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.

Advertisements

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.


Frontiers ebook | Sleep Spindles: Breaking the Methodological Wall

Source: Frontiers | Sleep Spindles: Breaking the Methodological Wall

PDF: OReilly et al ebook 2017

Edited by: Christian O’Reilly, Simon C. Warby, Tore Nielsen

Publisher: Frontiers Media SA

ISBN: 9782889451166

Product Name: Frontiers Research Topic Ebook


O’Reilly, Warby, Nielsen (2017)

Editorial: Sleep Spindles: Breaking the Methodological Wall, Frontiers in Human Neuroscience

pdf: oreilly-2017

Research on sleep spindles and their correlates has progressed steadily over the last decade. The subject has evolved from a simple topic of investigation to an emerging research field, as indicated this year by the first international conference on sleep spindles in Budapest, Hungary, as well as the launching of a scientific journal (i.e., Sleep Spindles and Cortical Up States: A Multidisciplinary Journal) on this topic. This increasing interest has been fueled by reports of associations of sleep spindle characteristics with diseases such as schizophrenia (Ferrarelli et al., 2007, 2010; Manoach et al.), Parkinson’s disease (Christensen et al.), REM sleep behavior disorder (Christensen et al., 2014; O’Reilly et al., 2015), Alzheimer’s disease (Montplaisir et al., 1995; Rauchs et al., 2008), autism (Limoges et al., 2005), and mental retardation (Shibagaki et al., 1982), with recovery processes following brain stroke (Gottselig et al., 2002), with cognitive faculties such as memory consolidation and intelligence (Fogel and Smith, 2011), and with sleep preservation (Landis et al., 2004; Dang-Vu et al., 2010; Schabus et al., 2012). Nonetheless, many methodological difficulties have been encountered in reliably detecting sleep spindles. Hence, this research topic was launched as a forum for proposing better practices in the study of sleep spindles and to provide new insights on spindle correlates. Authors were invited particularly to propose open-access resources that could help promote improved methods and support standardization in the field.

CONTRIBUTIONS

A total of 17 papers were accepted for publication on the research topic, with 10 being focussed particularly on methodological issues such as spindle detection and the remaining seven providing new insights on sleep spindle correlates.


O’Callaghan, Roig, Mongrain (2016)

Cell adhesion molecules and sleep, Neuroscience Research

pdf: ocallaghan-et-al-2016-review

Cell adhesion molecules (CAMs) play essential roles in the central nervous system, where some families are involved in synaptic development and function. These synaptic adhesion molecules (SAMs) areinvolved in the regulation of synaptic plasticity, and the formation of neuronal networks. Recent findingsfrom studies examining the consequences of sleep loss suggest that these molecules are candidates to actin sleep regulation. This review highlights the experimental data that lead to the identification of SAMsas potential sleep regulators, and discusses results supporting that specific SAMs are involved in differ-ent aspects of sleep regulation. Further, some potential mechanisms by which SAMs may act to regulatesleep are outlined, and the proposition that these molecules may serve as molecular machinery in thetwo sleep regulatory processes, the circadian and homeostatic components, is presented. Together, thedata argue that SAMs regulate the neuronal plasticity that underlies sleep and wakefulness.


Areal, Warby, Mongrain (2016)

Sleep loss and structural plasticity, Current Opinion in Neurobiology

pdf: areal-et-al-2017

Wakefulness and sleep are dynamic states during which brain
functioning is modified and shaped. Sleep loss is detrimental to
many brain functions and results in structural changes localized
at synapses in the nervous system. In this review, we present
and discuss some of the latest observations of structural
changes following sleep loss in some vertebrates and insects.
We also emphasize that these changes are region-specific and
cell type-specific and that, most importantly, these structural
modifications have functional roles in sleep regulation and brain
functions. Selected mechanisms driving structural
modifications occurring with sleep loss are also discussed.
Overall, recent research highlights that extending wakefulness
impacts synapse number and shape, which in turn regulate
sleep need and sleep-dependent learning/memory.


Gosselin et al (2016): BDNF Val66Met Polymorphism Interacts with Sleep Consolidation to Predict Ability to Create New Declarative Memories

PDF: gosselin-et-al-2016-bdnf-memory

It is hypothesized that a fundamental function of sleep is to restore an individual’s day-to-day ability to learn and to constantly adapt to a changing environment through brain plasticity. Brain-derived neurotrophic factor (BDNF) isamongthe key regulators that shape brain plasticity. However, advancing age and carrying the BDNF Met allele were both identified as factors that potentially reduce BDNF secretion, brain plasticity, and memory. Here, we investigated the moderating role of BDNF polymorphism on sleep and next-morning learning ability in 107 nondemented individuals who were between 55 and 84 years  of age. All subjects were tested with 1 night of in-laboratory polysomnography followed by a cognitive evaluation the next morning. We found that in subjects carrying the BDNF Val66Val polymorphism, consolidated sleep was associated with significantly better performance on hippocampus-dependent episodic memory tasks the next morning (-values from 0.290 to 0.434, p0.01). In subjects carrying at least one copy of the BDNF Met allele, a more consolidated sleep was not associated with better memory performance in most memory tests (-values from0.309 to0.392, p values from 0.06 to 0.15). Strikingly, increased sleep consolidation was associated with poorer performance in learning a short story presented verbally in Met allele carriers (0.585, p 0.005). This study provides new evidence regarding the interacting roles of consolidated sleep andBDNFpolymorphism in the ability to learn and stresses the importance of consideringBDNFpolymorphism when studying how sleep affects cognition.

Nadia Gosselin, Louis De Beaumont, Katia Gagnon, Andree-Ann Baril, Valerie Mongrain, Helene Blais, Jacques Montplaisir, Jean-Francois Gagnon, Sandra Pelleieux, Judes Poirier, and Julie Carrier; The Journal of Neuroscience, August 10, 2016 • 36(32):8390–8398