By Nipun Gorantla

Abstract

COVID-19, a novel viral infection characterized by high contagion rate, life-threatening symptoms, and constantly evolving characteristics, has developed into a noteworthy global pandemic which, as of May 1, 2020, has infected more than 3.2 million people in more than 187 countries. In response to the constantly evolving COVID-19 pandemic, government officials, lawmakers, politicians, and healthcare officials have developed responses to help “flatten the curve” or combat the significant evolution of the viral infection. In complement with officials, average citizens of nations have also engaged in intense measures to combat the spread of these viruses, including staying at home and “self-quarantining”. However, it should be recognized that during these unprecedented times, the very nature of the behaviour of the general population has dramatically shifted into “survival mode”. The majority of this paper will be discussing the responses that both the public and government officials have engaged in, and how these responses to the pandemic are supported by neuroscientific trends that are exhibited in crises.


I. Integrative Analysis of Trends in General Population’s Correlation of Risk When Exposed to Media in the COVID-19 Pandemic Using Neural Correlation

With the onset of the COVID-19 Pandemic, media and news outlets have been working to provide information to the mass public to educate them about COVID-19 and prepare them for a possible risk of the pandemic spreading to their zones. For example, meta news channels such as Fox News and CNN have been effortlessly working to provide a wall to wall coverage of the ongoing COVID-19 Pandemic. The only issue with the rapid proliferation and proportion with which the pandemic is being described? The level of panic that it causes in the common population. With neural information compiled after research conducted in past global health crises, such as the H1N1 Pandemic, a study recorded intra subject correlation (ISC) of neural time courses to record how brains of individual viewers of an H1N1 TV report correlated (Schmalzle, Hacker, Renner, Honey, Schupp et al.,2013). The researchers who conducted this study heavily devoted time and energy to the analysis of a risk perceptive factor in the terms of a cognitive framework when assessing people’s reactions in high-stress environments, such as a pandemic. The results of this study are strikingly similar to modern-day behaviors of populations. They found that there were enhanced ISC among viewers with overactive high-risk perceptions in the anterior cingulate, a feature of the brain which is responsible for the appraisal of threatening information [1].

The Anterior Cingulate plays an important role in the aspect of risk perception when provided with a threatening environmental stimulus. Indeed, an overactive anterior cingulate can lead to increasingly radical changes in the fight or flight, or stress response, in the human body, leading to certain behaviors and actions, such as hoarding, panic, and disorientation. As in the case with COVID-19, it is clear that the overactive anterior cingulate can result in massive physiological and psychological ramifications.  The TV report that the subjects were expected to watch included news such as severe disruptions in plane travel, discussion about vaccinations, the role played in the pharmaceutical industries, fatal consequences of the pandemic, and pictures of sick bays with bodies of victims [2]. The control stimulus, which was unrelated to H1N1, a TV documentary about astronomy, was also presented.

Using the ISC software, the researchers were able to assess voxel by voxel correlations between fMRI time courses from different people. The result was a two-step scheme that mapped out regions during the H1N1 TV report. This report is of paramount importance for the thesis as mentioned in the above space in the article, as it is clear that it draws an important synthesis that the frequently an H1N1 Pandemic news piece is broadcast, the level of risk perception dramatically increases.

Figure 1: Map regarding the functionalities and statistical significance/correlation of viewing H1N1 TV report and brain functionalities. It is shown in two varying observations that the p-values (probability value) of the studies were strikingly low, which means that the correlation between observing an H1N1 TV Report and spikes in brain functionalities were extremely high.

After calculating the average correlation coefficient for each voxel, the statistical significance was found to be that certain regions of the brain, including the anterior cingulate, the amygdala, and most notably the region of the limbic system, were extremely overactive during this series. This further develops the argument that the environmental risk factors as a result of the proliferation of data during a pandemic can have massive ramifications on the neuroanatomical structure of a human, thus creating stressful changes in the brain and enveloping anxiety. Recorded below in Figure 2 are the average fMRI results and their correlation to high H1N1 risk and low H1N1 risk perception groups based on voxel measurements.

Based on this statistical data, the researchers were able to perform a cross-group analysis in which they found that there were neural functions coupling in pgACC and adMCC regions [3]. It was found in human fMRI studies and animal electrophysiology that these regions were important for certain processes, such as emotional responsiveness, stress responsiveness, and more. The result of the experiment was a highly detailed Functional Connectivity Networks and ISC map, as seen in Figure 3 in which there were high amounts of functionality in certain regions of the brain that are instrumental to emotional and stress responsiveness. As seen in the ISC and Functionality Connectivity Network maps, crucial functionalities including executive control, which is a set of cognitive processes necessary for control of behaviour. Jeopardization of executive control in a state of anxiety or stress can lead to clouded judgement or decision making process [4].

