Neuron Publication for the Davidson Lab!

 

In this week’s Neuron, Dr. Jennifer Evans, until recently a post-doc in Dr. Alec Davidson’s lab, published a paper that helps to explain how different communication signals are used among neurons in the brain’s biological clock to control timing within the brain and body. Dr. Evans has recently begun an Assistant Professorship at Marquette University.

 

ResearchBlogging.org Mammals, and nearly all species, have evolved strategies to efficiently interact in a cyclic world where light and darkness alternate on a 24h schedule. To predict and prepare for environmental changes rather than simply react to them, animals have a biological clock that helps to organize all behavior and physiology within this 24h day. For example, body temperature begins to rise and metabolic hormones begin to increase well-before sun-up in day-active humans. Although all cells appear to have the capability to oscillate in this way, the central clock in the brain’s suprachiasmatic nucleus (SCN) seems to be the orchestra conductor, keeping time for all cells in the body. The SCN has direct input from the eyes to allow it to adjust its clock to light every day.

 

The SCN is made up of thousands of clock (AKA oscillator) neurons. To be a useful clock for the organism, these clocks need to all read the same time. Imagine a wall full of slightly imprecise clocks. Even if they were all started at the same time, they would drift apart and after a few days, all would show a different time. Not very useful. Thus these oscillator cells in the SCN need to communicate with one another to maintain synchrony, and this process is called ‘coupling’.

 

To see coupling in action, look at this cool YouTube video:


 

The study published by the Davidson lab investigates how this coupling occurs. Using state-of-the-art imaging techniques, they recorded the activity of SCN oscillator neurons while keeping them alive in brain slice culture. Using a gene borrowed from the firefly and integrated into the mouse genome, they were able to watch single SCN cells turn on and turn off over the day and night by recording tiny amounts of light being emitted by the cells. They observed that in brain slices from mice housed in normal 12:12 light:dark conditions, the population of cells is synchronized. But in long days consisting of 20h of light, regions of the SCN become desynchronized. They  authors leveraged this rearrangement, or photoperiodic reorganization of the SCN, to study how the SCN cells come back together, or resynchronize.  By blocking different types of communication amongst these SCN cells (e.g. all action potentials, vasoactive intestinal polypeptide (VIP), GABA), they determined that SCN cells use multiple signaling modalities, depending on what state the network is in. Sometimes these signals act to synchronize, such as GABA when cells are far apart in phase. Sometimes the same signals can be destabilizing, as is the case with GABA among cells that are already synchronized.  They also verified that VIP is indeed an important factor in SCN neuronal coupling, as has been reported by several other laboratories using other techniques.

 

The authors note that plasticity in the biological clock in the brain may reflect normal adjustment to different environmental conditions, but might also reflect a pathological state induced by long-days. Since we are all guilty of using artificial light to lengthen our days beyond the duration of sunlight, it would be useful to determine if altering our biological clocks in this way is harmful over a lifetime. Work in the Davidson lab is ongoing to investigate these types of questions.

 

Jennifer A. Evans, Tanya L. Leise, Oscar Castanon-Cervantes, Alec J. Davidson (2013). Dynamic Interactions Mediated by Nonredundant Signaling Mechanisms Couple Circadian Clock Neurons Neuron DOI: 10.1016/j.neuron.2013.08.022

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Joint Lab Publication: Novel Means to Quantify Physiological Sleepiness

For years, sleep researchers have characterized physiological sleepiness observed by EEG through one measure: slow wave activity. Slow wave activity is fairly ResearchBlogging.orgstraightforward to identify and represents EEG power at slower frequencies that essentially look like a roadside view of the Rocky Mountain Range. The amount of slow wave activity is directly proportional to the length of time that an animal is awake meaning that slow wave activity dominates the EEG after long periods of wake or sleep deprivation. While  slow wave activity has helped determine how a host of environmental, physiological, and genetic factors influence the ability to recover from sleep loss, it is limiting because it does not accurately quantify how an animal feels during sleep loss. And so, our lab teamed up with another electrophysiology lab that studies epilepsy to provide a means to quantify physiological sleepiness as it occurs, not after.

This was achieved through a period-amplitude analysis which looks at an individual EEG within a specific time frame, say 10 seconds, rather than all EEG waveforms within a specific time frame.   With this period-amplitude analysis, the number of slow wave peaks were counted across sleep loss with the mice being deprived of sleep for up to 24 hours. Early into the sleep deprivation, there were slow wave peaks here and there.

Early into Sleep Deprivation

But as time passed, particularly after the animal had been deprived of sleep for 6 hours, slow wave peaks began to dominate the EEG and persists until the end of 24 hours of sleep deprivation.
Late into Sleep Deprivation
We also found that the number of slow wave peaks was negatively correlated to the time it took the animal to fall asleep, in particular NREM sleep, which makes up 85-90% of total sleep and is the first type of sleep that an animal has unless the animal is narcoleptic. This relationship between slow wave peaks and time to NREM sleep corroborates the previously identified negative relationship between slow wave activity and time to NREM sleep.
Number of Slow Wave Peaks With Continued Sleep Deprivation
Finally, we found a time-dependent difference in the quantity of slow wave peaks based on whether the animal got sufficient sleep or was recovering from sleep loss. With sufficient sleep, slow wave peaks were more common during the light or rest/sleeping period of a nocturnal rodent. There were also more slow wave peaks during the middle of the night when most nocturnal rodents nap. This distinctive rhythm in slow wave peaks was absent in a mouse recovering from sleep loss. This is largely because there were more slow wave peaks during the night than that present with sufficient sleep.
Loss of Rhythm of Peaks After Sleep Deprivation

This study provides a means to better characterize changes in sleep and wake with inadvertent or voluntary sleep deprivation.

Ehlen JC, Jefferson F, Brager AJ, Benveniste M, & Paul KN (2013). Period-Amplitude Analysis Reveals Wake-Dependent Changes in the Electroencephalogram during Sleep Deprivation. Sleep, 36 (11), 1723-35 PMID: 24179307