SLEEP AND BRAIN ACTIVITY

277 Pages·10.288 MB·English

by  Marcos Gabriel Frank

SLEEP AND BRAIN
ACTIVITY
SLEEP AND BRAIN
ACTIVITY
Edited by
MARCOS G. FRANK
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PREFACE
Prior to the middle part of the 20th century, a tome entitled “Brain Activity
in Sleep” would likely be greeted with quizzical looks from the scientific
community. Although sleep had been shown to be accompanied by slow
electroencephalographic (EEG) waves in the 1920s and 30s, the significance
of these findings would elude scientists for decades to come. The general
consensus was consonant with prescientific thinking, which held that sleep
was a time when the brain rested—and, just as the body exhibited reduced
muscle activity during sleep, so did the brain. Sleep was thought to involve
either a near-complete cessation of brain activity or at best an idling brain
state. By the 21st century, it had become abundantly clear that far from a
time of brain silence, sleep was characterized by complex patterns of neural
and metabolic activity that in some cases exceeded what was observed dur-
ing wakefulness.
In the late 1940s, a prevailing view was that sleep was essentially a pas-
sive brain response to sensory deafferentation. This general idea was often
referred to as the “passive theory” of sleep. This passive theory of sleep gave
way under overwhelming scientific evidence of brain activity in sleep. REM
studies in the 1950s were the first scientific demonstrations that the sleeping
brain was crackling with neural activity. Since then, intracellular recordings
in vivo have revealed that slow oscillations typical of non-REM sleep (and
which were originally dismissed as brain idling) reflect the synchronized
activity of thousands, if not tens of thousands of neurons. During the corti-
cal “up” state component of the slow oscillation, cortical neurons fire at a
rate sometimes exceeding rates measured during wakefulness. REM sleep is
additionally accompanied by tonic and rapid firing in thalamic and cortical
neurons resembling that of the waking state. REM sleep also shows peculiar,
phasic activations of the brainstem, thalamus, and cortex.
Brain activity in sleep has also been shown in imaging studies and
molecular approaches. Functional imaging of the human brain has provided
stunning pictures of metabolic changes during sleep that are regional and
distinct from what is observed during wakefulness. Molecular approaches are
now identifying striking changes in gene expression across sleep and wake,
as well as the genetic basis of sleep EEG rhythmic activity. What is less clear
is to what end all of this brain activity is directed. That is to say, the ultimate
functions of brain activity in sleep remain mysterious.
ix
x Preface
In this book, leading scientists discuss some of the more dramatic devel-
opments in our understanding of brain activity in sleep. These include
recent findings from measurements of single brain cells in vivo and in vitro
that reveal the network and membrane mechanisms responsible for waking
and sleeping brain activity (Chapters 1-2). The discussion of cellular mecha-
nisms continues with treatments of possible roles of glial cells in the sleeping
brain (Chapter 3), the molecular basis of sleep EEG rhythms (Chapter 4),
and evidence that sleep may be an emergent property of smaller ensembles
of neurons (Chapter 5). The remainder of the book is dedicated to perhaps
the least understood, but no less important issue of function. We begin this
section with an intriguing set of findings indicating that brain activity in
songbird sleep contributes to singing (Chapter 6). We then explore the pos-
sible roles of REM sleep P-waves in rodent learning (Chapter 7), look inside
the sleeping human brain for evidence of memory processing (Chapters
8-9), and consider the possibility that ontogenetic changes in sleep EEG
rhythms might shape developing cortical circuits (Chapter 10). I am grateful
to all the contributors for their hard work and scholarship.
