By: Lea Kallo Edited by: Annette Dao Why I Picked This Topic When I first started learning about autism, I realized I didn’t want to stop at just understanding surface-level traits or stereotypes. I wanted to know what was actually happening in the brain—how structure and wiring might help explain the experiences and behaviours of people on the spectrum. Even though I don’t have a personal connection to autism, I’m fascinated by how the brain shapes how we think, feel, and interact. And the more I learned, the more I realized how much research has already been done—along with how much we still don’t know. Understanding the science behind autism can help us move away from labels and toward more supportive, inclusive environments that respect different ways of experiencing the world. What the Research Says About the Autistic Brain There’s no one “autistic brain”—just like there’s no one kind of autistic person. But researchers have found several consistent patterns that show up in brain development, structure, and connectivity. By looking at these patterns across studies, we can start to understand why certain behaviours, traits, or strengths are more common in people with autism. Below is a breakdown of three studies that helped me make sense of how autism affects the brain—and how that might connect to real-life experiences. 1. Brain Growth and Early Overdevelopment In a 2005 paper, researchers Eric Courchesne and Kathleen Pierce explored how autism affects early brain growth and organization. Their research focused especially on the association cortex, which is involved in higher-level functions like language, social thinking, and decision-making. They found that many children with autism show unusually rapid brain growth in the first few years of life—especially in these complex processing areas. At first, this might sound like a good thing. But according to Courchesne and Pierce, the issue isn’t just how fast the brain grows, but how it organizes itself. In autism, early overgrowth can lead to an excess of neurons and brain connections—but those connections might not form in the right patterns. Instead of being well-organized, the brain may become chaotic or overloaded in some areas, especially those that handle social communication and flexible thinking. This helps explain why some of the core traits of autism—like difficulty with social cues, speech, or multitasking—could be linked to brain growth that’s not following a typical path. The brain is working hard—but not always efficiently. 2. Brain Connectivity: Too Much Local, Not Enough Long-Distance While Courchesne and Pierce focused on structure, a 2016 review by Aboitiz, Billeke, and Aboitiz looked at connectivity—how well different parts of the brain talk to each other. Using studies that relied on tools like fMRI and EEG, the researchers found that many people with autism show unusual patterns in how their brain is wired. One of the main ideas is that local connectivity—communication between brain areas that are close together—is often higher than usual. But long-range connectivity, especially between far-apart regions like the front and back of the brain, is reduced. This means the brain may be very active in specific areas, but not as coordinated across larger networks. This wiring pattern can help explain why autistic individuals might focus intensely on details but struggle with bigger-picture thinking or switching between tasks. Their brains are highly active—but not always balanced. The review also points out that connectivity patterns can vary by age, meaning these differences may shift as someone gets older. Researchers used brain imaging tools like fMRI and EEG to study how different areas of the brain talk to each other. fMRI, or functional magnetic resonance imaging, shows which parts of the brain are more active by tracking changes in blood flow. Basically, it lets scientists see where  the brain is lighting up during certain tasks. On the other hand, EEG (electroencephalography) looks at the brain’s electrical activity using sensors on the scalp. It’s great for showing when  brain activity happens and whether it’s fast, slow, or out of sync. Both of these tools helped the researchers figure out that people with autism tend to have weaker connections between distant brain regions and stronger connections between nearby ones. That could explain why some autistic people are amazing at focusing on small details but might struggle with big-picture thinking or switching between tasks. 3. Specific Brain Structures and Their Role in Autism Not all research papers are packed with complex stats. Nicholette Zeliadt’s article from Spectrum News does a great job breaking down the brain differences seen in autism into terms that are easier to understand, while still being based on real science. Zeliadt explains how several major brain regions are affected in autism: Amygdala : This area helps regulate emotions. Some studies show it’s enlarged in young autistic children, which might be linked to stronger or more intense emotional responses. Corpus callosum : This structure connects the two halves of the brain. In many people with autism, it’s smaller than average, which may make communication between hemispheres less efficient. Cerebellum : Best known for movement and coordination, this part of the brain also plays a role in attention and social behavior. It often appears smaller or differently shaped in people with autism. Cortex : The outer layer of the brain sometimes shows unusual folding patterns or thickness in children with autism, especially in areas related to speech and social reasoning. These structural changes help connect what we see in behaviour (like sensory sensitivity, repetitive movement, or emotional overload) to what’s happening under the surface. Why This Matters Understanding autism through the lens of brain science doesn’t mean trying to “fix” anything. Instead, it shows us that autism is a different way of being—a different brain style, not a broken one. This kind of research helps shift the conversation from deficits to neurodiversity, where brain differences are seen as valuable and worth accommodating. When we understand how brain growth and connectivity influence behaviour, we become more capable of meeting autistic individuals with empathy, not judgment. It also helps parents, teachers, doctors, and communities build better supports that actually match how autistic brains work. Looking Ahead Autism research is still growing. Brain imaging is becoming more advanced, and new discoveries are constantly being made. But even now, the findings are already helping us understand how brain differences shape behaviour, learning, and interaction. And most importantly, this science reminds us that what we see on the outside is just one part of the story. Underneath every autistic trait is a brain doing its best to make sense of the world—just in its own way. References  Aboitiz, V. A., Billeke, D., & Aboitiz, F. (2016). Brain connectivity in autism spectrum disorder. Frontiers in Psychiatry, 7 , Article 107. https://doi.org/10.3389/fpsyt.2016.00107 Courchesne, E., & Pierce, K. (2005). The new neurobiology of autism: Cortex, connectivity, and neuronal organization. Current Opinion in Neurobiology, 15 (2), 225–230. https://doi.org/10.1016/j.conb.2005.03.003 Zeliadt, N. (2020, November 16). Brain structure changes in autism, explained. Spectrum News.   https://www.thetransmitter.org/spectrum/brain-structure-changes-in-autism-explained

By Alexa Kightlinger Experiencing trauma is a relatively common human experience. In fact, a WHO World Mental Health Survey examined responses from 68,894 individuals across 24 countries. From there, it was calculated that 70.4% of respondents had experienced some sort of lifetime trauma (Kessler et al., 2017), though I would argue this number may be even higher. When thinking about the different kinds of trauma people go through, what kind of events come to mind? For most, sexual assault, rape, and the unexpected death of a loved one spring to mind. Death has seemingly become synonymous with trauma. Birth, on the other hand, could never be a negative experience. Right? Sure, the nine months of carrying to term and laborious hours in the delivery room often are expected to be outweighed by the love a mother feels when she holds her child for the first time, but what about the aftereffects? The postpartum depression, heavy bleeding, and retained placenta are some common experiences that many women suffer from that are not talked about.  As with most things associated with pregnancy, the negatives are frequently and conveniently left out of the birth journey narrative. By leaving this information out, we tend to isolate women who experience these symptoms, making them unlikely to seek out the help they need in fear of judgment from others. As a quick reminder, post-traumatic stress disorder (PTSD) is a psychiatric disorder resulting from the stress of witnessing or experiencing a traumatic event (Iribarren et al., 2005). Luckily, despite the large percentage of individuals who at some point in their lives go through a traumatic event, only a small number will develop PTSD (Atwoli et al., 2015). Similar trends are seen within the pregnant population. Though 10-20% of women report birth as a traumatic event (Haagen et al., 2015), 3% of those women go on to develop PTSD (Yildiz et al., 2016). This percentage may be higher in high-risk populations, ranging from 20% to 24% (Yildiz et al., 2016; Muzik et al., 2017), depending on the research you reference. Symptoms Another difficult factor in diagnosing PTSD due to pregnancy is that many symptoms mirror those of postpartum depression. Both include negative mood and cognition, making it hard to differentiate between the two. Not only does misdiagnosis not allow mothers to get the needed treatment, but PTSD can continually worsen if not diagnosed and treated after delivery occurs (Khoramroudi, 2018). Additionally, these symptoms can be exacerbated if women have had prior reproductive trauma or a traumatic childhood (Musik et al., 2017).  Risks of maternal PTSD can be grouped into different factors: antenatal, perinatal, and postpartum. Research conducted in 2024 by Horsch and others attempted to categorize the symptoms among the predetermined groupings. Antenatal factors are considered depression in pregnancy, fear of childbirth, a history of sexual abuse, mental health problems, or complications during pregnancy, while perinatal factors are described as negative birth experiences (this is subjective), operative birth, lack of support, maternal morbidity, or dissociation. Meanwhile, postpartum factor examples can be depression, postpartum physical complications, and poor coping/stress.  The resulting children are also affected. A systematic literature review explored associations with material PTSD that were linked with low birth weight, low breastfeeding rates, issues with mother-infant bonding, and eating/sleeping difficulties in children (Cook et al., 2018). Self-reports on child development were also said to be negatively impacted, though research acknowledges the mother’s depressive symptoms may bias this data (Suarez & Yakupova, 2024). To remedy this, they suggest adding reports from the father and other caregivers, which may increase both reliability and accuracy of the mother's initial report.  Diagnostic Difficulties PTSD and pregnancy are a difficult combination to mix as human research is limited within this field. Maternal PTSD may increase poor pregnancy and birthing outcomes: ectopic pregnancy, miscarriages, early births, and low birth weight (Musik et al., 2017). The likelihood of mothers developing PTSD increases if there were complications during the pregnancy or childbirth process. However, there is a chance that PTSD may occur because of noncomplicated pregnancies.  Due to the gaps in the literature on childbirth-related PTSD and traumatic birthing experience, maternity services after childbirth are not able to offer the services needed to treat maternal PTSD. To further complicate matters, gaps in the literature make it hard to identify proper screening techniques, timings of when to screen, and who to screen. The lack of resources impacts the entire family and each interpersonal relationship within the family unit (eg, the couple dyad and both parent-infant dyads). Sexual dysfunction and blame for negative birth events occur between mother and partner, and it is often common for new mothers to report feelings of rejection towards the baby (Ayers et al., 2006). Additionally, 1% of birth partners are at risk of developing PTSD (Birth Trauma Association).  Insufficient treatment causes lasting effects on these relationships, leaving no room for healing or improvement. Personal Stories The Birth Trauma Association is a charity in the UK that is dedicated to supporting women and families who have experienced traumatic birth. One of the unique components of the website is a section devoted to stories mothers have been brave enough to share. This allows us a first-hand look at the feelings, fears, and experiences these women have faced, but more importantly, offers a feeling of connection between women who have gone through the same. After reading through several stories, I’ve compiled a short list of quotes below that struck a chord with me. “ The mood in the room lightens now that my baby has been born. The midwife who shouted at me to be quiet apologises and I tell her it’s ok, more to make her feel better; but it’s not ok, it never will be. I’ve never felt so betrayed by a woman who was supposed to be my advocate. But the assault is not over even now. ” - Jacqueline “I may physically be over what happened, but I struggled to have a smear test recently due to the trauma of the failed induction. I dread my next one because of this. I also have not been able to consider having another baby until very recently – though I'm still not sure I feel able to do this.” - Alison These women are not simply statistics in research. They are sisters, wives, mothers, and friends. Most importantly, though, they are human. We owe it to these individuals to make the birthing process a positive event. Advocating As with most things, educating yourself and others is the best way to stop the spread of misinformation, greatly reducing the negative stigma surrounding maternal PTSD. More likely than not, someone you know has gone through a negative birthing event, even if it did not result in PTSD.  Let's not stop there. Write to your local policymakers to collaborate with organizations advocating to amplify the voices of women with maternal PTSD. Work towards legislation that may encourage training for doctors, nurses, midwives, and OB/GYNs on how to recognize signs and correctly respond to each unique situation.  I think Olivia, who suffered a rare condition in which the placenta attaches to the womb, puts it best.  “ When people hear the term PTSD, they think they know what it is – a mental health condition that soldiers suffer after witnessing horrors of war. No one really believes that it can happen to a woman following the birth of her baby. No one wants to hear this. Birth is supposed to be a glorious, life-changing event when precious memories are created that will be cherished of the rest of the woman’s life. But that wasn’t the case for me I have been well for a long time now – but like those soldiers who witness the horrors of war, I can never forget. I can never unsee what I saw and I will never be who I was before that day.” References Atwoli, L., Stein, D. J., Koenen, K. C., & McLaughlin, K. A. (2015). Epidemiology of posttraumatic stress disorder: prevalence, correlates, and consequences. Current Opinion in Psychiatry, 28 (4): 307-311. https://doi.org/1 0.1097/YCO.0000000000000167 Ayers, S., Eagle, A., & Waring, H. (2006). The effects of childbirth-related post-traumatic stress disorder on women and their relationships: a qualitative study. Psychology, Health & Medicine, 11 (4): 389-398. https://doi.org/10.1080/13548500600708409   Cook, N., Ayers, S., & Horsch, A. (2018). Maternal posttraumatic stress disorder during the perinatal period and child outcomes: A systematic review. Journal of Affective Disorders, 1 (225): 18-31. https://doi.org/10.1016/j.jad.2017.07.045 Horsch, A., Garthus-Niegel, S., Ayers, S., Hartmann, K., Vaisbuch, E., & Lalor, J. (2024). Childbirth-related posttraumatic stress disorder: definition, risk factors, pathophysiology, diagnosis, prevention, and treatment. American Journal of Obstetrics and Gynecology, 230 (3), 1116-11276.   https://doi.org//S0002-9378(23)00713-5 Iribarren, J., Prolo, P., Neagos, N., & Chiappelli, F. (2005). Post-Traumatic Stress Disorder: Evidence-Based Research for the Third Millennium. Evidence-Based Complementary and Alternative Medicine, 2 (4): 503-512. https://doi.org/10.1093/ecam/neh127   Khoramroudi, R. (2018). The prevalence of posttraumatic stress disorder during pregnancy and postpartum period. Journal of Family Medicine and Primary Care, 7 (1): 220-223. https://doi.org/10.4103/jfmpc.jfmpc_272_17   Muzik, M., McGinnis, E. W., Bocknek, E., Morelen, D., Rosenblum, K., Liberzon, I., Seng, J., & Abelson, J. L. (2017). Depression and Anxiety, 33 (7): 584-591. https://doi.org/10.1002/da.22465   Suarez, A., & Yakupova, V. (2024). Effects of postpartum PTSD on maternal mental health and child socioemotional development - a two-year follow-up study. BMC Pediatrics, 24 (1): 789. https://doi.org/10.1186/s12887-024-05282   Yildiz, P. D., Ayers, S., & Phillips, L. (2016). The prevalence of posttraumatic stress disorder in pregnancy and after birth: A systematic review and meta-analysis. Journal of Affective Disorders, 208,  634-645. https://doi.org/10.1016/j.jad.2016.10.009

