The Relationship Between the Menstrual Cycle and Sleep

Post by Shireen Parimoo

What is the menstrual cycle?

The menstrual cycle describes the fluctuation of ovarian hormones that typically occurs over a 28-day period, but can range anywhere from 21 to 38 days. The cycle is divided into two distinct phases: the follicular phase and the luteal phase. The follicular phase begins on the first day of menses - more commonly known as the period - which lasts between three to eight days. This phase ends with ovulation, when an egg is released from the ovary into the uterus, followed by the luteal phase, which lasts until the next menses.

In each phase of the menstrual cycle, hormonal changes result from an interaction between the brain and the reproductive system. In the follicular phase, follicle stimulating hormone released from the pituitary gland in the brain prepares the ovaries for ovulation and triggers the release of estrogens, which prepares the uterus for ovulation. Toward the end of the follicular phase, high levels of estrogens act on the brain to facilitate the release of luteinizing hormone, which in turn triggers ovulation. Once the egg is released, there is an increase in progesterone and estrogen but if pregnancy does not occur levels decline, leading to the next menses.

How does it affect sleep?

Sleep promotes physical and mental recovery, maintenance, and repair within the brain, and facilitates learning and memory. Thus, it is important to maintain a consistent sleep schedule and get good quality sleep, since sleep disturbances can not only interfere with day-to-day functioning and general well-being but can also disrupt cognitive performance and increase the risk of disease and dementia. Good sleep hygiene, such as keeping a consistent bedtime/nighttime routine, getting enough sunlight during the day, exercising regularly, and avoiding nicotine, alcohol, and stimulants in the evening can help. In general, women self-report more frequent sleep problems, increased daytime sleepiness, and poorer quality of sleep compared to men, yet objective measures like sleep duration suggest that women get more quality sleep than men.

Ovarian hormones partially contribute to these conflicting sex differences in objective and subjective measures of sleep quality. Across the female lifespan, the most pronounced hormonal changes take place during puberty, menses, pregnancy, and menopause, which coincide with sleep disturbances. During reproductive years, women experience more subtle fluctuations in their quality of sleep over the course of the menstrual cycle. Sleep disturbances are primarily observed during the luteal phase, partly due to elevated levels of estrogens and progesterone. For example, women report higher daytime sleepiness and more awakenings at night during the luteal compared to the follicular phase. Core body temperature at night is also elevated during the luteal phase, which is related to both hypersomnia (excessive sleep) and insomnia (inability to sleep). However, some objective measures of sleep quality such as total time spent sleeping and sleep efficiency are not consistently affected by the menstrual phase.

How is the brain involved?

Sleep consists of recurring cycles that usually last about 90 minutes, with one night of sleep involving between three to five sleep cycles. Each sleep cycle includes rapid eye movement (REM) sleep, when dreaming occurs, and non-REM sleep, which involves light and deep sleep. Polysomnography studies show that the duration of REM sleep is lower during the luteal phase than the follicular phase, whereas the duration of non-REM sleep becomes longer.

Ovarian hormones are increasingly being recognized for their relevance in non-reproductive functions through their impact on the brain. For example, there is evidence of better memory after a nap for women in the luteal phase compared to those in the follicular phase of their menstrual cycle. Estrogens and progesterone, which are elevated during the luteal phase, act on receptors in the hippocampus and the frontal cortex, regions that are involved in learning, memory, and decision-making. Moreover, these hormones have also been shown to have neuroprotective effects on the brain’s structure across the lifespan.

Slow-wave sleep (deep sleep) and sleep spindles are prominent features of non-REM sleep, which is when much of the sleep-related recovery and memory consolidation takes place. Sleep spindles refer to bursts of oscillatory activity (11-16 Hz) that typically occur during stage 2 of non-REM sleep. Notably, sleep spindles occur more frequently during the luteal phase of the menstrual cycle and are associated with memory performance. Higher levels of progesterone are thought to modulate spindle activity by acting on GABA receptors in the brain. Conversely, slow-wave sleep is characterized by low frequency oscillatory activity (0.5-3 Hz) and is reduced during the luteal phase. However, it is currently unclear how brain activity during different sleep stages is linked to specific phases of the menstrual cycle. More research is needed to better understand the complex and interdependent relationship between the female reproductive system and brain in relation to sleep and cognition. 

References

Alonso et al. Sex and menstrual phase influences on sleep and memory. Current Sleep Medicine Reports (2021).

