Sawtooth Wave Oscillations During REM Sleep

Post by Cody Walters

What’s the science?

Sawtooth waves (STWs) are jagged oscillations seen in EEG recordings prior to bursts of rapid eye movement (REM) during sleep. Although REM sleep has been the focus of much research, little is known about the cortical origin and functional significance of STWs. This week in The Journal of Neuroscience, Frauscher et al. performed the first ever intracranial study of STWs in human subjects.

How did they do it?

The authors analyzed data from 26 epileptic human patients that underwent preoperative stereoelectroencephalography (SEEG) recordings to localize epileptogenic brain tissue. In addition to SEEG recordings, the authors had access to scalp EEG and polysomnography data.

What did they find?

The authors identified that STWs occur in a variety of brain structures including the parietal, lateral, and medial areas; the anterior insula; the lateral and orbital frontal cortices; and mesiotemporal structures. They then quantified the spectral power (by decomposing the SEEG signal into a sum of sine waves with varying frequencies and amplitudes) in a variety of brain regions during STWs. There was an increase in both low (2-4 Hz) and high (20-240 Hz) frequency power (i.e., the amplitude of the sine wave in those frequency ranges significantly contributed to the overall SEEG signal) during STWs in most brain regions.

Ripples are oscillations in the local field potential that occur in the high frequency range (i.e. 80-240 Hz ) and are known to play a role in memory consolidation and replay. The authors found that there was a significant increase in the ripple rate in a variety of brain structures (e.g., the angular gyrus, posterior cingulate gyrus, temporal pole, superior temporal gyrus and mesiotemporal structures) during STWs. Furthermore, the authors found that STWs were temporally and spatially highly variable both within and between brain regions. These data suggest that STW may synchronize the re-activation of activity to aid in complex memory consolidation.

What’s the impact?

This study is the first intracranial investigation of the electrophysiological correlates of sawtooth waves in human subjects. The authors found that (1) STWs occured in various brain regions, (2) STWs co-occurred with high frequency ripple events, and (3) STWs were both temporally and spatially variable. These data suggest that STWs may play an important role in coordinating the brain activity to aid memory consolidation during REM sleep. Further, this study sheds light on a largely unexplored aspect of REM sleep.

Frauscher et al. (2020). REM sleep sawtooth waves are associated with widespread cortical activations. The Journal of Neuroscience. Access the publication here.

Why Do We Reward Giving, but Not Giving Effectively?

Post by Amanda McFarlan

What's the science?

The concept of donating is one with which we are all familiar, and yet, very few of us actually take the time to think about the effectiveness of our giving. One potential explanation for this is that giving evolved to be mainly driven by social rewards - rewarding behaviours that are easily defined and observed. Thus, the efficacy and impact of donating, which are not easily defined or observed, may not have evolved to be important factors to consider when deciding to donate. This week in Nature Human Behaviour, Burum and colleagues performed a total of five studies to demonstrate that the social rewards associated with giving are sufficient to encourage people to give, but not to give effectively.

How did they do it?

In the first study, participants were divided into two groups, and each given a 30-cent bonus. The first group of participants was given the option of donating all, some, or none of their bonus to a charity of their choosing, while the second group of participants was given the option of saving all, some, or none of their bonus to receive later. In both groups, the amount of money donated or saved would be matched by a factor of 1 to 10. 

In a second study, participants were asked what percentage of their income they would be willing to donate in order to prevent one stranger, five strangers, one family member, or five family members from starving for a year. Participants in the third, fourth and fifth studies were matched with a donor who had selected their favorite charity from a list and was given the choice to keep or donate all of a bonus to that charity, varying with each study. In the third study, the donor was given the choice to donate all or some of a bonus that would either be matched (1:1) or not. In the fourth study, the donor had the same choice to keep or donate all of a bonus, but this time the participants were told that the bonus was either 10 cents or 20 cents. In the fifth study, the donor received a bonus of either 75 cents or 90 cents and was given the choice to donate one of several amounts. Participants were always matched with a donor who donated 60 cents. After reading about the donor’s decision, participants were given a 30-cent bonus and were given the option to reward the donor with some or all of their bonuses. Participants were also asked to rate the donor on characteristics including how generous, giving, good a person, ethical, moral, and deserving of praise they were. 

