Bowdoin Science Journal

Psychology and Neuroscience

Telepathic Technology: Novel Brain Implants Help Paralyzed Patients Communicate Again

by

Whether through lively classroom discussions, profound conversations with friends, or political debates at family dinners, speech resides at the crux of human connection. Tragic, life-altering events, such as stroke, traumatic brain injury, and the development of neurodegenerative diseases such as ALS can contribute to a loss of speech, often via vocal paralysis. When the nervous system is ravaged by disease or injury, severe paralysis and consequent locked-in syndrome can occur. In cases of locked-in syndrome, a patient loses all motor abilities and can only communicate via blinking or very minimal movements, rendering traditional speech aids such as typing and writing tools useless. While years of research have produced several assistive speech devices targeted at patients with severe paralysis, these devices are often extremely limited in their vocabulary range and only offer output options that are choppy, slow, and inauthentic. Despite allowing patients to communicate to some degree, the shortcomings of these devices remove much of the character and connection a person derives through speech, leaving patients feeling socially and emotionally isolated (Ramsey and Crone, 2023).

A recent study published in Nature has partially succeeded in remedying this issue. Through the development and use of brain-computer interfaces (BCIs), scientists have created a pathway that translates a patient’s neural signals into personalized text, speech audio, and animated, expressive facial avatars. In a case study involving a 47 year-old woman suffering from severe paralysis and complete loss of speech as a result of a brainstem stroke sustained 18 years earlier, researchers Metzger et al. designed and implanted a BCI into the left hemisphere of the patient’s brain, centered on her central sulcus and spanning the regions of her brain associated with speech production and language perception. The first of its kind, this BCI harnesses electrocorticography (ECoG) to decode neural signals for attempted speech and vocal tract movements into corresponding words, phrases, and facial expressions. Similar to the more familiar and established electroencephalography (EEG), which uses small electrodes attached to the scalp to monitor electrophysiological activity in the brain, electrocorticography can directly adhere to exposed surfaces of the brain to decode electric signals (“Electrocorticography”). The specific BCI used in this study contains a high-density array of 253 individual ECoG electrodes, connected to a percutaneous pedestal connector, which allows for the signals to be decoded and displayed on a computer interface (Metzger et al., 2023).

Following surgical implantation, the BCI and its connector were hooked up to a computer, which contained deep learning models that had already been “trained” via probability data to have predictive abilities that enable them to decode certain phones, silences, and articulatory gestures into words and phrases. An additional probability-based feature called a connectionist temporal classification loss function was added to the neural decoding network to distinguish the timing of the attempted speech and signals, which allowed for order and pauses between words to be identified. After being processed through this system, the decoded signals could either be translated directly into letters, discrete vocal speech units, or discrete articulatory gestures, which respectively produce artificial text, speech, and facial expressions (Fig. 1, Metzger et al., 2023). 

 

Figure 1. Overview of multimodal speech decoding in a patient with vocal paralysis. a. Overview of the speech-decoding pathway, from neural signal to speech output. b. Magnetic resonance imaging scan (MRI) of the patient’s brain, showing stroke-induced atrophy in the brainstem resulting in severe paralysis. c. MRI of the patient’s brain, overlaid with the implanted BCI in its actual location. d. Examples of simple articulatory movements attempted by the patient, coupled with their corresponding electrode-activation maps. Bottom graphs depict evoked cortical activity for each type of movement, along with the mean ± standard error across trials.

 

This novel neuroprosthesis provides an unprecedented sense of personalization and authenticity to communication for patients with extreme paralysis. While previous assistive speech technology could at best reach a rate of 14 words per minute, the BCI used in the case study averages 78 words per minute, which is much closer to the average adult rate of speaking, roughly 150 words per minute. Additionally, the speech function can be personalized to the patient’s voice prior to their vocal paralysis, and the facial expressions can be projected onto an avatar resembling the patient, adding another layer of personalization (Metzger et al., 2023).

Innovations in speech-targeted neuroprosthetic technology have the potential to change the lives of thousands of people dealing with severe paralysis. Naturally, these solutions are not perfect– the error rate of this particular BCI is approximately 20% for direct text decoding, and 50% for direct speech decoding (Ramsey and Crone, 2023). Despite sounding high, these numbers are a remarkable improvement from more established technologies and point to the vast potential of BCIs a few years down the road. Additionally, while this case is still in its early stages, there are several other similar BCIs being developed for the same purpose (Willett et al., 2023).  As research continues, it will be fascinating to observe the continued effects of this revolutionary technology on patients’ lives.

 

References

Electrocorticography—An overview | sciencedirect topics. (n.d.). Retrieved November 4, 2023, from https://www.sciencedirect.com/topics/neuroscience/electrocorticography

Metzger, S. L., Littlejohn, K. T., Silva, A. B., Moses, D. A., Seaton, M. P., Wang, R., Dougherty, M. E., Liu, J. R., Wu, P., Berger, M. A., Zhuravleva, I., Tu-Chan, A., Ganguly, K., Anumanchipalli, G. K., & Chang, E. F. (2023). A high-performance neuroprosthesis for speech decoding and avatar control. Nature, 620(7976), 1037–1046. https://doi.org/10.1038/s41586-023-06443-4

Ramsey, N. F., & Crone, N. E. (2023). Brain implants that enable speech pass performance milestones. Nature, 620(7976), 954–955. https://doi.org/10.1038/d41586-023-02546-0

Willett, F. R., Kunz, E. M., Fan, C., Avansino, D. T., Wilson, G. H., Choi, E. Y., Kamdar, F., Glasser, M. F., Hochberg, L. R., Druckmann, S., Shenoy, K. V., & Henderson, J. M. (2023). A high-performance speech neuroprosthesis. Nature, 620(7976), 1031–1036. https://doi.org/10.1038/s41586-023-06377-x

Aspartame Exposure May Lead to Learning and Memory Defects

by

If you’re reading this article, you have probably had aspartame today. Aspartame, an artificial sweetener commonly found in diet sodas and other sugar-free products, is consumed by millions of people each day. Yet, there is still doubt about the safety of aspartame—even at levels well below the FDA’s recommended maximum daily intake. 

As the correlation between aspartame consumption and risk for metabolic diseases and cancers becomes more widely recognized (WHO Advises Not to Use Non-Sugar Sweeteners for Weight Control in Newly Released Guideline, 2023), it is equally important to evaluate the possible effects of aspartame on cognitive abilities. When aspartame is digested, it is broken down into phenylalanine, aspartic acid, and methanol. Phenylalanine can cross the blood-brain barrier and is a precursor of the monoamine neurotransmitters dopamine, epinephrine, and serotonin. These three neurotransmitters control memory, mood, motivation, and motor function, which may explain how aspartame affects the central nervous system (CNS).  

