The Relationship Between Sleep, Circadian Rhythms, and Neuronal Myelination

Post by Meredith McCarty 

The takeaway

Myelination is pivotal for neuronal function and is altered in many neurodegenerative disorders including multiple sclerosis (MS). In this study, the authors find that myelination development and maintenance are dependent on the circadian transcription factor Bmal1. 

What's the science?

Myelination is the process by which neurons are encased in a myelin sheath, providing metabolic support and increased signaling efficiency of the neuron. Myelination is maintained by oligodendrocyte cells, which produce myelin sheaths for neurons throughout the central nervous system. Oligodendrocyte precursor cells (OPCs) are the precursor for oligodendrocytes, yet not much is known about their development and regulation. 

Bmal1 is a transcription factor involved in circadian rhythm regulation, and recent research has found the disruption of Bmal1 to be correlated with changes in OPC function. This week in Neuron, Rojo and colleagues investigate the role of Bmal1 in myelination processes, and how these dynamics are related to sleep disruption and circadian cycles. 

How did they do it?

To study the relationship between circadian rhythms, Bmal1 dynamics, and myelination throughout development, the authors conducted several genetic and behavioral experiments in mouse models. 

The mouse models used in this study were wild-type (i.e., normal) mice and mice that had the Bmal1 transcription factor knocked out from the OPC cells specifically (OPC-specific Bmal1 knockout mice). To quantify changes in OPC proliferation at different experimental time points, the authors injected a tag to measure DNA proliferation called EdU. To understand the role of Bmal1 in OPC regulation, the authors performed RNA-sequencing and circadian rhythmicity assessment on OPC cells from mice at varying experimental time points. The authors then quantified physical changes in myelination throughout the central nervous system using transmission electron microscopy (TEM) as well as tests of the integrity of the blood-brain barrier in wild-type and mice without Bmal1. 

To quantify changes in mouse behavior, the authors used measures of gait, stride length, and working memory. To study the role of sleep deprivation in Bmal1 function, the authors altered the mouse sleep/wake cycles and recorded during wake and sleep from implanted EEG electrodes. To probe the regulation of remyelination during adulthood, the authors quantified morphological complexity in older mice and performed a focal demyelination procedure in wild-type mice and mice without Bmal1. Lastly, the authors analyzed human genetic data to study the relationship between the risk of MS and sleep fragmentation. 

What did they find?

The authors found changes in OPC proliferation depending on the circadian rhythm. Additionally, mice without Bmal1 exhibited decreased OPC density and physical complexity in the corpus callosum (the white matter connecting the left and right hemispheres of the brain), but not in other brain regions. The authors also found a significant reduction in OPCs throughout cortical and subcortical brain regions in the developing brains of mice without Bmal1, suggesting reduced OPC migration in these mice. The authors next used RNA sequencing to compare genes expressed in OPCs at different time points in the circadian cycle and found that 10% of genes were rhythmically expressed in OPCs. This suggests that Bmal1 regulates OPC dynamics in specific brain regions at specific points in development and during the circadian rhythm. 

When measuring changes in myelination using transmission electron microscopy (TEM), the authors found the corpus callosum to have thinner myelination in mice without Bmal1 relative to wild-type mice. The authors found no differences in the integrity of the blood-brain barrier in mice without Bmal1-KO. Next, when the authors used a novel object recognition task to assess whether these changes in myelination altered the behavior of mice without Bmal1, they found deficits in working memory, stride length, and gait. suggesting that Bmal1 disruption and altered myelination results in behavioral and cognitive effects.  

EEG recordings during sleep deprivation experiments revealed mice without Bmal1  exhibited disrupted sleep and altered recovery from sleep deprivation relative to wild-type mice. The authors next compared the effect of Bmal1 disruption in early versus later developmental time points, quantifying changes in OPC morphology and migration. They found that Bmal1 knockout in adolescence led to significant disruption of OPC density and complexity. Interestingly, Bmal1 knockout in adulthood led to disrupted remyelination, but no changes in OPC density. 

What's the impact?

In this study, the authors find that Bmal1 regulation is tightly linked with circadian rhythms and the maintenance of myelination throughout specific regions of the mouse central nervous system. Interestingly, the authors note that since sleep disruption is associated with an increased risk of MS in humans, this novel evidence has implications for the treatment of demyelinating disorders like MS. Future research is necessary to clarify the relationship between MS and sleep in human studies.

