C H A P T E R
28 Sleep and Plasticity Georgia Sousouri*, Reto Huber*,†,‡ *Child Development Center, University Children’s Hospital, Z€ urich, Switzerland †Neuroscience Center Z€ urich (ZNZ), ‡ University of Z€ urich and ETH Z€ urich, Z€ urich, Switzerland Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Z€ urich, Z€ urich, Switzerland
I INTRODUCTION From classical antiquity to present, sleep has been a subject of inquiry. In 450 BC, the Greek physician Alcmaeon posits the earliest documented theory of sleep as a loss of consciousness brought about by inadequate blood circulation in the brain due to blood drainage from the body surface. About 100 years later, Aristotle links sleep to digestive processes rising from the heart, thought to be the center of sense and sensibility. In 162 BC, Galen establishes the brain as the seat of consciousness and thus links sleep to the brain. The first experimental studies on sleep were carried out by the Russian physician and scientist Marie de Manaceine, who reported in 1894 (De Manaceine, 1894) that total sleep deprivation for a few days is fatal in constantly active puppies and found the most severe lesions in the brain. In 1898, the Italian physiologists Lamberto Daddi and Giulio Tarozzi performed similar experiments in adult dogs (Daddi, 1898; Tarozzi, 1899) and confirmed a lethal effect of sleep deprivation. Moreover, Daddi observed degenerative alterations in neuronal cells and ascribed them to autointoxication of the brain during insomnia (Daddi, 1898). This hypothesis fostered the search for a “hypnogenic substance” in brain tissue (Ishimori, 1909). Henri Pieron and Rene Legendre were convinced that a “hypnotoxin” circulating in the blood of insomniac animals would induce sleep in a non-sleepdeprived one (Legendre & Pieron, 1908). Indeed, they reported that injection of cerebrospinal fluid (CSF) from a sleep-deprived dog into the cisterna magna of a sleep-satiated animal induced sleep in the recipient for 2–6 h following the injection (Legendre & Pieron, 1913) (reviewed in Bentivoglio & Grassi-Zucconi, 1997). In the 1930s—1950s, neural theories proposed by von Economo, Hess, Moruzzi, Nauta, and others overshadowed the humoral theories of sleep and drew the Handbook of Sleep Research, Volume 30 ISSN: 1569-7339 https://doi.org/10.1016/B978-0-12-813743-7.00028-1
attention to “regulating centers” (e.g., “waking center” and “sleep center”) located in the central nervous system (Hess, 1945; Moruzzi & Magoun, 1949; Nauta, 1993; Von Economo, 1930; von Economo & Koskinas, 1925). Key discoveries of that time included the description of the ascending activating system (Moruzzi & Magoun, 1949). The authors demonstrated that cortical synchronicity is disrupted by stimulation of the reticular formation of the brain stem, giving rise to low-voltage, fast activity. They also associated the mediation of that response with the thalamic projection system, thus supporting the possibility that the cortical arousal reaction occurs through this ascending reticular activating system, rather than being mediated by an intracortical spread of responses to sensory stimuli. In the 1960s–1970s, studies once again began to focus on the humoral regulation of sleep, and new investigations of the Pieron phenomenon were conducted, more advanced from the technical point of view, allowing experiments to be carried out under more physiological conditions than was possible in the past. Pappenheimer and colleagues demonstrated that CSF from sleepdeprived goats attenuates locomotor activity and increases sleep in rats (Pappenheimer, Miller, & Goodrich, 1967). Further biochemical analysis led to the identification of the sleep-promoting factor S—a low-molecular-mass fraction of goat CSF that progressively increases in concentration during 48 h of sleep deprivation (Fencl, Koski, & Pappenheimer, 1971)—its extraction (Pappenheimer, Koski, Fencl, Karnovsky, & Krueger, 1975) and purification (Krueger, Pappenheimer, & Karnovsky, 1978); and finally its characterization as muramyl peptide (Krueger, Pappenheimer, & Karnovsky, 1982a, 1982b), an immune adjuvant known to induce the production of interleukin-1, a central mediator of innate immunity and inflammation. Subsequently, sleep-inducing effects and sleep-regulating mechanisms of interleukin-1 were
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28. SLEEP AND PLASTICITY
reported (Krueger, Dinarello, & Chedid, 1983; Krueger, Walter, Dinarello, Wolff, & Chedid, 1984; Tobler, Borbely, Schwyzer, & Fontana, 1984), and the complex interactions between the immune system and sleep have been extensively explored (Krueger et al., 2008). However, as in the past, the debate about the roles of peripheral and central regulation of sleep persists, and we still do not have a generally accepted theory about the functional role of sleep and its underlying mechanisms.
II SLEEP STAGES Three main vigilance states are defined on the basis of physiological parameters, primarily as distinct neural patterns of brain activity: wakefulness, rapid eye movement sleep (REM) (also known as paradoxical or active sleep), and non-REM sleep (NREM) (also known as passive sleep). Brain activity, as measured by surface electroencephalogram (EEG), during wakefulness (Fig. 28.1, middle row, left) is characterized by high-frequency and low-amplitude waves. NREM sleep is a gradual process, which progressively deepens, as reflected by different substages (N1, N2, and N3) (Carskadon & Dement, 2005). Deep sleep (stage N3, also known as slow-wave sleep) is characterized by the occurrence of highly synchronized cortical activity, represented by low-frequency (0.5–4 Hz), high-voltage (>75 μV) slow waves and sleep spindles (i.e., waxing and waning oscillations at 12–16 Hz originating in the reticular nucleus of the thalamus) (Fig. 28.1, middle row, right). In addition, NREM sleep is associated with low muscle tone and increased arousal threshold. REM sleep, on the other hand, is characterized by desynchronized, wake-like brain activity, and is accompanied by muscle atony and episodic bursts of rapid eye movements. Typically, when falling asleep, we transition from N1 to N3 before entering REM sleep. This sequential occurring of NREM and REM sleep is defined as a sleep cycle, lasting approximately 90–120 min, which is repeated several times in the course of sleep (Carskadon & Dement, 2005).
III SLEEP AND HEBBIAN PLASTICITY A Regulation of Sleep Current prevailing theories about the function of sleep follow a neurocentric approach, elaborating on the regulation of sleep and waking through the central nervous system (CNS) (Saper, Chou, & Scammell, 2001; Saper, Scammell, & Lu, 2005; Tononi & Cirelli, 2003, 2006, 2014; Vyazovskiy & Harris, 2013; Honjoh et al., 2018). Borbely, in 1982, presented the “two-process model” of human sleep regulation, proposing an interaction
between homeostatic (homeostasis, the ability of an organism to maintain stability of its internal environment) and circadian (e.g., measured on a scale of 24 h) mechanisms of sleep regulation: a sleep-dependent process (Process S) and a sleep-independent circadian process (Process C) (Borbely, 1982). While these are two functionally and anatomically independent processes (Dijk & Czeisler, 1995), total sleep propensity and the duration of sleep are nevertheless assumed to be the combined result of these two components (Borbely, 1982). Process C reflects the circadian rhythms, which originate from the master circadian pacemaker located in the suprachiasmatic nucleus (SCN) in the anterior hypothalamus, above the optic chiasm (Moore & Eichler, 1972; Rosenwasser & Turek, 2015). The SCN generates a selfsustained circadian oscillation, driven by a transcriptional feedback loop, which coordinates clock-controlled internal rhythms synchronized by the daily light-dark cycle ( Jin et al., 1999; Reppert & Weaver, 2002). The second component important for the regulation of sleep is the homeostatic Process S, which is dependent on the prior history of sleep and waking. Specifically, with increasing time awake, sleep pressure (Process S) increases and dissipates in the course of sleep (Borbely, 1982). Slow-wave activity (SWA, the power density in the EEG between 0.5 and 4 Hz) best reflects these changes in sleep pressure and is mainly represented in the sleep EEG and/or local field potential (LFP) by sleep slow waves, the hallmark of deep sleep (Buzsaki, 2006). Numerous studies have confirmed that the proper interaction between Processes C and S is critical in order to maintain subjective alertness and sustained cognitive throughput (Cajochen, Khalsa, Wyatt, Czeisler, & Dijk, 1999).