A study published this year in Nature sought to clarify how aspartame affects cognitive skills, specifically learning and memory abilities, and if the effects of aspartame are inheritable (Jones et al., 2023).The researchers chose to solely study male mice because there is less research concerning the heritability of cognitive defects from males.  

The researchers used three groups of mice, each with a different level of aspartame in their water: 0.015% aspartame, 0.03% aspartame, and no aspartame (the control group). These levels are equivalent to 7-15% of the FDA’s daily limit, and thus reflect the amount of aspartame many people consume per day (about 2-4 small diet soda drinks). The mice were treated for 16 weeks to evaluate the effect of long-term aspartame exposure.  

The first generation (F0) were tested for spatial working memory defects in weeks 4, 8, and 12 using a Y-Maze test. Mice have an innate curiosity to visit new arms of the maze instead of returning to ones previously visited. Hence, Y- Maze tests demonstrate the intact working memory of mice by seeing how well they remember the arms of the maze they already visited (Kraeuter et al., 2019). While there were no significant cognitive differences in the defects shown between mice of the 0.03% treatment group and the 0.015% treatment group, there were significant cognitive defects recorded for both groups of aspartame mice compared to the control group. The mice treated with aspartame were less likely to remember which parts of the Y-Maze they had already explored. However, no defects were found in relearning tasks or in learned helplessness evaluations between the groups.  

Next, the researchers tested to see if the spatial working memory defect could be paternally passed down to the next generation of mice. The mice from each of the three groups (0.015% aspartame, 0.03% aspartame, and the no aspartame group) were bred with females who had been living of off plain drinking water. However, if they mated with an aspartame-treated mouse, the females unavoidably received the same aspartame water for 1-5 days during exposure. The researchers do not believe this was enough exposure to produce aspartame defects from maternal inheritability because there was no exposure during pregnancy or lactation. When the Y- Maze test was conducted for this next generation, the spatial working memory defect found in the F0 generation was passed down.  

A third generation (F2) was also studied for possible transgenerational heritability. For this generation, the F1 mice from the 0.03% aspartame lineage and the control lineage were bred with female mice drinking only plain (non-aspartame) water. The 0.03% group was selected because it was the group with the largest exposure to aspartame, so any transgenerational effects would have been the most apparent. Nevertheless, the spatial working memory defect was not passed down across two generations.  

Because spatial learning and working memory defects were seen in the F0 and F1 generations, the researchers believe the daily aspartame consumption impacted the mice’s amygdala. The amygdala regulates emotional functions, learning, and memory (Hermans et al., 2014), and thus is one region that could explain the observed effects. However, other brain regions are involved in spatial working memory, so more research needs to be done to conclusively establish the mechanism underlying the aspartame-induced behavioral changes. 

While these results leave a lot of questions unanswered, they rightfully raise awareness concerning aspartame’s possible adverse effects. If the defects caused by aspartame are inheritable, the amount of people potentially affected by aspartame is far greater than currently recognized. The results of this study call for more research, especially on the long-term effects of aspartame at the levels people consume it. 

So, maybe be a bit more cautious the next time you reach for a diet coke! 

Literature Cited

Hermans, E. J., Battaglia, F. P., Atsak, P., de Voogd, L. D., Fernández, G., & Roozendaal, B. (2014). How the amygdala affects emotional memory by altering brain network properties. Neurobiology of Learning and Memory, 112, 2–16. https://doi.org/10.1016/j.nlm.2014.02.005 

Jones, S. K., McCarthy, D. M., Stanwood, G. D., Schatschneider, C., & Bhide, P. G. (2023). Learning and memory deficits produced by aspartame are heritable via the paternal lineage. Scientific Reports, 13(1), Article 1. https://doi.org/10.1038/s41598-023-41213-2 

Kraeuter, A.-K., Guest, P. C., & Sarnyai, Z. (2019). The Y-Maze for Assessment of Spatial Working and Reference Memory in Mice. Methods in Molecular Biology (Clifton, N.J.), 1916, 105–111. https://doi.org/10.1007/978-1-4939-8994-2_10 

WHO advises not to use non-sugar sweeteners for weight control in newly released guideline. (n.d.). Retrieved November 17, 2023, from https://www.who.int/news/item/15-05-2023-who-advises-not-to-use-non-sugar-sweeteners-for-weight-control-in-newly-released-guideline 

Pupil Mimicry Strengthens Infant-Parent Bonding

by

Sometimes, it is a wonder how something so small can connect human beings on a deeper level, but that is what pupil mimicry does. Pupil mimicry describes the changes in pupil size that occur in both participants during eye contact, which can help with social bonding. It also reflects the different cognitive and emotional processes that occur during eye contact and socialization, such as showing social interest (Aktar et al., 2020). When pupil size synchronously dilates, meaning the pupil expands when eye contact is made, there is a promotion of trust and bonding between the two responders. The opposite is true for synchronous pupil constriction, which diminishes a positive social bond between the two responders.  

Pupil mimicry is an old, robust phenomenon (Prochazkova et al., 2018) that is modulated by oxytocin. This evolutionarily conserved neuropeptide acts as a hormone and neurotransmitter and facilitates social bonding (Aktar et al., 2020). Pupil mimicry has been observed in monkeys and chimpanzees, where it also increases trust and social familiarity (Kret et al., 2014). This effect occurs in infants as well, which suggests that pupil mimicry may help facilitate bonding between infants and their parents. But how can scientists measure that? 

It has been shown that young infants can differentiate between their own-race faces and other-race faces. Therefore, scientists hypothesized that infants would have quicker pupillary responses to pupils belonging to the same race as their parents when compared to other races. Researchers Aktar, Raijmakers, and Kret conducted a study with three aims to test this hypothesis: 

  1.  Do infants’ pupils react to dynamic videos of eyes with pupil sizes that change realistically? 
  2. Do parents and infants have the same speed in matching pupil size? 
  3. Do both the parents’ and infants’ pupils have differing rates of pupil mimicry between own-race faces and other-race faces

For the first aim, infants and parents watched black-and-white dynamic videos of same-race models (Dutch male and female) that had constricting, static, or dilating pupils while their pupillary reactions were tracked (Figure 1; Aktar et al., 2020). For the second and third aims, infants and parents watched black-and-white dynamic videos with two races: Dutch for the same-race category and Japanese for the other-race category (Aktar et al., 2020). The researchers compared the parents’ pupil mimicry speed to the infants’. 

Figure 1: Experimental set-up of infants and parents as they observe the stimuli (Aktar et al., 2020).

