Tag Archives: sleep

While you were sleeping: Neural reactivations promote motor learning

Originally published on the PLOS Neuroscience Community

Do you recall that moment when you first learned to ride a bike? After days of practice, it finally clicked. Almost effortlessly, your legs cycled in perfect harmony as you maneuvered gracefully around turns and maintained impeccable balance. You may have learned this skill decades ago but it will likely stick with you for the rest of your life. How did your brain accomplish this remarkable feat of transforming a series of forced and foreign actions into an automatic, fluid movement sequence? A new PLOS Biology study by Dr. Dhakshin Ramanathan and colleagues explored the neural substrates of motor memory acquisition, reporting that reactivation of task-related neural activity patterns during sleep promotes motor skill learning in mice.

Sleep enhances motor learning

To evaluate motor learning, the researchers trained mice to perform a task in which they had to reach for a food pellet. Since sleep is known to be important for memory consolidation, the mice were allowed to sleep before and again after the reach task. The mice performed the task a second time after sleeping to assess how sleep affected their performance. Accuracy on the reaching task improved over the course of the first training period, whereas the mice responded more quickly after sleeping; hence, “online learning” (during the task) improved accuracy and “offline learning” (while sleeping) improved speed. No changes occurred when the mice were sleep-deprived between task sessions, pinpointing sleep – rather than the passage of time – as the source of the performance boost.

Behavioral paradigm (Ramanathan et al., 2015)

Behavioral paradigm (Ramanathan et al., 2015)

Sleep-dependent neural changes

The researchers next explored the neurophysiological basis for this sleep-dependent learning, recording neural activity from the forelimb area of motor cortex. As co-author Dr. Karunesh Ganguly explained,

“Studies had previously studied hippocampal-based memory systems. It remained unclear specifically how the motor system (i.e., procedural memory) processes memories during sleep.”

When the mice performed the task the second time, after sleeping, the onset of neural firing (time-locked to the reach) peaked earlier, and this activity was more strongly modulated by the task. Notably, firing onset did not change after sleep deprivation, confirming that sleep was necessary for this temporal shift in the neural response.

Learning-related reactivation

Elsewhere in the cortex and hippocampus, during rest neurons will fire in a particular sequence matching the temporal pattern during a prior experience, an event known as “replay” that is thought to support formation of memory for that experience. The researchers speculated that reactivation or replay in motor cortex may similarly promote motor learning. Neural activity patterns identified from the reach task were more prevalent during sleep after the task, showing – as predicted – reactivation of task-related activity after motor learning. When mice performed the reach task on multiple days, over the course of all days the degree of reactivation during sleep correlated with reduced reaction time, linking stronger neural reactivation with behavioral improvements.

A) Reactivation of task-related neural activity after learning. B) Reactivation correlates with improvements in reaction time. (Ramanathan et al., 2015)

A) Reactivation of task-related neural activity after learning. B) Reactivation correlates with improvements in reaction time. (Ramanathan et al., 2015)

Since the authors observed neural reactivation during motor learning, they next wondered whether the temporal sequence of this reactivation may be an important element of the memory code. The neural activation pattern during sleep more closely matched the task-related activity pattern after learning than before, although some of the temporal information in the sleep sequence was lost. Although prior studies have shown a role for hippocampal or cortical replay in memory consolidation, Dr. Ganguly raises the important distinctions that here, they “did not find evidence of ‘replay’ (i.e., sequences) but ‘reactivation’ (i.e., synchronous bursts).”

Learning-related plasticity was evident even at the single neuron level. Those neurons with the highest task-related activity were most strongly reactivated during sleep, and those showing the strongest reactivation also happened to show the most dramatic shift in the onset of their response to reaching. The authors speculate that this increased temporal coupling of neural activity to the task could facilitate binding the neurons into a distributed “movement complex” that aids formation of the motor memory.