LIST OF CONTRIBUTORS
Jan Born
University of Lübeck, Department of Neuroendocrinology, Ratzeburger Allee 160, Hs 23a,
D-23538 Lübeck
Timothy P. Brawn
Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL,
USA
Vincenzo Crunelli
Neuroscience Division, School of Bioscience, Museum Avenue, Cardiff University, Cardiff
CF10 3AX, UK
Subimal Datta
Laboratory of Sleep and Cognitive Neuroscience, Departments of Psychiatry and
Neurology and Program in Neuroscience, Boston University School of Medicine, 85 East
Newton Street, Suite: M-902 Boston, Massachusetts 02118, USA
Gordon B. Feld
University of Tübingen, Department of Medical Psychology and Behavioral Neurobiology,
Gartenstraße 29, 72074 Tübingen, Germany
Ariane Foret
Cyclotron Research Centre (B30), University of Liège, 6, Allée du 8 Août, 4000 Liège,
Belgium
Marcos G. Frank
University of Pennsylvania, School of Medicine, Department of Neuroscience, 215
Stemmler Hall, 35th & Hamilton Walk, Philadelphia, PA 19104-6074, USA
Paul Franken
Center for Integrative Genomics, Genopode building, University of Lausanne, CH-1015
Lausanne-Dorigny, Switzerland
Reto Huber
University Children’s Hospital, Zurich, Steinwiesstrasse 75, CH-8032 Zurich, Switzerland
Stuart Hughes
Eli Lilly UK, Erl Wood Manor, Windlesham, Surrey, GU20 6PH, UK
Mathieu Jaspar
Cyclotron Research Centre (B30), University of Liège, 6, Allée du 8 Août, 4000 Liège,
Belgium
Salome K urth
Child Development Center, University Children’s Hospital Zurich, Steinweisstrasse 75,
CH-8032 Zurich, Switzerland and Department of Integrative Physiology, University of
Colorado at Boulder, CO, USA
xi
xii List of Contributors
Caroline Kussé
Cyclotron Research Centre (B30), University of Liège, 6, Allée du 8 Août, 4000 Liège,
Belgium
Pierre Maquet
Cyclotron Research Centre (B30), University of Liège, 6, Allée du 8 Août, 4000 Liège,
Belgium
Daniel Margoliash
University of Chicago, Department of Organismal Biology and Anatomy, 1027 East 57th
Street, Chicago, IL 60637, USA
Laura Mascetti
Cyclotron Research Centre (B30), University of Liège, 6, Allée du 8 Août, 4000 Liège,
Belgium
Christelle Meyer
Cyclotron Research Centre (B30), University of Liège, 6, Allée du 8 Août, 4000 Liège,
Belgium
Vincenzo Muto
Cyclotron Research Centre (B30), University of Liège, 6, Allée du 8 Août, 4000 Liège,
Belgium
David Rector
Department of Veterinary Comparative Anatomy, Pharmacology and Physiology,
Washington State University, 205 Wegner Hall, Pullman, WA 99164, USA
Igor Timofeev
Department of Psychiatry and Neuroscience, Laval University, Québec, G1V 0A6, Canada.
1
CHAPTER
Neuronal Oscillations in the
Thalamocortical System during
Sleeping and Waking States
Igor Timofeev
Département de Psychiatrie et de Neurosciences, The Centre de Recherche Université Laval Robert-Giffard
(CRULRG), Université Laval, Québec, (QC), G1J 2G3, Canada
All normal brain processes occur over three main states of vigilance: wake,
slow-wave sleep, and REM sleep. These states can be subdivided further to
passive and active wakefulness, three to four stages of slow-wave sleep, and
active and passive REM sleep. There are also abnormal states of the brain
like paroxysmal seizure activities or brain activities generated under anes-
thesia conditions. The states of vigilance by themselves originate via interac-
tion of circadian and homeostatic processes (Achermann & Borbely, 2003;
Borbely, Baumann, Brandeis, Strauch, & Lehmann, 1981) with a leading role
of interactions between suprachiasmstic nucleus and hypothalamic regions
(Fuller, Sherman, Pedersen, Saper, & Lu, 2011; Saper, Scammell, & Lu, 2005).
Different brain states are expressed as different forms of global electro-
graphic activities recorded from a brain surface (elecrocorticogram), which
are reflected on a head surface and recorded as an electroencephalogram
(EEG). These global electrical activities are mediated by synchronous synaptic
activities of neurons. Synchronous de- or hyperpolarization of neighboring
neurons will generate large amplitude global waves, the asynchronous activi-
ties of neurons will not generate the global field potential signals at all, and
intermediates neuronal synchrony will produce field potential waves of inter-
mediate amplitude. While the states of vigilance by themselves originate in
suprachiasmstic and hypothalamic regions and are transmitted to the other
brain structures via ascending activating systems (Steriade & McCarley, 2005),
the top level of the brain, primarily the thalamocortical system, generates the
electrical activities that are characteristic to different states of vigilance.
NEURONAL SYNCHRONIZATION
Neuronal synchronization requires some form of interactions between
neurons. I will only briefly overview this aspect. To understand the
Sleep and Brain Activity © 22001122 Elsevier Inc.
DOI: http://dx.doi.org/10.1016/B978-0-12-384995-3.00001-0 All rights reserved. 1
2 Sleep and Brain Activity
processes of neuronal synchronization, one has to separate local and long-
range synchronization. Local synchronization is required to produce
simultaneous de- or hyperpolarization of a local group of neurons that
mediates generation of local field potentials; and long-range synchroniza-
tion synchronizes the local field potentials generated at some distance. The
amplitude of field potential recordings with one electrode will provide
information on levels of local neuronal synchrony. Multisite recordings are
used to investigate long-range synchronization.