The researchers confirmed that both infants and parents were able to perform pupil mimicry. They also found that parents had quicker pupil response to dilated or constricted pupils than infants, possibly due to adults being more cognitively advanced than infants. Finally, they concluded that there was no significant difference in pupil mimicry response between own race and other races, but there were slight pupil mimicry delays. The researchers have several explanations for the slight delays. For instance, the infants’ pupils tend to stay dilated when they see a dilated pupil, regardless of race, since infants are still developing their pupil mimicry control. For adults, pupil mimicry tends to take about 2.5 milliseconds longer when given other-race stimuli. This may be from greater cognitive effort used to process other-race faces than own-race faces (Aktar et al., 2020). 

The key finding of the research is race does not affect the participants’ rate of pupil mimicry during emotionally neutral interactions (Aktar et al., 2020). So, though pupil mimicry helps strengthen parent-infant relationships, infants also have the skills to establish trust and awareness with strangers regardless of race. However, when infants are not in a neutral setting, meaning an environment where they feel unsafe and discontent, they are more likely to seek out their parents and less likely to make eye contact (Aktar et al., 2020). That is why, if the infants felt fussy or frightened, the researchers sat the parents right next to them to provide a feeling of safety (Figure 1). Eye contact conveys a great deal of information. Maybe the next time you make eye contact with someone, stare at them to see how their pupil responds to you!

References

Aktar, E., Raijmakers, M. E. J., & Kret, M. E. (2020). Pupil mimicry in infants and parents. Cognition and Emotion, 34(6), 1160–1170. https://doi.org/10.1080/02699931.2020.1732875

Kret, M. E., Tomonaga, M., & Matsuzawa, T. (2014). Chimpanzees and humans mimic pupil-size of conspecifics. PloS one, 9(8), e104886. https://doi.org/10.1371/journal.pone.0104886

Prochazkova, E., Prochazkova, L., Giffin, M. R., Scholte, H. S., De Dreu, C. K. W., & Kret, M. E. (2018, July 16). Pupil mimicry promotes trust through the theory-of-mind network – PNAS. Proceedings of the National Academy of Sciences. https://www.pnas.org/doi/10.1073/pnas.1803916115

Are Kids Innately Helpful? The Bystander Effect in Children

by

Many of us reflect on our childhoods as a time of blissful lack of social inhibition. As kids, we are often unaffected by the sense of embarrassment and self-consciousness which can hold us back as adults. This lack of social inhibition could explain why kids — even those as young as one year old — seem to have an inherent inclination to be helpful towards others (Plötner et al., 2015, p. 500). In fact, psychological forces which lead adults to resist offering their help can have a very different effect on kids. One such example is the bystander effect, which suggests that people are less likely to jump in and help someone in need when other onlookers are present. A previous study showed that the presence of bystanders can actually make children more likely to help (Plötner et al., 2015, p. 500). Researchers have identified three forces which may contribute to the bystander effect: social referencing, diffusion of responsibility, and shyness to act in front of others. Maria Plötner and colleagues recently conducted a study on the impact of diffusion of responsibility — the feeling of decreased sense of duty to help when more people are around — on children’s willingness to help an adult in need. The study calls into question our previous conceptions of kids as innately helpful, suggesting that children as young as five may be less likely to engage in helping behavior in the presence of other bystanders.

The researchers recruited sixty five-year-old participants to be assigned randomly to one of three conditions. In all conditions, the experimenter told the children that they were going to color a picture while she painted a cardboard wall. The test of helping came in after about thirty seconds, when the experimenter spilled her cup of dirty paint water all over her table. At regular intervals, she recited scripted dialogue (“oops,” “my cup has fallen over,” etc.) to let the children know she needed their help getting paper towels, which she had used to clean something up at the start of the experiment but were now out of her reach (Plötner et al., 2015, p. 501–502).

Figure 1. The alone (a), bystander (b), and bystander-unavailable (c) conditions.

For each of the three conditions, the researchers recorded whether or not children helped the experimenter by bringing her a paper towel. The first was the “alone” condition, which was identical to the other two except for the fact that, as you might guess, there were no other children present. When they were alone, the subjects helped the experimenter about ninety five percent of the time (Plötner et al., 2015, p. 503). On the other hand, in the “bystander” condition, two “confederates” — other children in on the study — were present when the experimenter spilled her water and did nothing to help her. Here only about fifty five percent of subjects helped (Plötner et al., 2015, p. 503). Some might account for this difference using the concept of social referencing, our tendency to look to others when an emergency or accident takes place. When those around us seem not to notice or care about what’s happening, we use their behavior to inform our own. It’s easy to see how this could make the five-year-old subjects much less likely to bring the experimenter a paper towel, considering that the confederates had seen what happened but were completely indifferent to the experimenter’s distress.

Figure 2. The percentage of children who helped in each of the three conditions.

But this study’s third condition, termed “bystander-unavailable,” put the social referencing explanation to the test. In this condition, while two other children were present, they were seated in such a way that they were physically unable to get up and help the experimenter (see Figure 1). Lo and behold, the children were much more likely to help when the other bystanders were “unavailable” than when they were also able to help. In fact, subjects helped just as often in the bystander-unavailable condition as when they were alone (Figure 2) (Plötner et al., 2015, p. 503). So social referencing really couldn’t explain why bystanders made the children less likely to help, since they were able to observe others’ indifference toward the experimenter in both bystander conditions. And if shyness to act in front of others had made the difference, we would have seen a low likelihood to help whenever others were present, not just in the regular bystander condition. So the researchers concluded that the diffusion of responsibility was the key force limiting these five-year-olds’ tendency to help while in the presence of others.

This study presents a novel discovery of the ability of even young children to dismiss their own sense of duty when they know they are not the only ones able to help. More broadly, it highlights the important role of taking responsibility for helping others in motivating us to actually step in. Meta-analyses of other studies on the subject have emphasized this, showing that people are less likely to take responsibility for helping when more bystanders are present and when “the need for help is ambiguous” (Plötner et al., 2015, p. 500). While it may not be possible to overcome the seemingly innate phenomenon of the bystander effect, being aware of it may allow us to take responsibility and offer our help in moments of need.

References

Plötner, M., Over, H., Carpenter, M., & Tomasello, M. (2015). Young Children Show the Bystander Effect in Helping Situations. Psychological Science, 26(4), 499-506. https://doi.org/10.1177/0956797615569579

Caution in STEM: Inhibition, Intuition, and Counterintuitive Reasoning

by

Imagine you’re on a 1950s game show. The host presents three doors and lays out the rules: Behind one door is a car, and behind the other two are goats. After you choose a door, the host, knowing what’s behind each door, opens one of the remaining two doors, revealing a goat. You have the opportunity to switch. Do you?

