26t h I n t e r n at I o n a l S u m m e r S c h o o l o f B r a I n r e S e a r c h
amSterdam, 29 June - 2 July 2010
Organized by
netherlands Institute for neuroscience royal netherlands academy of arts and Sciences Vu university amsterdam university of amsterdam leiden university medical center
Slow Brain Oscillations
organizing committee
Eus Van Someren Ysbrand Van Der Werf Pieter Roelfsema Huibert Mansvelder Fernando Lopes da Silva
Secretariat
Tini Eikelboom Wilma Verweij
T +31(0)20 5510833 during summer school hours
correspondence
26th International Summer School of Brain Research
Netherlands Institute for Neuroscience Meibergdreef 47 1105 BA Amsterdam T 020 5665500 F 020 5666121 e-mail: summerschool@nin.knaw.nl website: www.nin.knaw.nl/summerschool
Booklet design: Henk Stoffels Print: Gildeprint BV, Enschede
Contents
Sponsors 6 Introduction 7 General information 9 Scientific programme 12 Poster sessions 21abstracts invited speakers 27
abstracts participants 69
c.u. ariëns Kappers award 127
lecturers, chairpersons, organizers 129
sponsors
Main sponsor UCB Pharma Sponsors EGI BIAL MedCaT EASYCAP ONWAR NSWO
Philips Health Care Philips Consumer Lifestyle ZonMw
University of Amsterdam Leiden University Medical Center VU University Amsterdam
Royal Netherlands Academy of Arts and Sciences Netherlands Institute for Neuroscience
IntroduCtIon
Dear Participant,
We are pleased to welcome you at the 26th International Summer School of Brain
Research, in the monumental Trippenhuis, which has been the seat of the Royal Netherlands Academy of Arts and Sciences (KNAW) since 1812. Since 1964, the Summer School has been a biennial event organized by the Netherlands Institute for Neuroscience, one of the life science institutes of the KNAW.
The history of the Netherlands Institute for Neuroscience dates back to the beginning of the last century. At the meeting of the International Association of Academies held in Paris in 1901, the anatomist Wilhelm His proposed that research on the nervous system should be placed on an international footing. In 1904 this resulted in the formation of the International Academic Committee for Brain Research, which set itself the task of: “organizing a network of institutions throughout the civilized world, dedicated to the study of the structure and functions of the central organ...”. Several governments responded to this ambition by founding brain research institutes, among which was the Netherlands Central Institute for Brain Research, as it was called then, which opened its doors on 8 June 1909. Professor C.U. Ariëns Kappers (1877-1946) became the first director of the institute, a position he held until his death. The 26th International Summer School will focus on Slow Brain Oscillations of Sleep, Resting State and Vigilance.
The most important quest of cognitive neuroscience may be to unravel the mecha-nisms by which the brain selects, links, consolidates and integrates new information into its neuronal network, while preventing saturation to occur. During the last decade, neuroscientists working on the memory system within several disciplines and in many labs over the world have observed an important involvement of the specific types of brain oscillations that occur during sleep – the cortical slow oscillations; during the resting state – the MRI default mode and other networks; and during task perform-ance – the EEG power and performperform-ance modulations. Understanding the role of these slow oscillations thus appears to be essential for our fundamental understanding of brain function. Never before has an international meeting taken place that integrated these three fields of study and allowing for crossfertilization.
Brain activity is characterized by oscillations; in spike frequency, field potentials or blood oxygen level-dependent MRI signals. Environmental stimuli, reaching the brain through our senses, activate, or inactivate, neuronal populations and modulate ongoing activity. In the absence of sensory input, as is the case during rest or sleep,
brain activity does not cease. Rather, its oscillations continue and change with respect to their dominant frequencies and coupling topography. Studies ranging from the molecular, physiological and behavioral to the cognitive level have in the past decade clearly indicated that the study of these slow oscillations is essential for our under-standing of plasticity, memory, brain structure from synapse to default mode network, cognition, consciousness and ultimately for our understanding of the mechanisms and functions of sleep and vigilance. At this 26th International Summer School an inter-national selection of the most renowned scientists advancing these fields will present and discuss their work. The lectures will cover all levels of biological organization and are targeted at Ph.D-students, postdoctoral fellows and senior researchers, both in basic and clinical science, with an interest in plasticity, memory, sleep, vigilance, oscillations, default mode network activity, cognition and consciousness.
The Summer School aims at providing a forum for expressing new views and ideas on the above topics. The lectures are presented by internationally renowned scientists. One of the tangible results of this International Summer School will be a comprehensive volume published by Elsevier Scientific Publishers in their widely acclaimed Progress in Brain Research series.
We would like to express our gratitude to the various sponsors of this Summer School for their financial support, without which this Summer School would not have been possible, with a special mention of our main sponsor, UCB Pharma.
We are also very grateful to Tini Eikelboom and Wilma Verweij for their excellent organizational work and for compiling this booklet, and to Wilma Top and Rikesh Balgobind for their support in all matters financial. Finally, we thank Henk Stoffels for his fine artwork and preparation of the Summer School website, the program booklet and the poster.
We hope that the program provides the basis for an inspiring meeting and that its didactic format will succeed in enhancing our understanding of the slow oscillations of the brain.
On behalf of the organizing committee
General InformatIon
Venue
The Summer School will be held in the Tinbergen conference room of the Royal Netherlands Academy of Arts and Sciences (Het Trippenhuis), Kloveniersburgwal 29, 1011 JV Amsterdam, Tel. +31 (0)20-5510700. The venue is a 10-minute walk from Amsterdam Central Station, a 5-minute walk from any tram that stops at Dam Square, and a 2-minute walk from any Metro (subway) that stops at Nieuwmarkt.
Information desk
The information desk at the Summer School will be open each day from 8.00 a.m. to 9.00 a.m. and during the various breaks. Tel. secretariat on site: +31 (0)20 5510833.
Scientific programme
The scientific programme consists of ten symposia. Participants are given the chance to present their work in the form of posters. Please see the program for details.
Poster sessions
Posters will be on display every day during the lunch breaks.
Social events
On Tuesday 29 June at 18.30 hrs all speakers and participants of the Summer School are invited to a reception and buffet at the West-Indisch Huis (Herenmarkt 99) in Amsterdam.
On Thursday 1 July at 18:00 hrs all speakers and participants are invited to a dinner at Brasserie Harkema (Nes 67) in Amsterdam.
On Friday 2 July, on conclusion of the program, everyone is invited to a drink and some nibbles in the Trippenhuis.
