Decoding Brain Responses to Names and Voices across Different Vigilance States.

Past research has demonstrated differential responses of the brain during sleep in response especially to variations in paralinguistic properties of auditory stimuli, suggesting they can still be processed "offline". However, the nature of the underlying mechanisms remains unclear. Here, we therefore used multivariate pattern analyses to directly test the similarities in brain activity among different sleep stages (non-rapid eye movement stages N1-N3, as well as rapid-eye movement sleep REM, and wake). We varied stimulus salience by manipulating subjective (own vs. unfamiliar name) and paralinguistic (familiar vs. unfamiliar voice) salience in 16 healthy sleepers during an 8-h sleep opportunity. Paralinguistic salience (i.e., familiar vs. unfamiliar voice) was reliably decoded from EEG response patterns during both N2 and N3 sleep. Importantly, the classifiers trained on N2 and N3 data generalized to N3 and N2, respectively, suggesting similar processing mode in these states. Moreover, projecting the classifiers' weights using a forward model revealed similar fronto-central topographical patterns in NREM stages N2 and N3. Finally, we found no generalization from wake to any sleep stage (and vice versa) suggesting that "processing modes" or the overall processing architecture with respect to relevant oscillations and/or networks substantially change from wake to sleep. However, the results point to a single and rather uniform NREM-specific mechanism that is involved in (auditory) salience detection during sleep.

On the development of sleep states in the first weeks of life.

Human newborns spend up to 18 hours sleeping. The organization of their sleep differs immensely from adult sleep, and its quick maturation and fundamental changes correspond to the rapid cortical development at this age. Manual sleep classification is specifically challenging in this population given major body movements and frequent shifts between vigilance states; in addition various staging criteria co-exist. In the present study we utilized a machine learning approach and investigated how EEG complexity and sleep stages evolve during the very first weeks of life. We analyzed 42 full-term infants which were recorded twice (at week two and five after birth) with full polysomnography. For sleep classification EEG signal complexity was estimated using multi-scale permutation entropy and fed into a machine learning classifier. Interestingly the baby's brain signal complexity (and spectral power) revealed developmental changes in sleep in the first 5 weeks of life, and were restricted to NREM ("quiet") and REM ("active sleep") states with little to no changes in state wake. Data demonstrate that our classifier performs well over chance (i.e., >33% for 3-class classification) and reaches almost human scoring accuracy (60% at week-2, 73% at week-5). Altogether, these results demonstrate that characteristics of newborn sleep develop rapidly in the first weeks of life and can be efficiently identified by means of machine learning techniques.

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.

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.

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.

Event-related EEG power modulations and phase connectivity indicate the focus of attention in an auditory own name paradigm.

Estimating cognitive abilities in patients suffering from Disorders of Consciousness remains challenging. One cognitive task to address this issue is the so-called own name paradigm, in which subjects are presented with first names including the own name. In the active condition, a specific target name has to be silently counted. We recorded EEG during this task in 24 healthy controls, 8 patients suffering from Unresponsive Wakefulness Syndrome (UWS) and 7 minimally conscious (MCS) patients. EEG was analysed with respect to amplitude as well as phase modulations and connectivity. Results showed that general reactivity in the delta, theta and alpha frequency (event-related de-synchronisation, ERS/ERD, and phase locking between trials and electrodes) toward auditory stimulation was higher in controls than in patients. In controls, delta ERS and lower alpha ERD indexed the focus of attention in both conditions, late theta ERS only in the active condition. Additionally, phase locking between trials and delta phase connectivity was highest for own names in the passive and targets in the active condition. In patients, clear stimulus-specific differences could not be detected. However, MCS patients could reliably be differentiated from UWS patients based on their general event-related delta and theta increase independent of the type of stimulus. In conclusion, the EEG signature of the active own name paradigm revealed instruction-following in healthy participants. On the other hand, DOC patients did not show clear stimulus-specific processing. General reactivity toward any auditory input, however, allowed for a reliable differentiation between MCS and UWS patients.