Figure 2: A representation based on fMRI BOLD expression in the human brain based on the correlation with which the subjects viewed the H1N1 TV program. As seen in the map, it is clear that a high perceived H1N1 virus risk resulted in extremely overactive functionality of brain regions in the occipital and temporal areas, while a low perceived H1N1 risk was correlated with a lower amount of functionality in these areas.
Figure 3: ISC and Functional Connectivity Networks Maps. It is seen in the multiple branches surrounding the central figure of the brain that there are MRI scans of participants in the study. Each scan is organized by the functionalist which is clearly shown whilst watching an H1N1 TV report. Executive control is the functionality that governs most of the brain’s behavior and psychological operations. Dorsal Attention is the process by which the brain directs their attention following visual or some other environmental stimuli. The Default Mode Network is the process by which the brain initiates an “autopilot mode”. This is the network that is activated when the brain starts to relax and activate the parasympathetic nervous system. It is clear that with high H1N1 Risk perception, there is not an abundance of activity among the MRI scans in the Default Mode Network.

II. Analyzing Trends Associated With Response in Threats Using Neural Synchronization and Active Coping

In scenarios of crisis, such as the COVID-19 Pandemic, behaviour patterns among the general public can be divided into two main categories: those who take action in the face of threats and those who do not. This division between “action-takers” and “action-avoiders” is seen to exist as a result of active coping from neural synchronization between the amygdala, striatum, and medial prefrontal cortex (mPFC) [5]. Active coping can be defined as a coping style that is characterized by “solving problems, seeking information, seeking social support, seeking professional help, changing environments, planning activities, and the meanings of problems” [6]. Engaging in active coping has been seen to prove a strong negative correlation between the intensity of the stressor and the level of stress associated with the said stressor.

Indeed, active coping (AC) can vary among the general population and alter how people respond to a single stressor. AC can be studied through the biological study of brain regions that are activated during AC mechanisms to highlight stark differences between responses during crises. Sidman active avoidance (Sidman, 1953), is an experimental tool that is effective at establishing the differences in the functionality of brain regions as per their relation to automatic functionality of goal-directed/instrumental threat functionality [7]. Sidman avoidance’s experimental procedure aims to bolden these anatomical differences and their impacts on AC and responses to threats through placing animals into a two-compartment cage where shocks are delivered in certain short bursts of time. The initial response of these animals is to stay in place as an innate defence mechanism. However, with training, it was discovered that close to 80% of all rodents In the experiment learned to run innate defence mechanisms and continued to experience the shocks.

For brevity, the rodents who ran will be defined as “good performers” and the rodents who stayed still will be defined as “poor performers” [8]. Animal studies by world-renowned researchers in the field of psychology and neurology show that the caudate and lateral and basal amygdala play instrumental roles in active avoidance of threat (Allen, 1972; Allen and Davison, 1973; Delacour et al., 1977; Hart et al., 2010; Lazaro-Munoz et al.,2010) [9]. The caudate and lateral and basal amygdala are crucial brain structures that cooperate to synthesize a biological response to any incoming danger or stressful environment situation. The infralimbic cortex has been shown to control active coping through inhibiting the activity of the central amygdala and therefore deterring innate involuntary responses to threats, such as freezing in place.

Studies from a research study conducted by experienced psychologists and neurologists from renowned New York institutes such as the Friedman Brain Institute among humans in which 28 healthy male adults were given a virtual game-board modelled after Sidman animal studies (Collins, Mendelsohn, Cain, Schiller et al., 2014). Indeed, this experiment was administered to be as close to the methodology of the Sidman animal studies purposely, to gauge the levels of anxiety and AC that these adults would feel, and how neural synchronization between numerous brain structures correlates to a spike or decline in AC [10]. The virtual game board, consisting of E-prime 2 and MATLAB software was programmed with two different compartments, similar to the ones provided in the Sidman trials. Participants were required to move up, left, arrow, and down keys on their keyboards to correlate movement of a green marker on the screen. There were two different trials involved in the experiment, including an AC trial (blue) and a motor control trial (yellow). Subjects read these instructions:

“During the (blue/yellow) trials, you may receive electric shocks. You may also see lightning bolt images appear above the board. Your job is to try to avoid the lightning bolts. You can avoid the lightning bolts by moving the green marker around the game board. If you avoid the lightning bolts, you will also avoid the shocks. During the (blue/yellow) trials, you will not receive any shocks or see any lightning bolts. Your job is to move the marker continuously throughout the trial. It is important to keep it moving. [11]”

The directions provided above were directly gathered from the study’s instructions. As it is seen above, the methodology of the study relied mainly on the act of a participant playing a “board game”, but the board game loss risk coupled with an adverse physiological stimulation.