Locking to spindles and slow waves for widespread plasticity 

Burst of high-frequency activity – known as spindles – and slow wave oscillations have both been implicated in offline learning. If task-related reactivations during sleep are important for memory consolidation, the authors reasoned, they may be temporally linked to spindles or slow waves. After learning the reach task, reactivations were in fact more closely time-locked and phase-locked (i.e., occurred at a particular phase of the cycle) to fast spindles. Reactivations were also more strongly time-locked and shifted their phase-locking to slow oscillations. Thus, during sleep, neural activation patterns related to the motor task were not only more prevalent after training, but their timing was also refined to coincide with particular neural events that may facilitate memory formation. Since spindles may be involved in synchronizing long-range cortical activity, locking task-specific reactivations to spindles could tie them into neuroplastic changes throughout widespread brain networks supporting consolidation.

At a recent talk on sleep-dependent memory consolidation, the speaker compared the neural reorganization that takes place during memory formation to a house renovation. Just as it’s more comfortable and effective for us to check into a hotel while our house is renovated, learning may be more effective when the brain checks out – into the quietude of sleep – while neural reorganization occurs. This may explain why sleep is so important for learning, yet it doesn’t explain how the brain stores new memories during sleep. Past studies have identified neuronal reactivation, coordinated with spindles and slow waves, as critical for forming declarative memories. Dr. Ramanathan and colleagues’ findings suggest that these mechanisms also occur in the motor cortex to support a radically different form of memory – the kind that helped you learn to ride a bike many years ago.

Clarifying the neural dynamics of sleep-dependent learning holds profound implications not just for those of us hoping to learn to play a new instrument or refine our dance moves. It may also help us better understand the remarkable neuroplasticity that underlies rapid motor learning during early development, and hold potential to promote recovery from motor impairments following brain injury. Dr. Ganguly is optimistic about the possible applications of their findings:

“Motor learning is likely an essential process during rehabilitation. Surprisingly little is known about the role of sleep and replay during recovery. With further study, one could imagine using sleep and offline processing to maximize the learning during rehabilitation.”

References

Ji D, Wilson MA (2007). Coordinated memory replay in the visual cortex and hippocampus during sleep. Nat Neurosci. 10:100-107. doi: 10.1038/nn1825

Ramanathan DS, Gulati T, Ganguly K (2015). Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation. PLOS Biol. doi: 10.1371/journal.pbio.1002263

Stickgold R (2005). Sleep-dependent memory consolidation. Nature. 437:1272–1278. doi: 10.1038/nature04286

Image credit https://www.flickr.com/photos/echoforsberg/

Advertisements
Tagged , , , ,

#PLOS #SfN15 Recap: Hidden variables of behavior

Originally published on the PLOS Neuroscience Community

The Society for Neuroscience meeting is unique in both is breadth and depth. There are sessions on literally everything Neuro, each delving with exquisite detail and nuance into their given topic. While this level of focus is great for those seeking comprehensive coverage of their niche, it can be daunting for those looking for a broader sampling of the field’s cutting edge. The Hidden Variables of Behavior symposium was one of the rare sessions to stray from the single-track convention to elegantly bridge seemingly disparate topics, methodologies and applications, producing a standout session with exceptionally broad appeal. It accomplished this by exploring a theme that is perhaps the unifying motivation underlying nearly all Neuroscience research: how does the brain engender behavior? How does neural activity give rise to the thoughts, interactions with our environment, and engagements with others that define our experiences? In an enthralling series of talks by Loren Frank, Mark Schnitzer, Yang Dan and Catherine Dulac, the symposium covered topics ranging from learning and memory to sleep and social behavior. This session had it all.

Rapidly alternating representations of present and past in hippocampal-cortical networks

frankLoren Frank kicked off the symposium by exploring how the hippocampus supports our ability to remember the past and plan for the future. Hippocampal cells have a remarkable ability to replay past experiences via high-frequency oscillations during sharp waves known as ripples. When an animal traverses a path its hippocampal neurons will fire in a characteristic sequence that codes its trajectory; later, at rest or while sleeping, this spiking sequence will repeat, with the sequence sped up approximately twenty times the original rate! Disrupting hippocampal ripples impairs sequence learning, indicating that they’re critical for acquiring memories. However, the mechanisms, at both regional and whole-brain levels, by which sharp wave ripples (SWRs) help to consolidate memories are unclear.