B Hebbian Plasticity—LTP/LTD The established and well-studied changes in sleep pressure summarized above have been related to changes in synaptic dynamics, within the framework of homeostatic plasticity (Tononi & Cirelli, 2003). Synaptic plasticity refers to any alterations in synaptic connections among neurons, incorporating strengthening and weakening of synapses, changes in the distribution of receptor proteins affecting various postsynaptic signal transduction mechanisms, and changes in the absolute number and distribution of synapses formed between pairs of neurons (Benington & Frank, 2003). Synaptic plasticity is widely considered to be the cellular correlate and physical substrate of learning and memory consolidation (Diekelmann & Born, 2010; Frank & Benington, 2006; Peigneux, Laureys, Delbeuck, & Maquet, 2001; Smith, 1995; Stickgold, 2005; Tononi & Cirelli, 2014). A dominant, conceptual model for synaptic plasticity is
PART E. SLEEP, PLASTICITY, AND MEMORY
III SLEEP AND HEBBIAN PLASTICITY
FIG. 28.1 Brain plasticity and memory processes through the sleep-wake cycle. Top: Local, correlated neuronal activity during the day induces Hebbian, LTP-like plasticity that leads to synaptic potentiation. Increases in net synaptic strength result in cell stress and reduced signal-to-noise ratios (S/Ns), thus reducing selectivity of neuronal responses and leading to learning saturation. Global, non-Hebbian downscaling mechanisms (e.g., SHY, Tononi & Cirelli, 2014) activated during sleep renormalize synaptic weights and decrease net synaptic strength, thereby increasing S/Ns and enhancing neuronal selectivity and the ability to learn. Note that global, synaptic downscaling occurs proportionally, maintaining the relative strengths of the potentiated synapses. Middle: During wakefulness, brain activity is characterized by high-frequency, low-amplitude waves as depicted in the surface EEG. Alpha activity is one of the dominant rhythms during wakefulness. After prolonged wakefulness, sleep pressure builds up, which is reflected in the waking EEG by the occurrence of local theta waves. Global synaptic downscaling during sleep is mediated by low-frequency, high-amplitude slow waves accompanied by sleep spindles. The reduction of slow-wave activity during sleep is thought to mirror synaptic desaturation. Bottom: During wakefulness, newly acquired information is encoded into neuronal traces using the hippocampus as a temporary store. Consolidation processes occurring during wakefulness involve reactivations of these neuronal traces, thus facilitating their integration into preexisting neuronal networks. These reactivations are in a labile state and can be subject to interference from external stimuli, thereby causing plastic changes in the short-term memory store. Consolidation mechanisms during sleep drive frequent reactivations of memory-related activity patterns in a state without interference from external stimuli due to environmental disconnection during sleep. Slow oscillations promote the internal dialogue between the cortex and the hippocampus, thereby facilitating the stabilized integration of the neuronal ensembles into preexisting knowledge networks in the cortex. Sleep spindles are considered critical for the information transfer between the two storage sites and the mediation of plastic changes that occur in the long-term memory store. During subsequent wakefulness, retrieval of any previously consolidated mnemonic trace renders that trace into a labile state, sensitive to interference from external stimuli and subject to reconsolidation.
the Hebbian synapse (Fig. 28.1, top row, left to middle). Donald Hebb introduced his theory (also known as Hebb’s postulate and/or cell assembly theory) in his book, in 1949, “The organization of Behavior” (Hebb, 1949), in an attempt to explain the adaptation of neurons and synaptic modifications involved in learning and memory. The postulate describes a basic mechanism, where the occurrence of coincident activation of pre- and postsynaptic terminals should contribute to strengthening the synapse, while presynaptic release of neurotransmitters in the absence of a postsynaptic action potential (AP) should either have no effect on synaptic strength or should contribute to weakening the synapse. In his book, Hebb states as follows:
Let us assume that the persistence or repetition of a reverberatory activity (or “trace”) tends to induce lasting cellular changes that add to its stability. […] When an axon of cell A is near enough to excite a cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells such that A’s efficiency, as one of the cells firing B, is increased. (Hebb, 1949, p. 62)
A summary of Hebb’s theory is “Neurons that fire together, wire together.” In Hebbian plasticity, synaptic changes are associative, activity-dependent, rapidly induced, and input (synapse)-specific. Thus, information during the learning process is thought to be stored in neural circuits through long-lasting changes in synaptic
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strength (Hawkins, Kandel, & Siegelbaum, 1993; Hebb, 1949; Stent, 1973). In the last decades, numerous studies have investigated the relationship between sleep and brain plasticity by means of long-term synaptic potentiation (LTP) and its counterpart, long-term synaptic depression (LTD) (reviewed in Benington & Frank, 2003; Frank & Benington, 2006). LTP and LTD are the most intensively studied forms of synaptic plasticity, originally studied in the rabbit hippocampal formation (Bliss & GardnerMedwin, 1973; Bliss & Lømo, 1973), and refer to activitydependent, persistent strengthening or weakening of synaptic strength, respectively (Malenka & Bear, 2004). They operate in a synapse-specific manner (Linden & Connor, 1995; Madison, Malenka, & Nicoll, 1991) and produce associative changes in the strength of individual synaptic connections. Therefore, they are considered correlation-based, Hebbian mechanisms, crucial for information storage (Bliss & Collingridge, 1993; Turrigiano & Nelson, 2000). These two forms of synaptic plasticity can be triggered by Ca2+ influx through N-methyl-D-aspartate (NMDA)-sensitive glutamate receptors (NMDArs) (Bear & Malenka, 1994). Classically, LTP is induced by the strong activation of NMDArs following high-frequency stimulation (up to 100 Hz) of presynaptic inputs, which results in significant depolarization of the postsynaptic neuron above its threshold potential within a narrow time window (until 20 ms). LTD, on the other hand, is induced by low levels of postsynaptic NMDAr activation in response to low-frequency (1–5 Hz) stimulation, accompanied by the absence of a postsynaptic AP, a necessary condition for the induction of LTD (Dudek & Bear, 1992; Kemp & Bashir, 2001; Kirkwood & Bear, 1994; Mayford, Wang, Kandel, & O’Dell, 1995). In summary, pairing an AP and transmitter release from a presynaptic terminal with a subsequent excitatory postsynaptic potential (EPSP) in the postsynaptic neuron within a very narrow time window causes LTP; however, if the postsynaptic EPSP precedes the presynaptic AP within a short time interval (<20 ms), LTD is induced (Esser et al., 2006; Magee & Johnston, 1997; Markram, L€ ubke, Frotscher, & Sakmann, 1997; Sj€ ostr€ om & Nelson, 2002). This stringent temporal and spatial dependency between the generation of an AP and EPSP is a phenomenon referred to as spiketiming-dependent plasticity (STDP) (Benington & Frank, 2003). Experimentally, STDP has been widely used in order to induce and investigate associative plasticity (Caporale & Dan, 2008; Markram et al., 1997; Shulz & Jacob, 2010) and is thought to be promoted by cellular feedback mechanisms such as AP backpropagation and/or retrograde signaling (Hoffman, Magee, Colbert, & Johnston, 1997; Ohno-Shosaku, Maejima, & Kano, 2001). STDP is also considered to be of great relevance to the physiological processes of learning and memory (Bi & Poo, 2001; Park, Choi, & Paik, 2017).