Autumn Augustus In today's digital age, technology has become an inseparable part of daily life, from the constant ping of notifications to the hours spent scrolling through social media. While these advances have undoubtedly made our lives more convenient, they also come with serious consequences for mental health and interpersonal relationships. Technology has created a society where cognitive overload, emotional insecurity, and disconnection from reality are rampant, altering how we think, feel, and connect with others.The impact of technology on mental health is multifaceted. One of the most pressing concerns is cognitive overload. With the constant influx of information from various apps, websites, and social media platforms, our brains are continually working to process and respond to this barrage of stimuli. This can lead to heightened stress and anxiety, as we struggle to maintain focus and make clear, informed decisions. The pressure to stay connected and responsive can make it difficult to focus on anything for an extended period of time, contributing to feelings of mental fatigue and reduced cognitive clarity (Eppler & Mengis, 2004). Social media has also played a significant role in shaping mental health, particularly regarding self-esteem. Platforms like Instagram, TikTok, and Facebook promote idealized versions of life, often leading users to compare themselves to others in ways that are unrealistic and harmful. The constant exposure to curated content, showing others’ successes, beauty, and seemingly perfect lives, can leave individuals feeling inadequate or depressed. Studies have shown that social media use correlates with higher levels of anxiety and lower self-esteem, as users often measure their own worth based on external validation, such as likes and comments (Fardouly et al., 2015; Primack et al., 2017).Beyond comparison, the addictive nature of technology is another key factor affecting mental health. Many individuals find themselves increasingly reliant on their phones, checking for updates or messages even when they know it may not be healthy. This dependency on technology can lead to emotional numbness and isolation, as people may substitute online interactions for meaningful, face-to-face conversations. The constant need for external validation, from likes or messages, can also contribute to feelings of emptiness and loneliness, especially when those interactions are shallow or superficial (Kuss & Griffiths, 2017; Smetaniuk, 2014). Additionally, the rise of technology has disrupted many people's sleep patterns, further exacerbating mental health problems. The blue light emitted by screens interferes with the body’s natural circadian rhythm, making it harder for individuals to fall asleep and experience restorative rest. Poor sleep quality is closely linked to mood instability, increased anxiety, and an overall decline in mental well-being (LeBourgeois et al., 2017).Technology's impact extends beyond mental health, affecting relationships in both romantic and platonic contexts. One of the most significant ways in which technology has altered relationships is through the decrease in face-to-face interaction. With the rise of text messaging, social media, and dating apps, more and more communication happens through screens. While these platforms allow for easy and convenient contact, they often lack the emotional depth and nuance that come with in-person interactions. This reduction in meaningful communication can lead to misunderstandings, misinterpretations, and weakened emotional connections between individuals (Turkle, 2015). Instant gratification, a byproduct of the fast-paced nature of digital communication, has also created unrealistic expectations in relationships. With the ability to instantly message someone or receive an immediate response, individuals may expect the same level of quick feedback in their interactions. This can lead to impatience and frustration, particularly when expectations aren’t met in real-life interactions. Additionally, technology fosters a sense of immediacy that can pressure individuals into making decisions quickly, without taking the time to reflect or process emotions, which can harm relationships in the long term (Rosen et al., 2013). Another issue is the constant connectivity that technology promotes, particularly in romantic relationships. Many individuals feel pressured to be always available, responding to texts and social media messages even when they are busy or need time for themselves. This constant expectation can lead to relationship anxiety, as individuals may feel as though they need to be constantly engaged or risk seeming uninterested or neglectful. The resulting stress can cause emotional strain, jealousy, and even burnout in relationships, as both partners struggle to maintain a sense of balance between their digital lives and their personal connections (Toma et al., 2014).Technology also plays a role in fostering emotional insecurity, particularly in romantic relationships. The ability to monitor a partner's online activity—whether through social media profiles, messaging history, or location tracking—has introduced a new dynamic to interpersonal interactions. This constant surveillance can breed distrust and jealousy, causing significant strain on relationships. Instead of fostering trust and communication, technology can create a breeding ground for suspicion and insecurity, ultimately eroding the foundation of healthy relationships (Toma et al., 2014). Furthermore, the superficiality of digital communication only exacerbates these issues. Texting, DMs, and other forms of online interaction can lack the emotional depth and authenticity of face-to-face conversations. The ability to curate a version of oneself online means that individuals may not feel comfortable being their true selves in digital spaces. This lack of vulnerability and authenticity can prevent meaningful emotional connections from developing, leaving relationships feeling shallow or incomplete (Baym, 2015).The psychological effects of technology on communication and emotional bonding are profound. The constant use of digital devices creates a cognitive dissonance between the idealized world of social media and the real-world challenges of human interaction. People may find themselves torn between the need for validation and connection online and the desire for genuine, meaningful relationships in real life. This internal conflict can lead to stress, confusion, and emotional fatigue, as individuals struggle to reconcile their digital personas with their real identities (Baym, 2015). The loss of true connection is another significant psychological effect. As digital communication becomes more prevalent, face-to-face conversations are increasingly rare. This decline in in-person interactions hinders the development of emotional intelligence, as individuals rely on brief, impersonal messages to convey their thoughts and feelings. Without the ability to read body language, tone of voice, and facial expressions, people may struggle to fully understand each other's emotions, leading to communication breakdowns and emotional distance.As technology continues to advance, its impact on mental health and relationships will only grow. However, there are ways to mitigate these effects. Setting boundaries with technology, such as practicing digital detoxes, or limiting screen time before bed, can help individuals maintain better mental health. Prioritizing face-to-face communication and fostering authentic connections can also help strengthen relationships and counteract the superficiality of online interactions. In conclusion, while technology has undeniably transformed the way we live and interact, its effects on mental health and relationships cannot be ignored. From cognitive overload to emotional insecurity and disconnection, technology has reshaped how we think and how we connect with others. As we navigate the complexities of the digital age, it is crucial to find a balance between our online and offline lives to preserve our mental well-being and maintain meaningful, authentic relationships. References Baym, N. K. (2015). Personal connections in the digital age . Polity Press. Eppler, M. J., & Mengis, J. (2004). The concept of information overload: A review of literature. Information Society, 20 (5), 315-325. Fardouly, J., Diedrichs, P. C., Vartanian, L. R., & Halliwell, E. (2015). Social comparisons on social media: The impact of Facebook on young women’s body image concerns and mood. Body Image, 13 , 38-45. Kuss, D. J., & Griffiths, M. D. (2017). Social networking sites and addiction: Ten lessons learned. International Journal of Environmental Research and Public Health, 14 (3), 311. LeBourgeois, M. L., Hale, L., Chang, A. M., & Montgomery-Downs, H. E. (2017). Digital media and sleep in childhood and adolescence. Pediatrics, 140 (6), e20171742. Primack, B. A., Shensa, A., Sidani, J. E., Whaite, E. O., Lin, L., Rosen, D., Colditz, J. B., Radovic, A., & Miller, E. (2017). Social media use and perceived social isolation among young adults in the U.S. American Journal of Preventive Medicine, 53 (1), 1-8. Rosen, L. D., Lim, A. F., Carrier, L. M., & Cheever, N. A. (2011). An empirical examination of the educational impact of text message-induced task switching in the classroom. Computers in Human Behavior, 27 (3), 1212-1222. Smetaniuk, P. (2014). Internet addiction: The emergence of a new mental health disorder. American Journal of Public Health, 29 (4), 23-29. Toma, C. L., Hancock, J., & Ellison, N. B. (2014). Separating fact from fiction: An examination of deceptive self-presentation in online dating profiles. Personality and Social Psychology Bulletin, 40 (1), 7-17. Turkle, S. (2015). Reclaiming conversation: The power of talk in a digital age . Penguin Press.

Written by: Davis Hobley Behavior is rooted in complex neural circuitry. From face-selective regions like the fusiform face area to the dopaminergic reward system, neuroscience has mapped out key components of the brain that are implicated in perceiving, interpreting, and interacting with others (Adolphs, 2009). However, modeling these systems accurately has remained a challenge in neuroscience for decades. Deep learning may represent a promising path towards mechanistic models of neuroscience (Yamins & DiCarlo, 2016). Diverging from functional models, mechanistic models seek to accurately resemble the inner workings of the brain—reproducing the output, and reaching it in a similar manner. An analog and digital clock will both read 11:15 at the same time; however, the ways in which they get to that answer are drastically different. This makes the digital clock a functional model of the analog clock, as they both produce the same output, but not a mechanistic model, as they get to that output in very different ways.  What exactly is a mechanistic model of cognitive neuroscience? It may be easier to start with what makes a model non-mechanistic. For example, when we think about a Large Language Model (LLM), such as ChatGPT, it is generally agreed that the model may be functional, e.g. produce coherent thoughts and assemble information; however, the model does not mechanistically model how language is learned or communicated in humans (Sejnowski, 2023). This is a key distinction in the application of deep learning to neuroscience. So how does a model learn? How did ChatGPT reach a functional proficiency to model human language without mirroring human development? LLMs, generally speaking, reached the state of proficiency they are at today through three key scalable factors: model size, model architecture, and training data (Kaplan, et al. 2020). Model size refers to the number of parameters that a model has. GPT-3 has 175 billion parameters, and GPT-4 has 1 trillion (Brown, et al. 2020). Each parameter can be thought of as a variable in an equation that makes up the model. Each parameter has a value (often between -5 and 5) that represents some level of activation (Goodfellow, et al. 2016). Through the lens of neuroscience, we can think of a parameter as the objective synaptic strength between neurons in the brain. For example, neuron A may be connected to neurons B, C, and D. When neuron A is firing, perhaps neuron B fires 40% of the time, C 60% of the time, and D 5% of the time. This building of relationships between activation of neurons in the brain is very similar to how parameters define the relationship between neurons in a model (Richards, et al. 2019). Model architecture pertains to the overall structure and design of a model. For example, in OpenAI’s model CLIP (Contrastive Learning-Image Pretraining), the architecture contains an image encoder and a text encoder. The idea of this model was to allow CLIP to learn how to classify images with text descriptions (Radford, et al. 2021). This would mean that CLIP would learn what a dog looked like via images and how to label it as a dog through text. Specifically, this means the model was performing out-of-distribution classification: taking images it had never seen before, and based on generalizations of the training data, labeling them with a description. The image encoder sought to turn images into long strings of numbers, known as image embeddings. The text encoder did a similar process with text, creating a string to represent the label of an image. Together, the image embedding would represent the image of a dog while the text embedding would represent the word “Dog” (Illharco, et al. 2021). In neuroscience, model architecture can be thought of as neural circuits that connect different regions of the brain. An example includes the arcuate fasciculus, which connects Broca’s area (speech production) and Wernicke’s area (language comprehension) (Catani & Mesulam, 2008). Lastly, and most importantly for the scope of this paper, training data is paramount. Training data is more often than not, the divergence between mechanistic and functional models for deep-learning applied neuroscience (Goncalves, et al. 2020). When we think about a model like ChatGPT, it does contain a system which resembles the architecture of the brain, carrying millions of nodes analogous to neurons and over a trillion parameters. However, the way in which ChatGPT was trained is far from the reality of human learning. ChatGPT was trained on essentially the whole internet’s data, including textbooks, Reddit posts, and blogs. This is not  how a human learns (OpenAI, 2023). We do not start our journey of language learning by being exposed to every digital file accessible to us. The process is much slower, starting with simple readings, social interactions, and play, to gradually scaffold language as we age (Kuhl, 2004). Even with circuitry that may resemble the brain, the divergence in the training data makes ChatGPT a functional model, not a mechanistic one.  That being said, the creation of mechanistic models is well underway. One model that is close to mechanistic reliability is the VG-W2V2 model. This model is spectacular in quite a few ways, demonstrating that advanced Deep Learning models can be trained mechanistically. In VG-W2V2, the model is fed data from 123,000 crowd-sourced images with read-aloud image descriptions. Similar to CLIP, the goal is to get the model to generalize image descriptions over time, allowing it to analyze an image of a kitchen it has never seen before and label it as a kitchen (Khorrami, 2024). Based on what I’ve described so far, this should raise an important question: Isn’t exposure to 123,000 images extremely unrealistic for a child to be exposed to within a compact time? Yes, and that is exactly what the author thought while creating the model. So, the model was only exposed to 6 hours of spoken language a day, similar to how much a child would be exposed to. Note that the model had no prior understanding of language when it was built: it learned to encode language from waveform audio signals along with its training, an impressive feat that has been done before, but rarely within a model also learning how to label images with a data set of descriptions (contrastive model) (Baevski, 2020). This meant that, with the same amount of exposure to image, text, and audio, an infant may have, the model learned language, learned to associate text and image descriptions and was able to generalize these findings to out-of-distribution cases. The model represents a strong approach towards a mechanistic model for human language development. Figure 1 VG-W2V2 training data, including audio classification of a crowd-sourced image set Note:  The audios are waveforms, the model was not programmed with an understanding of language; rather, it learned language as an emergent property as the model was trained approaching mechanistic human language development (Khorrami, 2024). While impressive, this may leave you wondering what the purpose of all this is. Why should we attempt to make models mechanistic to human development and cognitive neuroscience? There are two key reasons: these models borrow from principles of neuroscience that have been built by evolution over the course of millions of years, potentially allowing these models to gain useful abilities rapidly. Additionally, if we can create a model that is both fully functional and mechanistic, experimentation may be done on this model as a sort of “model species” for humans, drastically changing the landscape of research. References Adolphs, R. (2009). The social brain: Neural basis of social knowledge. Annual Review of Psychology, 60 , 693–716.   