Place_Cell_Spiking_Activity_Example

Much attention has been paid to neurons in hippocampal subregions CA1 and CA3, which are excitable by high-speed motion and positively modulated by SWRs. However, Frank’s group identified a new group of hippocampal neurons – CA2P and CA2N – that are also positively and negatively modulated by SWRs, respectively. Notably, the CA2N population has an exceptionally high level of baseline activity and preferentially fires during rest or low speed motion. Because of their distinct function, these rebellious cells may be crucial for ongoing processing of the current state while maintaining representations of the past and future.

Although some (including yours truly) may hold a hippocampo-centric view of memory, Frank reminds us that memory is “not just a hippocampal thing.” Looking to the rest of the brain, his group found that SWRs recruit 35% of prelimbic regions, including cells that are both excited and inhibited by SWRs. Similar to the distinct populations of CA2P and CA2N cells, prefrontal cortex neurons may activate during either high-speed motion or immobility. This balance of excitation and inhibition in the hippocampus and surrounding cortex may promote rapid transitions between representations of the past and future, and facilitate their integration for learning and planning.

Large-scale ensemble neural dynamics underlying learning and long-term associative memory

schnitzer_mark_107Mark Schnitzer continued with this theme, presenting intriguing findings regarding the spatiotemporal properties of neural adaptations subserving learning. However, equally impressive are the advanced imaging tools his lab is developing to explore these issues. Their techniques allow neural recordings in behaving animals at unprecedented spatial depths and extents over long time scales. For instance, their current best is recording 1202 hippocampal cells in a freely moving mouse. Someone give this man the “I-recorded-the-most-neurons” award!

Using these tools, Schnitzer has been exploring hippocampal morphological and physiological changes that contribute to learning. CA1 neurons are a likely target for spatial learning, as they show place-cell activity, preferentially responding to particular regions of an animal’s environment. Surprisingly, dendrites in subregion CA1 are remarkably stable, suggesting that dendritic plasticity is unlikely to be the critical factor underlying learning. However, CA1 spine turnover is relatively rapid – on the order of 8-10 days – in comparison to cortical spines, of which 50% are permanent over a month. Schnitzer explained that although these cells are temporally stochastic in that they sometimes take breaks from their place-coding activity, when they return to the neuronal “spatial ensemble” they always return to encode the same place. What’s more, CA1 spatial representations are refined by learning, becoming both more accurate and reliable in their coding. I’ll be eagerly following Schnitzer’s work to see how their ongoing methodological innovations and applications advance our understanding of the hippocampal dynamics supporting long-term memories.

Neural circuits for sleep control

YIR_RH_DanYang Dan turned from this fast-paced discussion of rapid neural plasticity, spatial navigation and learning to examine neural regulation of sleep. Historically, neurons that trigger alertness and waking have been easy to identify, but researchers have struggled to track down those “sleep neurons.” Past lesion and c-fos studies have shown that hypothalamic – particularly preoptic – neurons are important for inducing sleep.

Combining optogenetics with electrophysiology, Dan’s lab has expanded upon these findings to pinpoint both the responsible cell types and their specific sleep-inducing effects. In particular, activating GABAergic preoptic cells projecting to the tuberomammillary nucleus (also of the hypothalamus) promotes non-REM sleep initially, and REM sleep later. The midbrain’s ventrolateral periaqueductal gray also promotes sleep, but only the non-REM type. Dan’s findings together suggest that mutual inhibition across these key hypothalamic and brainstem regions regulates transitions across three general brain states of waking, REM sleep and non-REM sleep.

Long-term changes in the representation of social information in the mouse medial amygdala

DulacAfter all this talk about sleep, my hypothalamic sleep neurons had begun batting the morning’s adenosine antagonists. Fortunately, Catherine Dulac’s captivating talk exploring the bases of social interactions and sex-specific behavior kept me alert and engaged. Two key circuits working in concert to process social information, she explained, are the olfactory and vomeronasal systems. This latter system in particular may act as a switch to promote appropriate (and suppress inappropriate) sex-specific behavior.

Dulac’s research, fusing molecular, genetic and electrophysiological techniques, has identified the medial amygdala as a critical stop along the vomeronasal circuit for mediating sex-specific social signaling in mice. Medial amygdalar encoding of social cues is not only sexually dimorphic, but is also regulated by salient social experiences including mating and co-housing. Furthermore, the efficiency of medial amygdalar signaling also changes after mating in a sex-specific manner, increasing in males but decreasing in females. Together, Dulac’s work has pinpointed the medial amygdala as an indispensible hub within an extensive neural circuit that regulates social behavior and in turn, is modulated by sexual and social experience.