tueSday 29 June
8:00 Registration
9:00 Eus Van Someren: welcome and overview
I. Slow oscillations in detail and in perspective
Chair: Inge Huitinga and Pieter Roelfsema
9:30 Cellular and network mechanisms of recurrent cortical network activity
David McCormick
10:15 Mechanisms of slow oscillations generation during sleep: neurons, glia and networks
Florin Amzica
11:00 Coffee break
11:30 Involvement of cytokines in slow wave sleep
James Krueger
tueSday 29 June
II. Sleep and resting state eeG-profile as heritable endophenotype and during development
Chair: Dorret Boomsma and Paul Franken
13:30 Genetic determination of sleep EEG profiles in mice
Paul Franken
14:15 Genetic determination of sleep EEG profiles in man
Hans-Peter Landolt
15:00 Developmental aspects of slow oscillations during sleep
Reto Huber
15:45 Tea break
III. Genetic, cellular and small scale network mechanisms of local use-dependent slow oscillations
Chair: Elly Hol and Marcos Frank
16:15 Modulation of cortical neuronal activity by vigilance state, behavior and preceding sleep-wake history: cellular correlates of sleep homeostasis in freely behaving rats
Vladyslav Vyazovskiy
17:00 Involvement of adenosine in slow wave sleep
Marcos Frank
WedneSday 30 June
IV. Phasic events during slow oscillations
Chair: Pierre Maquet and Huib Mansvelder
9:00 Impact of brainstem network activity on cortical dynamics during slow oscillations
Juan Mena-Segovia
9:45 Grouping of spindle activity during slow oscillations in sleep
Matthias Mölle
10:30 Coffee break
11:00 Spontaneous neural activity during human slow wave sleep
Pierre Maquet
WedneSday 30 June
V. thalamocortical network interactions during slow sleep oscillations
Chair: Fernando Lopes da Silva and Ysbrand Van der Werf
13:15 Neuronal plasticity in thalamocortical networks during sleep and waking oscillations
Igor Timofeev
14:00 Thalamocortical mechanisms of slow oscillations
Stuart Hughes
14:45 Tea break
VI. cortico-hippocampal network interactions and replay during slow oscillations
Chair: Francesco Battaglia and Guillén Fernández
15:15 The dynamics of memory reactivation and cortico-hippocampal interactions
Bruce McNaughton
16:00 Coordinated memory replay in the visual cortex and hippocampus during sleep
Matt Wilson
16:45 Communication between neocortex and hippocampus during sleep in rodents
thurSday 1 July
VII. cortical synchronization and travelling of slow oscillations
Chair: Marcello Massimini and Ole Jensen
9:00 Microvascular compliance changes across sleep and wake: mechanisms for local sleep regulation
David Rector
9:45 Long range synchronization of up and down states
Maxim Volgushev
10:30 Coffee break
11:00 Bistability, slow waves and information integration in thalamocortical circuits
Marcello Massimini
11:45 Sources of slow oscillation travelling waves; overlap with the default mode network?
Brady Riedner
thurSday 1 July
VIII. functional benefits of slow oscillations during sleep
Chair: Cyriel Pennartz and Peter Meerlo
14:15 Mechanisms of sleep-dependent consolidation of cortical plasticity
Marcos Frank
15:00 Sleep slow oscillations for memory consolidation
Jan Born
15:45 Tea break
16:15 Sleep benefits subsequent hippocampal functioning
Ysbrand Van Der Werf
frIday 2 July
IX. What comes to mind
Chair: Serge Rombouts and Martijn Van den Heuvel
9:00 Resting state networks as seen via fMRI: Characteristics and interpretations
Stephen Smith
9:45 Resting state default network oscillations interfacing vigilance and sleep
Linda Larson-Prior
10:30 Coffee break
11:00 Low frequency BOLD fluctuations during resting wakefulness and light sleep
Jeff Duyn
frIday 2 July
X. Slow oscillations in the competition between vigilant performance and resting state
Chair: Klaus Linkenkaer-Hansen and Scott Makeig
13:15 Daydreaming your way out of coma? fMRI restings state studies in disorders of consciousness
Steven Laureys
14:00 Very slow EEG fluctuations predict the dynamics of stimulus detection
Matias Palva
14:45 Tea break
15:15 Infraslow oscillations in drowsy performance and EEG
Scott Makeig
16:00 Introduction to the C.U. Ariëns Kappers lecture
Pieter Roelfsema
16:15 C.U. Ariëns Kappers lecture Two Views of Brain Function
Marcus Raichle
poster sessIons
Poster session 1 tuesday 12:15 - 13:30
mechanisms of sleep-wake states and oscillations
1 Origin of Active States in Local Neocortical Networks during Slow Sleep Oscilla-tion
Sylvain Chauvette, Maxim Volgushev and Igor Timofeev
2 Laminar properties of sleep slow oscillations in the human frontal lobe.
Richárd Csercsa, Balázs Dombovári, László Grand, Andor Magony, Lucia Wittner,
Loránd Erőss, György Karmos, István Ulbert
3 Dynamic changes in cortical oxygen and lactate concentrations across the sleep/ wake cycle in freely behaving rats.
Dash MB, Cirelli C, and Tononi G
4 Non neuronal modulators of synaptic transmission control spontaneous cortical activity in vivo.
Tommaso Fellin
5 Relaxin-3/RXFP3 networks and control of behavioural state, stress, biorhythms, cognition and emotion.
Gundlach AL, Ma S, Smith CM, Sang Q, Ryan PJ, Hossain MA, Wade JD, Bathgate RAD, Olucha-Bordonau FE, Blasiak A, Sutton SW, Lovenberg TW.
6 Differences in slow oscillations during NREM sleep between chronotypes. Valérie Mongrain, Julie Carrier, Jean Paquet, Ester McSween, Marie Dumont 7 Activation of reticular thalamic neurons by MT2 melatonin receptor ligands: in
vivo electrophysiological and electroencephalographic study.
Rafael Ochoa-Sanchez, Stefano Comai, Baptiste Lacoste, Gilberto Spadoni, Silvia Rivara, Annalida Bedini, Debora Angeloni, Franco Fraschini, Marco Mor, Giorgio Tarzia, Laurent Descarries and Gabriella Gobbi
8 Representation of the vestibular sense in the rodent cortex. Ede A. Rancz, Santiago Canals, Alan Brichta and Troy W. Margrie
9 Adenosine affects neurons in the rat prefrontal cortex in a cell type, subclass and layer specific way.
Van Aerde, K.I., Feldmeyer, D.
10 Overexpression of SK2-type K+ channels potentiates low-frequency oscillations in the nucleus reticularis thalami and modulates sleep architecture.
Ralf D. Wimmer, Chris T. Bond, Rudolf Kraftsik, John P. Adelman, Paul Franken, Anita Lüthi
11 Contribution of adenosine related genes to the risk of depression with disturbed sleep.
Natalia Gass, Hanna M. Ollila, Siddheshwar Utge, Timo Partonen, Erkki Kron-holm, Sami Pirkola, Johanna Suhonen, Kaisa Silander, Tarja Porkka-Heiskanen, Tiina Paunio
12 Markers of systemic inflammation and slow wave sleep in various sleep disorders. Marietta Keckeis, Zuzana Lattova, Eszter Maurovich-Horvat, Thomas Wetter and
Thomas Pollmächer
Poster session 2 Wednesday 11:45 - 13:15
electrophysiological markers and neuronal connectivity in sleep and wake-fulness
1 Changes in cortical responsiveness during up- and down-states of the human sleep slow oscillation revealed by EEG-triggered TMS.
Til O. Bergmann, Matthias Mölle, Marlit A. Schmidt, Christoph Lindner, Lisa Marshall, Jan Born, Hartwig R. Siebner
2 Influence of slow oscillating transcranial direct current stimulation on eeg and sleep related parameters of young healthy subjects.
C. Garcia, C. Schöbel, M. Glos, I. Fietze, T. Penzel
3 Sleep homeostasis and cortical synchronization: Evidence from spontaneously occurring K-complexes in humans.
Georg Gruber, Peter Anderer, Silvia Parapatics, Arnaud Moreau, Marco Ross, Georg Dorffner
4 Noradrenergic neurons of the locus coeruleus are phase-locked to cortical up-down states during sleep.
Cesare Magri, Oxana Eschenko. Stefano Panzeri, and Susan J. Sara 5 Cortical and hippocampal low delta activity: a human stereo-EEG study. Fabio Moroni, Fabrizio De Carli, Lino Nobili, Marcello Massimini, Daniela
Tem-pesta, Giorgio Lo Russo, Cristina Marzano, Carlo Cipolli, Luigi De Gennaro, Michele Ferrara
6 Monkey high density electrocorticogram (ECoG) reveals sleep spindle diversity. Ingrid LC Nieuwenhuis, Conrado A Bosman, Robert Oostenveld, Jan Born, Pascal
Fries
7 Inhibition recruitment in the prefrontal cortex during natural sleep spindles and gating of hippocampal inputs.
8 Cortical effective connectivity across the sleep-wake cycle: an intracerebral study in humans.
A. Pigorini, C. Szymanski, S. Casarotto, M. Rosanova, A. Casali, G. Lo Russo, M. Mariotti, L. Nobili, M. Massimini
9 Neocortical and hippocampal sharp potentials as EEG markers of the maturation of functional connectivity and sleep in the neonatal rat.
