Malgorzata Wislowska

Across the consciousness continuum—from unresponsive wakefulness to sleep

Advances in the development of new paradigms as well as in neuroimaging techniques nowadays enable us to make inferences about the level of consciousness patients with disorders of consciousness (DOC) retain. They, moreover, allow to predict their probable development. Today, we know that certain brain responses (e.g., event-related potentials or oscillatory changes) to stimulation, circadian rhythmicity, the presence or absence of sleep patterns as well as measures of resting state brain activity can serve the diagnostic and prognostic evaluation process. Still, the paradigms we are using nowadays do not allow to disentangle VS/UWS and minimally conscious state (MCS) patients with the desired reliability and validity. Furthermore, even rather well-established methods have, unfortunately, not found their way into clinical routine yet. We here review current literature as well as recent findings from our group and discuss how neuroimaging methods (fMRI, PET) and particularly electroencephalography (EEG) can be used to investigate cognition in DOC or even to assess the degree of residual awareness. We, moreover, propose that circadian rhythmicity and sleep in brain-injured patients are promising fields of research in this context.

Oscillatory brain responses to own names uttered by unfamiliar and familiar voices

Among auditory stimuli, the own name is one of the most powerful and it is able to automatically capture attention and elicit a robust electrophysiological response. The subject’s own name (SON) is preferentially processed in the right hemisphere, mainly because of its self-relevance and emotional content, together with other personally relevant information such as the voice of a familiar person. Whether emotional and self-relevant information are able to attract attention and can be, in future, introduced in clinical studies remains unclear. In the present study we used EEG and asked participants to count a target name (active condition) or to just listen to the SON or other unfamiliar names uttered by a familiar or unfamiliar voice (passive condition). Data reveals that the target name elicits a strong alpha event related desynchronization with respect to non-target names and triggers in addition a left lateralized theta synchronization as well as delta synchronization. In the passive condition alpha desynchronization was observed for familiar voice and SON stimuli in the right hemisphere. Altogether we speculate that participants engage additional attentional resources when counting a target name or when listening to personally relevant stimuli which is indexed by alpha desynchronization whereas left lateralized theta synchronization may be related to verbal working memory load. After validating the present protocol in healthy volunteers it is suggested to move one step further and apply the protocol to patients with disorders of consciousness in which the degree of residual cognitive processing and self-awareness is still insufficiently understood.

Better than sham? A double-blind placebo-controlled neurofeedback study in primary insomnia

See Thibault et al. (doi:10.1093/awx033) for a scientific commentary on this article. Neurofeedback has been claimed to have therapeutic efficacy in multiple disorders. In a double-blind, placebo-controlled trial in insomnia, Schabus et al. report that sensorimotor rhythm neurofeedback (12–15 Hz) neither changes the EEG nor objectively improves sleep. While patients do report subjective improvements, these do not differ from those seen with placebo feedback.

Night and day variations of sleep in patients with disorders of consciousness

Brain injuries substantially change the entire landscape of oscillatory dynamics and render detection of typical sleep patterns difficult. Yet, sleep is characterized not only by specific EEG waveforms, but also by its circadian organization. In the present study we investigated whether brain dynamics of patients with disorders of consciousness systematically change between day and night. We recorded ~24 h EEG at the bedside of 18 patients diagnosed to be vigilant but unaware (Unresponsive Wakefulness Syndrome) and 17 patients revealing signs of fluctuating consciousness (Minimally Conscious State). The day-to-night changes in (i) spectral power, (ii) sleep-specific oscillatory patterns and (iii) signal complexity were analyzed and compared to 26 healthy control subjects. Surprisingly, the prevalence of sleep spindles and slow waves did not systematically vary between day and night in patients, whereas day-night changes in EEG power spectra and signal complexity were revealed in minimally conscious but not unaware patients.

Preferential processing of emotionally and self-relevant stimuli persists in unconscious N2 sleep

HighlightsOwn names (SONs) and angry voice (AV) stimuli are salient during wakefulness.Intriguingly, SONs and AV stimuli remain salient during unconscious N2 sleep.Results suggest a ‘sentinel processing mode’ of the brain during unconscious sleep.A K‐complex‐like response indicates preferential processing of salient stimuli.Presumably, delta, theta and sigma ERS reflect the subsequent sleep‐protecting mechanism. Abstract Information processing has been suggested to depend on the current state of the brain as well as stimulus characteristics (e.g. salience). We compared processing of salient stimuli (subject’s own names [SONs] and angry voice [AV] stimuli) to processing of unfamiliar names (UNs) and neutral voice (NV) stimuli across different vigilance stages (i.e. wakefulness as well as sleep stages N1 and N2) by means of event‐related oscillatory responses during wakefulness and a subsequent afternoon nap. Our findings suggest that emotional prosody and self‐relevance drew more attentional resources during wakefulness with specifically AV stimuli being processed more strongly. During N1, SONs were more arousing than UNs irrespective of prosody. Moreover, emotional and self‐relevant stimuli evoked stronger responses also during N2 sleep suggesting a ‘sentinel processing mode’ of the brain during this state of naturally occurring unconsciousness. Finally, this initial preferential processing of salient stimuli during N2 sleep seems to be followed by an inhibitory sleep‐protecting process, which is reflected by a K‐complex‐like response.