https://doi.org/10.1146/annurev.psych.60.110707.163514 Baevski, A. Zhou, Y. Mohamed, A. & Auli, M. (2020). wav2vec 2.0: A framework for self‑supervised learning of speech representations . Advances in Neural Information Processing Systems, 33 .   https://arxiv.org/abs/2006.11477 Brown, T. B. Mann, B. Ryder, N. Subbiah, M. Kaplan, J. D. Dhariwal, P. ... & Amodei, D. (2020). Language models are few-shot learners . Advances in Neural Information Processing Systems, 33 , 1877–1901.   https://arxiv.org/abs/2005.14165 Catani, M. & Mesulam, M. M. (2008). The arcuate fasciculus and the disconnection theme in language and aphasia: History and current state. Cortex, 44 (8), 953–961.   https://doi.org/10.1016/j.cortex.2008.04.002 Goodfellow, I. Bengio, Y. & Courville, A. (2016). Deep Learning . MIT Press.   https://www.deeplearningbook.org Gonçalves, P. J. Lueckmann, J.-M. Deistler, M. Nonnenmacher, M. Öcal, K. Bassetto, G. Chintaluri, C. Podlaski, W. F. Haddad, S. A. Vogels, T. P. Greenberg, D. S. & Macke, J. H. (2020). Training deep neural density estimators to identify mechanistic models of neural dynamics. eLife, 9 , e56261.   https://doi.org/10.7554/eLife.56261 Ilharco, G. Wortsman, M. Wightman, R. Gordon, C. Carlini, N. Taori, R. ... & Zettlemoyer, L. (2021). OpenCLIP: An open-source reproduction of CLIP .   https://github.com/mlfoundations/open_clip Kaplan, J. McCandlish, S. Henighan, T. Brown, T. B. Chess, B. Child, R. ... & Amodei, D. (2020). Scaling laws for neural language models . arXiv preprint  arXiv:2001.08361.   https://arxiv.org/abs/2001.08361 Khorrami, K. & Räsänen, O. (2024). A model of early word acquisition based on realistic-scale audiovisual naming events. Speech Communication, 167 , 103169.   https://doi.org/10.1016/j.specom.2024.103169 Kuhl, P. K. (2004). Early language acquisition: Cracking the speech code. Nature Reviews Neuroscience, 5 (11), 831–843.   https://doi.org/10.1038/nrn1533 OpenAI. (2023). GPT-4 Technical Report .   https://cdn.openai.com/papers/gpt-4.pdf Radford, A. Kim, J. W. Hallacy, C. Ramesh, A. Goh, G. Agarwal, S. ... & Sutskever, I. (2021). Learning transferable visual models from natural language supervision . arXiv preprint  arXiv:2103.00020.   https://arxiv.org/abs/2103.00020 Richards, B. A. Lillicrap, T. P. Beaudoin, P. Bengio, Y. Bogacz, R. Christensen, A. ... & Kording, K. P. (2019). A deep learning framework for neuroscience. Nature Neuroscience, 22 (11), 1761–1770.   https://doi.org/10.1038/s41593-019-0520-2 Sejnowski, T. J. (2023). Large language models and the reverse Turing test. Neural Computation, 35 (4), 567–580.   https://doi.org/10.1162/neco_a_01604 Yamins, D. L. K. & DiCarlo, J. J. (2016). Using goal-driven deep learning models to understand sensory cortex. Nature Neuroscience, 19 (3), 356–365.   https://doi.org/10.1038/nn.4244

Author: Evelyn Fistler Editor: Camaria Polk Introduction Music is an important part of human life, where cultures and communities indulge in creating sound that expresses their characteristics as human beings. Intricate lyrics with melodies that soothe the soul are what we seek when life becomes stressful. People may listen to songs on the radio while some play instruments or sing. What many don’t notice is that music has the ability to affect our cognition, impacting us emotionally while strengthening neural networks. Mental disorders, such as depression, may severely impact an individual that their cognition becomes impaired, where deficits in memory and decision-making become prevalent as symptoms occur. Many people seek relatable songs to listen to as a form of coping through anxiety and stress, but how impactful could music be when dealing with depression? This article will provide insight on neuroplasticity in depression and how music impacts our cognitive capabilities. Created with Canva Neuroplasticity in Depression What is Neuroplasticity? Neuroplasticity refers to the brain's ability to adapt and change due to external and internal stimuli of learning and experience, such as reorganizing structure, functions, and connections (Puderbaugh & Emmady, 2023).  But how does depression cause this? Long-term depression causes structural and chemical changes in the brain which leads to decreased synaptic activity and weakened synaptic connections, with the hippocampus and amygdala being some of the most impacted, known to be regions that are responsible for cognitive skills such as learning and memory (Sekhon & Marwaha, 2023). Individuals may also suffer from cognitive deficits in attention, concentration, emotion processing and social learning (Rădulescu, et al, 2021). Major depression is known to severely affect certain areas of the brain. Notable demonstrations such as dendritic atrophy, referring to the shrinkage of neural branches that send and receive neural signals, and decrease in the number of glial cells, which maintain homeostasis and form myelin, a protective cover for neurons that also assist in communication sending. is prevalent in the prefrontal cortex. A decrease of synaptic activity in the hippocampal area is common due to withdrawal of dendrites and synapses, resulting in poor connection to and from the hippocampus (Rădulescu, et al, 2021). Music and its Effects on Neuroplasticity  Music engages the brain and affects neuroplasticity, promoting new neural connections while also enhancing cognitive function (Chatterjee, et al, 2021). One’s physical and emotional wellbeing may be altered when listening to music due to its ability to alleviate anxiety all while enhancing neural connections and reshaping networks. Starting as sound waves, music then transforms into an electric signal, progressing through the auditory nerve  to the brainstem , where sound can be perceived. Musicians, individuals who practice musical instruments and create music, are susceptible to major structural changes in the brain, such as the corpus callosum, where there is correlation to the area becoming larger through years of practice starting at ages as young as seven, in comparison to non-musicians and musicians who began practicing later on in life. Furthermore, white and gray matter changes are also prevalent in musicians—specifically pianists. Potential causes of grey and white matter adaptations could affect the number of synapses, glia volume, myelination , and axon diameter within the brain, found in motor regions. Cortical auditory representation is stronger in musicians, specifically on those who began playing at young ages. Playing instruments requires cognitive capabilities, highlighted through the motor cortex and structures responsible for coordination and movement. Even individuals involved on a short-term basis were found to have improved verbal memory (Muriel, et al, 2024). Animal studies utilizing mice suggests music being a beneficial variable when considering treatments for depression, where reduction in oxidative stress and inflammation in the brain, while neuronal death is also prevented. Because these factors are consistent in individuals with depression, studies such as these are useful in translational research and pre-clinical findings to humans (Fu, et al, 2023). How does this impact symptoms of cognitive deficits in depression? Cognitive deficits prevalent in depression show to be manageable through music intervention, though more concrete evidence should require further investigation, such as listening duration, genre of music, and age groups, but known studies and their suggestions show the importance of advancing this field of study. A study suggests that music intervention reduced cognitive impairment and improved moods in participants. Furthermore, listening to music improved memory recognition and depressive symptoms. Neuropsychological symptoms are alleviated through active parasympathetic and sympathetic nervous systems through the inducement of arousal and positive emotional responses. Additionally, fight-or-flight induced through the sympathetic nervous system may recede and promote relaxation through music with movement, instrumental music, or watching music videos, but the genre of music should be investigated further in order to provide substantial information. Music with movement such as dancing in a 12-week intervention showed improvement in cognition and depressive symptoms as well (Jordan, et al, 2022). Further investigation on music therapy exhibited reduction in depression symptoms when duration was three or more times weekly for about an hour in individuals of the average ages between 50-65 ( Tang, et al, 2020). While music through variations of utilization shows improvement in depression symptoms and overall mood, these studies help provide foundational information that can be used to advance known research on depression and cognition. Conclusion Neuroplasticity and cognitive changes resulting from mental disorders, specifically depression, are important matters to educate the public on due to the wide variety of treatments that may help depending on the individual. Pharmacotherapy, cognitive behavioral therapies, and somatic treatments are currently utilized for individuals suffering from major depressive disorder, but still, treatments vary per person and may affect each individual differently, which shows how useful other potential treatments may be for this disorder ( Karrouri, et al, 2021). More awareness on music’s ability to enhance neural networks and reduce stress could lead to further research on music and its effect on other brain related disorders or diseases. While listening to music showed significant results in positive emotional responses, brain stimulation and arousal, it is important to consider furthering this research through analyzing the genre of music, duration of time spent listening, and individuals’ ages when participating in studies. This is valuable for further research and may advance how treatments are utilized for mental disorders such as depression. By providing insight to this topic, the general public may be able to better understand cognition and how intricate the brain is, but there is still a need for further research on how daily activities such as listening to music can impact cognition.  Through further research on brain adaptations and changes through neuroplasticity, researchers can design interventions and valuable treatments for a wide range of neurological disorders, diseases, and overall mechanisms of cognition. Individual experiences and analyses of neuroplasticity and how sound and music affect the brain will provide useful information that could potentially shape the studies of cognitive sciences. Bibliography Catherine Jordan, Brian Lawlor, David Loughrey, A systematic review of music interventions for the cognitive and behavioural symptoms of mild cognitive impairment (non-dementia), Journal of Psychiatric Research,Volume 151, 2022, Pages 382-390, ISSN 0022-3956, https://doi.org/10.1016/j.jpsychires.2022.04.028 . Chatterjee, D., Hegde, S., & Thaut, M. (2021). Neural plasticity: The substratum of music-based interventions in neurorehabilitation. NeuroRehabilitation, 48(2), 155–166. https://doi.org/10.3233/NRE-208011 Fu, Q., Qiu, R., Chen, L., Chen, Y., Qi, W., & Cheng, Y. (2023). Music prevents stress-induced depression and anxiety-like behavior in mice. Translational psychiatry, 13(1), 317. https://doi.org/10.1038/s41398-023-02606-z Karrouri, R., Hammani, Z., Benjelloun, R., & Otheman, Y. (2021). Major depressive disorder: Validated treatments and future challenges. World journal of clinical cases , 9 (31), 9350–9367. https://doi.org/10.12998/wjcc.v9.i31.9350 Muriel T. Zaatar, Kenda Alhakim, Mohammad Enayeh, Ribal Tamer, The transformative power of music: Insights into neuroplasticity, health, and disease, Brain, Behavior, & Immunity - Health, Volume 35, 2024, 100716, ISSN 2666-3546, https://doi.org/10.1016/j.bbih.2023.100716 . Puderbaugh, M., & Emmady, P. D. (2023, May 1). Neuroplasticity. StatPearls - NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK557811/ Rădulescu, I., Drăgoi, A. M., Trifu, S. C., & Cristea, M. B. (2021). Neuroplasticity and depression: Rewiring the brain's networks through pharmacological therapy (Review). Experimental and therapeutic medicine, 22(4), 1131. https://doi.org/10.3892/etm.2021.10565 Sekhon, S., & Marwaha, R. (2023, July 3). Depressive cognitive disorders. StatPearls - NCBI Bookshelf. https://www.ncbi.nlm.nih.gov/books/NBK559256/#:~:text=Major%20depressive%20disorder%20mainly%20affects,and%20the%20verbal%20learning%20process . Tang, Q., Huang, Z., Zhou, H., & Ye, P. (2020). Effects of music therapy on depression: A meta-analysis of randomized controlled trials. PloS one , 15 (11), e0240862. https://doi.org/10.1371/journal.pone.0240862

Written by: Daisy Draper Edited by: Jacinda Taggett During my adolescence, frequent school transfers exposed me to the stark differences in traits between peers from higher-income households versus those with families facing financial hardship and instability. As I advanced in academia, these disparities became even more evident, not only in access to opportunities but also in the trajectory of their mental health and overall well-being.  Fast forward to today, these observations align with a growing body of evidence investigating the interplay between adverse childhood experiences (ACEs) and socioeconomic barriers in shaping mental health outcomes. Adverse childhood experiences are traumatic early-life stressors that can have lasting emotional, physical, and psychological effects (Young-Min Park et al., 2020). Their impacts often extend beyond childhood into adulthood and can shape lifelong health.    It’s safe to say that, as a population, we’ve all encountered some form of adversity. Whether it’s the stress of parental divorce, the social pressures of adolescence, the physiological response to public speaking (seriously, this still traumatizes me), or the widespread disruption of COVID-19. However, ACEs are particularly interesting because they don’t occur randomly. Factors like household income, parental education, and access to resources like healthcare create environments where some individuals are far more likely to experience adversity than others (Fisher et al., 2024). This raises the question: Why do we so often see people struggling with their mental health also facing financial hardships? Well, these disadvantages don’t just increase the risk of adversity, they shape who is most vulnerable from the very start, creating a systemic cycle of mental health problems. How Adversity Becomes a Mental Health Problem I would say we all have a general understanding that traumatic events and perpetual stress in childhood can lead to serious challenges later in life. But what’s more striking is how adversity physically alters the brain, negatively impacting its structure and function. For example, a study using fMRI (a technique measuring activity and structure of the brain) found that ACEs were linked to reduced gray matter in brain areas that help regulate emotions and process social interactions, such as in the frontal, temporal gyri, and the parietal regions (Chang et al., 2023). To help visualize this, think of the gyri as ridges and valleys in a mountainous trail (the ridges and folds in the brain) shaping how we feel by helping us process information and emotions. The parietal lobe is similar to a GPS (just behind the brain's frontal lobe), allowing us to navigate life by interpreting our surroundings and processing sensations. Now think of ACEs as natural disasters (destroying the mountainous trail and throwing off the GPS), therefore altering the brain’s ability to process emotions and engage in psychological resilience, which inevitably alters an individual's ability to cope with stress.  Figure 1 . Pink to blue bar is the BOLD signal. BOLD stands for ‘blood-oxygen-level-dependent functional magnetic resonance imaging.’ The pink color indicates more oxygenated blood and higher brain activity, while blue reflects lower oxygenation and reduced activity. Brain regions showing reduced gray matter volume (in blue) in individuals with higher ACE scores. Adapted from ‘The Mediating Role of Brain Structural Imaging Markers in Connecting Adverse Childhood Experiences and Psychological Resilience’ (Chang et al., 2023). That’s pretty shocking, right? Given that these structural brain changes can impact physical health, it’s no surprise that ACEs are associated with higher rates of mental health problems, such as depression (Humphreys et al., 2020). However, the type of childhood maltreatment modifies the strength of depression, with emotional abuse being most strongly associated with depressive symptoms, such as lack of interest or pleasure in life experiences (Van Veen et al., 2013). In addition, a recent systematic review analyzed data from over 14 million people globally, revealing that individuals with ACEs are 66% more likely to develop anxiety (Beletew Abate et al., 2024). They also discovered the following: Certain types of ACEs, like domestic violence, neglect, bullying, or sexual abuse, have the strongest associations with mental illness. The most common outcomes are depression and anxiety, with depression having an 87% increased risk. ACEs are a public health issue that requires early intervention to prevent further consequences. It is important to note that studies are never perfect, and the data used in this review varied by country. This suggests that some outcomes may be influenced by a country’s historical and political context and cultural factors. Now, what about individuals with genetic predispositions to mental illness? How do we even begin to account for, or confound for, these variables? Gene-environment interaction (GxE) studies help unpack this question. For example, a GxE study conducted in Barcelona used a tool called a polygenic risk score (PRS). A PRS assesses someone’s inherited sensitivity to their environment (essentially, how their genes may make them more or less affected by adversity) to evaluate how their genes interact with ACEs in shaping mental health. They discovered that individuals with higher PRS exhibited the following: They were more likely to have depression, anxiety, or display psychotic symptoms. They were less likely to display symptoms if they did not experience adversity. Some people’s genes interact with ACEs by either magnifying or moderating the impacts (Barrantes-Vidal et al., 2025). Overall, not everyone with ACEs may experience poor mental health outcomes later in life. That’s because genes are more like a roadmap, they can point to a destination, but it doesn’t mean an individual will arrive there. Whether or not someone develops a mental health condition often depends on the context of their journey, such as the severity or duration of exposure to ACEs. Interestingly, how our genes express is often contextual as well. Factors like poverty, housing instability, and access to resources like healthcare can all shape the trajectory of a person's health. Healthcare disparities are largely explained by adult socioeconomic status (Testa et al., 2022). Suffice it to say, if essential care is out of reach for a large population, mental healthcare is even less accessible, possibly deepening the cycle between adversity, poverty, and mental health risk. The Relationship Between Adversity, Socioeconomic Barriers & Mental Health Research also suggests that socioeconomic status is a key factor in shaping childhood adversity. Take, for example, a longitudinal UK study that tracked over 8,000 children from birth through adolescence to explore how growing up in poverty influenced their exposure to ACEs. The researchers found the following: Children born into poverty were almost 5x more likely to grow up in households with violence, addiction, and family conflict. Children in low-income families face a significantly higher risk of early childhood abuse. Poverty actively shaped who experienced ACEs and how severe they became (Farooq et al., 2024). Here’s the reality, when resources are limited, so are the options for improving difficult situations. Financial instability makes it harder to escape chronic stress, and socioeconomic barriers often keep individuals trapped in cycles of adversity. Additionally, think about one’s housing options when enduring poverty. This may also further exacerbate their challenges, exposing them to environments that increase the likelihood of experiencing mental health issues. However, those from higher-income households are less likely to experience higher numbers of ACEs, as those with financial security have more options when navigating adversity with fewer long-term consequences (Merrick et al., 2018). We’ve all heard the rhetoric of ‘pulling yourself up by the bootstraps’ when it comes to navigating hardship, but is that a reality for most people? While it may work for some, society often underestimates the lasting toll of traumatic events on people’s lives. It doesn’t just create obstacles, because it stacks the deck against you, especially regarding our youth and their educational achievement. Socioeconomic barriers can determine the quality of children's schooling, and persistent adversity may further hinder their academic performance. Let’s take a closer look at how ACEs may influence success. One study from 2023 tracked over 300 individuals at high risk for psychosis to examine how ACEs shaped their education and employment over time. The findings revealed: Childhood trauma, specifically emotional abuse, is linked to lower educational attainment. Adversity shrinks opportunities, as ACEs increase the likelihood of dropping out and struggling to earn higher qualifications. The impact extends beyond school, as those who experience physical abuse are more likely to be unemployed, while emotional neglect is linked to early workforce entry, possibly out of necessity and likely hindering the pursuit of higher education (Tognin et al., 2023). However, these findings should be read with caution, as they examined a smaller sample size and focused on individuals at high risk for psychosis, which limits how broadly these findings apply to the general population. More research is needed to see if these patterns apply to a wider demographic. Nonetheless, we can begin to see how the cycle unfolds. To take matters further, marginalized groups are often more likely to experience ACEs and mental health issues. According to a grounded theory analysis, researchers interviewed over 80 health and education providers, finding that race and poverty significantly shaped childhood adversity. They also identified that racism and chronic poverty created conditions where ACEs were more likely to occur, and that resources for assistance were often difficult to access (Camacho et al., 2022). This correlation aligns with recent literature, as multiracial individuals experience 46% higher rates of ACEs compared to white individuals (Giano et al., 2024). Overall, these findings underscore the significance of systemic inequities in shaping childhood adversity. Socioeconomic inequities don’t just create adversity. They are reinforced by systems that keep people stuck, setting them up for failure from the start. Evidence-Based Ways to Create Positive Change ACEs not only impact mental health, but they also cause a significant economic burden on our nation. ACEs cost the U.S. economy approximately $14.1 trillion each year due to lost healthy-life years and medical costs (Peterson et al., 2023). So, how do we break the cycle, and what are some evidence-based interventions that can help alleviate the impacts of ACEs caused by social and economic factors? The CDC offers key strategies for improvement (CDC and Prevention, 2019): Shift social norms around parenting  - Community education campaigns can reduce the stigma around seeking help, helping to promote shared responsibility for children’s safety. Tax Credits - Implementing tax credits, such as the Child Tax Credit, can increase family income, reduce poverty, and alleviate parental stress. These credits have been linked to an overall improvement in children’s health and behavioral problems. Childcare Subsidies  - Providing subsidies to help alleviate childcare costs can help families access higher levels of care. These subsidies are proven to reduce childhood abuse and neglect and reduce the rate of maternal depression. Paid Family Leave  - Paid family and medical leave ensures better care for children without income loss. These policies are associated with higher maternal employment and improved child development. Flexible Work Schedules  - Mandating employers to provide flexibility in scheduling can help parents balance work and family responsibilities, thus reducing stress and improving family interactions. State Dependent Care Tax Credits  - Mandating states to implement tax credits to supplement federal assistance can help low-income families with childcare expenses. These credits can vary in eligibility. Developmental Block Grants  - Federal grants can provide funds to states to help with childcare costs while parents attend work or higher education. These policies highlight myriad ways our nation can help reduce the long-term impacts of ACEs by creating safe, stable, equitable, and nurturing environments for children. ACEs are a public health issue, and I hope this article helped illuminate the urgent need to build stronger systems that support families, because that’s how we foster a healthier, more resilient, and mentally healthy society. References: Abate, B. B., Sendekie, A. K., Merchaw, A., Abebe, G. K., Azmeraw, M., Alamaw, A. W., Zemariam, A. B., Kitaw, T. A., Kassaw, A., Wodaynew, T., Kassie, A. M., Yilak, G., & Kassa, M. A. (2025). Adverse Childhood Experiences Are Associated with Mental Health Problems Later in Life: An Umbrella Review of Systematic Review and Meta-Analysis. Neuropsychobiology , 84 (1), 48–64. https://doi.org/10.1159/000542392   Barrantes-Vidal, N., Torrecilla, P., Mas-Bermejo, P., Papiol, S., Bakermans-Kranenburg, M. J., van IJzendoorn, M. H., Jolicoeur-Martineau, A., Kwapil, T. R., & Rosa, A. (2025). Genetic Susceptibility to the Environment Moderates the Impact of Childhood Experiences on Psychotic, Depressive, and Anxiety Dimensions. Schizophrenia bulletin , 51 (Suppl 2), S95–S106. https://doi.org/10.1093/schbul/sbad130 Camacho, S., & Henderson, S. C. (2022). The Social Determinants of Adverse Childhood Experiences: An Intersectional Analysis of Place, Access to Resources, and Compounding Effects. International journal of environmental research and public health , 19 (17), 10670. https://doi.org/10.3390/ijerph191710670   Centers for Disease Control and Prevention. (2019). Adverse Childhood Experiences Prevention. Retrieved from: https://www.cdc.gov/violenceprevention/pdf/ACEs-Prevention-Resource_508.pdf   Chang, Y. H., Yang, M. H., Yao, Z. F., Tsai, M. C., & Hsieh, S. (2023). The Mediating Role of Brain Structural Imaging Markers in Connecting Adverse Childhood Experiences and Psychological Resilience. Children (Basel, Switzerland) , 10 (2), 365. https://doi.org/10.3390/children10020365   Farooq, Bushra, et al. “The Association between Poverty and Longitudinal Patterns of Adverse Childhood Experiences across Childhood and Adolescence: Findings from a Prospective Population-Based Cohort Study in the UK.” Child Abuse & Neglect , vol. 156, 4 Sept. 2024, pp. 107014–107014, www.sciencedirect.com/science/article/pii/S0145213424004046 , https://doi.org/10.1016/j.chiabu.2024.107014 .  Fisher, Claire, et al. “The Association of Adverse Childhood Experiences with Household Income, Educational Attainment and Partnered Status among Adults Aged 30-39.” Deleted Journal , vol. 1, 1 Apr. 2024, pp. 100021–100021, https://doi.org/10.1016/j.chipro.2024.100021 . Giano, Z., Schwab-Reese, L., Mishra, A. A., Hubach, R. D., Johnson, R. L., Epperson, C. N., & Sammel, M. D. (2023). Intersectionality and Adverse Childhood Experiences: Comparing Subgroups of Sex, Race/Ethnicity, and Sexual Orientation. American journal of preventive medicine , 65 (1), 30–38. https://doi.org/10.1016/j.amepre.2023.01.025   Humphreys, K. L., LeMoult, J., Wear, J. G., Piersiak, H. A., Lee, A., & Gotlib, I. H. (2020). Child maltreatment and depression: A meta-analysis of studies using the Childhood Trauma Questionnaire. Child abuse & neglect , 102 , 104361. https://doi.org/10.1016/j.chiabu.2020.104361   Merrick, M. T., Ford, D. C., Ports, K. A., & Guinn, A. S. (2018). Prevalence of Adverse Childhood Experiences From the 2011-2014 Behavioral Risk Factor Surveillance System in 23 States. JAMA pediatrics , 172 (11), 1038–1044. https://doi.org/10.1001/jamapediatrics.2018.2537   Park, Y. M., Shekhtman, T., & Kelsoe, J. R. (2020). Effect of the Type and Number of Adverse Childhood Experiences and the Timing of Adverse Experiences on Clinical Outcomes in Individuals with Bipolar Disorder. Brain sciences , 10 (5), 254. https://doi.org/10.3390/brainsci10050254   Peterson, Cora, et al. “Economic Burden of Health Conditions Associated with Adverse Childhood Experiences among US Adults.” JAMA Network Open , vol. 6, no. 12, 6 Dec. 2023, p. e2346323, jamanetwork.com/journals/jamanetworkopen/fullarticle/2812583 , https://doi.org/10.1001/jamanetworkopen.2023.46323 . Testa, A., Jackson, D. B., Vaughn, M. G., Ganson, K. T., & Nagata, J. M. (2022). Adverse Childhood Experiences, health insurance status, and health care utilization in middle adulthood. Social science & medicine (1982) , 314 , 115194. https://doi.org/10.1016/j.socscimed.2022.115194 Tognin, S., Catalan, A., Aymerich, C. et al.  Association between Adverse Childhood Experiences and long-term outcomes in people at Clinical High-Risk for Psychosis. Schizophr  11, 23 (2025). https://doi.org/10.1038/s41537-025-00562-9   Van Veen, T., Wardenaar, K. J., Carlier, I. V., Spinhoven, P., Penninx, B. W., & Zitman, F. G. (2013). Are childhood and adult life adversities differentially associated with specific symptom dimensions of depression and anxiety? Testing the tripartite model. Journal of affective disorders , 146 (2), 238–245. https://doi.org/10.1016/j.jad.2012.09.011

By: Briana Barraza-Contreras Introduction Adolescence is often described as a difficult time, filled with academic expectations, social pressures, as well as navigating their emotional development. Teens are expected to excel academically and participate in extracurricular activities, all while navigating significant biological and emotional changes. Some levels of stress are normal, but chronic stress has become increasingly common in adolescence, a time when the brain is particularly vulnerable to certain stressors and its effects run deeper than mood swings or temporary burnout. This significant amount of stress can have negative, long-lasting effects on adolescents’ development and close relationships. Unlike adults, adolescents are in a critical period of brain plasticity, especially in regions responsible for decision-making, memory, and emotional regulation. This heightened plasticity allows for increased learning, but it also means that the brain is more impressionable, making it more vulnerable to negative experiences, such as chronic stress. Stressors, such as trauma or anxiety, can have long-lasting impacts that deeply shape long-term brain structure and function (Perica & Luna, 2023). One of the most vulnerable brain functions affected by chronic stress is working memory—the mental system responsible for temporarily holding and manipulating information. Working memory is essential for everyday academic tasks like solving math problems, following multi-step directions, and reading comprehension. However, its impact extends to adolescents’ social lives, where remembering details during conversations or coordinating plans is essential. When their working memory wavers, teens often feel overwhelmed, which can lower their motivation and harm their self-esteem, affecting both their academic success and social well-being. The Adolescent Brain and Stress During adolescence, the brain, especially the prefrontal cortex (PFC), which handles decision-making, memory, and impulse control, is still developing. Numerous impacts of stress undergo significant changes over time, show long-lasting consequences, or alter the course of developmental trajectories that impact cellular interactions and associated cognitive processes due to the PFC’s growth (Macht & Reagan, 2018). Moreover, teens' brains are more neuroplastic than adults’, meaning they are more open to change but also more vulnerable to damage. The impact of stress on learning, memory, and emotional regulation is greater during this period of their lives than at any other time besides infancy. Additionally, adolescence is marked by heightened emotional reactivity and evolving social cognition (McCormick et al., 2015). Teens are especially sensitive to peer rejection and performance-based evaluation, and often seek peer approval—common sources of modern adolescent stress. Without effective coping mechanisms or external support, this can increase the negative effects on brain development. When these pressures become chronic, they can exacerbate the emotional intensity already wired into the adolescent experience, leading to overactivation of stress-response systems, like the hypothalamic-pituitary-adrenal (HPA) axis (Tsai et al., 2021). Over time, this overactivation can result in dysregulated cortisol patterns, contributing to mood instability, impaired concentration, and disrupted sleep, all of which negatively affect brain development and cognitive function (Tsai et al., 2021). Working Memory and Cognitive Function Working memory is a core part of our cognitive system, responsible for holding and manipulating information over short periods of time. It is vital for learning, problem-solving, reading comprehension, and following instructions—all essential skills in academic settings (Almarzouki, 2024). The prefrontal cortex, which is still developing during adolescence, plays a central role in working memory.  