Access the original scientific publication here.

The Developmental Trajectories of Prosocial Behavior and Empathy Diverge from Childhood into Adolescence

Post by Shireen Parimoo

The takeaway

Prosocial behavior increases as children develop into adolescents, whereas parental reports of empathy show increases until late childhood followed by gradual declines in early adolescence. Brain activity in regions associated with feeling socially included, such as the ventral striatum and medial prefrontal cortex (mPFC), predicts future prosocial behavior in children. 

What's the science?

Empathy and prosocial behavior – social behavior that benefits others more than it benefits us – are important for forming and maintaining meaningful social connections. Prosocial behavior often involves empathy, as adopting someone else’s perspective makes it easier to act prosocially towards them. As empathy and perspective-taking develop throughout childhood, older children exhibit more prosocial behavior than younger children. Adolescents, on the other hand, show more cooperative prosocial behavior but less helping behavior.

The difference between children and adolescents may be explained by the developmental trajectory of brain regions supporting different aspects of prosocial behavior. For instance, a socio-cognitive network underlying perspective-taking, which includes the mPFC, may show a different developmental pattern than a socio-affective network that includes regions like the ventral striatum that support emotional processing. To distinguish between these possibilities, new work in NeuroImage by van der Meulen and colleagues investigated the neural mechanisms underlying the developmental trajectories of prosocial behavior and empathy from middle childhood to early adolescence.

How did they do it?

Children aged 7-13 years old participated in a longitudinal study that took place over five years. Data from three sessions were collected, with each session taking place 2-2.5 years apart. At each session, children played the prosocial cyber ball game which consisted of three other virtual players who tossed a ball to each other. In the Fair Game round, each player received the ball an equal number of times whereas in the Unfair Game round, player two (P2) only received the ball once from the other virtual players. Prosocial behavior was defined as the ratio of tosses from the participant to P2 in the Unfair compared to the Fair Game round. Parents also completed a questionnaire at each session reporting their child’s prosocial behavior and empathy toward others.

Functional MRI was used to record brain activity during the game at each session, except for the Fair Game round during T1. The authors examined whole-brain activity during the Unfair Round as well as activity in the socio-cognitive (temporoparietal junction, precuneus, posterior superior temporal sulcus, and mPFC) and in the socio-affective brain regions (anterior insula, ventral striatum, and dorsal anterior cingulate cortex ). Specifically, they contrasted the difference in brain activity during prosocial behavior (i.e., passing the ball to P2 during the Unfair Round) with non-prosocial behavior (i.e., passing the ball to the other players during the Unfair Round). Additionally, the authors examined brain activity associated with feeling socially included, that is, when the participant received the ball from P1 and P3 compared to when they did not receive the ball.

The authors used mixed effects statistical modeling to study the longitudinal change in 1) parental reports of prosocial behavior and empathy, 2) prosocial behavior during the cyber ball game, 3) brain activity associated with prosocial behavior and social inclusion, and 4) associations between prosocial behavior and brain activity.

What did they find?

Prosocial behavior from both the parental reports and the cyber ball game showed linear increases with age. Empathy, on the other hand, increased from middle to late childhood before showing a gradual decrease into early adolescence. Interestingly, empathy was positively correlated with parental reports of prosocial behavior but not with the prosociality measured by the cyber ball game. At the whole-brain level, no brain area showed greater activation during prosocial behavior at T1 and T2, while mPFC and visual regions showed increased activation at T3 (i.e., in early adolescence). Additionally, there was a gradual increase in ventral striatal activity with age until late childhood, after which it stabilized. However, changes in neural activity over time were not related to changes in prosocial behavior. Together, these results suggest that the neural correlates of prosocial behavior become more prominent during late childhood in socio-affective regions and during early adolescence in socio-cognitive regions.