The most investigated aspect of neuronal interactions are chemical synaptic
interactions (Eccles, 1964). Action potentials generated in excitatory neurons
will propagate to synaptic boutons and release excitatory neurotransmit-
ter (mainly glutamate within the thalamocortical system). The amplitude
of single-axon induced excitatory postsynaptic potentials (EPSPs) is small
from 0.1 to several millivolts with overall mean less than 2 mV (Thomson,
West, Wang, & Bannister, 2002). This neurotransmitter will exert depolar-
izing action on targets. Because each axon forms multiple contacts with tar-
get neurons it can produce sufficient local field effects if the target neurons
are located in proximity. If several neurons excite the same group of tar-
get cells nearly simultaneously, the overall postsynaptic effect will definitely
be larger due to spatial summation. That will produce summated effects,
which are sensed at the field potential level. Multiple excitatory connec-
tions are formed by long-axon neurons; therefore they are well positioned
to mediate long-range synchrony. Activities of inhibitory neurons within
the thalamocortical system release mainly GABA, an inhibitory neurotrans-
mitter. All known cortical interneurons have a short-axon (Markram et al.,
2004; Somogyi, Tamás, Lujan, & Buhl, 1998), which contacts multiple t arget
neurons in a local network. Therefore, cortical interneurons can contribute
to local, but not long-range synchronization. This is not the case for other
inhibitory cells. During development, GABAergic neurons of thalamic
reticular (RE) nucleus form variable patterns of connectivity from a com-
pact, focal projection to a widespread, diffuse projection encompassing large
areas of Ventro-Basal complex (VB) (Cox, Huguenard, & Prince, 1996).
Indirect action of reticular thalamic neurons that exerts diffuse projec-
tions onto thalamocotical neurons could likely be detected as synchronous
field potential events over some large cortical areas when thalamocorti-
cal neurons will fire action potentials. Neuronal constellations outside the
thalamocortical system may also to cortical synchronization. A recent study
(Eschenko, Magri, Panzeri, & Sara, 2012) has demonstrated that in rats, the
locus coreleus neurons fire in phase with cortical slow waves and they even
Thalamocortical Oscillations 3
preceded onsets of cortical neuronal firing, suggesting a contribution of
locus coreleus not only in setting up general cortical excitability, but also in
influencing the cortical synchronization processes.
The next mechanism contributing to neuronal synchronization is electri-
cal coupling between cells that is mediated by gap junctions. The astrocytic
network is tightly connected via gap junctions (Mugnaini, 1986). Gap junc-
tions were also found between multiple groups of cortical interneurons
(Galarreta & Hestrin, 1999; Gibson, Beierlein, & Connors, 1999). Electrical
coupling was demonstrated between neurons of reticular thalamic nucleus
(Fuentealba et al., 2004; Landisman et al., 2002). Dye coupling, presence of
spikelets, and modeling experiments suggest the existence of electrical cou-
pling between axons of hippocampal pyramidal cells (Schmitz et al., 2001;
Vigmond, Perez Velazquez, Valiante, Bardakjian, & Carlen, 1997). A single
study has found spikelets, an accepted signature of electrical coupling,
in thalamocortical neurons (Hughes, Blethyn, Cope, & Crunelli, 2002).
Indirect data on electrical coupling between thalamocortical neurons and
axo-axonal coupling are so far not supported by demonstration of the pres-
ence of gap junctions. The gap junctions, mediating electrical coupling,
form high resistance contacts between connected cells; therefore, they act
as low-pass filters (Galarreta & Hestrin, 2001). Confirmed gap junctions are
formed within dendritic arbor of connected cells, therefore, they can con-
tribute to the local synchronization only.
Ephaptic interactions constitute another mechanism of neuronal synchro-
nization. Changes in neuronal membrane potential produce extracellular
fields that affect the excitability of neighboring cells (Jefferys, 1995). The
extracellular fields produced by a single neuron are weak, however, when
a local population of neurons generate nearly simultaneous excitation or
inhibition, their summated effects can significantly influence the excitabil-
ity of neighboring neurons, contributing to local synchronization. External
electric field applied with intensities comparable to endogenous fields
applied to cortical slices modulated cortical slow oscillation (Frohlich &
McCormick, 2010). During seizure activity, the extracellular space reduces
and the effects of ephaptic interactions increase (Jefferys, 1995).
Neuronal activities are associated with movement of ions across mem-
brane due to activation of ligand- or voltage-controlled channels. Because
extracellular space in the brain is about 20% of total brain volume (Syková,
2004; Sykova & Nicholson, 2008) and an activation of ionic pumps,
ionic diffusion, etc., is a time-dependent process, the neuronal activities
alter extracellular concentration of implicated ions (Somjen, 2002). Changes are