Henna-Kaisa Wigren, Sebastian Schuchmann, Else A. Tolner, Kai Kaila
10 Interrelations of slow and high frequency activity in the NREM sleep EEG in the rat.
Roman Yasenkov and Tom Deboer
11 Integrated analysis of slow wave sleep, respiration and high frequency heart rate fluctuations across the lifespan.
Thomas RJ, Mietus JE, Peng CK, Gozal D, Montgomery-Downs H, Kothare S, Goldberger AL.
12 Spinal cord injury immediately changes the state of the brain.
J. Aguilar, D. Humanes-Valera, E. Alonso-Calviño, J.G. Yague, K. A. Moxon, A. Oliviero, G. Foffani
13 Immediate bilateral functional changes of the somatosensory cortex after unilateral spinal cord injury.
Josué G. Yague, Guglielmo Foffani, Juan Aguilar
Poster session 3 thursday 12:30 - 14:15
Sleep and oscillations in relation to cognition
1 Coherent theta oscillations and reorganization of spike timing in the hippocampal-prefrontal network upon learning.
Karim Benchenane, Adrien Peyrache, Mehdi Khamassi, Patrick Tierney, Yves Gioanni, Francesco P. Battaglia, Sidney I. Wiener
2 Dissociable consequences of memory reactivation during sleep and wakefulness.
Diekelmann S, Büchel, C, Born J, Rasch B
3 Statistical representations benefit from a nap. Simon Durrant, Charlotte Taylor, Penny Lewis
4 Sleep spindles and slow oscillations contained in naps increase after learning and correlate with consolidation success.
Oliver Markes, Simone Duss, Thomas Reber, Thomas König, Simon Ruch, Daniel Oppliger, Johannes Mathis, Corinne Roth, Katharina Henke
5 A time for learning and a time for sleep: the effect of sleep deprivation on contextual fear conditioning at different times of the day.
Roelina Hagewoud, Shamiso Whitcomb, Amarins N. Heeringa, Robbert Havekes, Jaap M. Koolhaas and Peter Meerlo
6 Chronic sleep restriction during adolescence: effects on hippocampal structure and emotionality.
Arianna Novati, Henriëtte J Hulshof, Peter Meerlo
7 Task-induced neuronal network connectivity is reactivated during sleep: an MEG study.
G. Piantoni, Y.D. van der Werf, O. Jensen, C.J. Stam, E.J.W. Van Someren 8 Fast and slow spindles relate inversely to motor skills in primary school aged
children.
Schutte RG, Raymann RJEM, Vis JC, Coppens JE, Kumar A, Bes E, De Weerd, A, Van Someren EJW
9 Learning by observation requires sleep.
Ysbrand D. Van Der Werf, Els Van Der Helm, Menno M. Schoonheim, Arne Ridderikhoff, Eus J.W. Van Someren
10 Dynamic changes in neurotransmitter levels in the basal forebrain during and after sleep deprivation.
Zant JC, Leenaars CHC, Kostin A, van Someren EJ and Porkka-Heiskanen T 11 Reduced sleep-associated consolidation of declarative memory in ADHD patients. Alexander Prehn-Kristensen, Robert Göder, Jochen Fischer, Ines Wilhelm, Mareen
Seeck-Hirschner, Josef Bernd Aldenhoff, Lioba Baving
12 Event-related activity and phase locking during a psychomotor vigilance task throughout sleep deprivation
Hoedlmoser, K., Griessenberger, H., Fellinger, R., Freunberger, R., Gruber, W., Klimesch, W., & Schabus M.
13 Alterations in sleep EEG might predict the ability to learn in a visual task in Wil-liams syndrome.
F. Gombos, P. Gerván, R. Bódizs, I. Kovács
14 Memory consolidation during sleep in schizophrenia
Poster session 4 friday 11:45 - 13:15
Slow wake oscillations andperformance
causes and consequences of disordered sleep
1 Slow Oscillations during baseline and recovery NREM sleep in somnambulism. Julie Carrier, Antonio Zadra, Jean Paquet, Hélène Blais, Jacques Montplaisir,
Valérie Mongrain
2 NREM Slow-wave oscillations rebound after sleep deprivation: effect of aging. Lafortune, M., Viens, I., Poirier, G., Vandewalle, G., Barakat, M., Martin, N.,
Filipini, D., & Carrier J.
3 NREM Slow-wave oscillations and early Alzheimer disease.
Lafortune, M., Petit, D., Gagnon, J.F., Massicotte-Marquez, J., Montplaisir, J.Y., & Carrier, J.
4 Ongoing alpha oscillations exhibit multi-fractal properties that are altered in early-stage Alzheimer’s disease.
Simon-Shlomo Poil, Espen Alexander Furst Ihlen, Philip Scheltens, Huibert D. Mansvelder, Klaus Linkenkaer-Hansen
6 Metabolic consequences of acute and chronic sleep disturbances in rats. Paulien Barf, Anton Scheurink, Peter Meerlo
7 Sleep spindles show different age-related changes across brain topography. Martin, N., Poirier, G., Robillard, R., Lafortune, M. & Carrier, J.
8 Consistent increases of delta sleep in individuals exposed to chronic sleep restric-tion.
John Axelsson, Göran Kecklund, Torbjörn Åkerstedt & Michael Ingre
9 Wake EEG markers determined by detrended fluctuation analysis correlate with neurocognitive deficits and subjective sleepiness during 40hours of extended wakefulness.
Denotti AL, Kim JW, Wong KKH, Bartlett DJ and Grunstein RR
10 Intrinsic connectivity networks, alpha oscillations and tonic alertness: A simulta-neous EEG/fMRI study.
Sepideh Sadaghiani, René Scheeringa, Katia Lehongre, Benjamin Morillon, Anne-Lise Giraud, Andreas Kleinschmidt
11 The phase of the slow oscillation determines the fate of incoming stimuli - an auditory EEG/fMRI study in human non-REM sleep.
Schabus, M., Dang-Vu,, Darsaud, A., Boly, M., Albouy, G., Sterpenich, V., Phillips, C. & Maquet, P.
12 Differential functional roles of slow-wave and oscillatory alpha-band activity in visual sensory cortex during anticipatory visual-spatial attention.
abstraCts InvIted speakers
Mechanisms of slow oscillations generation during sleep: neurons, glia and networks
Florin Amzica
School of Dentistry, Université de Montreal, Canada
Slow-wave sleep (SWS) is a state largely produced by the diffuse functional deafferenta-tion of the forebrain from its activating inputs mainly arising in the reticular midbrain structures. This induces an oscillatory state of cortical and cortico-thalamo-cortical networks during which several patterns of activity are generated. Among these, sleep spindles (7-14 Hz), arising in the thalamus, and delta oscillations (<4 Hz), mainly elicited in cortical circuits, play a leading role (for an extensive review, see Steriade, 2006). During my talk I will concentrate on the generation mechanisms of slow delta oscillations (mainly around 1 Hz) that create the central pattern of sleep oscillations during SWS. At the neuronal level they consist of alternative depolarized (around -60 mV) and hyperpolarized (around -75 mV) membrane potentials (Steriade et al., 1993). Virtually all neocortical neurons oscillate synchronously during these slow delta oscillations. The depolarizing phase, also called “up-state”, is made of post-synaptic potentials (both excitatory and inhibitory, reflecting the global balance between these inputs to any target neuron) and of intrinsic membrane currents (e.g. INa(p), IK(Ca), etc.). The hyperpolarizing phase, also termed “down-state”, mainly represents cortical disfacilitation, which results from the progressive depletion of extracellular calcium due to its prevalent post-synaptic uptake during the depolarizing phase of the slow oscillation (Massimini and Amzica, 2001). Lack of extracellular calcium leads to impaired synaptic efficiency and onset of the hyperpolarizing phase. Another factor that modulates the neuronal excitability is promoted by the spatial buffering of potas-sium by cortical glia. Simultaneous intracellular recordings of cortical neurons and glia have shown that the two cellular populations, although oscillating in coherence, reflect slightly different membrane dynamics (Amzica and Massimini, 2002): at the onset of the “up-state” glial depolarization lags the neuronal one by about 90 ms, prob-ably as a result of efficient buffering of potassium. In contrast, the glial repolarization anticipates the onset of the neuronal “down-state”, suggesting that the extracellular potassium starts to decrease while neurons are still in the “up-state”. This decreased extracellular potassium also reduces the neuronal excitability which, together with the extracellular calcium depletion, promotes the network disfacilitation leading to the “down-state. It is concluded that the slow (about 1 Hz) delta oscillatory state dominating SWS is the outcome of a neuronal-glial dialogue. The membrane potential
dynamics are then essential to determine the shape of the local field potentials and cortical dipoles for the genesis of electroencephalographic (EEG) potentials such as delta waves and K-complexes, which are main markers of SWS EEG.
