Solomonova et al (2017): Sleep-dependent consolidation of face recognition and its relationship to REM sleep duration, REM density and Stage 2 sleep spindles

Source: Sleep-dependent consolidation of face recognition and its relationship to REM sleep duration, REM density and Stage 2 sleep spindles

SUMMARY
Face recognition is a highly specialized capability that has implicit and
explicit memory components. Studies show that learning tasks with facial
components are dependent on rapid eye movement and non-rapid eye
movement sleep features, including rapid eye movement sleep density
and fast sleep spindles. This study aimed to investigate the relationship
between sleep-dependent consolidation of memory for faces and partial
rapid eye movement sleep deprivation, rapid eye movement density, and
fast and slow non-rapid eye movement sleep spindles. Fourteen healthy
participants spent 1 night each in the laboratory. Prior to bed they
completed a virtual reality task in which they interacted with computergenerated
characters. Half of the participants (REMD group) underwent
a partial rapid eye movement sleep deprivation protocol and half (CTL
group) had a normal amount of rapid eye movement sleep. Upon
awakening, they completed a face recognition task that contained a
mixture of previously encountered faces from the task and new faces.
Rapid eye movement density and fast and slow sleep spindles were
detected using in-house software. The REMD group performed worse
than the CTL group on the face recognition task; however, rapid eye
movement duration and rapid eye movement density were not related to
task performance. Fast and slow sleep spindles showed differential
relationships to task performance, with fast spindles being positively and
slow spindles negatively correlated with face recognition. The results
support the notion that rapid eye movement and non-rapid eye
movement sleep characteristics play complementary roles in face
memory consolidation. This study also raises the possibility that fast
and slow spindles contribute in opposite ways to sleep-dependent
memory consolidation.

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Sleep regulation of the distribution of cortical firing rates

Source: Sleep regulation of the distribution of cortical firing rates

pdf: Levenstein-2017-Sleep regulation of the distri

Sleep is thought to mediate both mnemonic and homeostatic
functions. However, the mechanism by which this brain state
can simultaneously implement the ‘selective’ plasticity needed
to consolidate novel memory traces and the ‘general’ plasticity
necessary to maintain a well-functioning neuronal system is
unclear. Recent findings show that both of these functions
differentially affect neurons based on their intrinsic firing rate, a
ubiquitous neuronal heterogeneity. Furthermore, they are both
implemented by the NREM slow oscillation, which also
distinguishes neurons based on firing rate during sequential
activity at the DOWN – UP transition. These findings suggest a
mechanism by which spiking activity during the slow oscillation
acts to maintain network statistics that promote a skewed
distribution of neuronal firing rates, and perturbation of that
activity by hippocampal replay acts to integrate new memory
traces into the existing cortical network.


Tout le monde rêve, même ceux qui disent ne jamais rêver | Réalités Biomédicales | Le Monde

Source: Tout le monde rêve, même ceux qui disent ne jamais rêver | Réalités Biomédicales


Cohen (2017): Where does EEG come from and what does it mean?

Source: Cohen-2017-Where Does EEG Come From and What D

Trends in Neuroscience
Where Does EEG Come From and What Does It Mean?
Michael X Cohen


Papalambros (2017): Acoustic Enhancement of Sleep Slow Oscillations and Concomitant Memory Improvement in Older Adults

Acoustic Enhancement of Sleep Slow Oscillations and Concomitant Memory Improvement in Older Adults
Nelly A. Papalambros1, Giovanni Santostasi1, Roneil G. Malkani1, Rosemary Braun2,3, Sandra Weintraub4, Ken A. Paller5 and Phyllis C. Zee1*

