Assignment

You will take your science-related topic and create an Inquiry-Based Research Paper geared to a standard academic audience. At the completion of the assignment, students will then write a 2-page individual  (B) Personal Reflection describing (1) their experience writing the assignment both educational and compositional; (2) their experience (3) their personal areas for improvement, and also (3) their plans for the future.

Purpose:

• provides you the opportunity to familiarize yourself with recent scholarship on your topic of focus.
• exposes you to a potentially familiar rhetorical situation and practice. 
• potentially educates you on a previously unknown science-related topic.
• provides opportunities to instill, reestablish or simply practice proper writing habits.
• Allows you to become your own “Scholar;” and become more familiar with the basic genre of science writing.
• Puts you in a likely (albeit not exact) professional atmosphere of coordination and group scholarship

Paper Requirements:

• Minimum of 6 Pages
• Minimum of 2 Pages for Post Assignment Solo Reflection
• At least 3 scholarly sources with decent usage
• Double-Spaced
• APA Citations

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Sample Student Paper (different length requirements)

Academic Stress and the Degradation of the Amygdala

Academic stress is a widespread experience that many students endure throughout their academic careers. Dusselier et al. ‘s (2005) quantitative study found that around 55 percent of college students at Iowa State University in the United States related stress to their schoolwork; and with intense academic stress, students undergo various negative effects such as emotional instability, sleep problems, and fatigue. According to Chen et al. ‘s (2015) study which administered questionnaires to Taiwanese high school adolescents, the students went through severe fatigue, sleep problems, and depressive symptoms. Additionally, Komarov et al., (2020) found positive correlations between stress, fatigue, and daytime sleepiness among graduate students in their hybrid study using quantitative and qualitative methods. It is clear that academic stress has a detrimental impact on students’ psychological health which manifests through sleep problems, fatigue, and emotional stability.

As students experience this constant and intense stress, they become more susceptible to various mental illnesses like anxiety disorders and depression which can even lead to suicide.

Since academic stress is widely experienced, many more people will undergo various mental health challenges. These challenges are dangerous to an individual’s well-being and negatively affect their lives. Understanding how stress affects the brain shows how mental health problems occur and can provide potential solutions for them. The brain’s amygdala is a region of interest because it is related to the development of anxiety and is important for processing fearful and threatening experiences (Roozendaal et al., 2009). Moreover, a process called long-term potentiation (LTP) is worth investigating because it is a process of strengthening synapses in the brain through pattern activities (Roozendaal et al. 2009). Since academic stress is consistent, it can cause LTP and can affect the functionality of the brain, specifically the amygdala. In this study, the relationship between constant academic stress, amygdala activity, and mental health is researched to understand how academic stress can be detrimental to students in the long run. As a result of persistent academic stress, students are more likely to experience an increase in mental health problems such as fatigue and anxiety, as well as negative degradation of the amygdala.

Methods

Firstly, Dusselier et al. ‘s (2005) study surveyed about 462 undergraduate students from Iowa State University. The survey consisted of 2 sections with a total of 76 questions. The first section inquired about the student’s feelings towards their residence halls and determined whether or not the residence hall is a source of stress. The second section surveyed how students recognized the impact of health and personal problems. This section was divided into two sets of questions that analyzed their lifestyles. One set of questions asked students about their electronic usage or extracurriculars that may detract from academic work. Another set of questions asked students about their overall health, relationships, and personal issues with alcohol use, drug use, sleep, and more. Furthermore, the survey prompted students to answer a qualitative question about what stresses them out the most. Lastly, backward linear regression analyses, which are common in many studies involving stress and its relationships with certain effects, were completed to determine possible predictors of stress.