When chronic stress disrupts this brain region, it can impair a teen’s ability to concentrate, retain new information, and complete complex tasks. These disruptions can lead to struggles in school and social situations, often misinterpreted as laziness or disinterest, when in fact, they are signs of an overwhelmed brain. Teens may have difficulty following instructions, keeping track of assignments, or participating in group discussions. Moreover, a decrease in working memory can affect emotional regulation, making it harder for teens to control impulsive behaviors or manage frustration (Perica & Luna, 2023). This can potentially strain relationships with peers, teachers, and family members, causing adolescents to feel misunderstood or left out, which may contribute to emotional withdrawal or social isolation. To prevent this, cognitive strategies such as chunking, rehearsal, and the use of visual aids can help adolescents retain information more effectively. With targeted support from family and teachers, and other caring adults or peers, it is possible to stop the cycle of chronic stress and help teens develop their ability to learn and thrive in and outside the classroom. Conclusion  Chronic stress has a lasting impact on the developing adolescent brain. It impairs working memory and facilitates academic struggles, emotional dysregulation, and long-term mental health challenges. When working memory is compromised, teens are not just forgetful or distracted. They are experiencing cognitive breakdowns that interfere with learning, socialization, and daily functioning. These are not temporary setbacks—they can reflect into adulthood if left untreated. Raising awareness about the biological and psychological effects of stress ensures that communities take action. Educators, mental health professionals, and caregivers must recognize the signs of cognitive overload and understand its neurological basis, and actively intervene by providing strategies and resources that support adolescents’ cognitive and emotional well-being. Schools can provide mental health education, incorporate brain breaks into classroom routines, and offer accessible counseling. Additionally, families can encourage open communication, model healthy coping strategies, and allow space for emotional processing. Ultimately, acknowledging the brain’s heightened plasticity during adolescence strengthens our understanding of how stress reshapes the adolescent brain, and we gain the insight and responsibility to build environments that support young minds. References Almarzouki, A. F. (2024). Stress, working memory, and academic performance: a neuroscience perspective. Stress , 27 (1).   https://doi.org/10.1080/10253890.2024.2364333   Macht, V. A., & Reagan, L. P. (2018). Chronic stress from adolescence to aging in the prefrontal cortex: A neuroimmune perspective. Frontiers in Neuroendocrinology , 49 , 31–42. https://doi.org/10.1016/j.yfrne.2017.12.001   McCormick, C. M., Hodges, T. E., & Simone, J. J. (2015). Peer pressures: Social instability stress in adolescence and social deficits in adulthood in a rodent model. Developmental Cognitive Neuroscience, 11 , 2–11. https://doi.org/10.1016/j.dcn.2014.04.002   Perica, M. I., & Luna, B. (2023). Impact of stress on excitatory and inhibitory markers of adolescent cognitive critical period plasticity. Neuroscience & Biobehavioral Reviews , 153 , 105378.   https://doi.org/10.1016/j.neubiorev.2023.105378   Tsai, N., Mukhopadhyay, S., & Quas, J. A. (2021). Stress and working memory in children and adolescents: Insights from a multisystem approach.  Journal of Experimental Child Psychology, 209 , 105176.   https://doi.org/10.1016/j.jecp.2021.105176

Catherine Gooch Background The experience of having a physical self, of understanding our bodies as our own and as being contained within certain physical boundaries, is something so automatic that we tend to not even consciously notice it. But this sense of embodiment can unfortunately become damaged by certain injuries. Because of this, researchers have been exploring embodiment as a clinically-useful outcome in various types of rehabilitation research. To better understand this, it will be helpful to have a clearer framework for what exactly embodiment is. One useful model consists of three main components: ownership, body representation, and agency (Segil et al., 2022) (Figure 1). Ownership is the belief or feeling that a limb, tool, or device is a part of yourself, rather than some external object. Body representation refers to “the knowledge, beliefs, and experiences we have of the physical structure of our bodies” (5). This includes things like knowing the size or location of our bodies, as well as understanding the boundaries of our bodies and which objects are close enough to be acted upon by us. Lastly, agency has to do with the self-attribution of our actions, or the feeling that we are in control of the movements we make. All three of these components can be experienced either consciously or subconsciously and are phenomenologically separable, meaning the subjective experience of one can occur without the subjective experiences of the other two. However, the processes that underlie each domain also play a role in the others, meaning they “are not separable on the level of neurophysiological mechanisms” (3). Figure 1. Framework depicting the three domains of embodiment. Conscious experience and subconscious mechanisms of each component are portrayed as distinct. Sensory information from both the external world and from inside the body enter the subconscious processes, which then create conscious experiences of embodiment (Segil et al., 2022). This work is licensed under the Creative Commons Attribution 4.0 International License,   https://creativecommons.org/licenses/by/4.0/ Applications This concept has relevance to fields such as prosthetics. It has been shown that prosthetic devices can become integrated into an individual’s perception of their own body (Marasco et al., 2011, Fritsch et al., 2021, Page et al., 2018). One interesting way of measuring this is by using the skin conductance response, which is the change in electrical conductivity of a person’s skin as they release sweat in response to some type of arousal. In one study, researchers used this response to investigate embodiment of a prosthetic hand after the participants experienced a version of the famous ‘rubber hand illusion’ (Ehrsson et al., 2008). The experiment was set up so that the participants’ stump was covered, and a realistic, rubber prosthetic hand was placed in full view in front of them. The researchers elicited a fear response by stabbing both the stump and the rubber hand with a needle attached to a syringe. While this was happening, they measured skin conductivity with electrodes placed over the area of the stump where the participants’ fingers would have been. This was performed in two conditions: one where the stump and the prosthesis were stabbed at precisely the same time, and one where they alternated. Interestingly, the researchers observed a greater skin conductance response in the synchronous condition compared to the asynchronous condition. This indicates that the participants experienced a greater sense of subconscious ownership of the prosthesis when it was stabbed at the same time as their stump. Further, it shows that embodiment, and particularly embodiment of external devices such as a prosthesis, is not static – rather it can be increased or decreased in response to external stimuli. Because embodiment can be modulated like this, researchers can design rehabilitation programs to promote this sense, and use it to improve recovery after certain injuries. One study took an embodiment-based approach to improve orthopedic rehabilitation in patients with distal radius fracture (Matamala-Gomez et al., 2022). In this study, researchers split the participants into three groups: an immersive virtual reality (VR) experimental group, a non-immersive VR control group, and another control group that performed conventional rehabilitation exercises focused on moving their hand and fingers. Patients in the immersive VR group wore a headset that displayed a virtual body through a first-person perspective, and performed a variety of tasks where they pressed a pedal to move their virtual arm while receiving tactile feedback on their immobilized real arm. Patients in the non-immersive VR group only observed virtual actions, rather than performing them by pressing the pedal; they also did not receive any sensory feedback. Participants in each group performed their exercises for 4-6 weeks after their arm was put in a cast, and then performed the same conventional rehabilitation exercises for 6 more weeks after their cast was removed. Participants in the immersive VR group had the greatest recovery in functional ability of their arm, as well as range of motion in their wrist. Additionally, results from an embodiment questionnaire showed that participants in the immersive VR group had stronger feelings of both ownership and agency of the virtual limb compared to participants in the non-immersive VR group. Importantly, there was a strong positive correlation between embodiment scores on the questionnaire and functional recovery of the participants’ arm after their cast was removed. The researchers suggest that “the feeling of ownership toward a virtual body may increase sensorimotor activation when the embodied virtual body performs movements over which the subject has a sense of agency,” which could be a reason for the improved motor function in the immersive VR group. Overall, this study shows the potential for utilizing embodiment-based approaches in rehabilitation. Another application of embodiment for rehabilitation is in robotic exoskeletons. These are a type of assistive device which can provide support for functions such as walking. There are a few different robotic exoskeletons currently available on the market, but their full potential for improving muscle function is not yet known. One newly-developed robotic exoskeleton was built by researchers to assist people with hemiparesis, or muscle weakness on one side of the body after stroke, in walking (Lora-Millan, J.S., et al., 2022). The researchers specifically examined how the exoskeleton was embodied by the users after gait training (Figure 2). This is important to examine, because the maximum benefit of these devices can only be achieved when they “are embodied by users, i.e. the wearer considers that the device’s action is performed by their own body rather than by an external tool” (Lora-Millan, J.S., et al., 2023). Figure 2 . A) REFLEX prototype (Lora-Millan et al., 2022). B) Experimental set-up (Lora-Millan et al., 2023). These works are licensed under the Creative Commons Attribution 4.0 International License,   https://creativecommons.org/licenses/by/4.0/ The researchers did this by looking at the changes in muscle activity of the wearers (Lora-Millan, J.S., et al., 2023). The idea is that as the exoskeleton becomes embodied by the user, their muscle activation patterns will change to reflect that. In particular, the researchers expected to see reduced muscle activity in the non-paretic leg as the participants develop greater senses of agency over the device, incorporating it into their body schema and reducing energy expenditure while walking. These results have been found in other studies that use robotic exoskeletons, but mostly with healthy subjects (Haufe et al., 2021, Wehbi et al., 2017, Steele et al., 2017, Gordon & Ferris, 2007). In this particular study, though, the researchers found mixed results, with some participants demonstrating these characteristics of embodiment and others not, which they attribute to the wide variety in time after stroke and degree of impairment among the study participants, as well as potentially not enough time actually using the device. Overall, these results emphasize the need to better understand how the nervous system’s ability to embody external devices is impacted by stroke, and how this crucial function can be restored to improve rehabilitation outcomes for stroke survivors. Future Directions Embodiment has been shown to be a clinically-relevant outcome in wide-ranging areas of rehabilitation research, from prosthetic devices to virtual reality to robotic exoskeletons and more. However, there are still many questions currently unanswered about how best to foster this in people with neurological or musculoskeletal injuries. Future research should aim to clarify the neurophysiological mechanisms that enable embodiment across different populations and injury types, as well as to standardize measures of embodiment used in research. By doing so, scientists, clinicians, and engineers can work to build improved technologies that restore not only motor function, but a more complete and intuitive sense of self for individuals recovering from injury. Works Cited 1.     Segil, J. L., Roldan, L. M., & Graczyk, E. L. (2022). Measuring embodiment: A review of methods for prosthetic devices. Frontiers in neurorobotics , 16 , 902162.   https://doi.org/10.3389/fnbot.2022.902162 2.     Paul D. Marasco, Keehoon Kim, James Edward Colgate, Michael A. Peshkin, Todd A. Kuiken, Robotic touch shifts perception of embodiment to a prosthesis in targeted reinnervation amputees, Brain , Volume 134, Issue 3, March 2011, Pages 747–758, https://doi.org/10.1093/brain/awq361 3.     Fritsch, A., Lenggenhager, B., and Bekrater-Bodmann, R. (2021). Prosthesis embodiment and attenuation of prosthetic touch in upper limb amputees - a proofof-concept study. Conscious. Cogn. 88, 103073.   https://doi.org/10.1016/j.concog.2020.103073 4.     Page, M. D., George, J. A., Kluger, D. T., Duncan, C., Wendelken, S., Davis, T., et al. (2018). Motor control and sensory feedback enhance prosthesis embodiment and reduce phantom pain after long-term hand amputation. Front. Hum. Neurosci. 12, 352.   https://doi.org/10.3389/fnhum.2018.00352 5.     H. Henrik Ehrsson, Birgitta Rosén, Anita Stockselius, Christina Ragnö, Peter Köhler, Göran Lundborg, Upper limb amputees can be induced to experience a rubber hand as their own, Brain , Volume 131, Issue 12, December 2008, Pages 3443–3452, https://doi.org/10.1093/brain/awn297 6.     Matamala-Gomez, M., Slater, M. & Sanchez-Vives, M.V. Impact of virtual embodiment and exercises on functional ability and range of motion in orthopedic rehabilitation. Sci Rep   12 , 5046 (2022).   https://doi.org/10.1038/s41598-022-08917-3 7.     Lora-Millan, J.S., Sanchez-Cuesta, F.J., Romero, J.P. et al.  A unilateral robotic knee exoskeleton to assess the role of natural gait assistance in hemiparetic patients. J NeuroEngineering Rehabil   19 , 109 (2022).   https://doi.org/10.1186/s12984-022-01088-2 8.     Lora-Millan, J.S., Sanchez-Cuesta, F.J., Romero, J.P. et al.  Robotic exoskeleton embodiment in post-stroke hemiparetic patients: an experimental study about the integration of the assistance provided by the REFLEX knee exoskeleton. Sci Rep   13 , 22908 (2023).   https://doi.org/10.1038/s41598-023-50387-8 9.     Haufe, F.L., Kober, A.M., Wolf, P. et al.  Learning to walk with a wearable robot in 880 simple steps: a pilot study on motor adaptation. J NeuroEngineering Rehabil   18 , 157 (2021).   https://doi.org/10.1186/s12984-021-00946-9 10.  Wehbi, F. et al. Active impedance control of a knee-joint orthosis during swing phase. In 2017 International Conference on Rehabilitation Robotics (ICORR) 435–440 (IEEE, 2017).   https://doi.org/10.1109/ICORR.2017.8009286 . 11.  Steele, K. M., Jackson, R. W., Shuman, B. R., & Collins, S. H. (2017). Muscle recruitment and coordination with an ankle exoskeleton. Journal of biomechanics , 59 , 50–58.   https://doi.org/10.1016/j.jbiomech.2017.05.010 12.  Gordon, K. E., & Ferris, D. P. (2007). Learning to walk with a robotic ankle exoskeleton. Journal of biomechanics , 40 (12), 2636–2644.   https://doi.org/10.1016/j.jbiomech.2006.12.006