Steriade, M. (2006) Grouping of brain rhythms in corticothalamic systems. Neuroscience, 137, pp. 1087-1106.
Steriade, M., Nuñez, A., Amzica, F. (1993) A novel slow (< 1 Hz) oscillation of neocortical neurons in vivo: depolarizing and hyperpolarizing components. J. Neurosci., 13, pp. 3252-3265.
Massimini, M. and Amzica, F. (2001) Extracellular calcium fluctuations and intracellular potentials in the cortex during the slow sleep oscillation. J. Neurophysiol., 85, pp. 1346-1350.
Amzica, F. and Massimini, M. (2002) Glial and neuronal interactions during slow wave and paroxysmal activities in the neocortex. Cereb. Cortex, 12, pp. 1101-1113.
Sleep slow oscillations for memory consolidation Jan Born
Department of Neuroendocrinology, University of Lübeck, 23538 Lübeck, Germany
Slow-wave sleep (SWS) facilitates the consolidation of declarative memory (for facts, episodes) a process assumed to involve the redistribution of the memory representa-tions from temporary hippocampal to neocortical long-term storage sites. Evidence will be provided indicating that this consolidation relies on a dialogue between neo-cortex and hippocampus which is essentially orchestrated by the <1 Hz EEG slow oscillation (SO). The SOs characterising SWS originate from neocortical networks. Their amplitude depends partly on the use of these networks for encoding of informa-tion, i.e., the more information is encoded during waking, the higher the SO amplitude during subsequent SWS. The SOs temporally group neuronal activity into up-states (of strongly enhanced activity) and down-states (of neuronal silence). This grouping is induced not only in the neocortex but also, via efferent pathways, in other structures relevant to consolidation, i.e., in the thalamus, generating 10-15 Hz spindles, and in the hippocampus, generating sharp-wave ripples which are well-known to accompany a replay of newly encoded memories taking place in hippocampal circuitries during SWS. The feedforward synchronizing effect of the SO enables memory-related inputs to be synchronously fed back from these (hippocampus, thalamus) and other struc-tures to the neocortex. The co-occurrence in the neocortex of these feedback-inputs possibly plays a critical role for the long-term storage of memories in neocortical networks. Indeed, induction of slow oscillations during NonREM sleep (but not dur-ing REM sleep or wakdur-ing) by slowly alternatdur-ing transcranial current stimulation not only enhances and synchronizes spindle activity but also improves the consolidation of declarative memory.
Low frequency BOLD fluctuations during resting wakefulness and light sleep Jeff Duyn
Laboratory for Advanced MRI, LFMI, NINDS, National Institutes of Health, www.amri.ninds.nih.gov
Much of the success of BOLD fMRI over the last two decades in studying the brain’s functional architecture can be attributed to its ability to map activity changes in response to carefully crafted behavioral tasks. Nevertheless, it is becoming increas-ingly clear fMRI may have important applications to the study of spontaneous brain activity as well. Similar to electrical and optical signals recorded from the brain (1,2), fMRI signals show a rich spatio-temporal structure even during periods of apparent mental or behavioral inactivity, and this structure appears, at least in part, to reflect the brain’s underlying functional architecture (3). BOLD fMRI of spontaneous activ-ity may therefore aid in the mapping of the brain’s major communication pathways, despite its temporal resolution being far too poor to cover the breadth of the spectrum of neural communications.
Although it has been realized that spontaneous fMRI signal fluctuations may have several non-neuronal contributions (including instrumental noise and drifts, motion and physiologic/vascular fluctuations), converging evidence suggest that it has a sub-stantial neural correlate. For example it has been shown that BOLD fMRI signals have a metabolic (4) and electrical (5) correlate and therefore may reflect glutamate cycling; their spatial extent is consistent with patterns from task activations studies (6), know fiber pathways (7), and MEG studies of electrical activity (8); and inter-hemispheric sig-nal correlation is reduced after surgical interruption of cortical neural connections (9). In as much spontaneous fMRI fluctuations represent neural processes, how do they relate to brain function? Do they simply represent ongoing and uncontrolled conscious mentation? Although initial reports of a dependence on behavioral state appeared to confirm this notion (10-12), it has been recently put into doubt by stud-ies showing maintained levels of spontaneous activity during conditions of reduced consciousness (13-18). Notably, in sleep, which is a natural conditions of reduced consciousness, spontaneous activity is largely maintained (15-18). This inspired the thought that spontaneous activity may not simply represent mentation and sensory processing, but at least in part surbserve more basic brain functions, possibly includ-ing (synaptic) homeostasis (16). In this presentation some of these findinclud-ings will be discussed in detail.
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2. Shoham D, Grinvald A. The cortical representation of the hand in macaque and human area S-I: high resolution optical imaging. J Neurosci 2001;21(17):6820-6835.
3. Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS, Buckner RL, Colcombe S, Dogonowski AM, Ernst M, Fair D, Hampson M, Hoptman MJ, Hyde JS, Kiviniemi VJ, Kotter R, Li SJ, Lin CP, Lowe MJ, Mackay C, Madden DJ, Madsen KH, Margulies DS, Mayberg HS, McMahon K, Monk CS, Mostofsky SH, Nagel BJ, Pekar JJ, Peltier SJ, Petersen SE, Riedl V, Rombouts SA, Rypma B, Schlaggar BL, Schmidt S, Seidler RD, Siegle GJ, Sorg C, Teng GJ, Veijola J, Villringer A, Walter M, Wang L, Weng XC, Whitfield-Gabrieli S, Williamson P, Windischberger C, Zang YF, Zhang HY, Castellanos FX, Milham MP. Toward discovery science of human brain function. Proc Natl Acad Sci U S A;107(10):4734-4739.
4. Fukunaga M, Horovitz SG, de Zwart JA, van Gelderen P, Balkin TJ, Braun AR, Duyn JH. Metabolic origin of BOLD signal fluctuations in the absence of stimuli. J Cereb Blood Flow Metab 2008;28(7):1377-1387. 5. Shmuel A, Leopold DA. Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey
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AR, Beckmann CF. Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci U S A 2009;106(31):13040-13045.
7. Krienen FM, Buckner RL. Segregated fronto-cerebellar circuits revealed by intrinsic functional con-nectivity. Cereb Cortex 2009;19(10):2485-2497.
8. de Pasquale F, Della Penna S, Snyder AZ, Lewis C, Mantini D, Marzetti L, Belardinelli P, Ciancetta L, Pizzella V, Romani GL, Corbetta M. Temporal dynamics of spontaneous MEG activity in brain networks. Proc Natl Acad Sci U S A;107(13):6040-6045.
9. Johnston JM, Vaishnavi SN, Smyth MD, Zhang D, He BJ, Zempel JM, Shimony JS, Snyder AZ, Raichle ME. Loss of resting interhemispheric functional connectivity after complete section of the corpus cal-losum. J Neurosci 2008;28(25):6453-6458.