Every SfN has at least one session that reminds me why I love the brain and re-ignites my passion for Neuroscience. This year, the Hidden Variables of Behavior symposium was it! It may be a year away, but I’m eagerly awaiting #SfN16 for similarly inspiring talks.

For an abbreviated play-by-play, visit my Storified live-tweeting of the symposium’s highlights.

References

Anderson EB, Grossrubatscher I, Frank L (2015). Dynamic Hippocampal Circuits Support Learning- and Memory-Guided Behaviors. Cold Spring Harb Symp Quant Biol. 79:51–58. doi: 10.1101/sqb.2014.79.024760

Attardo A, Fitzgerald JE, Schnitzer MJ (2015). Impermanence of dendritic spines in live adult CA1 hippocampus. Nature. 523:592–596. doi: 10.1038/nature14467

Bergan JF, Ben-Shaul Y, Dulac C (2014). Sex-specific processing of social cues in the medial amygdala. eLife. 3:e02743. doi: http://dx.doi.org/10.7554/eLife.02743

Brennan PA (2001). The vomeronasal system. Cell Mol Life Sci. 58(4):546–555.

Ego-Stengel V, Wilson MA (2010). Disruption of ripple-associated hippocampal activity during rest impairs spatial learning in the rat. Hippocampus. 20(1):1–10. doi: 10.1002/hipo.20707

Weber F, Chung S, Beier KT, Xu M, Luo L, Dan Y (2015). Control of REM sleep by ventral medulla GABAergic neurons. Nature. 526:435–438. doi: 10.1038/nature14979


Tagged , , , , , , ,

That All-Nighter is not without Neuroconsequences

Originally published on the PLOS Neuroscience Community

As you put the finishing touches on your paper, you notice the sun rising and fantasize about crawling in bed. Your vision and hearing are beginning to distort and the words staring back at you from the monitor have lost their meaning. Your brain … well, feels like mush. We’ve all been there. That debilitating brain fog that inevitably sets in after an all-nighter prompts the obvious question: what does sleep deprivation actually do to the brain? Neuroscientists from Norway set out to answer this question in their recent PLOS ONE study, examining how a night forgoing sleep affects brain microstructure. Among their findings, sleep deprivation induced widespread structural alterations throughout the brain. The lead author shares his thoughts on the possible biological causes of these changes, and whether they may be long-lasting.

Inducing sleep deprivation

The researchers assessed a group of 21 healthy young men over the course of a day. The participants underwent diffusion tensor imaging (DTI; a form of MRI that measures water diffusion and can be used to evaluate white matter integrity) when they first awoke, at 7:30 am. They were free to go about their day as normal before returning for a second DTI scan at 9:30 pm. They remained in the lab for monitoring until a final scan at 6:30 am the following morning, for a total period of 23 hours of continued waking. Since we’re now learning that anything and everything can influence brain structure on surprisingly short time-scales, the researchers finely controlled as many confounding factors as possible. The participants were not allowed to exercise or consume alcohol, caffeine or nicotine during the study, or to eat right before the scans. Since DTI measures water diffusion, hydration was evaluated at all sessions and accounted for in their analysis.

Rapid microstructural changes to waking

The researchers were interested in two main questions: How does the brain change after a normal day of wakefulness and after sleep deprivation? They focused on three DTI metrics to probe how different features of neuronal tissue may change with waking. Radial diffusivity (RD) measures how water diffuses across fibers, whereas axial diffusivity (AD) measures diffusion along the length of a tract. Fractional anisotropy (FA) is the ratio of axial to radial diffusivity and therefore measures how strongly water diffuses along a single direction.

From morning to evening, FA increased and this was driven mostly by reduced RD (Figure, left). From the evening to the next morning – after the all-nighter – FA values decreased to levels comparable to the prior morning, and this drop was coupled with a decrease in AD (Figure, right). Thus, over the course of a full day of wakefulness FA fluctuated, temporarily rising but eventually rebounding. In contrast, both RD and AD declined but at different rates, RD dropping by the end of a normal day, and AD dropping later, only after considerable sleep deprivation. These changes were non-specific, occurring throughout the brain, including in the corpus callosum, brainstem, thalamus and frontotemporal and parieto-occipital tracts.