pdf: fnhum-11-00109

Acoustic stimulation methods applied during sleep in young adults can increase slow wave activity (SWA) and improve sleep-dependent memory retention. It is unknown whether this approach enhances SWA and memory in older adults, who generally have reduced SWA compared to younger adults. Additionally, older adults are at risk for age-related cognitive impairment and therefore may benefit from non-invasive interventions. The aim of this study was to determine if acoustic stimulation can increase SWA and improve declarative memory in healthy older adults. Thirteen participants 60–84 years old completed one night of acoustic stimulation and one night of sham stimulation in random order. During sleep, a real-time algorithm using an adaptive phase-locked loop modeled the phase of endogenous slow waves in midline frontopolar electroencephalographic recordings. Pulses of pink noise were delivered when the upstate of the slow wave was predicted. Each interval of five pulses (“ON interval”) was followed by a pause of approximately equal length (“OFF interval”). SWA during the entire sleep period was similar between stimulation and sham conditions, whereas SWA and spindle activity were increased during ON intervals compared to matched periods during the sham night. The increases in SWA and spindle activity were sustained across almost the entire five-pulse ON interval compared to matched sham periods. Verbal paired-associate memory was tested before and after sleep. Overnight improvement in word recall was significantly greater with acoustic stimulation compared to sham and was correlated with changes in SWA between ON and OFF intervals. Using the phase-locked-loop method to precisely target acoustic stimulation to the upstate of sleep slow oscillations, we were able to enhance SWA and improve sleep-dependent memory storage in older adults, which strengthens the theoretical link between sleep and age-related memory integrity.


Vallat (2017) Evoked Potentials during Sleep: Implications for Dream Recall

Dream & Nightmare Lab

Vallat, R., et al. (2017). “Increased Evoked Potentials to Arousing Auditory Stimuli during Sleep: Implication for the Understanding of Dream Recall

PDF:Vallat-2017-Increased Evoked Potentials to Aro

High dream recallers (HR) show a larger brain reactivity to auditory stimuli during wakefulness and sleep as compared to low dream recallers (LR) and also more intra-sleep wakefulness, but no other modification of the sleep macrostructure. To further understand the possible causal link between brain responses, intra-sleep wakefulness and dream recall, we investigated the sleep microstructure of HR and LR, and tested whether the amplitude of auditory evoked potentials was predictive of arousing reactions during sleep. Participants (18 HR, 18 LR) were presented with sounds during a whole night of sleep in the lab and polysomnographic data were recorded. Sleep microstructure (arousals, rapid eye movements, muscle twitches, spindles, K-complexes) was assessed using visual, semi-automatic and automatic validated methods. Auditory evoked potentials to arousing…

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Nielsen (2017): Microdream neurophenomenology | Neuroscience of Consciousness

Source: Microdream neurophenomenology | Neuroscience of Consciousness | Oxford Academic

PDF: Nielsen 2017 Microdreaming

Tore Nielsen*
Dream & Nightmare Laboratory, Center for Advanced Research in Sleep Medicine, Hopital du Sacre-Coeur de Montreal and Department of Psychiatry, University of Montreal, Canada

Abstract
Nightly transitions into sleep are usually uneventful and transpire in the blink of an eye. But in the laboratory these transitions afford a unique view of how experience is transformed from the perceptually grounded consciousness of wakefulness to the hallucinatory simulations of dreaming. The present review considers imagery in the sleep-onset transition—“microdreams” in particular—as an alternative object of study to dreaming as traditionally studied in the sleep lab. A focus on microdream phenomenology has thus far proven fruitful in preliminary efforts to (i) develop a classification for dreaming’s core phenomenology (the “oneiragogic spectrum”), (ii) establish a structure for assessing dreaming’s multiple memory inputs (“multi-temporal memory sources”), (iii) further Silberer’s project for classifying sleep-onset images in relation to waking cognition by revealing two new imagery types (“autosensory imagery,” “exosensory imagery”), and (iv) embed a potential understanding of microdreaming processes in a larger explanatory framework (“multisensory integration approach”). Such efforts may help resolve outstanding questions about dream neurophysiology and dreaming’s role in memory consolidation during sleep but may also advance discovery in the neuroscience of consciousness more broadly.