10. McKiernan KA, Kaufman JN, Kucera-Thompson J, Binder JR. A parametric manipulation of factors affecting task-induced deactivation in functional neuroimaging. J Cogn Neurosci 2003;15(3):394-408. 11. Fransson P. Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the
resting-state default mode of brain function hypothesis. Hum Brain Mapp 2005;26(1):15-29.
12. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is in-trinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 2005;102(27):9673-9678.
13. Greicius MD, Kiviniemi V, Tervonen O, Vainionpaa V, Alahuhta S, Reiss AL, Menon V. Persistent default-mode network connectivity during light sedation. Hum Brain Mapp 2008;29(7):839-847. 14. Martuzzi R, Ramani R, Qiu M, Rajeevan N, Constable RT. Functional connectivity and alterations in
baseline brain state in humans. Neuroimage;49(1):823-834.
15. Horovitz SG, Braun AR, Carr WS, Picchioni D, Balkin TJ, Fukunaga M, Duyn JH. Decoupling of the brain’s default mode network during deep sleep. Proc Natl Acad Sci U S A 2009;106(27):11376-11381. 16. Fukunaga M, Horovitz SG, van Gelderen P, de Zwart JA, Jansma JM, Ikonomidou VN, Chu R, Deckers
RH, Leopold DA, Duyn JH. Large-amplitude, spatially correlated fluctuations in BOLD fMRI signals during extended rest and early sleep stages. Magn Reson Imaging 2006;24(8):979-992.
17. Horovitz SG, Fukunaga M, de Zwart JA, van Gelderen P, Fulton SC, Balkin TJ, Duyn JH. Low frequency BOLD fluctuations during resting wakefulness and light sleep: a simultaneous EEG-fMRI study. Hum Brain Mapp 2008;29(6):671-682.
18. Larson-Prior LJ, Zempel JM, Nolan TS, Prior FW, Snyder AZ, Raichle ME. Cortical network functional connectivity in the descent to sleep. Proc Natl Acad Sci U S A 2009;106(11):4489-4494.
Sleep-dependent consolidation of cortical plasticity Marcos Frank
University of Pennsylvania School of Medicine, Philadelphia, USA
Developing mammals sleep much more than adults, but why they do is a mystery. One possible explanation for the abundance of sleep in early life is that it contributes to brain development. Indeed, sleep undergoes dramatic changes at ages when the brain is rapidly growing and plastic; a term that refers to the ability of the brain to change the strength and number of its connections in response to new experience. We investigated a role for sleep in brain development by examining how it influences a classic form of neocortical plasticity (known as ocular dominance plasticity), origi-nally described by Torstein Wiesel and David Hubel in their seminal studies of the visual cortex. Ocular dominance plasticity is triggered by blurring patterned vision in one eye during a critical period of development, resulting in a rapid and profound rewiring of the (primary) cortical areas governing vision. We found that this plasticity was greatly enhanced by sleep, and inhibited by sleep loss. Subsequent investigations revealed that a number of activity-dependent mechanisms important in long-term synaptic potentiation (LTP) and long-term synaptic depression (LTD) were also ac-tivated during sleep. Our findings therefore demonstrate that sleep is critical for the consolidation of canonical forms of cortical plasticity in vivo. We are now determining precisely how sleep accomplishes this function.
Genetic determination of sleep EEG profiles in mice Paul Franken
Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
Although it has been long known that genetic factors affect sleep, until recently, very little was known on the specific genes and molecular pathways underlying these traits. Some 14 years ago we therefore began to map genomic loci contributing to the vari-ance found in sleep and EEG phenotypes in the mouse using quantitative-trait loci (QTL) analysis as a forward genetics approach. As in humans, EEG traits in particular proved to be under an exceptionally strong genetic control in the mouse with addi-tive genetic factors contributing over 80% of the variance. We identified variations in the Acads (acyl-coenzyme A dehydrogenase, short chain) and Rarb1 (retinoic acid receptor beta 1) genes to underlie the EEG differences in the frequency of theta (6-9Hz) oscillations(1) and the contribution of delta (1-4Hz) oscillations to the sleep EEG(2), respectively. These findings implicated fatty-acid beta oxidation and retinoic acid signaling in shaping rhythmic EEG activities.
Hypotheses on sleep’s neurobiological function agree that a need for sleep, which accumulates while awake, can only be efficiently alleviated during sleep. Sleep is thus thought to fulfill a homeostatic function. One homeostatically regulated aspect of sleep that has received considerable attention is the prevalence of delta oscillations in the sleep EEG, quantified as delta power, because it monotonically increases with time-spent-awake and decreases with time-spent-asleep. Inbred mouse strains differ greatly in the sleep-deprivation induced increase in delta power suggesting that genetic background affects the rate at which a need for sleep accumulates during wakefulness. Using QTL analysis we could map a locus (Dps1) on chromosome 13 explaining 49% of the variance of the increase in delta power after sleep loss(3). Subsequent analyses of the sleep-deprivation induced changes in the brain transcriptome and in silico analysis identified the activity-induced transcript Homer1a as a credible candidate for this QTL(4). Homer1a is involved in neuronal plasticity consistent with the suggested role for sleep in preserving the integrity of neuronal connectivity and signaling. These examples show that the QTL approach can be successful at indentifying novel molecular pathways underlying complex phenotypes. Our findings also illustrate that different aspects of the same variable (e.g. the sleep-wake dependent changes in delta power and the relative contribution of delta power to EEG spectral profiles) can be affected by different genes. Besides this forward genetics approach our lab has also extensively used reverse genetics; i.e., studying the effects of loss-of-function of genes of interest, on both these aspects of EEG delta power. These studies established that circadian clock genes, besides generating circadian rhythms, also play a role in sleep homeostasis(5) and that the thalamocortical oscillations characteristic of sleep
critically depend on Ca2+ signaling in which small-conductance Ca2+-activated K+
channels (SK2 or Kcnn2) play a prominent role(6).
1. M. Tafti et al., Nat Genet 34, 320 (Jul, 2003). 2. S. Maret et al., Science 310, 111 (Oct 7, 2005).
3. P. Franken, D. Chollet, M. Tafti, J. Neurosci. 21, 2610 (April 15, 2001). 4. S. Maret et al., Proc Natl Acad Sci U S A 104, 20090 (Dec 11, 2007). 5. P. Franken, D. J. Dijk, Eur J Neurosci 29, 1820 (May, 2009). 6. L. Cueni et al., Nat Neurosci 11, 683 (Jun, 2008).
Involvement of adenosine in slow wave sleep Marcos Frank
University of Pennsylvania School of Medicine, Philadelphia, USA
The glial brain cells known as astrocytes regulate a number of vital functions in the mammalian brain (Giaume et al., 2010, Halassa and Haydon, 2010). In addition to buffering ions, regulating neurotransmitter uptake, metabolism and cerebral vascu-lature, astrocytes are now known to be partners in synaptic transmission (Giaume et al., 2010, Halassa et al., 2009a, Halassa and Haydon, 2010). Astrocytes respond to and signal back to neurons with their own suite of chemical messengers (gliotransmitters) which can regulate neuronal excitability and function (Halassa et al., 2009a). Precisely how astrocytes release their own transmitters is debated (Hamilton and Atwell, 2010, Halassa and Haydon, 2010), but there is general consensus that the classic view of glia as simple, passive support cells is wrong (Haydon and Carmignoto, 2006, Halassa et al., 2009a). An emerging view is that they are instead partners with neurons in synaptic plasticity, computation and behavior (Halassa et al., 2009a). In this talk, I discuss cur-rent findings demonstrating that astrocytes play a crucial role in mammalian sleep. I begin with a brief overview of past findings which suggested that astrocytes influence sleep and wakefulness. This is followed by a review of new data that provide direct evidence that astrocytes are centrally involved in the accumulation and discharge of sleep need and the generation of slow-wave EEG oscillations.
Developmental aspects of slow oscillations during sleep Reto Huber
University Children’s Hospital Zurich, Switzerland.