Throughout the brain, FA values increase from morning to evening (left) and decrease from the evening to the next morning after a night without sleep (right). Elvsåshagen et al., 2015.

Throughout the brain, FA values increase from morning to evening (left) and decrease from the evening to the next morning after a night without sleep (right). Elvsåshagen et al., 2015.

How bad are the neuroconsequences of sleep deprivation?

Other studies have corroborated these reports that wakefulness alters the brain, including reduced diffusion with increasing time awake, and altered functional connectivity after sleep deprivation. How this plasticity reflects the consequences of waking on the brain, however, isn’t clear. Sleep is known to be essential to tissue repair and is particularly important for promoting lipid integrity to maintain healthy cell membranes and myelination. The question remains, therefore, how detrimental the structural reorganization from sleep deprivation really is. Does the plasticity reported here and elsewhere persist for days, weeks or longer, or can a long night of deep catch-up sleep reverse any detriment that all-nighter caused?

“My hypothesis,” says first author Dr. Torbjørn Elvsåshagen, “would be that the putative effects of one night of sleep deprivation on white matter microstructure are short term and reverse after one to a few nights of normal sleep. However, it could be hypothesized that chronic sleep insufficiency might lead to longer-lasting alterations in brain structure. Consistent with this idea, evidence for an association between impaired sleep and localized cortical thinning was found in obstructive sleep apnea syndrome, idiopathic rapid eye movement sleep behavior disorder, mild cognitive impairment and community-dwelling adults. Whether chronic sleep insufficiency can lead to longer-lasting alterations in white matter structure remains to be clarified.”

Is sleepiness really to blame?

It’s likely that multiple factors contribute to these distinct patterns of change in neuronal tissue. After sleep deprivation, the extent of AD decline correlated with subjective sleepiness ratings, suggesting that microstructural alterations may in fact be attributable to changes in alertness or arousal. This possibility is in line with the finding that changes occurred in both the thalamus and brainstem, regions important for arousal and wakefulness. However, the non-linear changes in FA suggest that some microstructural changes may be less related to sleepiness and more directly driven by circadian effects. FA increased late in the day, but – despite fatigue– dropped back after sleep deprivation to the same levels as the day prior. This rebounding may have been due to declining levels of AD and RD reaching equilibrium (reminder, FA is the ratio of AD to RD) or to neuronal features that fluctuate with our circadian rhythms, at least partially independent of our sleep habits. What’s more, other studies have found that presumably mundane activities, for example juggling or spatial learning, also induce gray and white matter changes within hours, and presumably many more as-of-yet unstudied activities also cause similarly rapid plasticity. Given that participants were free to engage in various physical and cognitive activities between the scans, it’s reasonable to assume that some of these behaviors may have also influenced brain structure. Whatever the mechanism, these effects underscore the importance of accounting for time of day in structural neuroimaging studies.

Dr. Elvsåshagen elaborates on these possible factors: “The precise neurobiological substrate for the observed DTI changes after waking remain to be clarified. We cannot rule out the possibility that both activity-independent and activity-dependent processes could contribute to DTI changes after waking. In support of potential activity-dependent white matter alterations, there is interesting evidence from in vitro studies indicating that hours of electrical activity can lead to changes in myelination. To further explore the possibility of activity-dependent white matter alterations, one could examine whether different physical or cognitive tasks lead to task-specific white matter changes.”

Sleepy outliers?

Notably, two of the 21 participants did not show the same rise in FA throughout the day as the others, and showed the smallest change in FA and AD after sleep deprivation. While variability across individuals in terms of brain structure and biological responses to the environment is expected, the remarkable consistency of the study’s other findings raises the possibility that some other factors may explain these outliers. Dr. Elvsåshagen conjectures, “These individuals were also the least tired individuals after sleep deprivation. Although highly speculative, one possible explanation for the lesser changes in these two participants might be a particular resistance to the effects of waking and sleep deprivation.” A follow-up with additional time-points and closer monitoring of activities could help more finely track how the patterns of brain microstructural change shift over periods of waking, and vary across individuals.