Author: Evelyn Fistler Editor: Morgann MacDonald Created using Canva: https://www.canva.com Introduction—What Is Alzheimer’s Disease?      Think of this: the aging population in the United States of America is rapidly growing. The elderly individuals in our lives could be susceptible to the development of neurological diseases, yet society still lacks definitive ways to prevent and cure forms of dementia, especially Alzheimer's.        So , what exactly is Alzheimer's Disease?  It’s the most common form of dementia— a progressive and degenerative condition that damages the brain, typically in individuals aged 65 and older. Over time, it impairs both cognitive and, eventually, physical functioning.       But   why does this happen? The primary biological drivers of Alzheimer’s are the buildup of Tau proteins–which form neurofibrillary tangles inside neurons, much like hair clogging a drain–and amyloid plaques which accumulate on the outside of neurons in the brain. These abnormalities disrupt cognitive abilities such as memory, language, and reasoning (Stages of Alzheimer’s Disease, 2024).        And   how does this happen? The exact cause of Alzheimer's disease remains unknown, and there is currently no known cure. However, a variety of risk factors–including social experiences, environmental exposures, genetics, and even influences of culture–are known to have an impact on the disease’s progression. This article offers a brief overview of Alzheimer's development and progression, exploring how aging and risk factors such as induced stress have an impact, through a sociological lens. Created using BioRender: https://app.biorender.com The Aging Population      Stress is a typical part of life, and the intensity often shifts in response to societal, environmental, and socioeconomic conditions. When considering the development of a neurological disease, stress is a key factor in analyzing and understanding the broader social issues that may elevate risks. By examining these stressors more closely, we can better assess the impact on the quality of life for older adults.      A major concern is the growing lack of accessible and affordable healthcare within communities—an issue closely tied to socioeconomic status and social support systems. These challenges are increasingly prominent due to the increasing elderly population. In fact, the number of people aged 65 and older in the US is estimated to increase by about 47% from 2022 to 2050 (Fact Sheet: Aging in the United States, n.d.).     Given Alzheimer's usually affects those aged 65 years and older, it’s important to consider how both social and environmental stressors correlate with the onset and progression of the disease when going over demographics for those typically vulnerable. By 2050, it’s estimated that around 13.8 million people will be living with Alzheimer’s in the US, with 7 million of those aged 85 and older (Hebert, 2013).      Factors such as health insurance eligibility, access to caregiving and support, fear of social isolation, environmental conditions, and place of residence are all areas where prolonged stress may occur. Exploring how stress impacts cognition and contributes to the development of Alzheimer's may lead to preventive strategies that improve the quality of life for aging individuals. Exploration of Alzheimer’s      Many sociological factors may contribute to the development and progression of Alzheimer’s disease, making them crucial areas for continued research. Sociology allows us to examine different communities within society where aspects such as race, sexual orientation, and gender identity are valuable to study due to discrimination, disparities, biological processes, and underlying factors. Neuropsychological studies of mental health and individual stressors can introduce new variables for reflection and future research. By understanding these influences, we can better recognize patterns that may contribute to the disease's development. Sociological Contributions      Through the analysis of society and the many biological variables that contribute to the development of Alzheimer's, we can begin to identify potential strategies to reduce risk. Sociological challenges that especially affect elders–such as isolation, stigma, socioeconomic status, social experience, and support–can have an impact on stress levels, which may influence the progression of the disease.      Ageism and other such harmful stereotypes, reinforced within some communities, can also impact their quality of life. It may lead to emotional distress and anxiety, which may in turn contribute to increased cellular stress and deteriorating health issues.       A study on positive self-perceptions and aging found that individuals with more positive self-views of aging tend to have better life quality, while those with negative perceptions of the self are more likely to experience cardiovascular issues, as well as neurofibrillary tangles and plaque within the brain that cause diseases such as dementia (Helio J, et al, 2024). This research highlights the importance of recognizing the impact of societal factors like discrimination and ageism and how they may alter perceptions of the self. Caregivers or institutions that many older individuals rely on may not provide the resources needed to ensure the best quality of life.          Another study conducted in China examined the activity in older participants in association with depression changes and found that increased physical activity improves both physical and cognitive aspects of individuals (Gao, et al, 2023). Returning to the idea of self-perception, regular physical activity, and social participation from aging individuals may contribute to a more positive self-perception that improves their health outcomes as they get older. Consistent activity and better quality of life lead to  reduced stress and anxiety, thus positively affecting cognition, while isolation and negative self-perception due to decreased activity do the opposite      When analyzing Alzheimer’s by demographic groupings, racial and ethnic disparities are notable to mention as Black and Hispanic Americans face a higher incidence of dementia than White Americans. Socioeconomic and educational status, as well as healthcare accessibility, potentially play a role in this disparity. A study based on self-reported experiences of discrimination and newly diagnosed cases of dementia shows that lifetime discrimination exposure was associated with a higher risk of dementia. It also suggests that discrimination in healthcare facilities may contribute to underdiagnosed early cognitive impairment, or the delay in care received (Bancks, et al, 2023).      Given the wide range of potential contributors to the disease, a deeper analysis of each risk factor down to the root could be key when it comes to treating the disease. Sociological inquiry allows us to discuss the more vulnerable populations with given demographics with Alzheimer’s. Lifestyle choices and prior cognition–such altering experiences could shape long-term brain health and daily functioning in older age. Psychological Factors       On a psychological level, factors such as mental health, cognitive testing, and accurate diagnoses may contribute to how Alzheimer’s is viewed. Symptoms that overlap with other mental disorders require careful investigation. Misdiagnosis, diagnostic bias, and behavioral changes–like emotional instability or altered coping mechanisms–are all relevant considerations. Indicated psychological symptoms can appear before the diagnosis of Alzheimer’s: 72% of patients experience depression, mood changes, social withdrawal, and suicidal thoughts two years before the diagnosis; 45% of patients develop hallucinations, paranoia, and delusions in less than a month following diagnosis; 21% exhibit irritability, agitation and aggressive behavior within a year following the diagnosis (Beata, et al, 2023).      A study on psychological distress in elderly individuals measured their resilience to contributing factors of Alzheimer’s, determined by their socioeconomic and educational status. The research showed that those with higher education and a higher-than-average income had lower psychological distress than those who did not. Lower incomes and less education were also associated with poorer lifestyle choices and worse levels of mental health. In addition, their resilience (particularly their self-perceived ability to withstand ageism) was linked to better mental health. However, loneliness remains a persisting factor that negatively affects the quality of life for elders (José Alberto Ribeiro-Gonçalves, et al,  2023).      It is essential to consider the elements that contribute to increased stress, as there is an increased risk for neurological issues and potentially dementia-related diseases. Chronic stress increases cognitive impairment and is prevalent in disorders such as depression (Wallensten, et al, 2023). Individuals facing social bias–whether due to age, gender identity, sexual orientation, or xenophobia–are especially vulnerable to chronic stress and its long-term impacts on brain health (Adkins-Jackson, et al, 2023). Biological Analysis of Stress      Rat models used to study the relationship between stress and Alzheimer's disease demonstrate a complex relationship. While Stress may not be a direct cause of the disease, increased levels show a detrimental impact on neuron functionality, pushing the neurons closer to death and accelerating the progression of Alzheimer's pathogenesis. Increased production of amyloid plaque and Tau protein–both of which disrupt cognitive function–is a hallmark of the disease, However, these biological markers alone do not fully explain how stress can impact Alzheimer’s disease, leaving room for the hypothesis that stress itself accelerates the progression of Alzheimer’s disease in humans (Justice, 2018).      Research also highlights the link between loneliness and the development of Alzheimer’s. In a ten-year study, it was shown that isolation was associated with the early stages of Alzheimer's neuropathogenesis. For lonely adults aged 80 and older without the APOE ε4 allele (the strongest known genetic risk factor for Alzheimer’s), the risks tripled. Loneliness was associated with poorer executive function, lower total cerebral volume (the measurement used for cognitive decline), and greater volume of white matter hyperintensities. This suggests that loneliness, particularly in middle-aged and young-old APOE ε4–negative adults in the early development of Alzheimer’s, may be a modifiable social and psychological risk factor. At the same time, brain atrophy and consequent cognitive decline in younger individuals point to additional biological vulnerabilities (Salinas et al., 2022).      Furthermore, oxidative stress–an imbalance of free radicals (reactive oxygen) and antioxidant defenses that help maintain homeostasis and cellular processes–also contributed to Alzheimer’s risk. This imbalance can be triggered by many factors, such as lifestyle choices, environmental and social exposures, and certain medical conditions. Without the antioxidants that neutralize the free radicals, unstable molecules damage cells, including neurons. Oxidative stress is linked to neuroinflammation, synaptic plasticity, serotonin, and mitochondrial dysfunction–all of which are associated with a heightened risk of depression(Correia, et al, 2023).       Because stress is such a crucial risk factor, identifying its sources may narrow down other potential causes of Alzheimer's disease. Depression is often accompanied by high cortisol levels, which are also commonly associated with an increased risk for Alzheimer's disease. With elevated cortisol levels, cognitive decline occurs faster, disrupting brain function (Ouanes & Popp, 2019). Cortisol  also promotes protein aggregation and induces oxidative stress ,  further damaging neurons and therefore inhibiting cognitive functioning like reasoning, language, memory, and learning (Signorello, et al, 2024).      Further biological analysis of the consequences of stress links chronic stress with higher levels of plasma T-Tau, a prevalent biomarker for neurodegeneration. Longstanding stress during midlife was similarly associated with higher levels of Tau in cerebrospinal fluid. Notably, this association does not differ significantly by race or gender. Social support is also suggested to promote better health and reduce the risk-related biological response, lowering the probability of neurodegeneration (Lee, et al, 2025). Public Health and Research—What Next?      Improving healthcare for older adults is essential, particularly as age increases vulnerability and risk. Understanding the socioeconomic factors that affect healthcare access is critical for addressing disparities in elder health One unsolved major challenge is the unfair and unequal distribution of medical resources (Jones, et al, 2024).      Public health efforts within communities must prioritize prevention, early detection, and diagnosis, as well as promoting brain health and support from caregivers. Early preventative measures such as lowering blood pressure, maintaining a healthy diet, and consistently exercising are among the most effective lifestyle changes recommended (The Public Health Approach | Alzheimer’s Association, n.d.).       By 2060, over 13 million people are projected to have dementia-related diseases, underscoring the urgency of funding and support from agencies (Federal Commitment to Addressing Alzheimer’s and Related Dementia, 2024). Federal funding for research and agencies’ efforts to disperse care and support institutions while still promoting public awareness is an efficient way to reach groundbreaking research on a macro level. For example, the Administration for Community Living focuses on funding for both individuals living with dementia and their caregivers. Their promotion of community-based programs is another particularly beneficial achievement. Thanks to administrations such as this, more funding can be provided for dementia-care services and research, allowing more accessible and improved healthcare for elders. Preventions—What Can Be Done?     As research on Alzheimer’s continues to explore the role of lifestyle and environmental factors, several modifiable habits have been shown to significantly reduce risk. Individuals who have limited their alcohol intake, avoided cigarette use, pursued a suitable diet, engaged in moderate to intensive physical activities, and committed to cognitive activities, have a 60% lower risk for Alzheimer’s in comparison to those who followed two of the listed factors–whose risk has been reduced by only 37% (Advances in Aging and Alzheimer's Research, 2025).       These findings raise further questions on how lifestyle, social engagement, and stress, such as social interactions, childhood experiences, and traumatic issues could potentially impact cognition later in life. The aforementioned stress levels and mental health should be taken into consideration to prevent worsened overall cognition and reduce the risk of developing a dementia-related disease.  Conclusion—Why Does This Matter?     Understanding the biological and societal impacts of Alzheimer’s is crucial as the aging population continues to grow. Awareness on the subject could bring forth more information that could be beneficial for research. Think of the older adults in your life who are facing daily struggles—whether they are cognitively impaired or suffering from other disorders that affect their ability to spend time with family and friends. Continued investment in research allows for new paths to be sought and discovered, providing new and valuable learning opportunities. By educating the general public, we encourage healthier lifestyles, reduce risk factors, and promote stronger support systems for aging individuals. Ultimately, greater awareness empowers caregivers, strengthens communities, and enhances the quality of life for those most vulnerable. References Adkins-Jackson PB,  George KM,  Besser LM, et al.  The structural and social determinants of Alzheimer's disease-related dementias. Alzheimer's Dement.  2023; 19: 3171–3185. https://doi.org/10.1002/alz.1302 Advances in Aging and Alzheimer's Research, 2025 https://www.nia.nih.gov/about/advances-aging-and-alzheimers-research#:~:text=NIH%2Dsupported%20investigators%20have%20made,just%20one%20of%20these%20behaviors Bancks, M. P., Byrd, G. S., Caban-Holt, A., Fitzpatrick, A. L., Forrester, S. N., Hayden, K. M., Heckbert, S. R., Kershaw, K. N., Rapp, S. R., Sachs, B. C., & Hughes, T. M. (2023). Self-reported experiences of discrimination and incident dementia. Alzheimer's & dementia: The Journal of the Alzheimer's Association, 19(7), 3119–3128. https://doi.org/10.1002/alz.12947 Beata, B. K., Wojciech, J., Johannes, K., Piotr, L., & Barbara, M. (2023). Alzheimer's Disease-Biochemical and Psychological Background for Diagnosis and Treatment. International journal of molecular sciences, 24(2), 1059. https://doi.org/10.3390/ijms24021059 Correia, A. S., Cardoso, A., & Vale, N. (2023). Oxidative Stress in Depression: The Link with the Stress. Response, Neuroinflammation, Serotonin, Neurogenesis and Synaptic Plasticity. Antioxidants (Basel, Switzerland), 12(2), 470. https://doi.org/10.3390/antiox12020470 Fact Sheet: Aging in the United States. (n.d.). PRB. https://www.prb.org/resources/fact-sheet-aging-in-the-united-states/#:~:text=The%20number%20of%20Americans%20ages,from%2017%25%20to%2023%25.