Sleep slow oscillations are the major electrophysiological characteristic of deep non-rapid eye movement sleep (Steriade et al., 1993). When these slow oscillations are synchronized and involve the majority of cortical neurons in a certain region, they become visible in the surface EEG as slow waves (Vyazovskiy et al., 2009). The activity of slow waves undergoes prominent changes during development: SWA increases in the first years of life, reaches its maximum shortly before puberty and declines thereafter throughout adolescence (Feinberg, 1982; Campbell and Feinberg, 2009). In addition, we found, by means of high-density EEG recordings in children and adolescence that the predominance of SWA shifts from occipital to frontal cortex in the first two decades of life. This shift along the postero-anterior axis is only present in the SWA frequency range and remains stable across the night. Interestingly, anatomical (e.g. synaptic density; Huttenlocher and Dabholkar, 1997), neuroimaging (e.g. MRI gray matter thickness; Shaw et al., 2008) and behavioural (e.g. executive functions; Luna and Sweeney, 2004) studies reported a similar time course of cortical maturation along the postero-anterior axis. This parallel time course of cortical maturation and sleep SWA is of interest in the context of the accumulating evidence that sleep plays an important role in brain plasticity, i.e. the remodelling of neuronal connections in the brain after learning (Tononi and Cirelli, 2006). More specifically, the hypothesis by Tononi and Cirelli proposes that SWA is closely related to changes in synaptic strength. Using multi-unit activity recordings in rats, Vyazovskiy et al. (2009) showed that the level of synchronization of cortical activity across neurons depends on the level of synaptic strength. The denser and stronger synapses are, the faster they synchronize their activity and the larger is the resulting potential change measured by standard EEG over the cortex. Thus, age related changes in cortical connectivity and excitability might be directly reflected in the activity of slow waves during sleep. Whether sleep SWA merely reflects cortical maturation or even plays an active role remains to be determined. No matter what, in the future, tracking sleep SWA might be used as a clinical tool to detect aberrations in cortical maturation.
Campbell IG, Feinberg I (2009) Longitudinal trajectories of non-rapid eye movement delta and theta EEG as indicators of adolescent brain maturation. Proc Natl Acad Sci U S A 106:5177-5180.
Feinberg I (1982) Schizophrenia: caused by a fault in programmed synaptic elimination during adolescence? J Psychiatr Res 17:319-334.
Huttenlocher PR, Dabholkar AS (1997) Regional differences in synaptogenesis in human cerebral cortex. J Comp Neurol 387:167-178.
Luna B, Sweeney JA (2004) The emergence of collaborative brain function: FMRI studies of the develop-ment of response inhibition. Ann N Y Acad Sci 1021:296-309.
Shaw P, Kabani NJ, Lerch JP, Eckstrand K, Lenroot R, Gogtay N, Greenstein D, Clasen L, Evans A, Rap-oport JL, Giedd JN, Wise SP (2008) Neurodevelopmental trajectories of the human cerebral cortex. J Neurosci 28:3586-3594.
Steriade M, McCormick DA, Sejnowski TJ (1993) Thalamocortical oscillations in the sleeping and aroused brain. Science 262:679-685.
Tononi G, Cirelli C (2006) Sleep function and synaptic homeostasis. Sleep Med Rev 10:49-62.
Vyazovskiy VV, Olcese U, Lazimy YM, Faraguna U, Esser SK, Williams JC, Cirelli C, Tononi G (2009) Cortical firing and sleep homeostasis. Neuron 63:865-878.
Thalamocortical mechanisms of slow oscillations Stuart Hughes
Discovery Sleep Research, Lilly UK Ltd., Windlesham, Surrey, UK
During sleep and some types of anesthesia, neurons in the neocortex and thalamus exhibit alternating UP and DOWN states, characterized by periods of sustained, in-tense network activity/action potential firing and widespread neuronal quiescence, respectively. This structured form of spontaneous activity is considered to represent the fundamental dynamics of neocortical and thalamocortical networks and may be important for ‘off-line’ processing and memory consolidation. When examined in brain slice preparations under appropriate conditions both neocortical and thalamic neurons retain the innate ability to exhibit UP and DOWN states thereby facilitating an in-depth analysis of their basic mechanisms. Consistent with in vivo data, widespread synchronized UP and DOWN states in neocortical slices can be readily elicited by applying the Ach receptor agonist carbachol. These UP states show a diverse array of manifestations depending on the cell-type under examination, are accompanied by faster network oscillations in the gamma (>20 Hz) band and are overwhelmingly reliant on fast glutamatergic and GABAergic synaptic activity. When neocortical slices are subject to higher concentrations of carbachol, UP states take on a more paroxysmal appearance reminiscent of epileptiform activity whereas the addition of noradrena-line to lower concentrations of carbachol converts normal rhythmic UP and DOWN states into a continuously-activated state. Unlike UP/DOWN states in the neocortex, in both thalamocortical (TC) relay neurons and neurons of the nucleus reticularis thalami (NRT), UP and DOWN states appear following activation of metabotropic glutamate receptor 1 (mGluR1) and are wholly generated by intrinsic mechanisms. Specifically, UP and DOWN states emerge due to the bistability brought about by an
interaction between the window component of the T-type Ca2+ current and a leak K+
current. In both TC and NRT neurons, UP/DOWN states are also critically shaped
by several additional ionic currents including the h-current and a Ca2+-activated
non-selective (CAN) cation current. UP and DOWN states in TC and NRT neurons show a remarkable similarity to those observed in vivo including the presence of a
robust T-type Ca2+-channel-mediated burst of action potentials at the beginning of
each UP state which may provide a critical timing signal for initiating network events elsewhere in the relevant thalamocortical module. In summary, the use of reduced in vitro preparations has shed significant light on the cellular and network basis of UP and DOWN states in both neocortical and thalamic neurons thus providing important insights into the fundamental operation of thalamocortical networks.
Involvement of cytokines in slow wave sleep James M. Krueger
Sleep and Performance Research Center, Program in Neuroscience, Washington State University, Pull-man, WA, USA
Cytokines such as tumor necrosis factor alpha (TNF) and interleukin-1beta (IL1) play a role in sleep regulation in health and disease. For instance, brain levels of TNF mRNA or TNF protein have diurnal variations with higher levels associated with greater sleep propensity and sleep loss enhances brain TNF. Central or systemic TNF injection enhances sleep. Inhibition of TNF using the soluble TNF receptor, or anti-TNF antibodies, or a TNF siRNA reduces spontaneous sleep. Mice lacking the TNF 55 kD receptor or both TNF receptors have less spontaneous sleep. Injection of TNF into hypothalamic sleep regulatory circuits promotes sleep. In normal humans, plasma levels of TNF co-vary with EEG slow wave activity (SWA) and in multiple disease states plasma TNF co-varies with sleep propensity. Many of the symptoms induced by sleep loss, e.g. sleepiness, fatigue, poor cognition, enhanced sensitivity to pain, can be elicited by injection of exogenous TNF into normal animals.
Neuronal use induces cortical neurons to express IL1 and TNF. IL1 or TNF unilateral application to the surface of the cortex induces state-dependent enhancement of EEG SWA ipsilaterally suggesting that regional sleep intensity is enhanced. State oscillations occur within cortical columns and are defined using evoked response potentials. One such state, so defined, shares properties with whole animal sleep in that it is dependent on prior cellular activity, it shows homeostasis, it is induced by TNF, and experimen-tally the cortical column sleep-like state is associated with performance detriments. Data from the developmental and memory literatures also suggests that local sleep is use-dependent. Thus experimental interventions ranging from whisker stimulation in rats to unilateral somatosensory stimulation, arm immobilization, adroit learning paradigms in humans, and selective neonate sensory deprivation indicate that local-ized changes in sleep EEG delta power or blood flow are enhanced if, during prior waking, the areas were activated.
