Reduced neural feedback signaling despite robust neuron and gamma auditory responses during human sleep

Hayat, Hanna; Marmelshtein, Amit; Krom, Aaron J; Sela, Yaniv; Tankus, Ariel; Strauss, Ido; Fahoum, Firas; Fried, Itzhak; Nir, Yuval

doi:10.1038/s41593-022-01107-4

Cited by 30 publications

(32 citation statements)

References 89 publications

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“…During slow-wave sleep (SWS), the membrane potential of neocortical and thalamocortical neurons fluctuates between depolarized (UP) and hyperpolarized (DOWN) states, reflecting the balance between network excitation and inhibition, with the emergence of cortical gamma oscillations occurring primarily during UP-states 14,15,16 . These phenomena, as well as state heterogeneity between brain regions 17 , may serve in evolutionarily-preserved vigilance and a level of sustained processing of environmental stimuli even during SWS 18,19 .…”

Section: Introductionmentioning

confidence: 99%

Tailoring Human Sleep: selective alteration through Brainstem Arousal Circuit Stimulation

Deli

Duchet

et al. 2023

Preprint

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Brainstem nuclei, such as the pedunculopontine nucleus, send activating projections to cortex, modulating states of sleep, wakefulness and arousal levels. Surgical modulation of subcortical activity using deep brain stimulation (DBS) is utilised in the management of pain and movement disorders. DBS of brainstem arousal circuits in a state-dependent manner could offer an attractive alternative in severe pharmacoresistant cases of hypersomnia and in disorders of consciousness, where behavioural activation is desired. We wanted to investigate if we can selectively induce wakefulness and/or alter sleep state through DBS of the PPN region (PPNR). To this end, we used the opportunity of implanted PPNR electrodes in stimulation-naive patients with multiple systems atrophy. PPNR activity was recorded during both slow wave sleep (SWS) and quiet wakefulness with simultaneous cortical EEG, in order to identify differences in brainstem oscillatory patterns during different states of excitability. PPNR DBS in two gamma frequency protocols (40Hz and 100Hz) was delivered during SWS of the same sleep stage and with comparable pre-trial levels of slow wave activity. Additionally, SHAM trials were used as a control where no stimulation was applied. We examined changes in cortical oscillatory power, changes in functional connectivity (coherence and causality) from pre- to post- stimulation and phase-locking of cortical oscillations with DBS frequencies and their sub-harmonics during stimulation. We also evaluated connectivity changes induced by DBS and corresponding differences in circuit dynamics between SWS and wakefulness. Beta and gamma PPNR oscillatory power increased when wake was compared to sleep. We saw clear PPNR power modulation by the phase of EEG slow wave, with significant increase in gamma compared to beta power during the excitable part of the slow-wave cycle. Gamma PPNR DBS induced transitions to wakefulness and REM, while it truncated sleep time compared to baseline. The 40Hz stimulation protocol was more efficient in reducing slow wave activity and increasing cortical beta power, compared to PPNR DBS at 100Hz. Furthermore, intrinsic cortical rhythms phase-locked with 40 Hz PPNR DBS to a significantly higher degree compared to the 100Hz protocol, with regional differences in phase-locking, suggesting a complex biological phenomenon. Finally, functional connectivity changes induced by PPNR DBS were consistent with differences in circuit dynamics between SWS and wakefulness. Overall, these results highlight the possibility of using DBS of brainstem arousal circuits to promote arousal and wakefulness, which opens new perspectives for using closed-loop approaches to modulate vigilance states in humans for therapeutic benefit.