&text=The%20U.S%20population%20is%20older,the%20top%20of%20the%20list.&text=The%20older%20population%20is%20becoming,from%2075%25%20to%2060%25.&text=The%20rising%20diversity%20among%20older,%25)%20were%20non%2DHispanic%20white.&text=But%20research%20shows%20that%20there,part%20of%20the%20white%20majority Federal Commitment to Addressing Alzheimer’s and Related Dementias (December 5th, 2024) https://www.alzheimers.gov/taking-action/federal-response Gao, Y., Jia, Z., Zhao, L., & Han, S. (2023). The Effect of Activity Participation in Middle-Aged and Older People on the Trajectory of Depression in Later Life: National Cohort Study. JMIR public health and surveillance, 9, e44682. https://doi.org/10.2196/44682 Hebert, L. E., Weuve, J., Scherr, P. A., & Evans, D. A. (2013). Alzheimer's disease in the United States (2010-2050) estimated using the 2010 census. Neurology, 80(19), 1778–1783. https://doi.org/10.1212/WNL.0b013e31828726f5 Helio J. Coelho-Junior, Riccardo Calvani, Anna Picca, Francesco Landi, Emanuele Marzetti, The influence of ageism on the hallmarks of aging: Where age stigma and biology collide, Experimental Gerontology, Volume 196, 2024, 112575, ISSN 0531-5565, https://doi.org/10.1016/j.exger.2024.112575 . Jones, C. H., & Dolsten, M. (2024). Healthcare on the brink: navigating the challenges of an aging society in the United States. npj Aging, 10(1), 22. https://doi.org/10.1038/s41514-024-00148-2 Justice N. J. (2018). The relationship between stress and Alzheimer's disease. Neurobiology of stress, 8, 127–133. https://doi.org/10.1016/j.ynstr.2018.04.002 José Alberto Ribeiro-Gonçalves, Pedro Alexandre Costa, Isabel Leal, Loneliness, ageism, and mental health: The buffering role of resilience in seniors, International Journal of Clinical and Health Psychology, Volume 23, Issue 1, 2023, 100339, ISSN 1697-2600, https://doi.org/10.1016/j.ijchp.2022.100339 . ( https://www.sciencedirect.com/science/article/pii/S1697260022000473 ) Lee, J. K., Johnson, L., Hall, J. R., Bateman, J. R., Barnes, L. L., O'Bryant, S., & Mielke, M. M. (2025). Chronic stress, social support, and Alzheimer's blood-based biomarkers in the HABS-HD study. Alzheimer's & dementia: the journal of the Alzheimer's Association, 21(3), e70043. https://doi.org/10.1002/alz.70043 Newcomb, B. (2021, December 2). To better understand aging, look at both biological and social factors. USC Leonard Davis School of Gerontology. https://gero.usc.edu/2020/12/03/usc-aging-biology-social-factors/ Ouanes, S., & Popp, J. (2019). High Cortisol and the Risk of Dementia and Alzheimer's Disease: A Review of the Literature. Frontiers in aging neuroscience, 11, 43. https://doi.org/10.3389/fnagi.2019.00043 Salinas, J., Beiser, A. S., Samra, J. K., O’Donnell, A., DeCarli, C. S., Gonzales, M. M., Aparicio, H. J., & Seshadri, S. (2022). Association of loneliness with 10-year dementia risk and early markers of vulnerability for neurocognitive decline. Neurology, 98(13). https://doi.org/10.1212/wnl.0000000000200039 Signorello, M. G., Ravera, S., & Leoncini, G. (2024). Oxidative Stress Induced by Cortisol in Human Platelets. International journal of molecular sciences, 25(7), 3776. https://doi.org/10.3390/ijms25073776 Stages of Alzheimer’s disease. (2024, February 29). Johns Hopkins Medicine. https://www.hopkinsmedicine.org/health/conditions-and-diseases/alzheimers-disease/stages-of-alzheimer-disease . The public health approach | Alzheimer’s Association. (n.d.). Alzheimer’s Association. https://www.alz.org/professionals/public-health/public-health-approach Wallensten, J., Ljunggren, G., Nager, A., et al. Stress, depression, and risk of dementia – a cohort study in the total population between 18 and 65 years old in Region Stockholm. Alz Res Therapy 15, 161 (2023). https://doi.org/10.1186/s13195-023-01308-4

Magdalene Dochnal Early in the throes of World War I, the French military found itself in crisis. As infantry units sustained great losses across a rapidly modernizing Western Front, France found itself stuck in the mud of the trenches in its royal blue jackets and accompanying bright red pants. In response, the French military would come to form the Camoufleurs. The special department largely composed of contemporary painters was tasked with bringing the military’s uniforms into the 20th century and designing camouflage to hide and obscure large military equipment. Within two years, infantry units ditched their vibrant blue coats and red pants for duller, but more effective uniforms, allowing them to blend in with the Western European landscape. Hidden inside a (likely apocryphal) story of military history, the Camoufleurs exemplify the broad reach of cognitive psychology. At the same time that the Camoufleurs were studying the art of hiding in plain sight, the field of Gestalt psychology was taking shape in Germany with the aim of understanding how our brains group objects and concepts together to make sense of the world. Using lessons from Gestalt psychology, we can come to better understand how camouflage patterns scramble our visual cognition, and how cognitive psychologists can take their work beyond academia. Gestalt visual perception Gestalt psychology has its origins in the early 20th century with the work of Czech psychologist Max Wertheimer and his collaborators at the Humboldt University of Berlin (Wagemans et al., 2012). Wertheimer primarily studied motion perception and theorized that people perceive motion by grouping together changes in an object’s position, creating a cohesive trajectory (1912). This notion was later generalized to a range of sensory modalities to form the broader Gestalt theory which suggests that perception is primarily a process of organization into closed, cohesive, and continuous units, taking its name from the German word for “shape” (Wertheimer, 1923). In Gestalt theory’s initial form, Wertheimer (1923) described seven principles by which people group visual stimuli when all other conditions are accounted for or assumed: Proximity: items that appear close to each other are grouped together. Similarity: items that appear similar are grouped together. Common fate: items that appear to move in the same direction are grouped together. Symmetry: items that are symmetrical reflections of one another are grouped together. Parallelism: items that are parallel to one another are grouped together. Continuity: items that form a continuous unit are grouped together. Closure : items that form a closed unit are grouped together. The advent of cognitive psychology in the decades after World War II would bring renewed interest in Gestalt theory and with it, additional grouping principles such as common region  (items that occupy the same space are grouped together; Palmer, 1992) and synchrony  (items that change at the same time are grouped together; Alais et al., 1998). These mechanisms appear to manifest both behaviorally and neurally with memory recall benefits for unfamiliar objects that abide by Gestalt principles (Palmer, 1977) and increased neuronal signal change when binding follows Gestalt principles (Mihalas et al., 2011). Camouflage in the natural world In nature, camouflage typically adopts one of two approaches, each targeting a different layer of survivability. The most commonly thought of approach is crypsis where an organism possesses a trait or displays a behavior that makes it more difficult to detect (Merilaita et al., 2017; Petersson, 2018). While there are many methods of crypsis across organisms, each of them attempts to disrupt visual grouping processes by influencing a stimulus’ signal-to-noise ratio, the ratio of visual information that is meaningful in detecting an organism to that which is meaningless (Merilaita et al., 2017). Given that Gestalt processes are dependent on differences in stimuli to distinguish groupings from one another, organisms can increase their survivability by reducing the amount of meaningful visual information about themself (reducing signal), increasing the amount of useless visual information (increasing noise), or by doing some combination of the two. An example of camouflage that reduces signal is countershading whereby the ventral portion, or stomach, of an animal is lighter than the dorsal portion, its back (Rowland et al., 2008; Ruxton et al., 2004). This is especially common in marine organisms like the blue shark ( Prionace glauca ), which has a white stomach and a steel blue back. Countershading reduces visual information about the blue shark’s shadow, making it more difficult for another marine organism to make judgments about the shark’s shape. When looking from below, the blue shark’s stomach matches the sunlight’s glare on the ocean surface; from above, its blue back blends with the ocean’s depths. This increases the similarity of the blue shark to its environment which makes it difficult for predators such as orcas to detect where the shark’s body begins or ends, enhancing the blue shark’s survivability. Even when detected, organisms have other ways of enhancing their survivability with the masquerading approach to camouflage (also known as “mimesis”). In masquerading, an organism leverages traits or behaviors that make them more difficult to identify in their environment or increase the likelihood of misidentification (Petersson, 2018; Skelhorn & Rowe, 2016). A great example of such a behavior can be seen in American peppered moth ( Biston betularia cognataria ) caterpillars who are able to change colors from brown to green to mimic birch and willow branches, respectively (Noor et al., 2008). Even if there are slight discrepancies in the disguise, the fact that a twig-like object shares a common region with branches and looks similar enough to a branch significantly decreases the likelihood that a preying bird will group the caterpillar into any category other than “branch,” or more importantly “inedible” (Rowland et al., 2008; Ruxton et al., 2004; Skelhorn & Rowe, 2016). Applying nature’s lessons: Military camouflage Like many other organisms, humans too engage in camouflage. Every military has a vested interest in increasing the survivability of its personnel, and so military camouflage is often used to address the two foremost principles of survivability: do not be detected and do not be identified (Dodge & McKelvey, 2013; Talas et al., 2017). It is these principles of survivability that have guided camouflage design from the khaki uniforms of British occupying forces in India in the mid-1800s to the woodland camouflage of Western European militaries today (Talas et al., 2017). In testing the quality of new camouflage patterns, military researchers leverage findings from cognitive psychology to design computational metrics and assessments of camouflage efficacy. Such metrics and assessments specifically take into account features of human visual cognition such as perceived color differences (Bai et al., 2020), allocation of visual attention (Yang et al., 2021), and how humans group visual stimuli (Li et al., 2023). A common feature of military camouflage is the technique of background matching, where the colors of a pattern’s operational environment (the setting in which the pattern will be deployed; Dodge & McKelvey, 2013) are used in the pattern’s design to reduce the amount of visual information about the wearer’s shape (Merilaita et al., 2017; Talas et al., 2017). Background matching makes it more difficult to identify an object’s edges, which in turn makes it more difficult to group as a distinct object in the environment (Fraser et al., 2007). However, most contemporary military uniforms are not monochromatic and instead feature many patches of color (Talas et al., 2017). This too aids in camouflage via the technique of disruptive coloration where patches of color are used to mask distinctive features that may lead to identification (Merilaita et al., 2017). These patches introduce false edges and illusory continuities on the wearer’s body which make it more difficult to accurately group the whole body as a continuous unit. Conclusion Camouflage is one of countless examples of how cognitive psychology is applied out in the world. This blog article could have just as easily been about the design of highway signage (see Elbardawil, 2022) or how information processing affects driving behaviors (see Macuga et al., 2019). In a time of crisis for American research universities with dramatic cuts in federal grant spending and increased financial precarity for researchers (see Associated Press, 2025; Jaswal et al., 2025), the realm of applied science offers an alternative route for aspiring scientists to continue their careers in psychology. With so much disruption to traditional paths to pursuing a career in science, it is important that educators in psychology do not let their students’ passions for the field waver. Stories from the world of applied science may offer an additional way to foster enthusiasm among young psychology students for research beyond the research university. References Alais, D., Blake, R., & Lee, S.-H. (1998). Visual features that vary together over time group  together over space. Nature Neuroscience , 1 (2), 160–164. https://doi.org/10.1038/414 Associated Press. (2025, June 21). Judge blocks the Trump administration's National Science  Foundation research funding cuts. The Washington Post .  https://www.washingtonpost.com/politics/2025/06/21/research-funding-trump-cuts/57a2d bf8-4ec3-11f0-8fff-262d6ec54ab9_story.html Bai, X., Liao, N., & Wu, W. (2020). Assessment of camouflage effectiveness based on perceived  color difference and gradient magnitude. Sensors , 20 (17), 4672.  https://doi.org/10.3390/s20174672 Dodge, M. N., & McKelvey III, R. F. (2013). Why the survivability onion should include Reliability, Availability, and Maintainability (RAM)  [Master’s Thesis, Naval Postgraduate  School]. Calhoun: The NPS Institutional Archive DSpace Repository.  https://hdl.handle.net/10945/37614 Elbardawil, S. (2022). Legibility research of highway signage typefaces: A critical review and a  potential design-centred approach. The Design Journal , 25 (1), 86–103.  https://doi.org/10.1080/14606925.2021.2004716 Fraser, S., Callahan, A., Klassen, D., & Sherratt, T. N. (2007). Empirical tests of the role of  disruptive coloration in reducing detectability.  Proceedings of the Royal Society B:  Biological Sciences , 274 (1615), 1325–1331. https://doi.org/10.1098/rspb.2007.0153 Jaswal, V. K., Koenig, M. A., Gelman, S. A., Bauer, P. J., Callanan, M. A., Goldin-Meadow, S.,  Newcombe, N. S., Wellman, H. M., & Uttal, D. H. (2025). The future of cognitive  development in challenging times. Journal of Cognition and Development , 26 (3),  351–353.   https://doi.org/10.1080/15248372.2025.2494830 Li, N., Qi, W., Jiao, J., Li, A., Li, L., & Xu, W. (2023). SPCC: A superpixel and color clustering  based camouflage assessment. Multimedia Tools and Applications , 83 (9), 26255–26279.  https://doi.org/10.1007/s11042-023-16425-3 Macuga, K. L., Beall, A. C., Smith, R. S., & Loomis, J. M. (2019). Visual control of steering in  curve driving. Journal of Vision , 19 (5), 1-12. https://doi.org/10.1167/19.5.1 Merilaita, S., Scott-Samuel, N. E., & Cuthill, I. C. (2017). How camouflage works.  Philosophical Transactions of the Royal Society B: Biological Sciences , 372 (1724),  20160341. https://doi.org/10.1098/rstb.2016.0341 Mihalas, S., Dong, Y., Von Der Heydt, R., & Niebur, E. (2011). Mechanisms of perceptual  organization provide auto-zoom and auto-localization for attention to objects.  Proceedings of the National Academy of Sciences , 108 (18), 7583–7588.  https://doi.org/10.1073/pnas.1014655108 Noor, M. A. F., Parnell, R. S., & Grant, B. S. (2008). A reversible color polyphenism in  American peppered moth ( Biston betularia cognatari a) caterpillars. PLoS ONE , 3 (9),  e3142. https://doi.org/10.1371/journal.pone.0003142 Palmer, S. E. (1977). Hierarchical structure in perceptual representation. Cognitive Psychology ,  9 (4), 441–474. https://doi.org/10.1016/0010-0285(77)90016-0 Palmer, S. E. (1992). Common region: A new principle of perceptual grouping. Cognitive  Psychology , 24 (3), 436–447. https://doi.org/10.1016/0010-0285(92)90014-S Pettersson, R. (2018). Visual camouflage. Journal of Visual Literacy , 37 (3), 181–194.  https://doi.org/10.1080/1051144X.2018.1529884 Prudic, K. L., Stoehr, A. M., Wasik, B. R., & Monteiro, A. (2015). Eyespots deflect predator  attack increasing fitness and promoting the evolution of phenotypic plasticity.  Proceedings of the Royal Society B: Biological Sciences , 282 (1798), 20141531.  https://doi.org/10.1098/rspb.2014.1531 Rowland, H. M., Cuthill, I. C., Harvey, I. F., Speed, M. P., & Ruxton, G. D. (2008). Can't tell the  caterpillars from the trees: Countershading enhances survival in a woodland. Proceedings  of the Royal Society B: Biological Sciences , 275 (1651).   https://doi.org/10.1098/rspb.2008.0812 Ruxton, G. D., Speed, M. P., & Kelly, D. J. (2004). What, if anything, is the adaptive function of  countershading? Animal Behavior , 68 (3), 445-451.  https://doi.org/10.1016/j.anbehav.2003.12.009 Skelhorn, J., & Rowe, C. (2016). Cognition and the evolution of camouflage. Proceedings of the  Royal Society B: Biological Sciences , 283 (1825), 20152890.  https://doi.org/10.1098/rspb.2015.2890 Wagemans, J., Elder, J. H., Kubovy, M., Palmer, S. E., Peterson, M. A., Singh, M., & Von Der  Heydt, R. (2012). A century of Gestalt psychology in visual perception: I. Perceptual  grouping and figure–ground organization. Psychological Bulletin , 138 (6), 1172–1217.  https://doi.org/10.1037/a0029333 Wertheimer, M. (1912). Experimentelle studien über das sehen von bewegung [Experimental  studies on the seeing of motion]. In T. Shipley (Ed.), Classics in Psychology  (pp.  1032-1089). Philosophical Library.  Wertheimer, M. (1923). Untersuchungen zur Lehre von der Gestalt. II [Investigations into the  theory of Gestalt]. Psychologische Forschung , 4 (1), 301–350.  https://doi.org/10.1007/BF00410640 Yang, X., Xu, W., Jia, Q., & Liu, J. (2021). MF-CFI: A fused evaluation index for camouflage  patterns based on human visual perception. Defence Technology , 17 (5), 1602–1608.  https://doi.org/10.1016/j.dt.2020.08.007