Similarly, to Drusselier et al. (2020), a quantitative study by Chen et al. (2015) analyzed how academic stress affected fatigue, sleepiness, and depression in 757 senior high school Taiwanese students along with the relationships between stress and the aforementioned mental health effects. The high school students were divided into four groups such as Grade 1, Grade 2, Grade 3T, and Grade 3S. Grade 1 had 261 students, and grade 2 had 228 students. Grade 3T had 199 students who must take an upcoming college entry exam, while Grade 3S had 69 students who finished their college entry exams and applications. All the groups were required to fill out questionnaires which included the Chinese version of the Multidimensional Fatigue Symptom Inventory-Short Form (MFSI-SF-C), the Pittsburgh Sleep Quality Index-Taiwan Form (PSQI-T), the Chinese version of the Epworth Sleepiness Scale (CESS), and the Chinese version of the Beck Depression Inventory (C-BDI-II). The MFSI-SF-C contained 30 questions that measured general, physical, emotional and mental fatigue. These questions prompted students to determine how true a statement was. They answered the questions using a five-point scale where 0 was equivalent to not true and 4 was extremely true. A linear regression model for the MFSI- SF-C was created to determine if depression, sleepiness, and sleep quality correlated to physical, emotional, and mental fatigue. The PSQI-T was a 19-item questionnaire that assessed sleep quality. The students answered the questions using scores from 0 to 3 where 0 is no difficulty and 3 is very difficult. The CESS evaluated daytime sleepiness with eight questions describing different situations. The students had to score each situation using the numbers 0 to 3 like the PSQI-T. Lastly, the C-BDI-II was a 21-item questionnaire that measured levels of depression by analyzing depressive symptoms, cognitive function, and physical symptoms. Similar to the PSQI-T, each question was scored from 0 to 3 where higher scores indicated more severe depression. After administering the questionnaires, the average score of each questionnaire was calculated to determine the students’ conditions. Overall, higher averages of each questionnaire indicated the students’ worsening condition from academic stress.

Komorav et al. ‘s (2020) study analyzed the relationships between stress, fatigue, sleepiness, and sleep quality and observed the brain activity in students in contrast to the previous sources. This study employed a Daily Sampling System (DDS) where eighteen graduate students used their smartphones to fill out reports about their stress, fatigue, sleepiness, and sleep quality throughout the academic semester. Each report was formatted like a questionnaire where students had to rate their conditions. When reporting their self-evaluated stress and fatigue, the students rated their condition from 0 to 100 with 0 demonstrating no effect from stress and 100 is severe effects from stress. This format remained the same when students rated their sleep quality. When self-reporting daytime sleepiness, students rated their feelings from a 1, which is extremely alert, to a 9, which is extremely sleepy. At the end of the semester, the response scores from each report were averaged and standard deviations were calculated. Bivariate correlation analyses and multiple regression analyses were also conducted to determine relationships between the evaluated conditions from the DSS. In addition to the DSS, the authors used Depression, Anxiety, and Stress Scales (DASS-21) to assess students’ depression, anxiety, and stress throughout the semester. They had students complete a 21-item questionnaire that utilized a depression scale for measuring symptoms of dysphoric moods, an anxiety scale for measuring symptoms of panic attacks or fear, and a stress scale for evaluating tension as well as irritability. Bivariate correlation analyses were also done with the DASS-21s at the end of the semester to analyze the relationships between depression, anxiety, and stress. The students completed DASS- 21s every two weeks along with a resting-state EEG which measures electrical activity within the

brain as well as various brain waves. The EEG scans provide insight to how the brain functions during different points in the semester. Though, different studies approached studying the effect of stress on the brain through other methods.