C Neural Activity in NREM Sleep The transition from wakefulness to sleep elicits remarkable changes in the thalamocortical neuronal activity pattern (McCormick & Bal, 1997). During the waking state (and REM sleep), cortical neurons engage in tonic bursts and irregular firing, represented by low-amplitude, high-frequency EEG activity. With the onset of NREM sleep, virtually all neocortical pyramidal neurons exhibit rhythmic recurrent oscillations in the delta (1–4 Hz or slow-wave range) and sigma (12–16 Hz or spindle range) frequency bands, which appear to be coordinated by a slower rhythm (<1 Hz or slow oscillation) (SanchezVives & McCormick, 2000; Steriade, Nuñez, & Amzica, 1993, Steriade, 2006). Intracellular recording studies have demonstrated that, during slow-wave sleep (SWS), cortical neurons undergo intermitted periods of cyclic, long-lasting (0.3–0.5 sec), high-amplitude (8–20 mV) hyperpolarizations (Steriade, Timofeev, & Grenier, 2001), which occur more frequently as sleep deepens. Throughout these sustained hyperpolarizing phases (also called down or off states), cortical neurons cease their firing activity and remain silent, while the subsequent depolarizing epochs (also called up or on states) of this slow oscillation (<1 Hz) coincide with high discharge rates similar to those during waking and REM sleep (Steriade, 1999; Steriade, McCormick, & Sejnowski, 1993). The transitions between on and off states occur in great synchrony among individual units and, therefore, manifest as highamplitude slow waves on the cortical surface (Volgushev, Chauvette, Mukovski, & Timofeev, 2006; Vyazovskiy et al., 2009). The slow oscillation was first discovered by Steriade, Nunez, and Amzica (1993) and has been thoroughly analyzed in terms of its origin and generation on both the cellular and molecular level (Achermann & Borbely, 1997; Steriade, Contreras, Dossi, & Nunez, 1993; Steriade, Nunez, & Amzica, 1993; Steriade, Nuñez, & Amzica, 1993; Timofeev, Grenier, Bazhenov, Sejnowski, & Steriade, 2000; Timofeev, Grenier, & Steriade, 2001). A cardinal drive for these state-specific changes in neuronal activity appears to be the differential release of arousalpromoting neuromodulators, such as acetylcholine, norepinephrine, serotonin, and histamine (McCormick, 1992; McCormick & Bal, 1997). In waking, these neuromodulators are released at high levels, reducing neuronal potassium (K+), thereby causing neurons to be tonically depolarized (Hirsch, Fourment, & Marc, 1983). Neuronal behavior in REM sleep is regulated in a similar fashion by the release of acetylcholine alone, since serotonin, norepinephrine, and histamine are minimally released in this state (Aston-Jones & Bloom, 1981; McGinty & Harper, 1976). On the other hand, decreased activity of these neuromodulators during NREM sleep increases potassium leak currents (IKL). Consequently, the resting membrane
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potential of cortical neurons is reduced, thereby facilitating hyperpolarization and eventually the emergence of the slow oscillation. Thus, the slow oscillation exhibits a bimodal membrane potential distribution: a disfacilitated, silent down state and a depolarized, high-conductance up state (Hill & Tononi, 2005). During the off state, the cell is driven toward depolarization, mainly because of the increase of both the persistent sodium current (INa(p)) and the hyperpolarization-activated cation current (Ih). However, throughout the on state, the increase of the activitydependent, depolarization-activated potassium current (IDK) ordains the initiation of the next off state (Hill & Tononi, 2005; McCormick, 1992). In adult animals, the slow oscillation originates in frontal cortical regions and propagates in an anterior-posterior direction (with an average speed of 2.7 0.2m/s) as a traveling wave (Massimini, Huber, Ferrarelli, Hill, & Tononi, 2004).
D Local Aspects of Sleep and Plasticity Sleep is usually regarded as a global, unitary phenomenon affecting the whole brain uniformly and concomitantly (Sejnowski & Destexhe, 2000). Typically, sleep and wakefulness are studied as two exclusively different behavioral states in an “all-or-none view” (Siclari & Tononi, 2017). However, recent research has challenged this holistic view. Unquestionably, sleep and wake are two clearly separable brain states, both on the behavioral and electrophysiological level; nevertheless, they are critically dependent on each other. As already highlighted, sleep slow waves alternate between two stable, longlasting (e.g., hundreds of milliseconds) voltage levels. Apart from the aforementioned intrinsic properties of neurons driving this bistability during SWS, it has also been proposed that the switch to the active on state could arise from a gradual buildup of synaptic drive prior to its onset, originating from spontaneous transmitter release of deep-layer neurons and propagating to other cortical regions (Chauvette, Volgushev, & Timofeev, 2010; Nir et al., 2011). In the last several years, many studies have demonstrated “local intrusions” of sleep during wakefulness and wakefulness-like behavior during sleep (Vyazovskiy et al., 2011; Nobili et al., 2011; Nobili et al., 2012; Fattinger, Kurth, Ringli, Jenni, & Huber, 2017; see also Krueger et al., 2008). An extraordinary example of “local sleep,” known for some time, has been observed in some birds and marine mammals, in which one cerebral hemisphere exhibits sleep activity patterns, while concurrent activity of the other one resembles wakefulness (Mukhametov, Supin, & Polyakova, 1977; Lyamin, Manger, Ridgway, Mukhametov, & Siegel, 2008; Lesku, Vyssotski, Martinez-Gonzalez, Wilzeck, & Rattenborg, 2011). While unihemispheric sleep does not occur in
healthy humans, it is nevertheless of interest to mention a recent study that showed hemispheric asymmetries in slow-wave activity and brain reactivity to external stimuli during the first night of sleep in an unfamiliar laboratory environment, hypothetically acting as a “night watch” monitoring novel surroundings (e.g., first-night effect) (Tamaki, Bang, Watanabe, & Sasaki, 2016). Accordingly, a new hypothesis of sleep has emerged within the past years, incorporating the local regulation of sleep and its use-dependency on the neuronal group level (Krueger & Obál, 1993; Krueger et al., 2008; Krueger & Tononi, 2011). Supporting this hypothesis, prolonged wakefulness leads to the progressive intrusion of sleeplike states into the waking EEG by enhanced low-frequency activity in the theta (4–8 Hz) range (Fig. 28.1, middle row, middle), acting as an endogenous homeostatic component (Cajochen, Brunner, Krauchi, Graw, & Wirz-Justice, 1995). Theta power increases as a function of prior waking time, thereby reflecting the buildup of sleep pressure and homeostatic sleep regulation (Finelli, Baumann, Borbely, & Achermann, 2000; Leemburg et al., 2010). Intriguingly, the combination of LFP and multiunit recordings has shown that short intermittent periods of silence occur in local neuronal populations of awake, sleep-deprived animals, closely resembling the off state of NREM sleep (Vyazovskiy et al., 2011). Furthermore, these brief periods of silence became more frequent and widespread with time spent awake and were associated with (i) locally increased excitability after intensive training, (ii) a local increase in theta activity, and (iii) impaired task performance. Recent human studies have also shown region-specific, use-dependent increases in theta activity after sleep deprivation coinciding with taskperformance errors (Bernardi et al., 2015; Fattinger, Kurth, et al., 2017; Hung et al., 2013). Remarkable demonstrations of a local regulation of sleep also come from human studies showing regional regulation of SWA depending on daytime use. For example, the application of local transcranial magnetic stimulation (TMS)-induced synaptic potentiation was followed by a remarkable increase (40%) in SWA in the respective cortical region (Huber et al., 2007). Similarly, training on a visuomotor-learning task leads to a local increase of SWA in the previously activated circumscribed cortical region, with this increase correlating with overnight performance improvement (Huber, Ghilardi, Massimini, & Tononi, 2004). Conversely, reduced synaptic activity due to arm immobilization during the day resulted in a local decrease of SWA over the contralateral motor cortex (Huber et al., 2006), further reflecting the local, usedependent regulation of sleep. Altogether, these findings support the notion of sleep as a dynamic phenomenon emerging from a progressive, local increase in excitability based on learning- and activity-related synaptic
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potentiation, which in turn is tightly associated with an increase in neuronal bistability (Krueger & Tononi, 2011; Tononi & Cirelli, 2006).
