CONTEXTO SOCIAL Y DE LA POBLACIÓN EN SITUACIÓN DE RIESGO

A large number of studies in humans and animals suggest that sleep is required for memory consolidation and learning, which are based on processes of long-term synaptic potentiation (LTP) and depression (LTD) (Rasch & Born, 2013). In classical Hebbian plasticity, the process of presynaptically emerged action potential activating the postsynaptic neuron, strengthens the synapse and forms the neurophysiological mechanism for memory (Fox & Stryker, 2017). New synapses are formed and strengthened in the course of the learning process. However,

this cannot continue endlessly, a restriction mechanism is needed. Homeostatic plasticity offers such mechanism(s).

The theory which links sleep with synaptic homeostasis (synaptic homeostasis hypothesis or SHY) was proposed by Tononi and Cirelli (Tononi & Cirelli, 2003, 2006) in order to explain the fundamental function of sleep as restoration of synaptic homeostasis. According to SHY, wakefulness is accompanied by synaptic potentiation in a large fraction of cortical circuits, resulting in a LTP and a net increase in synaptic weight. Rodent studies show a diffuse increase in synaptic density in animals exposed to enriched environments (Klintsova & Greenough, 1999) and activation of LTP-related genes, including the BDNF, during spontaneous wakefulness (Cirelli & Tononi, 2000a). The amount of SWA during sleep is tied to the amount of synaptic potentiation that has occurred during previous wakefulness (Cirelli & Tononi, 2000b; Huber et al., 2004; Kelly & Deadwyler, 2003). This relation between synaptic potentiation and following SWA is proportional: the higher the amount of synaptic potentiation in cortical circuits during wakefulness, the higher the increase in SWA during subsequent sleep. Moreover, it has been shown that SWA increases locally in the brain areas which were involved in synaptic potentiation during wakefulness, and this increase correlates with post-sleep improvements in performance (Huber et al., 2004). Evidence for a relationship between synaptic density and SWA also comes from the developmental studies: both synaptic density and SWA reach their peak in adolescence, after which they gradually decline (Feinberg & Campbell, 2010; Feinberg et al., 2006; Feinberg et al., 1990).

Moreover, according to SHY, SWA is associated with generalized depression or downscaling of synaptic strength, i.e. proportional reduction in the strength of all synapses converging onto the same neuron (de Vivo et al., 2017; Tononi & Cirelli, 2003, 2014). In other words, sleep in needed for weakening synapses through a process of downscaling or synaptic renormalization. Indirect evidences supporting this idea come from the experiments demonstrating a downregulation of LTP- related genes (Cirelli & Tononi, 2000a) and upregulation of molecules implicated in depotentiation/depression during spontaneous sleep (Cirelli et al., 2004) indicating that sleep may be an unfavourable condition to synaptic potentiation. Two recent imaging studies of cortical dendrite spine morphology support the idea

that sleep may play an important role in downscaling the strength and number of synaptic connections (Maret et al., 2011; Yang & Gan, 2012). These studies showed that the ratio of spines eliminated versus those formed was greater after a period of sleep than a period of wakefulness. Authors of SHY also speculate that a process of generalized downscaling may not be compatible with wakefulness, while it would be ideally compatible with sleep, a state during which the brain is both spontaneously active and virtually disconnected from the environment; and the reduced activity of the noradrenergic system during sleep would ensure that only downscaling occurs, and not potentiation (Tononi & Cirelli, 2003).

SHY is consistent with other theories that propose a similar synaptic weakening effect of sleep (Crick & Mitchison, 1983; Giuditta et al., 1995). A number of studies in insects, rodents and humans provide evidences that are in line with SHY (Gilestro et al., 2009; Huber et al., 2004; Liu et al., 2010; Vyazovskiy et al., 2008). However, this hypothesis also meets criticism since very little is known about the mechanisms that underlie the process of synapses weakening during sleep (Frank, 2012, 2013).

According to another view, homeostatic plasticity stabilizes neuronal excitability and maintains the so called neuronal firing rate homeostasis through mechanisms of intrinsic and synaptic plasticity (Turrigiano, 2011). Moreover, neuronal activity in neuronal circuits returns to baseline individual set point after periods of quietness and activity. It has been shown that the firing rate homeostasis is gated by sleep/wake states, and that sleep inhibits rather than promotes, firing rate homeostasis. This idea was prompted by the research studying the effects of visual deprivation on neuronal firing rates in the primary visual cortex using monocular deprivation model (Hengen et al., 2016). It has been shown that in spite the fact that monocular deprivation depressed at first the firing rate of individual neurons in visual cortex, firing rates returned precisely to the neuron’s individual baseline later during periods of active wake, but not during the sleep. Thus, this finding suggests that the relationships between sleep and homeostatic plasticity are opposite to those that has been proposed in SHY. According to this model wakefulness but not sleep state enables the expression of homeostatic plasticity.

Recently, another model was introduced, which suggests that sleep is needed for homogenization of the firing rate distribution through cortical neurons

(Levenstein et al., 2017; Watson et al., 2016). This model is based on a study using large-scale recordings to examine the activity of neurons in the frontal cortex of freely behaving rats (Watson et al., 2016). It has been shown that the distribution of pyramidal cell firing rates was wide and strongly skewed toward high firing rates. Furthermore, neurons from different parts of this distribution were differentially modulated by sleep stages: NREM sleep reduced the activity of high firing rate neurons and tended to upregulate firing of slow-firing neurons, whereas REM sleep reduced firing rates across the entire rate spectrum.

All above mentioned theories have strong experimental support and whether they are describing the same phenomenon will be studied in future.