The electrodes were attached to a participant’s wrist that delivered electric shocks through intensities that increased in intensity as the experiment grew in difficulty. The motor control trials mentioned above were used as a control method to compare the brain activity observed in the subjects during the AC trials to the motor control trials. The motor control trials did not produce any aversive stimuli, such as shocks. Through this methodology and fMRI data acquisition, the researchers were able to model and computerise brain regions of interests to compile information collected from both trials and compare the two to observe functionalities in terms of AC response.

The results of the study proved that there were drastic differences in the exhibition of AC behaviour, as the range of the participant’s aversion of aversive stimuli was between 0 and 89%. There were also diverse patterns of changes in behaviour over time; as it was proven that with more exposure to the stimuli, there were more changes in AC functionality [12]. However, among the experiment’s sample population it was seen that five participants mastered the task and engaged in all AC performances, two participants had shown no improvement in AC functionality, and the remaining 21 participants had varying levels of performances. The difference in “good” and “poor” learners is a crucial concept to grasp when understanding the different behaviours that individuals adopt of a population when they are presented with a threat of some type of crisis level, including a pandemic such as COVID-19.

Figure 4: Correlational data analysis between poor learners and good learners as well as their performance(s).

The subjects themselves reported more anxiety during the task compared with their report before and after the task. In terms of the larger picture, this mass difference in self-reported anxiety can be correlated with the anxiety experienced during the COVID-19 pandemic. It predicts that stress and anxiety-geared responses will stem from proximity to the crisis. For example, if the COVID-19 pandemic were to reach Person A’s house, then Person A would experience intense anxiety during the situation, but not nearly the same amount of intensity before or after the situation at hand.

As the neuroimaging results from the fMRI were analyzed, it was found that Blood Oxygen Level Dependent factors were of high abundance in the right caudate, a C-shaped structure that plays a vital role in how the brain learns, as well as in the processing and integration of memories [13]. The conclusion of the study lies within the results that AC performance in the face of impending threats relies on the connectivity between the Caudate, the anterior mPFC, and the Amygdala. The caudate’s primary functionality in this neural circuit is to act as an encoder for action-outcome associations, while the anterior mPFC is instrumental for action selection instrumental learning. Interactions between the caudate and the mPFC allow individuals to use experience and sculpt their responses in the face of a threat to this experience. In addition to this, a vital neural circuit existing between the Amygdala’s central nucleus and the infralimbic cortex is instrumental in the prevention of innate threat response that would deter AC in the face of threats.

Moreover, the research study outlined above has a massive role to play in the discussion of responses made in the face of threats, such as the COVID-19 Pandemic. The type of behaviour seen in the Sidman experiment iterations proves that the synchronized effort between the amygdala, caudate, and the anterior mPFC is important when it comes to human behaviour in the face of threat, as they play instrumental roles in the creation of an active coping response. The phenomenon of AC can serve as a testament to the mass panic and hysteria surrounding the COVID-19 Pandemic.

III. Conclusion

The highly mutating COVID-19 pandemic is dictated by unprecedented standards and taking an increasingly dominant position in our present-day society. As such, it is important to examine how this pandemic can lead to the formation of responses to the pandemic from a scientific and neurobiological perspective. To gain an understanding of a general behavioural pattern of our population, it is important to understand why these responses originate in the first place, and modern neuroscience has helped us in this endeavour. When navigating in these difficult times, modern neuroscience has taught us as an important lesson. As Barry Neil Kaufman famously said, “The way we choose to see the world creates the world we see.”


Nipun Gorantla is a 15 year old 9th grader from North Carolina, United States, and is currently studying at Marvin Ridge High School. He enjoys researching, reading, playing his guitar, and hanging out with his family. In the future, he hopes to attend his dream school, Harvard College, and pursue a career as a Neurologist.