This is, of course, the infamous Monty Hall problem. Assuming you prefer the car over the goat, the answer is to always switch, since it will give you double the probability—⅔ rather than ⅓—of winning the car. Here’s an explanation that goes through each possible case (Table 1):

Table 1: Possible outcomes for staying and switching in the Monty Hall problem (Saenen et al., 2018)

If you got it wrong, you’re not alone—between 79% and 87% of adults get it wrong, too (Saenen et al., 2018). But what is behind this phenomenon? Solving unintuitive problems like the Monty Hall problem is thought to require the inhibition of misleading information, such as from prior knowledge or false cues (Dumontheil et al., 2022; Saenen et al., 2018). However, a 2018 study by Brookman-Byrne et al. and a 2022 study by Dumontheil et al. shine a new, more nuanced light on the connection between inhibitory control and (counter)intuition.

Both studies had British schoolchildren aged 11-15 undergo a volley of tests assessing their response inhibition (the ability to manage and filter out conflicting information), semantic inhibition (the ability to suppress responses driven by impulse ), vocabulary, reasoning, and working memory. Researchers then had participants complete a set of intuitive (control) and counterintuitive math and science problems. Dumontheil et al. (2022) measured neural activity using fMRI (functional Magnetic Resonance Imaging, an imaging technique which measures blood-oxygen levels to determine which parts of the brain are active) throughout.

Unsurprisingly, researchers consistently found that participants were more accurate and faster in solving intuitive problems than counterintuitive problems. Furthermore, in counterintuitive reasoning, response inhibition predicted response times, whereas semantic inhibition predicted accuracy. Interestingly, however, the only predictors of counterintuitive reasoning ability found in both studies were a more extensive vocabulary and increased age, both of which also predicted response inhibition (Brookman-Byrne et al., 2018; Dumontheil et al., 2022). Given these unexpected findings, neuroimaging results by Dumontheil et al. (2022) were necessary to provide some insight into what goes on in participants’ brains. 

Figure 1: Brain regions showing greater activation for (A) counterintuitive versus control (intuitive) problems, (C) response inhibition versus no response inhibition, and (D) semantic inhibition versus no semantic inhibition (Dumontheil et al., 2022). 

Figure 2: A comparison between areas showing increased activation during counterintuitive reasoning and (A) complex inhibition behavior, and (B) interference control behavior (Dumontheil et al., 2022). 

Since the overlap is limited in Figure 2, researchers concluded that the relationship between inhibitory control and counterintuitive problem solving was not direct (Dumontheil et al., 2022). They posit that the role of inhibition in counterintuitive reasoning may be limited to specific types of inhibition. In particular, semantic inhibition might be a better explanation than just response inhibition (Dumontheil et al., 2022). 

Neurosynth (an fMRI image database) also associates areas activated during counterintuitive reasoning with “working memory,” “calculation,” “symbolic,” “attention,” “visually,” and “spatial,” suggesting that inhibition is not the only factor at play (Dumontheil et al., 2022). They highlight that two areas known as the intraparietal sulcus (IPS) and Brodmann area 7 (BA 7)—which together are responsible for visuo-spatial attention—show increased activation during counterintuitive reasoning, response inhibition, and semantic inhibition (Dumontheil et al., 2022). Hence, they also suggest that visuo-spatial attention may be another factor in counterintuitive reasoning (Dumontheil et al., 2022). 

So what does this mean, practically? For educators, it seems that curriculum design in STEM should not be done in isolation. Given the impact of semantic reasoning, it would be prudent to balance training in purely symbolic reasoning with training in semantic reasoning (e.g., by requiring humanities classes be taken with STEM classes). For cognitive neuroscientists, this research suggests that there may be another dimension to understanding counterintuitive reasoning: the complex causal relationships between visuo-spatial attention, inhibitory control, and counterintuitive reasoning. Indeed, this is a cautionary tale about the importance of inhibition in science itself—causation is difficult to establish, and the most intuitive models in science may not always be right, either.

 

References

Brookman-Byrne, A., Mareschal, D., Tolmie, A. K., & Dumontheil, I. (2018, June 21). Inhibitory control and counterintuitive science and maths reasoning in adolescence. PLoS ONE, 13(6), 1-19. https://doi.org/10.1371/journal.pone.0198973

Dumontheil, I., Brookman-Byrne, A., Tolmie, A. K., & Mareschal, D. (2022). Neural and Cognitive Underpinnings of Counterintuitive Science and Math Reasoning in Adolescence. Journal of Cognitive Neuroscience, 34(7), 1205. https://doi.org/10.1162/jocn_a_01854

Saenen, L., Heyvaert, M., Van Dooren, W., Schaeken, W., & Onghena, P. (2018). Why Humans Fail in Solving the Monty Hall Dilemma: A Systematic Review. Psychologica Belgica, 58(1), 128-158. https://doi.org/10.5334/pb.274

 

Retrieving Forgotten Memories With Optogenetics

by

Alzheimer’s disease is a neurodegenerative disorder in which patients experience a progressive decline in memory (Roy et. al, 2016). The early stages involve struggles to remember mostly episodic memories, which are memories of personal experiences that are linked to activity in the hippocampus. To discover potential treatments, scientists have tried to better understand the biological mechanisms behind the disease. Optogenetic activation of engram cells and calculation of dendritic spine density are two methods to research the disease. Dendritic spines are extensions from dendrites, which are the structures that receive signals from other neurons and pass them to the cell body. Memory is associated with changes in dendritic spines or the formation of new ones, which makes scientists believe that they serve as sites for memory formation and storage (Roy et. al, 2016). Engram cells are biological traces of established memories that store the information and are reactivated when memories are retrieved (Ortega-de San Luis et al., 2022). Optogenetics is a technique in which genes for light sensitive proteins are injected into specific neurons in the brain so that they can be activated by laser lights that open ion channels (Figure 1). 

Figure 1. The process of optogenetics (Adapted from Buchen, 2010). 

A 2016 study affiliated with the Massachusetts Institute of Technology used optogenetics to activate engram cells in the hippocampus of mouse models of early Alzheimer’s disease (AD) (Roy et al., 2016). With direct activation of hippocampal cells, the mice were able to retrieve the memories they had forgotten, indicating that the issue is retrieving memories; the memories are still available in the brain, but there is a problem in recalling them. Amnesia in early AD mice is also correlated with a reduction of spine density of engram cells in the hippocampus. The study showed that restoring dendritic spine density with optogenetic activation allows for the retrieval of long-term memory. Spine density was restored as the optogenetic activation allowed sodium ions to flow into the neurons and consequently depolarized them (meaning the cell is less negatively charged). This depolarization triggers long term potentiation, which is the process of strengthening connections between neurons that underlies memory and learning. Thus, restoration of spine density could lead to an effective strategy for treating memory loss in patients with early Alzheimer’s disease. 