IV SLEEP AND NON-HEBBIAN PLASTICITY A Non-Hebbian Scaling During wakefulness, learning- and activity-related local changes in synaptic strength take place (Krueger & Tononi, 2011). As discussed, such processes occur primarily through Hebbian plastic mechanisms, such as LTP, that favor synaptic potentiation, that is, increase excitability. However, these locally induced forms of plasticity require the activation of controlling, stabilizing mechanisms, operating at the neuronal level, in order to prevent neural activity from runaway excitation (e.g., continuous LTP) or total quiescence (e.g., continuous LTD) (Miller & MacKay, 1994, Miller, 1996). Bienenstock, Cooper, and Munro (1982) published a theory (known as the BCM theory) attempting to address this issue in the case of stimulus selectivity in the visual cortex, by proposing mathematically a form of synaptic correlation modification as a general organizational principle. They proposed a temporal form of synaptic competition between incoming patterns that determines the efficacy of a given synapse, in analogy to the history of the postsynaptic neuron’s firing rates, resulting in a sliding modification voltage threshold θM (Bienenstock et al., 1982). Consequently, in the late 1990s, a non-Hebbian type of plasticity was proposed that would up- or downregulate the total synaptic strength of a neuron, or network of neurons, in a multiplicative manner, according to global changes in activity. This type of plasticity was called “synaptic scaling” or “homeostatic synaptic plasticity” (Pozo & Goda, 2010; Turrigiano, 1999, 2008). Synaptic scaling was first described in 1998 (Turrigiano, Leslie, Desai, Rutherford, & Nelson, 1998) in cultured neocortical neurons. The authors manipulated the activity of the culture by chronically (48 h) blocking either neuronal firing (using tetrodotoxin: TTX; sodium channel blocker) or GABA (γ-aminobutyric acid)-mediated cell inhibition (using bicuculline; a GABA inhibitor). By using whole-cell, voltage-clamp recordings to measure the amplitude of miniature excitatory postsynaptic currents (mEPSCs), they demonstrated that, by increasing neuronal activity, mEPSC amplitudes and firing rates decreased over a 48-h period. Conversely, chronic blockade of activity caused an enhancement of the amplitude of mEPSCs. Furthermore, their experiments showed that these automodulations of quantal amplitude of α-amino-3hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAr)-mediated synaptic inputs were regulated in a multiplicative manner, as a function of previous activity,
while preserving the relative differences between inputs, hence, suggesting a global mechanism of synaptic scaling. No effects on mEPSCs were observed upon blockade of NMDArs. In contrast to LTP- or LTD-like types of plasticity (which are mediated by NMDArs and are induced quickly), changes in homeostatic synaptic scaling occur relatively slowly and cumulatively (Turrigiano & Nelson, 2004), ranging from hours to days of altered activity in order to obtain measurable modifications in synaptic strength (Turrigiano et al., 1998). In brief, neuronal activity, like many other physiological processes, is subject to a homeostatic, “tuning” feedback control that stabilizes it around some set-point value (Cannon, 1932) in order to restore neuronal selectivity and facilitate efficient acquisition and integration of constantly flowing information. The architecture and function of such a negative feedback system remain unknown. A minimum logical requirement, though, would be that there is a mechanism to “buffer” or integrate the history of neuronal signaling over a longer timescale (minutes to hours), relative to the time required for information transmission (milliseconds to minutes), in order to analogically adjust synaptic properties according to that set-point value (Davis, 2006). In general, there are different factors that can account for state-dependent changes in neuronal plasticity (Timofeev & Chauvette, 2017). For example, the variant secretion of neuromodulators directly affects multiple neuronal channels (see Section III.C), thereby affecting cell intrinsic excitability (Krishnan et al., 2016; Lee & Dan, 2012). Moreover, several studies have shown that cortical excitability and quantal/synaptic scaling can be influenced by the expression of plasticity-related genes, such as brain-derived neurotrophic factor (BDNF), tumor necrosis factor (TNF) or Homer (Beattie et al., 2002; Desai, Rutherford, & Turrigiano, 1999; Diering et al., 2017; Rutherford, Nelson, & Turrigiano, 1998; Stellwagen & Malenka, 2006). All these factors are differentially regulated during wakefulness and sleep, implicating a combination of sleep regulatory mechanisms that actively contribute to non-Hebbian plastic changes during sleep and cannot be separated from activity-dependent, sleeprelated synaptic stabilization processes.
