested that it is possible to define network-specific cut-off values that could help in outcome prediction: (1) high functional connectivity and high MD, associated with good outcome; (2) low functional connectivity and low MD, associated with poor outcome; (3) low functional connectivity and high MD, associated with uncertain outcome.DiscussionResting-state functional connectivity and mean diffusivity-three days after cardiac arrest are strongly associated with neurological recovery-six months later in a complementary fashion. The combination of fMRI and MD holds potential to improve prediction of outcome.

Project description:Deviants are stimuli that violate one's prediction about the incoming stimuli. Studying deviance detection helps us understand how nervous system learns temporal patterns between stimuli and forms prediction about the future. Detecting deviant stimuli is also critical for animals' survival in the natural environment filled with complex sounds and patterns. Using natural songbird vocalizations as stimuli, we recorded multi-unit and single-unit activity from the zebra finch auditory forebrain while presenting rare repeated stimuli after regular alternating stimuli (alternating oddball experiment) or rare deviant among multiple different common stimuli (context oddball experiment). The alternating oddball experiment showed that neurons were sensitive to rare repetitions in regular alternations. In the absence of expectation, repetition suppresses neural responses to the 2nd stimulus in the repetition. When repetition violates expectation, neural responses to the 2nd stimulus in the repetition were stronger than expected. The context oddball experiment showed that a stimulus elicits stronger neural responses when it is presented infrequently as a deviant among multiple common stimuli. As the acoustic differences between deviant and common stimuli increase, the response enhancement also increases. These results together showed that neural encoding of a stimulus depends not only on the acoustic features of the stimulus but also on the preceding stimuli and the transition patterns between them. These results also imply that the classical oddball effect may result from a combination of repetition suppression and deviance enhancement. Classification analyses showed that the difficulties in decoding the stimulus responsible for the neural responses differed for deviants in different experimental conditions. These findings suggest that learning transition patterns and detecting deviants in natural sequences may depend on a hierarchy of neural mechanisms, which may be involved in more complex forms of auditory processing that depend on the transition patterns between stimuli, such as speech processing.

Project description:BackgroundThe number of resuscitated cardiac arrest patients suffering from anoxic-ischemic encephalopathy is considerable. However, outcome prediction parameters such as somatosensory evoked potentials need revision because they are based on data predating the implementation of mild therapeutical hypothermia and because data from our own laboratory suggest that they may fail to predict prognosis accurately. The present research project "Hypoxia and Outcome Prediction in Early-Stage Coma" is an ongoing observational prospective cohort study that aims to improve outcome prediction in anoxic coma by limiting the effects of falsely pessimistic predictions at the intensive care unit.MethodsOur outcome analysis is based on functional and behavioural definitions. This implies the analysis of the positive predictive value of prognostic markers yielding either positive or negative results. We also analyse the effect of covariates adjusted for age and sex such as sociodemographic variables, prognostic variables and treatment factors on functional and behavioural outcomes, with mixed effects regression models (i.e. fixed and random effects). We expect to enrol 172 patients based on the result of previous research. The null hypothesis is that there is a probability of <10 % that a positive outcome will be observed despite the presence of any of the predictors of a poor/negative outcome. We test the null hypothesis against a one-sided alternative using a Simon's two-stage design to determine whether it is warranted to recruit the full number of patients suggested by a power analysis. The second stage has a design with a Type I error rate of 0.05 and 80 % power if the true response rate is 25 %.DiscussionWe aim to make a significant contribution to the revision and improvement of current outcome prediction methods in anoxic-ischemic encephalopathy patients. As a result, neurocritical care specialists worldwide will have considerably more accurate methods for prognosticating the outcome of anoxic-ischemic encephalopathy following cardiac arrest. This will facilitate the provision of treatment tailored to individual patients and the attainment of an optimal quality of life. It will also inform the decision to withdraw treatment with a level of accuracy never seen before in the field.Trial registrationClinicalTrials.gov NCT02231060 (registered 29 August 2014).

Project description:ObjectiveIn 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.MethodsWe 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.ResultsGroup-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.InterpretationTopological 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.

Dataset Information

Somatosensory and auditory deviance detection for outcome prediction during postanoxic coma.

Objective

Methods

Results

Interpretation

Publications

Somatosensory and auditory deviance detection for outcome prediction during postanoxic coma.

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