In the experiment, the scientists labeled engram cells in the mouse models of Early Alzheimer’s disease and tested memory recall with contextual fear conditioning and long-term memory testing. Contextual fear conditioning is when mice are trained to associate a particular context (a cage) with a shock, creating a memory of the association in the hippocampus. Long-term memory was tested by placing them back into the cage and measuring their freezing behavior, a commonly used measure of fear. They also calculated dendritic spine density. 

The researchers found that optogenetic activation of memory engrams restores fear memory in early AD mice and that reversal of engram-specific spine deficits rescues memory in early AD mice. The diagram below shows the process of fear conditioning (panel t): the mice were trained to associate a shock with a certain context, and then their long term memory was tested by placing them back into the cage and observing their “freeze response,” which demonstrates that they feel fear in the cage because of the memory of the shock in the cage. The graphs show that when the engrams were activated optogenetically, the mice froze more than they did when the engrams were not activated, indicating that the optogenetic activation of the engram cells restored the mice’s memory of the fearful context. 

Figure 2. Optogenetic activation of memory engrams restores fear memory in early AD mice (Adapted from Roy et al., 2016, Fig. 1). 

The results of this study provide directions and hope for future treatments for Alzheimer’s Disease. If the amnesia is due to retrieval impairments, memory could be restored by technologies involving brain stimulation, like the optogenetic activation did in the mouse models. However, optogenetics is currently a technique only possible in animals since it is invasive, so further research will have to be done to discover a technique employing the principle in a more plausible way for humans, perhaps by finding a way to restore spine density in engram cells. There are other limitations when it comes to future treatments for Alzheimer’s because of the fact that they studied only early AD mice and episodic memory. Even though they showed that amnesia in early AD mice impairs memory retrieval, long-term memory storage in advanced stages of AD may also be impaired and eventually lost as neuronal degeneration progresses. Also, humans with early AD often exhibit non-episodic memory deficits as well, which involve brain structures outside of the medial temporal lobe that the current study did not investigate. However, overall, the findings contribute to a better understanding of memory retrieval deficits in early cases of AD, which may also apply to other neurological diseases in which patients have difficulty with memory retrieval, such as Huntington’s Disease. 

 

Literature Cited

Buchen, L. (2010). Neuroscience: Illuminating the brain. Nature, 465(7294), Article 7294. https://doi.org/10.1038/465026a

Ortega-de San Luis, C., & Ryan, T. J. (2022b). Understanding the physical basis of memory: Molecular mechanisms of the engram. The Journal of Biological Chemistry, 298(5), 101866. https://doi.org/10.1016/j.jbc.2022.101866

Roy, D. S., Arons, A., Mitchell, T. I., Pignatelli, M., Ryan, T. J., & Tonegawa, S. (2016d). Memory retrieval by activating engram cells in mouse models of early Alzheimer’s disease. Nature, 531(7595), 508–512. https://doi.org/10.1038/nature17172

Yuhas, D. (n.d.). Forgotten Memories May Remain Intact in the Brain. Scientific American. Retrieved November 4, 2023, from https://www.scientificamerican.com/article/forgotten-memories-may-remain-intact-in-the-brain/

ChatGPT Beats Humans in Emotional Awareness Test: What’s Next?

by

In recent times, it can seem like everything revolves around artificial intelligence (AI). From AI-powered robots performing surgery to facial recognition on smartphones, AI has become an integral part of modern life. While AI has affected nearly every industry, most have been slowly adapting AI into their field while trying to minimize the risks involved with AI. One such field with particularly great potential is the mental health care industry. Indeed, some studies have already begun to study the uses of AI to assist mental health work. For instance, one study used AI to predict the probability of suicide through users’ health insurance records (Choi et al., 2018), while another showed that AI could identify people with depression based on their social media posts (Aldarwish & Ahmed, 2017). 

Perhaps the most wide-spread AI technology is ChatGPT, a public natural language processor chatbot that can help you with a plethora of tasks, from writing an essay to playing chess. Much discussion has been done about the potential of such chatbots in mental health care and therapy, but few studies have been published on the matter. However, a study by Zohar Elyoseph has started the conversation of chatbots’ potential, specifically ChatGPT, in therapy. In this study, Elyoseph and his team gave ChatGPT the Levels of Emotional Awareness Scale (LEAS) to measure ChatGPT’s capability for emotional awareness (EA), a core part of empathy and an essential skill of therapists (Elyoseph et al., 2023). The LEAS gives you 20 scenarios, in which someone experiences an event that supposedly elicits a response in the person in the scenario, and the test-taker must describe what emotions the person is likely feeling. Two examinations of the LEAS, one month apart, were done on ChatGPT to test two different versions of ChatGPT. This was done to see if updates during that month would improve its ability on the LEAS. On both examinations, two licensed psychologists scored the responses from ChatGPT to ensure reliability of its score. On the first examination in January 2023, ChatGPT achieved a score of 85 out of 100, compared to the French men’s and female’s averages of 56.21 and 58.94 respectively. On the second examination in February 2023, ChatGPT achieved a score of 98: nearly a perfect score, a significant improvement from the already high score of 85 a month prior, and a score that is higher than most licensed psychologists (Elyoseph et al., 2023).

This study shows that, not only is ChatGPT more capable than humans at EA, but it is also rapidly improving at it. This has massive implications for in-person therapy. While there is more to being a good therapist than just emotional awareness, it is a major part of it. Therefore, based on this study, there is potential for chatbots like ChatGPT to rival, or possibly even replace, therapists if developers are able to develop the other interpersonal traits of good therapists. 

However, ChatGPT and AI needs more work to be done before it can really be implemented into the mental health field in this manner. To start, while AI is capable of the technical aspects of therapy, such as giving sound advice and validating a client’s emotions, ChatGPT and other chatbots sometimes give “illusory responses”, or fake responses that it claims are legitimate (Hagendorff et al., 2023). For example, ChatGPT will sometimes say “5 + 5 = 11” if you ask what 5 + 5 is, even though the answer is clearly wrong. While this is a very obvious example of an illusory response, harm can be done if the user is not able to distinguish between the real and illusory responses for more complex subjects. These responses can be extremely harmful in situations such as therapy, as clients rely on a therapist for guidance, and if such guidance were fake, it could harm rather than help the client. Furthermore, there are concerns regarding the dehumanization of therapy, the loss of jobs for therapists, and the breach of a client’s privacy if AI was to replace therapists (Abrams, 2023). ​​

Fig 1. Sample conversation with Woebot, which provides basic therapy to users. Adapted from Darcy et al., 2021. 