Assessment of SOMNOwatch plus EEG for sleep monitoring in healthy individuals.

Polysomnography (PSG) is still the standard in sleep monitoring, with several alternative solutions developed, including simplified electroencephalographic recorders such as SOMNOwatch plus EEG. In this study, we aimed to evaluate the validity of the recordings and of the analysis of the proprietary software of this solution, compared to PSG and semiautomatic scoring, respectively. From thirteen healthy adults, we recorded 27 nights simultaneously with a classical EEG amplifier (NeuroScan system) and the ambulatory SOMNOwatch plus EEG. Thereafter, we performed (semi-) automatic sleep analysis in Somnolyzer 24x7 and DOMINO Light (SOMNOwatch software). AASM scoring sensitivity of SOMNOwatch plus EEG, as revealed by Somnolyzer 24x7, was 97.79%, and specificity 87.19%. Paired T tests revealed no significant differences between the recordings of the two EEG systems, with intraclass correlation coefficients ranging from moderate to very good. When data were analyzed in DOMINO Light, sensitivity was 92.99% and specificity was 80.90%. Our data suggest that SOMNOwatch plus EEG might serve as a reliable instrument for recording sleep in healthy individuals, but its proprietary software, DOMINO Light, still seems to have weaknesses in terms of automatic sleep staging.

Can self-relevant stimuli help assessing patients with disorders of consciousness?

Emotional and self-relevant stimuli are able to automatically attract attention and their use in patients suffering from disorders of consciousness (DOC) might help detecting otherwise hidden signs of cognition. We here recorded EEG in three Locked-in syndrome (LIS) and four Vegetative State/Unresponsive Wakefulness Syndrome (VS/UWS) patients while they listened to the voice of a family member or an unfamiliar voice during a passive. Data indicate that, in a passive listening condition, the familiar voice induces stronger alpha desynchronization than the unfamiliar one. In an active condition, the target evoked stronger alpha desynchronization in controls, two LIS patients and one VS/UWS patient. Results suggest that self-relevant familiar voice stimuli can engage additional attentional resources and might allow the detection of otherwise hidden signs of instruction-following and thus residual awareness. Further studies are necessary to find sensitive paradigms that are suited to find subtle signs of cognition and awareness in DOC patients.

The Voice of Anger: Oscillatory EEG Responses to Emotional Prosody.

Emotionally relevant stimuli and in particular anger are, due to their evolutionary relevance, often processed automatically and able to modulate attention independent of conscious access. Here, we tested whether attention allocation is enhanced when auditory stimuli are uttered by an angry voice. We recorded EEG and presented healthy individuals with a passive condition where unfamiliar names as well as the subject’s own name were spoken both with an angry and neutral prosody. The active condition instead, required participants to actively count one of the presented (angry) names. Results revealed that in the passive condition the angry prosody only elicited slightly stronger delta synchronization as compared to a neutral voice. In the active condition the attended (angry) target was related to enhanced delta/theta synchronization as well as alpha desynchronization suggesting enhanced allocation of attention and utilization of working memory resources. Altogether, the current results are in line with previous findings and highlight that attention orientation can be systematically related to specific oscillatory brain responses. Potential applications include assessment of non-communicative clinical groups such as post-comatose patients.

Sleep in patients with disorders of consciousness characterized by means of machine learning

Sleep has been proposed to indicate preserved residual brain functioning in patients suffering from disorders of consciousness (DOC) after awakening from coma. However, a reliable characterization of sleep patterns in this clinical population continues to be challenging given severely altered brain oscillations, frequent and extended artifacts in clinical recordings and the absence of established staging criteria. In the present study, we try to address these issues and investigate the usefulness of a multivariate machine learning technique based on permutation entropy, a complexity measure. Specifically, we used long-term polysomnography (PSG), along with video recordings in day and night periods in a sample of 23 DOC; 12 patients were diagnosed as Unresponsive Wakefulness Syndrome (UWS) and 11 were diagnosed as Minimally Conscious State (MCS). Eight hour PSG recordings of healthy sleepers (N = 26) were additionally used for training and setting parameters of supervised and unsupervised model, respectively. In DOC, the supervised classification (wake, N1, N2, N3 or REM) was validated using simultaneous videos which identified periods with prolonged eye opening or eye closure.The supervised classification revealed that out of the 23 subjects, 11 patients (5 MCS and 6 UWS) yielded highly accurate classification with an average F1-score of 0.87 representing high overlap between the classifier predicting sleep (i.e. one of the 4 sleep stages) and closed eyes. Furthermore, the unsupervised approach revealed a more complex pattern of sleep-wake stages during the night period in the MCS group, as evidenced by the presence of several distinct clusters. In contrast, in UWS patients no such clustering was found. Altogether, we present a novel data-driven method, based on machine learning that can be used to gain new and unambiguous insights into sleep organization and residual brain functioning of patients with DOC.

What Can We Learn About Brain Functions from Sleep EEG? Insights from Sleep of DOC Patients

Disorder of Consciousness (DOC) patients are often reported to have alterations in sleep architecture and sleep-specific graphoelements. The reappearance of non-REM oscillatory patterns such as sleep spindles has been associated with diagnosis and presumably prognosis.