References:

  1. Schmalzle, R., F. Hacker, B. Renner, C. J. Honey, and H. T. Schupp. “Neural Correlates of Risk Perception during Real-Life Risk Communication.” Journal of Neuroscience 33, no. 25 (2013): 10340–47. https://doi.org/10.1523/jneurosci.5323-12.2013.
  2. Schmalzle, R., F. Hacker, B. Renner, C. J. Honey, and H. T. Schupp. “Neural Correlates of Risk Perception during Real-Life Risk Communication.” Journal of Neuroscience 33, no. 25 (2013): 10340–47. https://doi.org/10.1523/jneurosci.5323-12.2013.
  3. Schmalzle, R., F. Hacker, B. Renner, C. J. Honey, and H. T. Schupp. “Neural Correlates of Risk Perception during Real-Life Risk Communication.” Journal of Neuroscience 33, no. 25 (2013): 10340–47. https://doi.org/10.1523/jneurosci.5323-12.2013.
  4. Quinn, Meghan E., and Jutta Joormann. “Control When It Counts: Change in Executive Control under Stress Predicts Depression Symptoms.” Emotion 15, no. 4 (2015): 522–30. https://doi.org/10.1037/emo0000089.
  5. Collins, K. A., A. Mendelsohn, C. K. Cain, and D. Schiller. “Taking Action in the Face of Threat: Neural Synchronization Predicts Adaptive Coping.” Journal of Neuroscience 34, no. 44 (2014): 14733–38. https://doi.org/10.1523/jneurosci.2152-14.2014.
  6. Intern. “Intern.” WisPolitics.com, May 1, 2020.  https://www.wispolitics.com/2020/uw-health-actively-coping-with-stress-during-covid-19-pandemic/.
  7. Smeets, Paul M., Simon Dymond, and Dermot Barnes-Holmes. “Instructions, Stimulus Equivalence, and Stimulus Sorting: Effects of Sequential Testing Arrangements and a Default Option.” The Psychological Record 50, no. 2 (2000): 339–54. https://doi.org/10.1007/bf03395359.
  8. Collins, K. A., A. Mendelsohn, C. K. Cain, and D. Schiller. “Taking Action in the Face of Threat: Neural Synchronization Predicts Adaptive Coping.” Journal of Neuroscience 34, no. 44 (2014): 14733–38. https://doi.org/10.1523/jneurosci.2152-14.2014.
  9. “Caudate Nucleus.” Healthline. Healthline, n.d. https://www.healthline.com/human-body-maps/caudate-nucleus.
  10. Collins, K. A., A. Mendelsohn, C. K. Cain, and D. Schiller. “Taking Action in the Face of Threat: Neural Synchronization Predicts Adaptive Coping.” Journal of Neuroscience 34, no. 44 (2014): 14733–38. https://doi.org/10.1523/jneurosci.2152-14.2014.
  11. Collins, K. A., A. Mendelsohn, C. K. Cain, and D. Schiller. “Taking Action in the Face of Threat: Neural Synchronization Predicts Adaptive Coping.” Journal of Neuroscience 34, no. 44 (2014): 14733–38. https://doi.org/10.1523/jneurosci.2152-14.2014.
  12. Collins, K. A., A. Mendelsohn, C. K. Cain, and D. Schiller. “Taking Action in the Face of Threat: Neural Synchronization Predicts Adaptive Coping.” Journal of Neuroscience 34, no. 44 (2014): 14733–38. https://doi.org/10.1523/jneurosci.2152-14.2014.
  13. “Caudate Nucleus.” Healthline. Healthline, n.d. https://www.healthline.com/human-body-maps/caudate-nucleus.

Figure Citations:

Figure 1: Schmalzle, R., F. Hacker, B. Renner, C. J. Honey, and H. T. Schupp. “Neural Correlates of Risk Perception during Real-Life Risk Communication.” Journal of Neuroscience 33, no. 25 (2013): 10340–47. https://doi.org/10.1523/jneurosci.5323-12.2013.

Figure 2: Schmalzle, R., F. Hacker, B. Renner, C. J. Honey, and H. T. Schupp. “Neural Correlates of Risk Perception during Real-Life Risk Communication.” Journal of Neuroscience 33, no. 25 (2013): 10340–47. https://doi.org/10.1523/jneurosci.5323-12.2013.

Figure 3: Collins, K. A., A. Mendelsohn, C. K. Cain, and D. Schiller. “Taking Action in the Face of Threat: Neural Synchronization Predicts Adaptive Coping.” Journal of Neuroscience 34, no. 44 (2014): 14733–38. https://doi.org/10.1523/jneurosci.2152-14.2014.

Figure 4: Collins, K. A., A. Mendelsohn, C. K. Cain, and D. Schiller. “Taking Action in the Face of Threat: Neural Synchronization Predicts Adaptive Coping.” Journal of Neuroscience 34, no. 44 (2014): 14733–38. https://doi.org/10.1523/jneurosci.2152-14.2014.