What did they find?

In the first study, the authors found that participants responded to efficacy when saving, but not when donating to charity. Matching the donation by a factor of 1 to 10 did not have an effect on the amount the participants donated, but it did have a significant effect on the amount they saved. In the second study, the authors showed that participants donated effectively when helping their family members, but not when helping strangers. Participants were willing to donate substantially more when helping family members compared to strangers. They were also willing to donate more when five family members were in need compared to one family member, while the amount they were willing to donate to strangers was similar regardless of the number of people in need. 

Next, the authors showed that in the third, fourth, and fifth studies, the donors who chose to donate were rewarded more by the participants than the donors who did not choose to donate. Notably, the authors found that the participants did not reward the donor more if their donation was larger, whether they donated a small or large bonus, or if the donor gave a larger portion of their total bonus. The participants’ character assessments of the donors mirrored these results such that donors that chose to give were seen as generous, but donors who gave more were not seen as more generous than donors who gave less.

What’s the impact?

This study demonstrates that people do not attend to efficacy when donating to charity but are capable of attending to efficacy in situations that are not dictated by social rewards, such as saving for the future and helping family members. Additionally, people are insensitive to the impact of giving. All together, these findings support the notion that donors evolved to be sensitive to clearly defined behaviors that bring social rewards.

Burum et al. An Evolutionary Explanation for Ineffective Altruism. Nature Human Behaviour (2020). Access the original scientific publication here.

Smartphone Use in School and Academic Performance

Post by Shireen Parimoo

What's the science?

Ever since smartphones gained popularity and became a seemingly indispensable part of our lives, they have been linked to a number of negative outcomes including low grades, poor sleep, reduced social engagement, and even depression. How much of this is supported by research? Does smartphone use really have a negative impact on students’ grades? This week in Psychological Science, Bjerre-Nielsen and colleagues report results from a 2-year observational study that investigated the relationship between smartphone use among young adults and their academic performance.

How did they do it?

Participants were 470 students (19-29 years old) who were part of the Copenhagen Networks Study in Denmark. The students agreed to have their smartphone usage tracked over the course of two years, which included tracking their GPS location, social interactions, and the on/off status of the smartphone screen. The authors combined the frequency of smartphone use and class attendance to obtain measures of in-class and out-of-class smartphone usage. They also obtained student-specific characteristics such as the participants’ age, sex, personality traits (e.g., Big Five Inventory), and grade-point averages (GPA) for each course.

The authors first assessed the correlation between in-class and out-of-class smartphone use and GPA. They then specified two types of statistical models to assess the effect of smartphone use on academic performance, each of which had its own set of advantages. Panel models included data for every student’s grades and smartphone use in each of their courses, which allowed the authors to examine the effect of both student characteristics (e.g., age) and course characteristics (e.g., difficulty) on academic performance. Conversely, a cross-sectional model included data for each student’s overall grade and smartphone use and allowed them to compare their results to previously published research on the topic.

What did they find?

Greater smartphone use was related to poorer academic performance, and this relationship was stronger for in-class smartphone use. Smartphone use was also negatively correlated with students’ high-school GPA. Thus, students who used their smartphone more frequently in class had a lower GPA in both high school and university. The cross-sectional model largely replicated previous findings on this topic, namely, that greater smartphone use during class is associated with lower grades. However, results from the panel model indicate that the relationship between smartphone use and academic performance is not as strong as previously thought. Specifically, when student and course-specific characteristics like personality traits and class difficulty are accounted for in the model, the association between smartphone use and GPA is not reliable.

What's the impact?

Smartphone use may not have as detrimental an impact on students’ academic performance as previously thought. Moreover, the study highlights the importance of considering individual- and context-specific characteristics in performing this type of research, which can have an impact on the magnitude of the observed effects. Overall, these findings provide some reassurance as smartphones are becoming increasingly pervasive and students are exposed to them at younger ages than before.  

Bjerre-Nielsen et al. The negative effect of smartphone use on academic performance may be overestimated: Evidence from a 2-year panel study. Psychological Science (2020). Access the original scientific publication here.