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Section: Introductionmentioning

confidence: 99%

Tailoring Human Sleep: selective alteration through Brainstem Arousal Circuit Stimulation

Deli

Duchet

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…In the last decades, a large body of research has supported the idea that the brain maintains a certain level of responsiveness toward environmental events during sleep (Beh & Barratt, 1965;Blume et al, 2017Blume et al, , 2018Chen et al, 2016;Formby, 1967;Hayat et al, 2022;Legendre et al, 2019;Moyne et al, 2022). However, sensory stimuli often do not reach conscious perception during sleep and whether the brain prioritizes certain acoustic features over others remains unclear.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, brain responses aligned maximally with stimuli in the roughness range (~30-80Hz), supporting the current finding that stimuli in this range enhance neural synchronization. Noteworthy, although it is moderately diminished compared to wakefulness, auditory steady-state response and ITPC in the delta and theta frequency range following 40Hz click trains can still be observed in NREM sleep (Hayat et al, 2022;Lustenberger et al, 2018;Picton et al, 2003). In the visual domain, emotional stimuli, like angry or happy faces, can boost the activity of neural populations in emotional (e.g.…”

Section: Reliable Brain Responses To Screams During Nrem Sleepmentioning

confidence: 99%

Scream’s roughness confers a privileged access to the brain during sleep

Legendre

Moyne

Domínguez-Borràs

et al. 2022

Preprint

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During sleep, recognizing threatening signals is crucial to know when to wake up and when to continue vital sleep functions. Screaming is perhaps the most salient and efficient signal for communicating danger at a distance or in conditions of limited visibility. Beyond the intensity or the pitch of the sound, rapid modulations of sound pressure in the so-called roughness range (i.e. 30-150 Hz) are particularly powerful in capturing attention and accelerating reactions. Roughness is an acoustic feature that characterizes alarm signals such as screams. However, whether rough sounds are also processed in a privileged manner during sleep is unknown. We tested this hypothesis by stimulating sleeping human participants with low-intensity screams and neutral calls. We found that screams trigger more reliable and better time-locked responses in wakefulness and NREM sleep. In addition, screams boosted sleep spindles, suggesting elevated stimulus salience. The increase in sleep spindle power was linearly proportional to the roughness of vocalizations, but not to their pitch. These findings demonstrate that, even at low sound intensity, scream's roughness conveys stimulus relevance and enhances processing in both the waking and sleeping states. Preserved differential neural responses based on stimulus salience may ensure adaptive reactions -and ultimately survival- in a state where the brain is mostly disconnected from external inputs.

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“…Therefore, feature extraction is the common ability of visual cortex, but what really takes effect to conscious recognition is the feature remembrance, i.e., the ability of understanding the meanings of the activated neuron groups. Another recent research also showed that, even during sleeping, extensive auditory responses persist [46], which means that the peripheral auditory nervous system is still extracting the features of input sound, but the system responsible for auditory consciousness is not working.…”

Section: Discussionmentioning

confidence: 99%

Activated Neuron Group Based Extract-Remember Model for Humanlike Object Recognition

Duan

Peng

2022

Preprint

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Convolutional neural networks (CNNs) exhibit similarities to the human visual cortex, such as in structure and response to images. However, when it comes to the learning process, CNN and visual neural network are quite different: CNNs are generally trained by complex mathematical analysis based gradient descent algorithms, while the human brain seems to learn how to recognize objects in a more intuitive way: extract and remember the features of the object. This difference raises an interesting question --- could CNN recognize objects in a more humanlike way? In this work, a straightforward but novel ''extract-remember'' model (ERM) is proposed: first, we observed that different image classes can stably trigger the activation of different neuron groups even in a randomly weighted CNN, which means that the activated neurons jointly represent the core features of the stimuli; then, the simple remember operation is involved: try to identify the commonly activated neuron group (CANG) related to a specific object during the training process, instead of adjusting the huge amount of values of the weights. After mapping different object classes into different CANGs, the CNN can directly recognize the object in a new image by determining whether its response contains the remembered CANGs, and recognition results on different image datasets show the significant efficiency of ERM. The proposed ERM has high interpretability, suggesting a new direction for artificial neuron network design, which may in turn, also help to understand the object recognition process of human brain.

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Reduced neural feedback signaling despite robust neuron and gamma auditory responses during human sleep

Cited by 30 publications

References 89 publications

Tailoring Human Sleep: selective alteration through Brainstem Arousal Circuit Stimulation

Tailoring Human Sleep: selective alteration through Brainstem Arousal Circuit Stimulation

Scream’s roughness confers a privileged access to the brain during sleep

Activated Neuron Group Based Extract-Remember Model for Humanlike Object Recognition

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