However, rudimentary AI programs are already sprouting that try to bolster the mental health infrastructure. Replika, for instance, is an avatar-based chatbot that offers therapeutic conversation with the user, and saves previous conversations to remember them in the future. Woebot provides a similar service (Figure 1), providing cognitive-behavioral therapy (CBT) for anxiety and depression to users (Pham et al., 2022). While some are scared about applications such as these, these technologies should be embraced since, as they become more refined, they could provide a low-commitment, accessible source of mental health care for those who are unable to reach out to a therapist, such as those who are nervous about reaching out to a real therapist, those who live in rural environments without convenient access to a therapist, or those who lack the financial means for mental health support. AI can also be used as a tool for therapists in the office. For example, an natural language processing application, Eleos, can take notes and highlight themes and risks for therapists to review after the session (Abrams, 2023). 

There are certainly some drawbacks of AI in therapy, such as the dehumanization of therapy, that may not have a solution and could therefore limit AI’s influence in the field. There is certainly a chance that some people would never trust AI to give them empathetic advice. However, people said the same when robotic surgeries began being used in clinical settings, but most people seem to have embraced that due to its superb success rate. Regardless of whether these problems are resolved, AI in the mental health industry has massive potential, and we must make sure to ensure that the risks and drawbacks of such technology are addressed and refined so that we can make the most of this potential in the future and bring better options to those who need it. 

 

Citations

Abrams, Z. (2023, July 1). AI is changing every aspect of psychology. Here’s what to watch for. Monitor on Psychology, 54(5). https://www.apa.org/monitor/2023/07/psychology-embracing-ai

 

Aldarwish MM, Ahmad HF. Predicting Depression Levels Using Social Media Posts. Proc – 2017 IEEE 13th Int Symp Auton Decentralized Syst ISADS 2017 2017;277–80.

 

Choi SB, Lee W, Yoon JH, Won JU, Kim DW. Ten-year prediction of suicide death using Cox regression and machine learning in a nationwide retrospective cohort study in South Korea. J Affect Disord. 2018;231(January):8–14.

 

Darcy, Alison & Daniels, Jade & Salinger, David & Wicks, Paul & Robinson, Athena. (2021). Evidence of Human-Level Bonds Established With a Digital Conversational Agent: Cross-sectional, Retrospective Observational Study. JMIR Formative Research. 5. e27868. 10.2196/27868. 

 

Elyoseph, Z., Hadar-Shoval, D., Asraf, K., & Lvovsky, M. (2023). ChatGPT outperforms humans in emotional awareness evaluations. Frontiers in psychology, 14, 1199058. 

 

Hagendorff, T., Fabi, S. & Kosinski, M. Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT. Nat Comput Sci 3, 833–838.

 

Pham K. T., Nabizadeh A., Selek S. (2022). Artificial intelligence and chatbots in psychiatry. Psychiatry Q. 93, 249–253.



PedPRM Unveils Promising Treatment for Insomnia in Children with Autism Spectrum Disorder

by

Getting enough sleep is widely considered crucial to our well-being. However, for some individuals with Autism, getting enough quality sleep is not as easy as it sounds. Autism Spectrum Disorder (ASD) is a developmental disorder that widely affects the U.S. population, as 1 in 36 children aged eight has been diagnosed with autism (CDC, 2023). The kind and severity of the symptoms that individuals with ASD may exhibit can vary along the ASD spectrum.  Sleeping, in particular, is a common challenge for those with ASD, as many suffer from problems related to the REM (Rapid Eye Movement) sleep phase, a phase critical for memory consolidation (Devnani & Hedge, 2015). This can quickly become a vicious cycle for some, as lack of sleep can increase the severity of other Autism-related symptoms but also affect both the individual and the quality of life of their family or caretakers. 

To investigate possible treatments, a recent study by Malow et al. focuses on the pharmacological approach of using melatonin to treat sleep disorders. Melatonin is a hormone produced by the body’s pineal gland to regulate circadian rhythm, allowing the body to relax according to the appropriate light-darkness cycles of the day. This study uses a small, long-release tablet (PedPRM) used to mimic endogenous melatonin secretion. It focused mainly on a younger population, from children to adolescents, who met two criteria. The first was that they had confirmed diagnoses of either Autism or Smith-Magenis syndrome and had also experienced sleep abnormalities. Smith-Magenis syndrome is also a developmental disorder that involves symptoms similar to those of ASD, affecting behavior, cognition, and sleep. They also had to have not previously seen improvements when using sleep hygiene treatments to be included in the study, such as establishing a strict bedtime routine and taking steps to provide a calm and comfortable sleep environment (CHOC, 2023).

With the study sample set, the study was conducted over 108 weeks and divided into four phases (Graph I). Throughout the four phases, participants’ caregivers would document sleep quality and total sleep time in a sleep and nap diary to record the efficacy of treatment. The study participants started the first stage with a 2-week period in which they were given placebos. If participants showed improvement while receiving a placebo, they would be removed from the study to reduce the possibility of external factors affecting the results (Scott et al., 2021). After clearing Phase 1, participants entered the second phase, which consisted of a double-blinded 13-week period in which they were randomly placed into either a placebo or treatment group (PedPRM). After this, Phase 3 comprised a longer 91-week open-label period in which both groups were combined. For the final phase of the study, participants were placed again in a 2-week single-blind placebo period to ensure that the drug had been completely removed from the participants with no adverse effects after stopping treatment.

Upon concluding the experimental period, the results to be considered for this study could be divided into three groups: participants’ sleep quality, caregivers’ well-being, and the participants’ growth development. With this data, the researchers found a significant

 decrease in sleep disturbance (Graph II-A) and an increase in caregiver satisfaction (II-B) and quality of life (II-C). These were most pronounced during the first half of the treatment. In the latter half of treatment, sleep disturbance continued to decrease but at a slower pace than the initial treatment phase. Fortunately, there were no reported deaths, and most adverse reactions included daytime fatigue and mood swings. However, the severity and extent of these were not detailed in the results of the study and offer the potential to be analyzed further.  

The study shows compelling initial evidence that PedPRM is an effective treatment for sleep disorders in individuals with ASD. However, as noted by the researchers, it also shows that constant active treatment is required as most sleep quality improvements are removed upon halting treatment. Since medications for children are generally more strictly controlled, PedPRM consistently demonstrates a possibility for effective pediatric treatment, even if for long-term medication. In particular, these findings are essential as it has been found that rapid-release melatonin is not helpful with maintaining sleep a couple of hours after administration, and it had long been considered a challenge to find small, swallowable prolonged-release tablets for children (Fliesler, 2022). As sleep interruption is something that mainly affects those with neurodevelopmental disorders, this is a significant step towards adequate treatment. However, it is essential to note that this pharmacological alternative should only be considered if behavioral interventions and sleep hygiene modifications have been attempted but have been found unsuccessful. 