Baker & Driver. Self-reported sleep across the menstrual cycle in young, healthy women. Journal of Psychosomatic Research (2004).

Bixler et al. Women sleep objectively better than men and the sleep of young women is more resilient to external stressors: the effects of age and menopause. Journal of Sleep Research (2009).

Brann et al. Neurotrophic and neuroprotective actions of estrogen: basic mechanisms and clinical implications. Steroids (2007).

Brinton et al. Progesterone receptors: form and function in the brain. Frontiers in Neuroendocrinology (2008).

Brown & Gervais. Role of ovarian hormones in the modulation of sleep in females across the adult lifespan. Endocrinology (2020).

Dorsey et al. Neurobiological and hormonal mechanisms regulating women’s sleep. Frontiers in Neuroscience (2021).

Driver et al. The menstrual cycle effects on sleep. Sleep Medicine Clinics (2008).

Genzel et al. Sex and modulatory menstrual cycle effects on sleep related memory consolidation. Psychoneuroendocrinology (2012).

Gervais et al. Ovarian hormones, sleep and cognition across the adult female lifespan: an integrated perspective. Frontiers in Neuroendocrinology (2017).

Johnson et al. Epidemiology of DSM-IV insomnia in adolescence: lifetime prevalence, chronicity, and an emergent gender difference. Pediatrics (2006).

Mong & Cusmano. Sex differences in sleep: impact of biological sex and sex steroids. Philosophical Transactions of the Royal Society B (2016).

Plante & Goldstein. Medoxyprogesterone acetate is associated with increased sleep spindles during non-rapid eye movement sleep in women referred for polysomnography. Psychoneuroendocrinology (2013).

Romans et al. Sleep quality and the menstrual cycle. Sleep Medicine (2015).

Schumacher et al. Progesterone: therapeutic opportunities for neuroprotection and myelin repair. Pharmacology and Therapeutics (2007).

De Zambotti et al. Menstrual cycle-related variation in physiological sleep in women in the early menopausal transition. Journal of Clinical Endocrinology and Metabolism (2015).

Decision Processes Leading to Unhealthy Food Choices

Post by Andrew Vo

What's the science?

After making poor dietary choices, we often blame our actions on either a strong preference for tasty (but oftentimes unhealthy) food, or on poor self-control. Traditional computational models characterize such value-based decisions as a dynamic accumulation of evidence that biases us towards one option over another. These models, however, do not account for distinct contributions of separable attributes to a decision (e.g., how health and taste attributes are integrated with different weights and at different times in evidence accumulation). This week in Nature Human Behaviour, Sullivan and Huettel use an updated computational framework to better understand how distinct attributes influence decision processes that could lead to unhealthy food choices.

How did they do it?

The authors recruited a group of young adults who arrived hungry at the lab after a four-hour fast. They were then asked to rate 30 different snack foods based on tastiness, healthiness, and ‘wanting’ attributes. Before beginning the main task, participants received a behavioral primer that emphasized the importance of either healthy or tasty choices. During a main, binary choice task, they were presented with pairs of food items (that they had previously rated) and were asked to indicate which they would like to eat more. Of the 300 self-paced trials, half were designed to be “conflict trials” in which one option was tastier but less healthy than the other, whereas the other half were non-conflict trials in which both options were closely matched.

Participants’ food choices and response times (RTs) were fitted using a multi-attribute, time-dependent, drift diffusion model (mtDDM) (a statistical model). This model has the advantage of distinguishing the various contributions of different attributes to a decision. To do this, it estimates (1) drift slope, which captures the rate of evidence accumulation for each attribute, and (2) drift latency, which describes when each attribute begins to exert its influence during evidence accumulation.

What did they find?

The authors found faster RTs for conflict versus non-conflict trials, as participants made fast unhealthy choices over healthier ones. Those participants who were primed with health information were found to put less weight on taste information, which marginally increased their likelihood of making healthy choices.

The mtDDM estimated that taste drift slopes were larger (steeper) than health drift slopes and taste drift latencies were earlier than health drift latencies. These results suggest that bias towards tasty versus healthy food choices is due to a greater weighting and earlier entry of taste information into evidence accumulation. To test whether slope and latency independently influenced food choices, multiple linear regressions of drift slope and latency differences (i.e., taste minus health) were performed. Both drift slopes and latencies predicted individual differences in the likelihood of healthy food choices. Finally, examining the relationship between trial-by-trial RTs and healthy choices in conflict trials, the authors found that longer RTs were associated with healthier food choices. This suggests that longer RTs allow time for slower-processed healthy information to influence evidence accumulation.