Social inclusion was linked with more widespread activation in regions of the socio-affective and socio-cognitive networks. The dorsal anterior cingulate cortex, insula, ventral striatum, and the precuneus showed a U-shaped trajectory, with a reduction in activation from middle to late childhood followed by an increase in activity into early adolescence. Thus, in contrast to prosocial behavior, neural correlates of social inclusion are most apparent in middle childhood and adolescence. Notably, there was a negative relationship between changes in neural activity and changes in prosocial behavior. Specifically, a stronger decrease in mPFC and ventral striatal activity during social inclusion was related to greater increases in prosocial behavior over time. Altogether, these findings highlight how the neural and cognitive processes underlying social inclusion interplay with prosocial behavior over the course of development.

What's the impact?

This study found that prosocial behavior and empathy follow different developmental trajectories as children transition into adolescence. The finding that the regions sensitive to social inclusion predict prosocial behavior over time paves the way for future research to investigate the mental processes these regions facilitate in order to support prosocial behavior. 

Access the original scientific publication here.

How the Orbitofrontal Cortex Learns How to Learn

Post by Rachel Sharp

The takeaway

The orbitofrontal cortex (OFC) plays a crucial role in the brain's ability to learn and adapt to new situations. By studying mice and computational models, researchers show how the OFC supports meta-reinforcement learning, a process where learning evolves to incorporate better strategies and allow for better decision-making.

What's the science?

Meta-learning, or 'learning to learn', has been a fundamental concept in psychology and artificial intelligence (AI), enabling humans and AI models to quickly acquire new skills based on generalized knowledge from past experiences. In the AI field, this concept has been used to enhance deep learning models, allowing them to refine their learning algorithms across multiple episodes. Similarly, in neuroscience, meta-reinforcement learning (meta-RL) involves the quick neural computation of multiple reinforcement learning (RL) processes at parallel, but different timescales. This week in Nature Neuroscience, Hattori and colleagues unravel how the brain, specifically the orbitofrontal cortex (OFC), manages this complex form of learning. The authors explore whether, and how, synaptic plasticity (a mechanism for adaptive learning at the cellular level) and neural activity-based mechanisms in the OFC collaborate to facilitate meta-RL, using a combination of mouse models and deep RL algorithms.

How did they do it?

The researchers trained mice on a probabilistic reversal learning task, where mice had to choose between two options with varying reward probabilities. This task was designed to mimic the complexities of decision-making in changing environments. Additionally, they focused on the OFC as a key area for mediating meta-RL. To test the role of synaptic plasticity in the OFC, they used paAIP2, a light-inducible inhibitor of CaMKII kinase activity, which blocks synaptic plasticity without affecting pre-existing neural connectivity. Put another way, they injected a virus containing an inhibitor of synaptic plasticity into neurons in the OFC. They were then able to control when this inhibitor was active or not by shining a light at these neurons. When they applied a light to the neurons, paAIP2 would become active, and inhibit the activity of CaMKII.

They then trained deep RL models on the same task, using a meta-RL framework. This meta-RL framework allowed the models to update their strategies across sessions, similar to how the mice learned. The researchers then examined the neural mechanisms behind this learning process, focusing on the OFC in mice, and comparing their findings with the computational models. 

What did they find? 

The study revealed that synaptic plasticity in the OFC is essential for efficient across-session meta-RL in mice. When synaptic plasticity was inhibited in the OFC, mice showed delayed learning and stabilization of reward-based choices. However, once the mice became task experts, blocking OFC plasticity did not affect their performance, indicating that OFC activity (as opposed to plasticity) is required for fast or immediate RL These results when taken together, suggest that CaMKII-mediated plasticity in the OFC is necessary for meta-learning of RL which supports long-term strategy development, while OFC activity supports more immediate decision-making. The findings also demonstrate that both mice and deep RL models improved their decision-making over time, adapting their choices based on reward history, indicating the OFC's vital role in conducting meta-RL. 

What's the impact?

This study sheds light on the dual role of the orbitofrontal cortex (OFC) in managing both slow and fast reinforcement learning (RL). The OFC uses CaMKII-dependent synaptic plasticity for slow, across-session meta-learning, in which generalized knowledge is accumulated and stored over long periods. For immediate, trial-by-trial decision-making, the OFC relies on its neural activity. The study's findings align closely with deep reinforcement learning models, demonstrating a remarkable parallel between artificial and biological learning systems. The insights from this study not only enhance our understanding of the brain's learning mechanisms but also pave the way for more sophisticated AI models.  

Access the original scientific publication here.