For instance, Suvrathan et al. (2014) conducted a qualitative experiment to analyze how stress impacts the brain’s structure. They utilized 60 to 65-day-old Male Wistar rats where the experimental group of animals underwent chronic immobilization stress for ten days, while the control group was kept in separate cages and conditions. In this experiment, the chronic immobilization stress model involved the experimental group of rats being restricted from moving for two hours. This kind of stress model was utilized to analyze LTP in the brain’s synapses, specifically in the amygdala and thalamus. After ten days of employing chronic stress immobilization, both groups of rats underwent fear conditioning. Twenty-four hours prior to this part of the experiment, rats were introduced to two new settings for 15 minutes each. One chamber was lit with dim house lights and had a metal floor. The other setting was a chamber with Plexiglass walls with a bright, white light. When it was time for fear conditioning, the first chamber was placed in a cubicle where sound is dampened. Each rat was tested separately, and they were placed in the chamber for three minutes before starting the fear conditioning protocol. During fear conditioning, rats listened to five separate 20-second tones of 70 decibels (dB) and received a foot shock at the end of the last tone. They were returned to their original cages after this process. The rats were then separately tested again in the other chamber the next day. They were exposed to the same tones from the previous day and were observed for freezing or motionless behavior. Lastly, the neurons and spin density of the rats were studied through Golgi staining and a spine density analysis. In order to complete this analysis, the rats’ brains were extracted through humane methods and were submerged in Golgi-Cox fixative which stained any present neurons. Then the stained neurons of the thalamus and lateral amygdala were analyzed to deduce the magnitude of N-methyl-D-aspartate (NMDA) receptor activity and quisqualate (AMPA) receptor activity which demonstrates evidence of stress in the specific brain structures. The activity of each receptor in the distinctive regions was compared and contrasted because the thalamus undergoes LTP from stress, while the lateral amygdala does not undergo LTP from stress. Since the thalamus constantly undergoes stress, the synapses’ structures and neurons should be unaffected. By determining similarities and differences between the receptor activity of both structures, the authors were able to deduce how LTP from stress affects the lateral amygdala.

Results

Through linear regression statistical analysis and the backward selection process, Dussilier et al. (2005) deduced the predictors of stress. The authors determined that depression and stress had a positive correlation, and depression was found to be a significant predictor of frequent stress.

Approximately 18% of the respondents reported feeling depressed. Respondents who were feeling depressive symptoms were more likely to report that they were experiencing frequent stress. Physical illness such as mononucleosis was found to have a negative relationship with stress which conveys that students who had mononucleosis experienced less frequent stress.

Other predictors of stress such as concern for an individual, alcohol use, and sleep difficulties had positive correlations with stress. Around 13% of the total respondents who reported concern for a troubled friend or family member were 67.4% more likely to experience frequent stress.

Along with concern for others, 33.1% of the total surveyed students who claimed to consume alcohol were 67.1% more likely to experience frequent stress. Moreover, 37% of the respondents who expressed sleep troubles were 67.4% more likely to experience frequent stress. Respondents who reported using drugs (3.7%) and underwent police action (10.3%) were about 64% less likely to report frequent stress. The respondents who lived comfortably in residence halls, which was about 94.7%, were 67.1% more likely to report frequent stress. However, those who felt they could study in their rooms and were respected by their roommates were 64.6% likely to experience less frequent stress. For the qualitative question, 25% of students reported personal issues were causing stress; approximately 20% of respondents reported that either time management issues, environmental issues, or financial issues were causing stress. There were also very minimal responses regarding illnesses and physical conditions. However, about 55% of the respondents reported that academic-related tasks were causing their stress which could contribute to certain effects in their lives.

To understand these effects, Chen et al. (2015) analyzed how academic stress negatively impacts mental health and found that higher-grade students had elevated physical, emotional, and mental fatigue. Based on the PSQI-T, students frequently went to sleep after 12 AM and slept on average below seven hours. Grade 3T was observed to have the worst sleep quality with an average score of 2.85 and the shortest total sleeping hours. Students in higher grades had higher average C-BDI-II scores. Grade 3T had the highest average score of 9.24, while Grade 3S had the lowest average score of 6.31 out of the other groups. The linear regression model of the MFSI-SF-C conveyed that the students’ depression levels, sleepiness, and sleep quality were associated with physical fatigue. It also demonstrated that depression levels and sleep quality were also connected to emotional fatigue. Lastly, grade levels, depression levels, sleepiness, and sleep quality were shown to be tied to mental fatigue. Overall, the authors attributed these results to the intense academic stress that the students were undergoing.