What's the impact?

This study demonstrates how the influence of different attributes on decision-making processes might explain our food choices. The results provide insight into how we can augment our thinking to make better decisions for our long-term benefit, such as considering the healthiness alongside the tastiness of a food item or taking more time to seek out health information on a food choice. Understanding the timing of decision processes in the brain might also be key to creating effective interventions that help people make better choices — not just in terms of diet but also in financial decisions, for example. Much like how you should look before you leap, consider pausing before you place that next restaurant order.

Sullivan & Huettel. Healthful choices depend on the latency and rate of information accumulation. Nature Human Behaviour (2021). Access the original scientific publication here.

Psilocybin Triggers Synaptic Remodeling

Post by Shireen Parimoo

What's the science?

Psychedelics are hallucinogenic compounds that alter perception, mood, and cognition. Although they are most commonly known for recreational use, psychedelics are increasingly being recognized for their therapeutic potential to treat psychiatric disorders like major depression and PTSD. Psilocybin, commonly referred to as magic mushrooms, is a serotonergic psychedelic substance that is currently being tested in clinical trials to treat depression. Currently, the efficacy of psilocybin use on depressive symptoms and its impact on neuronal functioning are unclear. This week in Neuron, Shao and colleagues examined the impact of a single dose of psilocybin on learned helplessness behavior in mice, as well as its impact on dendritic structure and functioning of the medial prefrontal cortex (mPFC).

How did they do it?

Six- to ten-week-old mice underwent a learned helplessness protocol in which they were given hundreds of inescapable foot shocks on two consecutive days. On the third day, the mice were tested on their learned helplessness behavior by receiving foot shocks that they could avoid by escaping into a different chamber. The authors measured the time it took the mice to escape (escape latency) and recorded escape failures if the mice failed to leave the chamber within 10 seconds of receiving the foot shocks. They used a statistical clustering technique (k-means clustering) on these measures to classify mice as “susceptible” or “resilient” to learned helplessness. After receiving a single dose of either saline, ketamine, or psilocybin treatment on the fourth day, the mice were once again tested on learned helplessness on the fifth day (Test 2).

To examine the effects of psilocybin on mPFC neurons, 2-photon imaging was performed to characterize dendritic spine properties prior to and after treatment. Specifically, the authors assessed spine width, protrusion length, density, formation rate, and elimination rate up to 34 days after saline and psilocybin treatment. Lastly, they performed whole-cell electrophysiological recordings to determine whether psilocybin modulated the activity of pyramidal neurons in the mPFC.

What did they find?

Psilocybin treatment reduced escape failures at Test 2, including in nearly all the mice that were considered to be particularly susceptible to learned helplessness. A single dose of psilocybin also resulted in morphological changes on dendrites, including an increase in spine width, longer spine protrusions, and greater spine density due to an increase in the formation of new dendritic spines (rather than reduced elimination of spines). In fact, half of the newly formed spines remained intact after a week, and up to 37% of those remained intact after a month. Interestingly, the long-term stability of these new dendritic spines was only seen on some dendrites, which suggests that certain sub-population of neurons in the cingulate/premotor mPFC might be more amenable to the effects of psilocybin than others. The authors further replicated this pattern of results in the primary motor cortex and in the prelimbic/infralimbic regions of the mPFC, showing the generalizability of psilocybin’s effects on dendritic remodeling. Lastly, psilocybin led to increased miniature excitatory potentials 24 hours after administration, as compared to saline treatment. Together, these findings demonstrate that a single dose of psilocybin triggers dendritic remodeling, enhances excitatory neurotransmission in the mPFC, and is sufficient to reduce depressive symptoms like learned helplessness in mice.

What's the impact?

This study shows that a single dose of psilocybin quickly triggers both behavioral and synaptic changes in the mouse mPFC. The therapeutic potential for psilocybin use is particularly exciting because many antidepressants take several weeks to have a noticeable effect on behavior and cognition. This study provides a promising first step in understanding how psilocybin affects different brain regions implicated in major depression and paves the way for future research to extend these findings to humans.  

Shao et al. Psilocybin induces rapid and persistent growth of dendritic spines in frontal cortex in vivo. Neuron (2021). Access the original scientific publication here.