We posit that the property of waking that is responsible for cytokine release is extracellular ATP released during neuro- and glio-transmission. ATP interacts with purine type 2 receptors (PR2) located on glia and other cells to release TNF and IL1. The ATP agonist, BzATP enhances sleep while several ATP antagonists, e.g. OxATP, inhibit sleep. Further, P2X7 receptor mRNA has a diurnal rhythm and is altered in brain by sleep loss and IL1. Mice lacking the P2X7 receptor have attenuated sleep rebound responses after sleep loss. These mice do, however, respond normally to IL1; release of IL1 is downstream from the ATP-P2 interaction. TNF and IL1 activate NFkB. NFkB promotes production of the adenosine A1 receptor and the gluR1 component
of the AMPA receptor. The altered receptor expression changes neuron sensitivity to adenosine and glutamate. Thus the sensitivity, or gain, of the post-synaptic neuron is scaled to the activity in the pre-synaptic neuron. TNF’s role in synaptic scaling is well characterized. Because the sensitivity of the post-synaptic neuron is changed, the same input will result in a different output signal from the network within which the scaling occurred. By definition this is a state change.
The top-down paradigm of sleep regulation requires intentional action from sleep/ wake regulatory brain circuits to initiate whole-organism sleep. This raises unresolved questions as to how such purposeful action might itself be initiated. Within the new paradigm, local sleep is a direct consequence of prior local cell activity and whole-organism sleep is a bottom-up, self-organizing, and emergent property of the collective states of cortical columns throughout the brain.
Genetic determination of sleep EEG profiles in humans Hans-Peter Landolt1,2
1Institute of Pharmacology & Toxicology, University of Zürich, Switzerland, 2Center for Integrative Human
Physiology, University of Zürich, Switzerland
Individual patterns of EEG activity during sleep are among the most heritable traits in humans, yet the distinct genetic and neurochemical mechanisms underlying sleep EEG phenotypes are virtually unknown. Especially large inter-individual variation and high intra-individual stability is observed in frequencies below 15 Hz in both non-rapid-eye-movement (nonREM) sleep and rapid-eye-movement (REM) sleep. These slow brain oscillations reflect functional and regulatory aspects of wakeful-ness and sleep. They show heritability of over 90 % (De Gennaro et al., Ann Neurol, 2008), suggesting that the percentage of variance explained by genetic effects is very high. Uncovering the genes contributing to these sleep EEG traits provides one of the most promising avenues to foster our understanding of the neurobiology of sleep in health and disease.
A few polymorphic variations in genes contributing to trait-like inter-individual variation in sleep EEG oscillations have now been identified. Healthy carriers of the
PER35/5 genotype of a variable-number-tandem-repeat polymorphism of the clock
gene PER3 have more delta (1-2 Hz) activity in nonREM sleep, as well as higher alpha
(9-11 Hz) activity in REM sleep than individuals with the PER34/4 genotype (Viola
et al., Curr Biol, 2007). The 22G>A polymorphism of adenosine deaminase (ADA) reduces ADA enzymatic activity and adenosine metabolism. This polymorphism also affects the spectral composition of the sleep EEG. Delta (~ 0.5-5 Hz) activity in nonREM sleep and theta/alpha (~ 6-12.5 Hz) frequencies in nonREM and REM sleep are higher in G/A allele carriers than in homozygous G allele carriers (Rétey et al., PNAS, 2005). Moreover, the G/A genotype shows reduced performance on attention tasks, yet the rate at which sleep need accumulates during prolonged wakefulness is not affected by this polymorphism. A synonymous 1976T>C polymorphism located
in the coding region of the adenosine A2A receptor gene (ADORA2A) also affects the
sleep EEG. This polymorphism is linked to a 2592C>Tins polymorphism in the 3’-UTR of ADORA2A, which may alter protein expression. EEG activity in the theta/alpha (~ 7-10 Hz) range in nonREM and REM sleep is higher in subjects with 1976T/T genotype than in individuals with the 1976C/C genotype (Rétey et al., PNAS, 2005). The gene encoding catechol-O-methyl transferase (COMT) is located in proximity to ADORA2A. This gene contains a common, functional Val158Met variation that alters the amino acid sequence and enzymatic activity of COMT protein. Sleep variables do not differ between male carriers of Val/Val and Met/Met genotypes (Bodenmann et al., Clin Pharmacol Ther, 2009). By contrast, EEG power in nonREM and REM sleep
is consistently lower in the upper-alpha (11-13 Hz) range in Val/Val compared to Met/Met homozygotes (Bodenmann et al., J Neurosci, 200). This difference is present before and after sleep deprivation, and persists after administration of the stimulant modafinil.
Taken together, the molecular genetics of human sleep is only at the beginning of being explored. Accumulating data demonstrate that genetic variations in neuro-chemical systems contributing to circadian and homeostatic sleep-wake regulation are associated with robust inter-individual differences in the sleep EEG. Because most currently known differences are not sleep-state specific and independent of elevated sleep pressure, it needs to be established whether these polymorphisms modulate EEG generating mechanisms rather than sleep-wake regulation. Finally, all currently known polymorphic variations explain only small portions of the variation in sleep EEG phenotypes and many more genetic contributions to sleep oscillations remain to be discovered.
Research supported by Swiss National Science Foundation grants # 310000-120377 and the Zürich Center for Integrative Human Physiology (ZIHP).
Functional network organization through slow oscillatory activity in the descent to sleep
Linda Larson-Prior
Washington University School of Medicine, Department of Radiology, St. Louis, USA
Slow oscillatory activity is a hallmark of sleep, becoming more prominent as sleep deepens cyclically through the night. However, recent data has strongly indicated a role for such slow activity in the absence of sleep, suggesting that it may underlie a state-independent network connectivity structure that is fundamental to normal brain function. Whether focused on neuronal activity assessed via magneto-electro-physiological recordings both invasive and non-invasive [1,2,3] , or as indexed by hemodynamic coupling (the blood oxygen level dependent (BOLD) signal) [4,5,6,7], evidence is mounting to indicate that functional brain network organization relies in part on correlated rhythmic activity in neural networks.
The development of multi-modal, non-invasive imaging methods such as EEG/fMRI provides a powerful mechanism with which to investigate neural network organization both during wake - whether during quiet rest or task performance - and sleep. In ad-dition to allowing for identification of sleep state, this method enables the investigator to examine both infra-slow oscillatory activity (< 1 Hz) and those faster rhythms that can be imaged using high density EEG (hdEEG) or electrocorticography (ECoG) so that the potential interactions between them that provide the framework for functional neural network activity can be elucidated in human subjects. While EEG/fMRI allows the investigator to examine global network interactions, local interactions in these low frequencies are also of significant interest, and can be investigated by use of ECoG, an invasive technique used in pre-surgical planning for human subjects. This technique is not used lightly, and its use is confined to individuals for whom the clinical need is great, but it enables the capture of local field and multiple unit electrophysiological signals that add important information on the interactions between local networks [8].
1. de Pasquale F, Della Penna S, Snyder AZ, Lewis C, Mantini D, Marzetti L, Belardinelli P, Ciancetta L, Pizzella V, Romani GL, Corbetta M (2010) Temporal dynamics of spontaneous MEG activity in brain networks.PNAS 107:6040-6045
2. Britz J, Van De Ville D, Michel CM (2010) BOLD correlates of EEG topography reveal rapid resting-state network dynamics.Neuroimage doi:10.10.16/j.neuroimage.2010.02.052
3. Mazzoni A, Whittingstall K, Brunel N, Logothetis NK, Panzeri S (2009) Understanding the relationship between spike rate and delta/gamma frequency bands of LFPs and EEGs using a local cortical network model. Neuroimage doi.10.10.16/j.neuroimage.2009.12.040
4. Fox MD, Corbetta M, Snyder AZ, Vincent JL, Raichle ME (2006) Spontaneous neuronal activity dis-tinguishes human dorsal and ventral attention systems. PNAS 103:10046-10051
5. Honey DJ, Kotter R, Breakspear M, Sporns O (2007) Network structure of cerebral cortex shapes func-tional connectivity on multiple time scales. PNAS 104:10240-10245.