Linking diffusion to neurons

How sleep, fatigue, activity or circadian rhythms affect particular neuronal structural properties remains to be seen. RD and AD are thought to depend on myelin and axon integrity, respectively, but DTI metrics in general are sensitive to various other tissue features as well, including cell membrane permeability, axon diameter, tissue perfusion or glial processes. While these properties are difficult to image in living humans, insight from animal studies will help determine how waking impacts specific neuronal characteristics.

Longer-term studies are needed to answer these questions. Dr. Elvsåshagen shared that his team has since replicated their results in a larger sample, and are planning a follow-up study on the effects of waking and sleep deprivation on gray matter structure. Until these outstanding questions are answered, keeping a regular sleep schedule – and avoiding those early morning paper-writing marathons – may be better option for your brain health.

References

Bellesi M, Pfister-Genskow M, Maret S, Keles S, Tononi G, Cirelli C (2013). Effects of sleep and wake on oligodendrocytes and their precursors. J Neurosci. 33: 14288–14300. doi: 10.1523/JNEUROSCI.5102-12.2013

Budde MD, Xie M, Cross AH, Song SK (2009). Axial diffusivity is the primary correlate of axonal injury in the experimental autoimmune encephalomyelitis spinal cord: a quantitative pixelwise analysis. J Neurosci. 29: 2805–2813. doi: 10.1523/JNEUROSCI.4605-08.2009

De Havas JA, Parimal S, Soon CS, Chee MW (2012). Sleep deprivation reduces default mode network connectivity and anti-correlation during rest and task performance. NeuroImage. 59: 1745–1751. doi: 10.1016/j.neuroimage.2011.08.026

Driemeyer J, Boyke J, Gaser C, Buchel C, May A (2008). Changes in gray matter induced by learning—revisited. PLOS ONE. 3: e2669. doi: 10.1371/journal.pone.0002669

Elvsåshagen T, Norbom LB, Pedersen PØ, Quraishi SH, Bjørnerud A, Malt UK (2015). Widespread Changes in White Matter Microstructure after a Day of Waking and Sleep Deprivation. PLOS ONE. 10(5): e0124859. doi: 10.1371/journal.pone.0127351

Hinard V, et al. (2012). Key electrophysiological, molecular, and metabolic signatures of sleep and wakefulness revealed in primary cortical cultures. J Neurosci. 32: 12506–12517. doi: 10.1523/JNEUROSCI.2306-12.2012

Hofstetter S, Tavor I, Tzur Moryosef S, Assaf Y (2013). Short-term learning induces white matter plasticity in the fornix. J Neurosci. 33: 12844–12850. doi: 10.1523/JNEUROSCI.4520-12.2013

Jiang C, , et al. (2014). Diurnal microstructural variations in healthy adult brain revealed by diffusion tensor imaging. PLOS ONE. 9: e84822. doi: 10.1371/journal.pone.0084822

Joo EY, et al. (2013). Brain Gray Matter Deficits in Patients with Chronic Primary Insomnia. Sleep. 36(7): 999-1007. doi: 10.5665/sleep.2796

Rayayel S, et al. (2015). Patterns of cortical thinning in idiopathic rapid eye movement sleep behavior disorder. Mov Disord. 30(5): 680–687. doi: 10.1002/mds.25820

Sanchez-Espinosa MP, Atienza M, Cantero JL (2014). Sleep deficits in mild cognitive impairment are related to increased levels of plasma amyloid-β and cortical thinning. NeuroImage. 98: 395-404. doi: 10.1016/j.neuroimage.2014.05.027

Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH (2002). Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage. 17: 1429–1436. doi: 10.1006/nimg.2002.1267

Sexton CE, et al. (2014). Accelerated changes in white matter microstructure during aging: a longitudinal diffusion tensor imaging study. J Neurosci. 34(46): 15425–15436. doi: 10.1523/JNEUROSCI.0203-14.2014

Wake H, Lee PR, Fields RD (2011). Control of Local Protein Synthesis and Initial Events in Myelination by Action Potentials. Science. 333(6049): 1647–1651. doi: 10.1126/science.1206998

Tagged , , , , ,