B Synaptic Homeostasis Hypothesis (SHY) One of the most influential hypotheses about the function of sleep in relation to brain plasticity is the synaptic homeostasis hypothesis (SHY), first described by Tononi and Cirelli (Tononi & Cirelli, 2003). SHY suggests a universal, seminal role of sleep for brain plasticity and cognitive functions by restoring synaptic homeostasis. According to SHY, the neuronal network is burdened by a use-dependent, LTP-like increase in synaptic strength (caused mainly by synaptic potentiation) during
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the waking period and synaptogenesis during development. Synaptic potentiation, in turn, is mirrored in the homeostatic regulation of slow-wave activity that increases with time spent awake. During subsequent sleep, global synaptic downscaling takes place (Fig. 28.1, top row, middle to right), which is associated with a gradual decrease of slow-wave activity in the course of sleep. This global normalization of synaptic strengths accounts for the beneficial effects of sleep on performance and on memory acquisition, consolidation, and integration (Tononi & Cirelli, 2003, 2014). Indeed, numerous studies on the molecular, electrophysiological, and structural level provide evidence that net synaptic strength undergoes sleep-wake dependent changes. Specifically, experimental results demonstrate a net synaptic increase prevailing during wakefulness and a global synaptic downscaling occurring during sleep (Bushey, Tononi, & Cirelli, 2011; De Vivo et al., 2017; Diering et al., 2017; Gilestro, Tononi, & Cirelli, 2009; Huber et al., 2012; Kuhn et al., 2016; Maret, Faraguna, Nelson, Cirelli, & Tononi, 2011; Norimoto et al., 2018; Vyazovskiy, Cirelli, Pfister-Genskow, Faraguna, & Tononi, 2008). In agreement with SHY, during a waking episode, learning and continuous interactions with the environment trigger plastic changes in the brain and, accordingly, increase synaptic strength, mainly by synaptic potentiation and the formation of new synapses. Such a continuous increase in synaptic strength, though, burdens the neuron both energetically (e.g., less “economic” firing) and informationally (e.g., decreased selectivity). Biological costs are increased energy consumption and higher demands of supplies that support cellular functions, ultimately leading to high cellular stress. Moreover, accumulating information encoding reduces signal-to-noise ratio, reflected in (i) broadening of neuronal distribution of input patterns; (ii) reduced selectivity; and, consequently, (iii) saturated learning capacity. Accordingly, neurons need, at some point, to renormalize net synaptic strength and restore synaptic homeostasis (Tononi & Cirelli, 2014). SHY suggests that this global reorganization takes place during sleep, specifically slow-wave sleep, when the brain is disconnected from the environment and unbiased by (conscious) external interactions (Massimini et al., 2005). Spontaneous “offline” activity during deep sleep allows neurons to “comprehensively” sample and integrate all the information acquired over a broader timescale, ranging from evolutionary heritage, to development, to acute environmental interactions and learning (Edelman, 1987; Tononi, Edelman, & Sporns, 1998). According to SHY, synaptic refinement takes place during slow-wave sleep through activity-dependent synaptic depression or downscaling. In other words, global synaptic strength is proportionally decreased during sleep, with synapses residing below a minimal strength threshold becoming virtually ineffective, thus favoring
the maintenance of utilitarian network communication and the depression of “expensive,” redundant synaptic connections. Thereby, cellular functions are restored, and the neuronal network is “recalibrated,” supporting functional neuronal selectivity and improving signal-tonoise ratio. Hence, the capacity for encoding of new information through associative plasticity is enhanced, and task and mental performance are improved. Abundant molecular, electrophysiological, and structural evidence exist that synaptic downscaling occurs during sleep (Hinard et al., 2012; Huber et al., 2008; Huber, Tononi, & Cirelli, 2007; Lubenov & Siapas, 2008; Maret et al., 2011). On the molecular level, for instance, expression or removal of AMPArs in the synaptic membrane reflects major mechanistic adjustments mediating synaptic potentiation and depression, respectively (Kessels & Malinow, 2009). Recently, a study showed that, during sleep, low levels of the neuromodulator noradrenaline allow the expression of the immediate early gene Homer1a at the excitatory synaptic sites, causing the weakening of synapses through AMPAr removal or dephosphorylation (Diering et al., 2017). Another recent study demonstrated a sleep-dependent reduction of the synaptic axon-spine interface (ASI), providing further structural evidence that sleep promotes synaptic downscaling (De Vivo et al., 2017). On the electrophysiological level, synaptic strength is mirrored in the slope of evoked responses caused by electric stimulation in animals (Vyazovskiy et al., 2008) or TMS in humans (Huber et al., 2012). Studies in animals and humans have shown that the decline in the slope of cortical responses correlates with the decline of slow-wave activity, presumably reflecting renormalization of synaptic strengths. Interestingly, results of a recent study support a slow-wave dependence of synaptic downregulation by inhibiting hippocampal sharp-wave ripples (i.e., transient excitatory bursts (200 Hz “ripple” oscillations) originating in the hippocampus) during slow-wave sleep, which in turn intercepted synaptic downscaling and impaired the acquisition of new memories (Norimoto et al., 2018). In a nutshell, the core claim of SHY is that sleep serves an essential, universal function. This function is the preservation of synaptic homeostasis that is challenged by continuous plastic changes occurring during wakefulness, mainly biased toward a net increase in synaptic strength that results from massive synaptogenesis during development and learning during adulthood (Tononi & Cirelli, 2012).
V SLEEP AND MEMORY A Memory Systems Although the concept of a global downscaling during sleep meets broad acceptance, a major challenge remains
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28. SLEEP AND PLASTICITY
to reconcile SHY with evidence for synaptic potentiation during sleep (Aton et al., 2009; Aton, Suresh, Broussard, & Frank, 2014; Chauvette, Seigneur, & Timofeev, 2012). One possibility is that “replay” or “reactivation” occurs against a background of global downscaling. One of the most exciting hypotheses in the field is that sleep crucially contributes to memory processes (Born, Rasch, & Gais, 2006; Diekelmann & Born, 2010; Giuditta, 2014; Lewis & Durrant, 2011; Maquet, 2001; Rasch & Born, 2013; Smith, 1995; Stickgold & Walker, 2013). Although the idea that sleep facilitates memory exists for a long time ( Jenkins & Dallenbach, 1924; M€ uller & Pilzecker, 1900), extensive research on the behavioral, neurobiological, and computational level was triggered after Pavlides and Winson (1989), demonstrated for the first time in rat hippocampal place cells that spatiotemporal patterns of neuronal activity in the awake state are “reactivated” or “replayed” in the same sequential order during subsequent sleep, possibly representing information processing during sleep (Pavlides & Winson, 1989). In neuropsychology, human memory is mainly classified into two major systems, the declarative and the nondeclarative memory system (Schacter & Tulving, 1994). Declarative memory encompasses consciously acquired and explicitly (i.e., with awareness) accessible memories of fact-based information (i.e., knowing “what”) and its formation critically depends on structures in the medial temporal lobe, especially the hippocampus (Eichenbaum, 2000). Important subcategories of the declarative system are episodic memory, which includes autobiographical memories for past events, and semantic memory incorporating context-independent, general knowledge (Tulving & Murray, 1985). In contrast, nondeclarative memory is regarded as nonconscious and includes procedural memories (i.e., knowing “how”) for motor and perceptual skills or habits and implicit (i.e., without awareness) learning and certain forms of conditioning. Acquisition and recall in the nondeclarative memory system can be implicit and does not require the involvement of medial temporal lobe structures, but relies on different areas of the brain (Squire & Zola, 1996).