6. Cohen AL, Fair DA, Dosenbach NU, Meizin FM, Dierker D, Van Essen DC, Schlaggar BL, Petersen SE (2008) Defining functional areas in individual human brains using resting functional connectivity MRI. Neuroimage 15:45-57.
Neural correlates of human NREM sleep oscillations Pierre Maquet
Cyclotron Research Centre, University of Liège, Belgium
In humans, non rapid eye movement (NREM) sleep, and especially slow wave sleep (SWS) has long been viewed as a state of brain quiescence. In contrast, animal neu-rophysiology has demonstrated that the main NREM sleep oscillation, spindles and slow waves are associated with distinct firing patterns in thalamo-cortical loops. Recently, using simultaneous electro-encephalography (EEG) and functional magnetic resonance imaging (fMRI), we characterized in non-sleep deprived human volunteers the activity associated with slow waves and spindles, identified as discrete events that emerge from the background NREM sleep EEG activity.
An activation pattern common to both slow (11-13 Hz) and fast (13-15 Hz) spindles involved the thalami, paralimbic areas (anterior cingulate and insular cortices) and superior temporal gyri. No thalamic difference was detected in the direct compari-son between slow and fast spindles although some thalamic areas were preferentially activated in relation to either spindle type. Beyond the common activation pattern, the increases in cortical activity differed significantly between the two spindle types. Slow spindles were associated with increased activity in the superior frontal gyrus. In contrast, fast spindles recruited a set of cortical regions involved in sensorimotor processing, as well as the mesial frontal cortex and hippocampus. The recruitment of partially segregated cortical networks for slow and fast spindles further supports the existence of two spindle types during human NREM sleep, with potentially different functional significance.
Significant increases in activity were also associated with slow (> 140 µV) and delta waves (75-140 µV) during SW in several cortical areas including inferior frontal, me-dial prefrontal, precuneus and posterior cingulate. As compared to baseline activity, slow waves were associated with significant activity in the parahippocampal gyrus, cerebellum and brainstem whereas delta waves were related to frontal responses. No decrease in activity was ever observed.
These finding demonstrate that NREM sleep is an active state during which brain activity is temporally organized by spontaneous oscillations (spindles and slow oscil-lation), in a regionally specific manner.
Bistability, slow waves and information integration in thalamocortical circuits Marcello Massimini
Department of clinical Science, faculty of Medicine, University of Milan
Upon falling asleep, brainstem activating systems reduce their firing rates thus increas-ing the influence of depolarization-dependent potassium currents in thalamic and cortical neurons (McCormick et al., 1993). Due to these currents, cortical neurons become bistable and inevitably tend to fall into a silent, hyperpolarized state (down-state) after a period of activation (up-(down-state). This bistability provides the mechanism for the slow oscillations of sleep, where large populations of cortical neurons sponta-neously alternate between up and down-states (Hill and Tononi, 2005).
In my talk I will argue that intrinsic bistability in thalamocortical circuits may represent not only the key mechanism responsible for the occurrence of the sponta-neous slow oscillations of sleep, but also the reason why information processing and consciousness are impaired during NREM sleep and other states. Recent experiments (Massimini et al., 2007; Massimini et al., 2005; Massimini et al., 2009b) employing a combination of transcranial magnetic stimulation (TMS) and high-density EEG (hd-EEG) suggested that, while during wakefulness and REM sleep (Massimini et al., 2010) the brain is able to sustain long-range specific patterns of activation, dur-ing NREM sleep, while consciousness fades, this ability is lost;, the thalamocortical system, despite being active and reactive, either breaks down in causally independent modules (producing a local down-state), or it bursts into an explosive and non-specific response (producing a global down-state and a full-fledged EEG slow wave). In no case during NREM sleep early in the night cortical stimulation with TMS resulted in a balanced, long-range, differentiated pattern of activation.
While sleep is a physiological and reversible processes, pharmacological interven-tions and pathological processes may also result in alterainterven-tions of the brain’s ability to integrate information and in loss of consciousness through bistability. To corroborate this hypothesis, I will present TMS/hd-EEG data collected in healthy subjects during anaesthesia (Midazolam, Propofol and Xenon) and in brain-damaged patients affected by disorders of consciousness. These findings suggest that stereotypical responses resembling slow waves represent a general mechanism underlying loss of conscious-ness in different conditions (Massimini et al., 2009a).
Hill, S., Tononi, G., 2005. Modeling sleep and wakefulness in the thalamocortical system. J Neurophysiol. 93, 1671-98.
Massimini M., et al., 2010. Cortical reactivity and effective connectivity during REM sleep in humans. Cognitive Neuroscience, in press.
Massimini, M., et al., 2009a. A perturbational approach for evaluating the brain’s capacity for conscious-ness. Prog Brain Res. 177, 201-14.
Acad Sci U S A. 104, 8496-501.
Massimini, M., et al., 2005. Breakdown of cortical effective connectivity during sleep. Science. 309, 2228-32. Massimini, M., et al., 2009b. Slow waves, synaptic plasticity and information processing: insights from
transcranial magnetic stimulation and high-density EEG experiments. Eur J Neurosci. 29, 1761-70. McCormick, D. A., et al., 1993. Neurotransmitter control of neocortical neuronal activity and excitability.
The Cortical Slow Oscillation: a model system for studying the rapid modulation of cortical neuronal responses by network activity David A. McCormick
Dept. Neurobiology, Yale University School of Medicine, New Haven, CT USA
The cerebral cortex is a large sheet of massively interconnected neurons. The path that activity takes varies on a moment to moment basis, as determined by past experience, expectations, and context. The basic mechanism underlying the direction of this complex flow of neuronal activity is thought to be rapid gain control. Owing to its importance, we are investigating the mechanisms by which the neuronal activity and responsiveness may be rapidly modulated through the investigation of the mechanisms of generation and the influence of the cortical slow oscillation.
Intracellular recordings in vivo in naturally sleeping or anesthetized animals reveal the rhythmic recurrence of Up and Down states. During quiet waking, the membrane potential of cortical cells appears similar to that of a maintained Up state, with rapid variations around a steady level of depolarization. We examined here how changes in local neuronal network activity may influence the properties and responsiveness of cortical neurons, with the goal of obtaining clues on how gain modulation may occur during the wake, behaving state.
Our results indicate that recurrent networks in the cerebral cortex operate through a balance of re-entrant excitation and local inhibition. Rapid variations in this balance (e.g. excess excitation and withdrawal of inhibition) mediates rapid depolarizations and the initiation of action potentials. In the presence of this membrane potential variance, changes in the average membrane potential result in near multiplicative gain changes to sensory stimuli (e.g. visual stimuli of varying contrast). These changes in membrane potential can result either through the intracellular injection of current or spontaneously through variations in synaptic activity. The ability of changes in membrane potential to result in large multiplicative changes in neuronal gain indicates that the control of functional connectivity in the cortical sheet may occur through variations in membrane potential (as well as conductance and variance) mediated by rapid alterations in synaptic bombardment (as a consequence of variations in network activity).
Our results are consistent with the Hebbian view that cortical neurons interact in dynamically defined neuronal assembles, which are ever changing and evolving accord-ing to behavioral demands, past experience, and future goals. Rapid gain modulation is a major functional component to the operation of the cerebral cortex, giving it the flexibility it needs to perform complex computational tasks and behavior. Supported by NIH and the Kavli Foundation.
Haider, B., Krause, M.R., Duque, A., Yu, Y., Touryan, J., Mazer, J.A., McCormick, D.A. (2010) Synaptic and network mechanisms of sparse and reliable visual cortical activity during nonclassical receptive field stimulation. Neuron, 65: 107-121.
Ros, H., Sachdev, R.N.S., Yu, Y., Sestan, N., and McCormick D.A. (2009) Neocortical networks entrain neuronal circuits in cerebellar cortex. J. Neurosci. 29: 10309-10320.
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