In addition to the previous research results, Komarov et al., (2020) solidify the correlations of academic stress and conditions related to mental health and sleep. For the entire group, the average scores with standard deviations were 49.4±17.4 for stress, 50.3±17.6 for fatigue,

59.6±10.2 for sleep quality, 55.6±13.3 for mood on awakening, and 51.4±13.2 for alertness on awakening. These results are based on the questions where students rated their condition from 0 to 100. For the aspects of their condition rated from 1 to 9, the average scores for the group were

6.1±1.5 for daytime sleepiness and 7.4±0.5 hours for sleep duration. Bivariate correlation analyses of the DSS conveyed that stress had a positive correlation of 0.626±0.233 with fatigue and negative correlations of −0.335±0.264, −0.299±0.308, and −0.237±0.255 with sleep quality, alertness, and mood upon awakening, respectively. However, fatigue had a positive correlation of 0.403±0.311 with daytime sleepiness and negative correlations with sleep quality (−0.267±0.233), sleep duration (−0.248±0.230), alertness (−0.414±0.360), mood upon awakening (−0.290±0.284). Bivariate correlation analyses of the DASS-21s demonstrated that depression and stress had a strong positive correlation of 0.773. Depression and anxiety had a strong positive correlation of 0.842, and anxiety and stress had a strong positive correlation of 0.823. Multiple regression analyses of DASS-21 conveyed that the stress levels of the participants were increased on Mondays and Tuesdays but decreased on Saturdays. Certain EGG data were analyzed based on clusters of elevated stress, anxiety, and depression determined by the DAAS- 21s. Through analyzing theta brain waves and elevated levels of anxiety through EEG, higher

activity of the brain was detected throughout the entire head. Additionally, more activity was discovered around the prefrontal sites when analyzing alpha brain waves and anxiety. When analyzing increased stress levels under delta, theta, and alpha waves, there was elevated brain activity in the prefrontal and occipital areas. Under increased levels of depression, theta and alpha brain wave analysis exhibited more frontal and prefrontal activity. Lastly, there was noticeable activity in the temporal lobes of the brain during resting-state EEGs. However, this study did not analyze the brain any further.

Though, Suvrathan et al. ‘s (2014) experiment had a more in-depth analysis of the brain which demonstrated that the magnitude of LTP in the lateral amygdala was greater in the experimental group compared to the control group. Through analyzing the potential activity for the lateral amygdala’s NMDA receptors and the potential activity for the lateral amygdala’s AMPA receptors, the authors discovered that the ratio of NMDAR activity to AMPA activity was doubled in the experimental group. This also demonstrated that more stress increased the ratio of NMDAR activity to AMPA activity in the amygdala which conveys higher long-term potentiation of stress in this region. These findings suggest the creation of “silent synapses,” so the authors analyzed synapses further to solidify the existence of these structures. First, they deduced that silent synapses existed if an increase in stress would promote NMDA receptor activity. The authors used a coefficient of variation (CV) to measure the variability of synaptic responses for each cell so that an accurate conclusion can be made. It is important to note that CV varies inversely with the original measured activity. With further analysis, they found that increasing stress decreased the ratio of CVs of NMDA activity to AMPA activity which exhibits an increase in NMDA receptor activity. Next, they solidified their conclusion that chronic stress created silent synapses by analyzing the ratios of AMPAR and NMDAR activity in the stressed lateral amygdala. If the ratios did not differ from the unstressed synapses, the presence of “silent synapses” would be proven which was the case. Through further cellular analysis, the authors observed that chronic stress reduced the inhibition of activity in the lateral amygdala neurons. After conducting the spine density analysis, the authors discovered that the spine densities were higher in the experimental group.

This change in density conveyed that chronic stress could lead to the development of silent synapses in the amygdala and explained the increase in LTP from stress. Lastly, both groups of animals developed fear conditioning at the same rate in context A because they exhibited the same freeze behaviors throughout the session. However, the experimental groups exhibited more freezing behaviors in context B compared to the control group in context B.

Discussions

Based on the reported results, the data support the idea that academic stress is a large, persistent contributor of stress in students. Dussilier et al. (2005) identified that 55% of the respondents stated some form of academic work was a source of stress (p. 18). Komorav et al. (2020) analyzed graduate students’ conditions throughout the semester, and their overall score average of experienced stress was approximately 50 out 100 with a standard deviation of 17.4 (p.798). The aforementioned findings demonstrate that academics contribute to stress. It is also suggested that it is a major contributor to students’ experienced of stress because many respondents attributed academics as a source of stress. Throughout the semester, students had moderate experiences of stress with 50 out of 100 average scores (Komorav et al, 2020, p. 798). Overall, it now seems likely that academic stress is a large, persistent source of stress in students.