B Memory Consolidation Memory involves three fundamental processes: acquisition, consolidation, and retrieval (Born et al., 2006). Acquisition involves the encoding of new information into a neuronal trace or neuronal ensemble as a temporary store. Consolidation refers to the progressive transformation (qualitative reorganization), stabilization, and integration of postacquired memory into preexisting neocortical “knowledge circuits” representing long-term memory storage, while retrieval refers to the recall of
stored memories (Diekelmann & Born, 2010). Sleep, as a state of environmental disconnection, characterized by the loss of consciousness, constitutes an ideal time window for memory consolidation by protecting the brain from external interference and offering the possibility of triggering plastic changes through the spontaneous activity during sleep (i.e., slow oscillations, spindle oscillations, and sharp-wave ripples) (Fig. 28.1, bottom row). On the other hand, memory encoding and retrieval most constructively occur during awake (Diekelmann & Born, 2010; Tononi & Cirelli, 2014), even though there is also abundant evidence delineating the detrimental consequences of inadequate sleep on subsequent, successful memory encoding, from both human (Harrison & Horne, 2000; Drummond et al., 2000; Yoo, Hu, Gujar, Jolesz, & Walker, 2007; Killgore, 2010; Van Der Werf, Altena, Vis, Koene, & Van Someren, 2011) and animal studies (Beaulieu & Godbout, 2000; Guan, Peng, & Fang, 2004; McDermott et al., 2003). Interestingly, when examining the historical development of these concepts, a few years after the establishment of the term “consolidation” in memory research, attributed to M€ uller and Pilzecker (Konsolidierung) (M€ uller & Pilzecker, 1900), Burnham published a landmark paper on retroactive amnesia (or retrograde amnesia, i.e., remote memory is spared relative to more recent memory). By integrating findings from experimental psychology and neurology, Burnham (1903) further elaborated on memory maturation, emphasizing its postexperience, dynamic nature: There must be time for the processes of organization and assimilation (of memory) to take place. There must be time for nature to do her part… Hurry defeats its own end.
Burnham’s “time” actually refers to two types of consolidation that have already attained the status of tenet in the neurobiology of memory, namely, “synaptic consolidation” and “systems consolidation” (Dudai, 2004). Synaptic consolidation refers to LTP-/LTDmediated, enduring, synaptic modifications of neurons representing a memory trace (Frankland & Bontempi, 2005; Kandel, 2001; Redondo & Morris, 2011). Systems consolidation builds on synaptic consolidation and encompasses the plastic processes that contribute to the transformation, redistribution, and integration of newly encoded memory ensembles into neuronal networks capable of long-term storage (i.e., memory association) (Marr, Willshaw, & McNaughton, 1991; Buzsáki, 1989; McClelland, McNaughton, & O’reilly, 1995; Squire & Alvarez, 1995; Frankland & Bontempi, 2005; Rasch & Born, 2007). Sleep is thought to be of major importance for systems consolidation, due to blockade of external interference and, thus, insusceptible memory processing. Together, these processes are included under the umbrella term “memory consolidation.” LTP represents
PART E. SLEEP, PLASTICITY, AND MEMORY
V SLEEP AND MEMORY
the main mediator of memory consolidation, due to the overlap in the characteristics of LTP induction (e.g., rapid induction, activity dependence, input specificity, associativity, and persistence) and the behavioral properties of associative long-term memory (Abraham & Robins, 2005). An important question arises from the foregoing discussion: how can new memories interact and be incorporated in the knowledge network without overriding older memories? This problem has been called “catastrophic interference” (McClelland et al., 1995; Robins, 1995) and has led to the formulation of the “stability versus plasticity dilemma”: The brain, as any dynamic learning system, should be organized in a way that allows for sequential learning (i.e., new information can be integrated with old information at any time), in parallel with the continuous ability to modify its synaptic configurations (i.e., encoding) and, thus, minimize memory loss (Abraham & Robins, 2005). The most influential model that addresses this quandary is the standard two-stage model of memory consolidation (Carr, Jadhav, & Frank, 2011; Marr et al., 1991; McClelland et al., 1995). The key assumption of the model is the existence and cooperation of two separate memory stores. One store is considered as the “fast learner” with the capability of quickly and efficiently storing newly acquired information for a short time period, functioning as a buffer in the memory system. The second store is the “slow learner” and serves for long-term memory storage. Regarding the declarative memory system, the fastand slow-learning stores are represented by the hippocampus and the neocortex, respectively (Corkin, 2002; Frankland & Bontempi, 2005). Initially, both stores commit to the parallel encoding of new information. Subsequently, in further consolidation periods, the fastlearning store drives concurrent reactivations in both modules ( Ji & Wilson, 2007), thereby gradually redistributing and establishing the representations in the preexisting networks of long-term storage (Gais et al., 2007). The time interval a memory trace requires to reach permanent consolidation and hippocampus-independent retrieval can vary from 1 day to several years of reconsolidation (Nader & Hardt, 2009), depending on the type of the memory and whether there already is a preexisting “schema” (Gilboa & Marlatte, 2017) or cognitive framework in the knowledge network (Groch, Schreiner, Rasch, Huber, & Wilhelm, 2017; Tse et al., 2007; Wang & Morris, 2010).
C Reactivation of Memories During Sleep In the last decades, several hypotheses have been proposed in an attempt to adequately describe a mechanism for the evident contribution of sleep to memory
consolidation. The “dual-process hypothesis” suggests that the different sleep stages benefit the consolidation of different types of memory (Gais & Born, 2004; Maquet, 2001), with SWS serving declarative memory and REM sleep consolidating nondeclarative memory. Nevertheless, findings showing that procedural memory can also benefit from NREM2 and SWS have challenged this approach (Clemens, Fabo, & Halasz, 2005; Gais, Plihal, Wagner, & Born, 2000; Huber et al., 2004, 2006; Laventure et al., 2016). Giuditta and colleagues introduced the “sequential hypothesis” that emphasizes the importance of the cyclic succession of sleep stages (REM following NREM or SWS) for memory formation. Initially, during SWS, memories to be retained are distinguished or “tagged,” while nonadaptive memories would be weakened or eliminated. In a second step, during REM sleep, “tagged” memories are potentiated and integrated into preexisting knowledge networks, a process supported by high frequencies in the neocortical EEG and hippocampal theta activity prevailing during REM sleep (Giuditta et al., 1995). The sequential hypothesis shares some mechanism with SHY, in the sense that no potentiation should occur during SWS, but only a global downscaling of synaptic strengths. Probably the most influential theory in memory research, though, is the “active system consolidation hypothesis” that integrates aspects of both aforementioned suppositions. According to this hypothesis, sleep provides an ideal time window for memory consolidation through the repeated reactivation of novel memory representations in the absence of external stimulus interference. These reactivations occur during NREM sleep and underlie the transformation and integration of temporarily stored memories into the preexisting knowledge networks of the long-term store, where these representations are selectively being stabilized through a synaptic consolidation process, assumed to take place during REM sleep (Diekelmann & Born, 2010; Rasch & Born, 2013). An intriguing theoretical assumption of this model is that such reactivations cause the extraction of invariant repeating features of the explicitly learned material, which then leads to processes of general rule abstraction, insight, and integration, thereby forming the basis for the creation of prototypes and the development of cognitive schemata (context-independent and interferenceresistant memories) (Lewis & Durrant, 2011; Rasch & Born, 2013). The sleep slow oscillation (<1 Hz) is considered the most prominent candidate of a sleep-specific, “spontaneous trigger” for memory processing and consolidation of declarative (Marshall, Helgadóttir, M€ olle, & Born, 2006) and nondeclarative memories (Miyamoto et al., 2016). Evidence for such a specific role of slow oscillations in memory reactivation comes from behavioral and