 

 

Sources 

CDC. (2022, December 9). Centers for Disease Control and Prevention. https://www.cdc.gov/ncbddd/autism/facts.html 

CDC Newsroom. (2016, January 1). CDC. https://www.cdc.gov/media/releases/2023/p0323-autism.html 

CHOC – Children’s Hospital of Orange County. (2023, March 2). Autism and Sleep Hygiene – Children’s Hospital of Orange County. Children’s Hospital of Orange County. https://www.choc.org/programs-services/autism-neurodevelopmental/co-occurring-conditions-program/autism-and-sleep/ 

Devnani, P., & Hegde, A. U. (2015). Autism and sleep disorders. Journal of Pediatric Neurosciences, 10(4), 304. https://doi.org/10.4103/1817-1745.174438 

Fliesler, N. (2022, June 13). Melatonin for kids: Is it effective? Is it safe? – Boston Children’s Answers. Boston Children’s Answers. https://answers.childrenshospital.org/melatonin-for-children/#:~:text=There%20is%20some%20evidence%20to,the%20ability%20to%20swallow%20capsules. 

Furfaro, H. (2023, March 10). Sleep problems in autism explained. Spectrum | Autism Research News. https://www.spectrumnews.org/news/sleep-problems-autism-explained/ 

Lemoine, P., Garfinkel, D., Laudon, M., Nir, T., & Zisapel, N. (2011). Prolonged-release melatonin for insomnia – an open-label long-term study of efficacy, safety, and withdrawal. Therapeutics and Clinical Risk Management, 301. https://doi.org/10.2147/tcrm.s23036 

Malow, B. A., Findling, R. L., Schröder, C., Maras, A., Breddy, J., Nir, T., Zisapel, N., & Gringras, P. (2021b). Sleep, Growth, and puberty after 2 years of Prolonged-Release Melatonin in children with Autism Spectrum Disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 60(2), 252-261.e3. https://doi.org/10.1016/j.jaac.2019.12.007 

Scott, A., Sharpe, L., Quinn, V. F., & Colagiuri, B. (2022). Association of single-blind placebo run-in periods with the placebo response in randomized clinical trials of antidepressants. JAMA Psychiatry, 79(1), 42. https://doi.org/10.1001/jamapsychiatry.2021.3204 

Signs & Symptoms | Autism Spectrum Disorder (ASD) | NCBDDD | CDC. (2023, January 11). Centers for Disease Control and Prevention. https://www.cdc.gov/ncbddd/autism/signs.html

Distributions, not Differentiation: New Studies Find Cell Proportions at the Heart of Neurological Specialization

by

The human brain is organized into cortices, lobes, hemispheres, and more, with every designation serving as a location where a particular function necessary for survival is hosted. In understanding the cell types of the brain, scientists can further shape the understanding of the nature of human life. Current work strives toward comprehending the functions and capacities of the brain and developing stronger foundations for modeling brain physiology to support future research and medical applications. All of the advancements discussed originated from Alyssa Weninger and Paola Arlotta’s Science review article, A family portrait of human brain cells, which compiles recent findings in brain mapping research as aligned with the National Institute of Health’s BRAIN Initiative. In the article, Weninger and Arlotta summarize and discuss the work of multiple groups of neuroscientists that have developed new findings about the brain’s composition and variability across regions, individuals, and species (specifically five primates of interest and mice).

As suggested by the article, recent research from multiple teams of neuroscientists utilized a variety of study mechanisms to compare the composition of the brain. One of the most important tools used in the studies included single-cell profiling. This profiling technique analyzes cellular behavior through multiple methods that include their transcriptome (range of genetic information produced to control cell behavior), proteome (range of proteins produced by the cell), and epigenome (range of modifications and markings that control the genetic information expressed by a cell) to organize them into groups based on their functional similarities. Models that encompass these methods and human organoids (structures of organs derived from STEM cells that mimic organ tissue) are developed to model the brain and its cells. They are also used in mapping and developing comparative analyses to determine significant findings and understanding of the brain organization.

Comparisons of cell composition in regions across the brain resulted in findings from researchers under Siletti from the University of North Carolina at Chapel Hill and Jorstad from Harvard University. The two groups found that rather than mainly having different types of cells in different parts of the brain, some different parts of the brain shared the same cells but had different proportions of these cells. There were some exceptions, such as inhibitory neurons in the primary visual cortex, although the explanation of this finding is unclear. Such results change the understanding of evolutionary diversity in that diversification does not depend heavily on having many different cell types, but rather on having varying proportions of cells with small differences.

Jorstad’s group also developed a significant result in identifying differences in brain composition between human individuals. One cell type from 75 individuals was profiled and resulted in different classes of cells bearing contrasting levels of variability among individuals. Most of the explanatory factors were beyond demographic differences, such as gender, ancestry, or age. The reason for such differences is still unclear. Scientists are further encouraged to study bigger cohorts of people to further examine the origin of differences in variability across humans.

The finding of varying cell proportions held as Jorstad’s group conducted interspecies comparisons, comparing human compositions with other primates (specifically chimpanzees, gorillas, rhesus macaques, and marmosets). The exceptional cognitive ability found in humans was largely supported by differences in proportions of brain cell types rather than the variability of cell types. Additionally, faster evolutionary divergence may explain the differences in gene expression found between supportive tissue, known as glial cells, in the brain. This allowed for further species-specific development across primates. Only a limited number of gene patterns specific to humans were found, most of them concentrated in parts of the brain with human evolutionary change. As such, scientists have come to understand that attributes of the human brain are derived from very few cellular or molecular changes, leaving differences in cell proportions as the most prominent explanatory factory for human brain development. Furthermore, understanding the brains of related primates and their relation to human brains will help scientists develop new models for brain pathways and understand the kinds of questions that they will be able to answer with such knowledge in the future.

Neuroscientists today continue to work hard toward developing human brain models. Current studies are focusing on developing accurate organoids – three-dimensional tissue models of stem cells developed to mimic organs in structure and function. Velmeshev’s group of researchers worked towards profiling different cortical (outer layers of the upper brain) areas and related areas in fetuses to track developments across human births. Kim’s group of researchers investigated single-cell transcriptomes of the thalamus (the processor of sensory data) during its development but was missing an investigation of the thalamus cellular compositions. The work of these scientists contributes to the idea of molecular mechanisms as the driver of cellular diversity in the brain, but also calls for more innovation in external biological investigations to better model the brain and further study its composition. In doing so, neuroscientists will come even closer to understanding one of the most complex systems in the human body and develop more answers for current-day neurological problems.