The fact that academic stress is persistent, it can impact students’ mental health through fatigue which can translate into other possible mental health issues. Komorav et al. (2020) deduced that there is “a strong positive correlation between self-evaluated measures of fatigue and stress, for all subjects” (p. 802). Chen et al. (2015) found positive correlations between physical, emotional, and mental fatigue with depressive symptoms. This data suggests that even though stress causes only fatigue, this fatigue is connected with other mental health issues like depression. There is no direct correlation between mental health challenges and stress, but it is indirectly connected through an indirect faceted system. It is important to understand that academic stress can cause fatigue through the intensity of the stress. Chen et al. (2015) found students going to sleep after midnight, especially the group of students who have not completed their college applications. This group of students experienced the worst sleep quality with an average score of 2.85 and an average sleep duration of about 6 hours (Chen et al., 2015, p. 744).

This suggests that the students are sacrificing sleep to complete some form of academic work by either studying or completing homework which contributes to the students’ high fatigue levels.

Through elevated levels of fatigue caused by consistent academic stress, students become more likely to develop mental health issues such as depression.

As a result of persistent stress, overactivity in the brain’s amygdala causes detrimental changes to the structure. The results of Suvrathan et al. ’s (2014) study demonstrate that synapses in the amygdala are consistently being strengthened through LTP from chronic induced stress, and silent synapses are being formed. Not only are the amygdala’s synapses strengthened, but their inhibitory mechanism is also reduced while the promotion of activity is favored (Suvrathan et al., 2014). These findings overall demonstrate that chronic stress alters the structure of the amygdala by favoring constant activity and weakened inhibition. Even though the study utilized chronic stress mobilization on rats, academic stress can exhibit the same effects in the human amygdala because it is a constant feeling that students experience based on previous notions. This idea is further supported in Komarov et al. ‘s (2020) EEG anxiety and depression scans where there was brain activity in the temporal region which is where the amygdala is located.

All in all, academic stress can be attributed to negative alterations within the amygdala which can lead to the development of mental health disorders like anxiety. Since the amygdala is commonly associated with the development of anxiety due to its function of encoding fear memories and processing threatening stimuli, the deteriorated condition of the synapses within the amygdala can explain why mental health issues like anxiety occur (Roozendaal et al., 2009; Suvrathan et al., 2014). Suvrathan et al. (2014) convey this idea by stating, “that exposure to severe or prolonged stress renders the LA (lateral amygdala) network hyperresponsive to subsequent emotional experiences, thereby triggering affective symptoms such as abnormally high fear and anxiety observed in stress-related psychiatric disorders” (p. 9). These results convey that persistent academic stress can be contributing to the degradation of amygdala synapses because students who undergo constant academic stress also experience anxiety. This is demonstrated by Komorav et al.’s (2020) findings of a positive correlation between anxiety and stress. Overall, this is how academic stress can negatively affect the amygdala and cause the onset of mental health issues such as anxiety.

Although this paper attempts to extend the understanding of how consistent academic stress can have detrimental effects on the amygdala and quality of life, there were some limitations. For example, each study analyzed different age groups of students varying from high school, undergraduate college, and graduate school. Stress and anxiety can vary between each grade and academic level. Additionally, the scheduling of classes can differ from each grade and affect outcomes within this paper. Moreover, students from various regions of the world were studied, and different regions of the world contain different education/academic systems. In addition, the connection between academic stress degrading the amygdala and only anxiety was supported. Other mental health challenges such as depression could not be justified and further research will have to be done to understand how the hyperactivity of amygdala function can affect the development of depression. These were the overall limitations of this paper.

Conclusion

Overall, constant academic stress can cause the degradation of the amygdala synapses which contributes to the onset of mental health issues, specifically anxiety. Many students frequently experience stress along with other mental health challenges such as fatigue, anxiety, and depression. As a result of this frequent stress, the overall function of the amygdala is negatively altered due to the promotion of activity in the structure and lack of inhibition. This can explain why many students experience mental health challenges. These findings also imply whether or not academic systems should be assessed for more efficient ways of teaching without imposing mental health problems upon students. In conclusion, constant academic stress negatively impacts the amygdala synapses through degradation which contributes to the development of mental health problems, specifically anxiety, within students.