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28. SLEEP AND PLASTICITY
invasive electrophysiological studies (see Rasch & Born, 2013). According to this idea, the alternation between increased neuronal firing and total quiescence provided by the slow oscillation establishes a dialogue between the neocortex and subcortical structures, thereby facilitating the transformation and redistribution of encoded information into the long-term store (Sirota & Buzsáki, 2005; Sirota, Csicsvari, Buhl, & Buzsáki, 2003). Through a top-down control, the cortically generated slow oscillation seems to coordinate the bidirectional flow of information between the neocortex and the hippocampus by temporally grouping plasticity-related activity of thalamocortical and hippocampal circuits (Sirota et al., 2003). Hippocampal sharp-wave ripples (SW-Rs; negative “sharp waves” in the CA1 stratum radiatum and transient fast “ripple oscillations” (150–250 Hz) in the CA1 pyramidal layer Buzsáki, 1986) during SWS represent the reactivation of neuronal ensembles active during prior wakefulness (Nádasdy, Hirase, Czurkó, Csicsvari, & Buzsáki, 1999; Pavlides & Winson, 1989; Peyrache, Khamassi, Benchenane, Wiener, & Battaglia, 2009; Wilson & McNaughton, 1994) and are thought to promote synaptic potentiation (Buzsáki, Haas, & Anderson, 1987; King, Henze, Leinekugel, & Buzsáki, 1999). The sleep-dependent replay of the spatiotemporal patterns of neuronal firing recurs in the same sequential order but on a compressed timescale (as in fastforward mode) compared with previous encoding (Nádasdy et al., 1999; Wilson & McNaughton, 1994). Concurrently, SW-Rs are time-locked to thalamocortical spindles (Sirota et al., 2003; Wierzynski, Lubenov, Gu, & Siapas, 2009) that seem to play a crucial role in memory consolidation and information processing (Cairney, El Marj, & Staresina, 2018). Specifically, each SW-R event is nested in an individual spindle trough (Siapas & Wilson, 1998). This ripple-spindle complex becomes subject to feed-forward control of the slow oscillation, thereby providing a mechanism for efficient and widespread information transfer between the hippocampus and the neocortex during the depolarized up phases of the slow oscillation (Batterink, Creery, & Paller, 2016; Destexhe, Hughes, Rudolph, & Crunelli, 2007; Dudai, Karni, & Born, 2015; Kitamura et al., 2017; Miyamoto, Hirai, & Murayama, 2017). Several neuroimaging studies have demonstrated spindle-dependent reactivations of memory traces for both declarative and nondeclarative memories (Rasch & Born, 2013). Synchronous spindle input at the neocortical circuitry may lead to longterm plastic changes as a result of calcium influx into cortical pyramidal cell dendrites (Massimini & Amzica, 2001; Niethard, Burgalossi, & Born, 2017; Sejnowski & Destexhe, 2000). This is supported by evidence that repeated spindle-associated spike discharges can trigger long-term potentiation in neocortical synapses (Lindemann, Ahlbeck, Bitzenhofer, & Hanganu-Opatz, 2016; Rosanova & Ulrich, 2005).
Several studies in rats and humans provide further support for the reactivation of memories through the orchestrated slow-wave-spindle-ripple activity. These studies demonstrate a learning-dependent increase in slow-wave, spindle, and ripple activity for both declarative tasks and procedural memories (see Rasch & Born, 2013). In humans, for instance, visually acquired declarative memory is slow-wave-dependent, while spindle activity is critical for nondeclarative motor memory (Born et al., 2006; Fogel & Smith, 2011; Lustenberger et al., 2016; Marshall et al., 2006; Ngo, Martinetz, Born, & M€ olle, 2013; Peigneux et al., 2001; Stickgold & Walker, 2013; Walker & Stickgold, 2006). Mnemonic reactivation processes occur also spontaneously during periods of relaxed wakefulness (see Carr et al., 2011). These reactivations are also accompanied by SW-Rs, although they occur less frequently compared with SWS reactivation rates (Buzsáki, 1986; Cheng & Frank, 2008; O’Neill, Senior, & Csicsvari, 2006), and are considered active contributors to the consolidation process (Diekelmann, B€ uchel, Born, & Rasch, 2011; Oudiette, Antony, Creery, & Paller, 2013). Interestingly, in the cases of spatial navigational learning or episodic memory, where the sequence of events plays a significant role in the correct mnemonic representation, sequential replay in hippocampal assemblies occurs in a temporally reversed order after the completion of the task, suggesting a mechanism similar to principles of reinforcement learning models (Foster & Wilson, 2006). Apart from the actual perceptual input, remote mnemonic representations are also reactivated in wakefulness (Karlsson & Frank, 2009) during memory retrieval or because of a reminder. Another relevant concept for the relationship between sleep and memory consolidation is the concept of reconsolidation (consolidation occurs repeatedly upon a memory and not just once). According to this concept, memory representations can exist either in an “active” or an “inactive” state (Nader & Hardt, 2009). Upon posttraining reactivations or reactivations during retrieval, memories enter an active state where they are transiently labile and susceptible to external interference and need to go through a reconsolidation process in order to be restabilized and reintegrated in the long-term storage, thus returning to the inactive state, where they remain stable and resistant to amnestic treatments (Nader, 2003; Sara, 2000). The mechanisms supporting memory retrieval and its interaction with ongoing cognitive processes still remain unknown. It has been observed that theta oscillations (4–8 Hz), associated with encoding and complex behaviors requiring mnemonic processes, coordinate the communication between the hippocampus and prefrontal cortex in navigational decision-making ( Jones & Wilson, 2005) and seem to reflect the modification of synaptic connections (Buzsáki, 2002). Active neural reactivations during working memory tasks seem to be
PART E. SLEEP, PLASTICITY, AND MEMORY
coordinated by the phase of coherent theta (O’Keefe & Recce, 1993), with this coordination correlating with working memory performance (Fuentemilla, Penny, Cashdollar, Bunzeck, & D€ uzel, 2010). A recent study showed that memory reactivations during sleep and wakefulness share common neural signatures, that is, their coordination by the phase of theta oscillations. Moreover, during SWS, these reactivations reoccur at a range of 1 Hz, indicating a supraordinate orchestration by the sleep slow oscillation (Schreiner, Doeller, Jensen, Rasch, & Staudigl, 2018). Information encoding and organization by the brain’s myriad neural circuits hold the key to understanding cognitive functions such as perception, emotion, action, attention, and memory and have the potential to offer insights into the causes and symptoms of neurological and psychiatric diseases.