Bibliography

Weninger, Alyssa, and Paola Arlotta. “A Family Portrait of Human Brain Cells.” Science, vol. 382, no. 6667, Oct. 2023, pp. 168–69, https://doi.org/10.1126/science.adk4857.

The Contraceptive Brain Drain: How Birth Control Alters Women’s Brains

by

There are millions of women taking steroids every day. But how is this possible? Are they just getting really buff? It feels like we always hear stories about how performance-enhancing drugs, namely steroids, are giving world-class athletes the boost they need to beat out their competition. But women across the globe are taking steroids every day as well, in the form of hormonal birth control. Despite their widespread use, side effects of hormonal contraceptives are largely unstudied, or have been until recently. In the last ten years, several studies have come out about the effect of taking a daily dose of steroids on women’s brains and mental health, which until now has been a severely neglected area where lack of knowledge affects millions of people worldwide. 

People take hormonal birth control, or hormonal contraceptives, for a myriad of reasons, from the obvious (preventing pregnancy) to the not-so-obvious (lessening iron deficiency) and everything in between. This type of medication simply refers to methods of pregnancy prevention that act on the endocrine system. The endocrine system controls growth, development, metabolism, and reproduction via signaling molecules called hormones. Two hormones in particular, estrogen and progesterone, control the menstrual cycle and are therefore the major components of hormonal birth control. Types of hormonal contraceptives come in many forms including the pill, the patch, the implant, injections, and hormonal intrauterine devices or IUDs, but despite the wide variety in the forms this medication takes, all of them contain one or both of these two hormones. As steroids, both estrogen and progesterone affect other body systems besides the reproductive system.

To study the impacts of taking a daily dose of steroids on other areas of the body, specifically the brain, Dr. Belinda Pletzer and her colleagues conducted a study in 2010. The brain is particularly susceptible to change due to an influx of synthetic hormones because it contains a very high quantity of hormone receptors. The brain needs to act as a “sponge” for these molecules since it plays an important role in creating the appropriate responses in the rest of the body. Pletzer’s study investigated how the sponginess of the brain would affect changes in its structure by comparing images of the brains of adult men, adult women during different stages of their menstrual cycle, and adult women taking hormonal contraceptives. To perform this comparison they used a technique called voxel-based morphology on MRIs of study participants (Pletzer et al., 2010). Voxel-based morphology measures differences in the concentration of tissue and the size and shape of different areas of the brain.

Overall, they found that women taking hormonal birth control had smaller areas of gray matter, or areas of the brain that have a high concentration of the cell bodies of nerve cells, when compared to “naturally cycling women” in both their follicular and luteal menstrual phases (Figure 1). Pletzer’s study also found interesting gendered differences in gray matter volume. While men had greater gray matter overall, the volume of gray matter in the prefrontal cortex, the pre-and postcentral gyri, and the supramarginal gyrus of both naturally cycling women and women taking hormonal contraceptives was higher than the volume of gray matter in these areas in men (Figure 2). These areas are involved in decision-making and problem-solving, controlling motor function, and emotional responses. However, the higher amount of gray matter in women in these areas was overshadowed by the larger volume of gray matter in men in the hippocampus, hypothalamus, parahippocampal and fusiform gyri, putamen, pallidum, amygdala, and temporal regions of the brain during the early follicular phase (A), mid-luteal phase (B), and in women taking hormonal birth control (C) (Figure 2). Many of these areas of reduced gray matter are ones of high importance for neurophysical ability and mental health.

Additionally, a study done by Rush University Medical Center showed an association between higher levels of gray matter and better cognitive function (“Everyday Activities Associated with More Gray Matter in Brains of Older Adults”). These findings suggest that taking birth control, and the associated decrease in gray matter, could be directly causing some of the symptoms women on hormonal contraceptives experience, such as brain fog, mood changes, and even anxiety and depression. For example, a smaller hypothalamus, one of the areas of decreased gray matter, is associated with heightened irritability and depression symptoms (“Study Finds Key Brain Region Smaller in Birth Control Pill Users”). Pletzer’s research and the work of others after her on the impact of birth control on structures of the brain represent important first steps in proving a causative relationship between birth control, symptoms associated with it, and structural changes in the brain.

Although this research has made some crucial preliminary steps into researching how taking a daily dose of steroids affects the brains of women taking hormonal contraceptives, the highly complex nature of the brain and its relationship with the regulation of the rest of the body means that further research is necessary. The sheer number of people that this issue affects means that it is essential to continue researching the impacts of this widely used drug. More importantly, knowing the potentially serious negative side effects enables millions of people to make more informed decisions concerning their health and their bodies.

 

Works Cited

Rush University Medical Center. (2018, February 14). Everyday activities associated with more gray matter in brains of older adults: Study measured amount of lifestyle physical activity such as house work, dog walking and gardening. ScienceDaily. Retrieved March 11, 2023 from www.sciencedaily.com/releases/2018/02/180214093828.htm.

Lewis, C. A., Kimmig, A. C. S., Zsido, R. G., Jank, A., Derntl, B., & Sacher, J. (2019). Effects of hormonal contraceptives on mood: a focus on emotion recognition and reactivity, reward processing, and stress response. Current psychiatry reports, vol. 21, no.11, 2019, p 115. PubMed Central, https://doi.org/10.1007/s11920-019-1095-z.

Meyer, Craig H., Kinsley, Elizabeth A. “Women’s Brains on Steroids.” Scientific American, https://www.scientificamerican.com/article/womens-brains-on-steroids/. Accessed 7 Mar. 2023.

Nemoto, Kiyotaka. “[Understanding Voxel-Based Morphometry].” Brain and Nerve = Shinkei Kenkyu No Shinpo, vol. 69, no. 5, May 2017, pp. 505–11. PubMed, https://doi.org/10.11477/mf.1416200776.

Pletzer, Belinda, et al. “Menstrual Cycle and Hormonal Contraceptive Use Modulate Human Brain Structure.” Brain Research, vol. 1348, Aug. 2010, pp. 55–62. ScienceDirect, https://doi.org/10.1016/j.brainres.2010.06.019.

Sharma, Rupali, et al. “Use of the Birth Control Pill Affects Stress Reactivity and Brain Structure and Function.” Hormones and Behavior, vol. 124, Aug. 2020, p. 104783. ScienceDirect, https://doi.org/10.1016/j.yhbeh.2020.104783.

“Study Finds Key Brain Region Smaller in Birth Control Pill Users.” ScienceDaily, https://www.sciencedaily.com/releases/2019/12/191204090819.htm. Accessed 7 Mar. 2023.