VI PARADOXICAL/REM SLEEP AND PLASTICITY The previously discussed, current hypothesis about the functional relationship between sleep and learning/ memory proposes a central role of NREM sleep and its electrophysiological characteristics in the consolidation process. From a historical point of view, the discovery of REM sleep was critical to initiate the quest of how sleep is related to learning and memory, starting with the first characterization of REM sleep in 1953 (Aserinsky & Kleitman, 1953). Kleitman and colleagues reported sleep intervals of rapid, jerky, and binocularly symmetrical eye movements, accompanied by increased respiration and heart rates and emergence of low-amplitude, fast cortical activity similar to wakefulness, all of which pointing to an activation of the sympathetic autonomous nervous system. These sleep intervals were also called active of paradoxical sleep, due to their resemblance to waking electrophysiological activity. Furthermore, awakenings and interrogations immediately following the occurrence of eye mobility sleep periods revealed a significant association of REM sleep with dreaming (Aserinsky & Kleitman, 1953), thereby giving birth to more intensive research about dreaming, consciousness, brain states, and their underlying neural correlates (Edelman, 1989; Koch, Massimini, Boly, & Tononi, 2016; Siclari et al., 2017; Tononi, 2004; Tononi & Edelman, 1998). Initial indications for the role of REM sleep in learning and memory came in the 1970s from experiments in rats in Jouvet’s laboratory ( Jouvet-Mounier, Astic, & Lacote, 1969). In the next decades, Carlyle Smith and his colleagues further investigated paradoxical sleep deprivation (PSD) in animals and identified PS “windows,” that is, small “windows” of time within the 24 h day where REM sleep is necessary for learning (Smith & Butler, 1982; Smith, 1985). Subsequently, Datta and colleagues
provided compelling electrophysiological and pharmacological data to further emphasize the importance of REM sleep and “transition sleep” (e.g., the transition state between SWS and REM sleep) for memory processing and plasticity (Datta, 2000; Datta, Mavanji, Ulloor, & Patterson, 2004). A seminal experiment of Blumberg and Lucas (1994), who studied myoclonic twitches during REM in neonatal rats after thoracic spinal transection (Blumberg & Lucas, 1994), additionally highlighted the role of REM sleep in the development of plastic processes. In their study, they demonstrated that limb twitches during REM sleep, prevalent during early development, are partially triggered by descending motor activation from the brain stem. These findings, together with the fact that cessation of REM-related twitches coincides with the completion of the development of the musculoskeletal system in terms of synaptic independence (Brown, Jansen, & Van Essen, 1976), set the basis for the idea that REM sleep actively contributes to plastic developmental organization. This contribution is thought to rely on an internal communication between the central and the peripheral nervous system for the establishment of synaptic connections (Blumberg & Lucas, 1994; Blumberg, Marques, & Iida, 2013; Tiriac, Del Rio-Bermudez, & Blumberg, 2014). These studies support an important role of REM sleep during brain maturation (Roffwarg, Muzio, & Dement, 1966). The fact that, across species, the amount of REM sleep after birth is closely related to the maturational status further suggests the involvement of REM sleep in early cortical maturation ( Jouvet-Mounier et al., 1969). REM sleep was also proposed to be related to STDP. For example, synchronized spiking phase-locked to theta oscillations typical for REM sleep could lead to STDP, favoring either potentiation or depression, depending on the timing of spikes (Harris et al., 2002). Interestingly, in a recent study, Funk and colleagues demonstrated the local occurrence of slow waves during REM sleep in primary sensory and motor areas, which could account for sensory disconnection during this stage, despite its resemblance to wakefulness (Funk, Honjoh, Rodriguez, Cirelli, & Tononi, 2016); however, the functional relevance of this observation is unknown. At this point, it is of importance to highlight the fact that sleep and its contribution to plastic processes, learning, and memory should be studied from a holistic perspective; the characteristics, contributions, and interactions of different sleep stages should be taken into account in order to adequately understand the precise functions played by sleep.
VII DISCUSSION Considering the aforementioned indications, sleep can be seen as a multicomponent, dynamic process that bidirectionally influences synaptic plasticity, concurrently
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28. SLEEP AND PLASTICITY
promoting synaptic up- and downscaling in separate neuronal networks. One prominent way to explore such dynamics and gain insight into the mechanisms underlying sleep functions and their influence on synaptic plasticity is the study of electrophysiological, molecular, and behavioral outcomes following “manipulation” of ongoing brain activity during sleep. Indeed, numerous animal and human studies have employed various kinds of intervention in order to unravel causal effects of sleep processes on restorative functions and performance improvements (for a review, see Rasch & Born, 2013). Since the discovery of electricity, numerous different techniques of transcranial electric and magnetic stimulation (TES and TMS) have been widely applied in clinics and research for therapeutic and investigational reasons, respectively (Devinsky, 1993; Miniussi, Paulus, & Rossini, 2012). Over the years, TES and TMS have been the most prominent approaches, in virtue of the wellestablished biophysical observation that externally applied electric fields can affect neuronal excitability, induce neuroplasticity, and influence biological and behavioral outcomes (Miniussi et al., 2012). Recently, a novel, noninvasive approach has been introduced to modulate ongoing brain activity during sleep by means of auditory closed-loop stimulation (Ngo et al., 2013). Studies indicate that the precise targeting of acoustic stimuli to sleep slow waves at the up or down phase can enhance or decrease SWA, respectively (Fattinger et al., 2017; Papalambros et al., 2017). Furthermore, in-phase stimulation of the slow oscillation during the up phase also enhances spindle activity, which might contribute to memory consolidation (Ngo et al., 2013). Hence, auditory closed-loop stimulation constitutes a prominent tool for safe, noninvasive, and nonpharmacological intervention for sleep-slow-wave modulation. Another widely used approach to study the mechanism(s) of memory consolidation during sleep is experimentally induced targeted memory reactivation (TMR) through memory cueing methods (learning of a new memory in association with a presented stimulus). This approach has proved successful for different types of cues, such as odors, sounds, melodies, or verbal materials (Antony, Gobel, O’hare, Reber, & Paller, 2012; G€ oldi, van Poppel, Rasch, & Schreiner, 2017; Groch et al., 2017; Laventure et al., 2018; Lehmann, Schreiner, Seifritz, & Rasch, 2016; Oyarzún, Morís, Luque, de DiegoBalaguer, & Fuentemilla, 2017; Rasch, B€ uchel, Gais, & Born, 2007; Rihm, Diekelmann, Born, & Rasch, 2014; Schreiner, Lehmann, & Rasch, 2015; Schreiner & Rasch, 2014). Very recently, it has been shown that phaselocking of the cued memory to the up phase of the sleep slow oscillation using closed-loop TMR remarkably enhances recall performance, while phase-locking the cues to the down phase did not unveil any mnemonic benefits (G€ oldi et al., 2017). Together, the experimental
work highlights the significance of the different phases of the sleep slow oscillation and its entrained endogenous rhythms for the understanding of the underlying neuronal consolidation mechanisms. Finally, and although not within the scope of this work that focuses in the reciprocal interactions of sleep and plasticity of the CNS, it is of importance to make an additional note. The recent discovery and characterization of the glymphatic paravascular pathway in the brain that facilitates clearance of interstitial solutes (Iliff et al., 2012) revive old theories about “toxic” substances circulating in the brain and their metabolic regulation. Recent work also emphasizes again the relationship between activity of the CNS during sleep and the periphery (Besedovsky et al., 2017). Currently, there is a lack of consensus in the scientific community as to what exactly it is in the body and brain that drives such a vital need for sleep. Integral approaches taking into consideration interdependencies of the central and peripheral nervous systems and immune and metabolic functions may prove fruitful in the future in shedding further light on the functions of sleep.
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