Written by: Alondra Alamillo Jocelyn, a 12-year-old girl, has just begun her sixth-grade year and is thrilled to transition from elementary school to a middle school environment.. However, her parents are worried about the changes that are to follow as their daughter enters adolescence. Since Jocelyn’s parents strongly believe this is a time of rebellion and misconduct, they have set stricter rules and expectations to help adapt to the stereotypical behavior expected during adolescence. These include maintaining A’s in all of her classes and making her way back home immediately after school with no exceptions as to limit her opportunities to “act out.” She is expected to meet these demands no matter what, as her parents would not be accepting of anything less. Although Jocelyn believes these are beyond unreasonable and attempted to have a conversation with her parents, she is reminded that these are not up for debate and that she must never question what her parents ask of her. This embodies the authoritarian parenting style, which largely relies on heavy punishment-based disciplinary attitudes and demands, often leading adolescents to feel discouraged from having open communication with their parents about their own standards. The ways in which parents choose to respond to their adolescent’s behavior, is a factor that can contribute to their tendency to engage in risky behaviors as they develop like substance use.  Is there a correct parenting style? While parenting styles are referred to in many different ways across family relationships research, there are two important concepts that help understand parental behavior. A ccording to Maccoby and Martin (1983), parent responsiveness and parent demandingness are important elements when characterizing parental attitudes and behavior. Parent responsiveness refers to the parents’ ability to respond to their adolescents' needs in a supportive manner. Parent demandingness, however, measures the extent to which parents expect “adult-like” behavior such as taking on responsibilities that are not prototypical of children. Together, these two elements create four possible combinations that result in four main parenting styles including authoritarian, authoritative, indifferent and indulgent (Maccoby & Martin, 1983). Authoritarian parenting styles, as introduced above, is low on the responsiveness scale yet high on the demandingness scale. This suggests that in parent-adolescent relationships, parents expect absolute obedience from their child. In this case, when a child demonstrates complete submission to their guardians, the parents may not necessarily view open communication with their child as productive when establishing standards for them. On the other hand, parents who fall under the indulgent parenting style are high on the responsiveness scale yet low on demandingness. As the complete opposite of the authoritarian style, indulgent parents do encourage honest and respectful conversations while maintaining fewer rules and little-to-no real discipline. Low demandingness and low responsiveness, for instance, are characterized as indifferent parenting. Indifferent parents expect less of their child, are less responsive to their needs often demonstrating poor communication or emotional availability. Oppositely, the authoritative parenting style values both high responsiveness and high demandingness. This combination means there is an equal importance placed on open communication, and assertive expectations through monitorization, or a form of self-monitoring. This results in a unique opportunity where adolescent autonomy is encouraged and respected by parents, often taking the adolescents’ opinions into account. Given that autonomy is very important for adolescent development, the authoritative style is considered to be the most optimal choice since it aligns with adolescents’ autonomy needs best (Bosca & Cojocaru, 2023). At this particular stage in development, especially those entering early adolescence will experience a shift in the autonomy and emotional independence they are searching for due to changes in their school social structure. By parents trusting and involving their adolescence in making more decisions regarding themselves, this helps build toward a  better parent-adolescent relationship and overall aids with adolescent identity development. However, it is important to recognize that aspects such as individual growth, peers, and culture amongst others influence development too. While the “right” parenting style for one family or individual might be a good fit for them, it may not work the same way for another.  Literature on the topic often focuses on the effects of parenting styles and their corresponding attitudes. These include emotional closeness, open communication, the extent of parental restrictiveness, permissiveness in academic settings, and how it may relate to adolescents’ when it comes to academic performance. However, the impact that various parenting styles have on other behaviors that adolescents are more likely to engage in, such as risky behavior is also important to consider. Particularly substance use, as first use is a common reckless choice that is made at this stage. Parenting and substance use An early study by Baumrind (1991) sought to investigate the interaction between parenting styles and substance use by using the responsiveness and demandingness variables to first characterize parenting styles and then determine the effects each type of parenting style had on substance use. By rating responsiveness and demandingness at various levels (low, medium-low, medium-high, and high), Baumrind determined six different parenting types- authoritative, democratic, directive, good-enough, non-directive, and unengaged families. The author decided to categorize into six categories, first based on either passiveness and authoritarian attitudes, and later subdivided into more specific categories. On Baumrind’s scale, authoritative parenting styles still refer to high demand and high response. Democratic, is a blend of high response, moderate demand. Directive families resulted in medium to high scores of restrictiveness and equal demand, yet low to medium scores in responsiveness. The good-enough category was a space for medium to low to medium to high scores overall. Non-restrictive showed significantly higher scores in responsiveness compared to parental restrictable and non-engaged scores which were consistently low. Using self-reporting surveys to assess adolescent’s attitudes on substance use, values, and behavior, showed that individuals from authoritative and democratic families in particular had negative views towards these attitudes (Baumrind, 1991). Not only this, but prosocial behavior, behavior that benefits other people, and self-regulation were found to be highest for people who were categorized as a part of these families compared to the other four parenting styles (Baumrind, 1991). Together with a higher ability to restrain themselves from engaging in substance use and competency, does suggest that there is a benefit in parenting styles having an assertive combination of expectations while still trusting adolescents. References Flaticon. (n.d.). Family free icon designed by Freepik . https://www.flaticon.com/free-icon/family_2061474?term=family&page=1&position=33&origin=search&related_id=2061474 Questa Soft. (2023). The influence of parenting styles on adolescent development . https://www.ceeol.com/search/article-detail?id=1175933 Baumrind, D. (1991). The influence of parenting style on adolescent competence and substance use. The Journal of Early Adolescence , 11 (1), 56–95. https://doi.org/10.1177/0272431691111004 Strugaru, V. (n.d.). The influence of parenting styles on adolescent development . https://www.rcis.ro/en/current-issue/3166-the-influence-of-parenting-styles-on-adolescent-development.html Maccoby, E., & Martin, J. (1983). Socialization in the context of the family: Parent–child interaction. In E. M. Hetherington (Ed.), Handbook of child psychology:  Vol. 4. Socialization, personality, and social development  (pp. 1–101). Wiley.

By: Alexander Hindeleh Introduction: As the population of the United States continues to age, the prevalence of  neurodegenerative disorders, such as Alzheimer’s, Huntington’s, and Parkinson’s disease (PD), is steadily increasing (1). These disorders are associated with a progressive loss of neurons, leading to increased neuroinflammation and neural circuitry dysfunction. Over time, neurodegenerative diseases affect motor function, memory, speech, and day-to-day activities. Scientists believe that neurodegenerative disorders are caused by a combination of environmental, genetic, and lifestyle factors (2). Since the cause of these disorders is not fully understood, we must explore personalized approaches for treatment or reduction of symptoms. In this article, I will describe recent advancements in precision medicine that have shown great potential for the treatment and diagnosis of neurodegenerative disease. Alzheimer’s Disease: As described by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), Alzheimer’s disease is characterized by a decline in learning and memory and at least one other cognitive domain that affects daily functioning (3). This disorder affects 1 in 9 individuals in the United States aged 65 or older; , therefore, appropriate and early diagnoses and treatments are necessary (4). The common pathophysiological hallmarks include abnormal accumulations of amyloid-beta and tau protein plaques. These plaques contribute to microglial dysfunction—the brain’s main immune cells responsible for removing toxic proteins—and disrupt neural circuits by building up around synapses, reducing communication between neurons. Alzheimer’s does not have an exact, defined cause, and is believed to be a result of many possible factors, ranging from mutations in the APOE gene, lifestyle factors like smoking or excessive alcohol consumption, and high cholesterol.  In a recent clinical trial, researchers at the UCLA David Geffen School of Medicine treated 25 Alzheimer’s disease patients with personalized protocols over a 9-month test period (5). After evaluating potential Alzheimer’s contributors in each patient through genetic, biochemical, and brain scan testing, the researchers developed a strict, detailed protocol for each patient, whether anti-inflammatory herbal supplements for patients with elevated systemic inflammation or vitamin D supplements for patients with inadequate nutrient levels. The results showed an average increase of 0.3% of brain gray matter through magnetic resonance imaging (MRI), contrary to traditional Alzheimer’s pathology, which decreases gray matter. The researchers also observed a significant increase in serum vitamin D levels, an improvement in lipid profile (a lower ratio of triglycerides-to-high-density-lipoproteins). Conversely, they observed a decrease in systemic inflammation and glycation (hemoglobin A1C levels), indicating some recovery from traditional Alzheimer’s symptoms. Overall, this pilot study lays a successful framework for future personalized medicine protocols for diverse Alzheimer’s patient populations.  Emerging Technologies for Precision Medicine: Recent advancements in next-generation sequencing have greatly expanded our understanding of the molecular mechanisms underlying neurodegenerative disorders. Single-cell transcriptomics enables researchers to profile gene expression changes within individual cells, attributing specific messenger RNA (mRNA)  upregulations or downregulations to particular brain cell types, like microglia or astrocytes (6). This high-resolution approach offers deeper insight into cell-type–specific pathological processes, providing context that traditional bulk RNA-sequencing methods don’t account for. Recent studies have shown the importance of accounting for cellular heterogeneity when studying neurodegenerative disease, especially in identifying disease-relevant cell states that vary across patients (7). A newer technique, spatial transcriptomics, was named “Method of the Year” in 2020 by Nature  for its major contributions to the study of structure-function relationships in many complex tissues, including the brain (8). One widely used platform, 10x Genomics’ Visium, uses specialized glass slides with spatially barcoded capture areas to measure gene expression throughout brain regions. This method adds spatial context to transcriptional studies and can be integrated with single-cell RNA-seq data to achieve spatial, single-cell–level resolution. Additionally, the emergence of spatial genomic brain atlases will allow scientists to compare their own datasets with reference maps from control or diseased brains to identify region-specific gene expression changes. In the context of PD, for example, the striatum plays a vital role in controlling dopaminergic signaling—typically impaired in PD pathology. Through spatial transcriptomics, researchers can precisely map gene expression changes in the striatum to uncover disrupted signaling pathways and understand how these may cause motor and cognitive deficits. This technology will allow for identification of patient-specific molecular signatures that can guide further personalized treatment strategies and ameliorate clinical outcomes.  Precision Medicine for Parkinson’s Disease: Parkinson’s disease is a progressive, neurodegenerative disorder that causes motor deficits, such as tremor, rigidity, and bradykinesia, which is slowness of movement. Scientists believe that PD is caused by a combination of environmental and genetic factors, and is identified by the loss of dopaminergic neurons in the brain region titled substantia nigra pars compacta (9). This loss impacts the “direct pathway” in the basal ganglia, another brain region, which directly projects to motor neurons which are responsible for controlling movement in the body. Another hallmark of PD is an abnormal buildup of alpha-synuclein proteins in the brain, which interrupt normal brain cell functions. Due to the heterogeneity in PD causes and pathology, personalized treatments are needed to properly treat the diverse patient populations affected by this disorder.  A recent study used spatial transcriptomics to identify T-cell interactions in the substantia nigra, which are increased as a result of dopaminergic neurodegeneration in typical PD pathology (10). They found that PD induced an alteration in the spatial organization of T cells in specific cell types, such as astrocytes and endothelial cells, which was shown through co-integration with single-nucleus RNA sequencing (snRNA-seq). These changes can help scientists and clinicians understand how immune cells are activated in different brain regions and how to use these immune responses to improve outcomes for PD patients. Other emerging studies and clinical trials have shown promising improvements in symptoms following strict, personalized regimens of exercise, diet, and lifestyle. More research is needed, but with spatial transcriptomics becoming more commercially available, I am confident that more biomarkers for PD will become recognized in the scientific community. Then, clinicians and scientists can work on personalized treatments to benefit patients based on their individual needs.  Conclusion: Although precision medicine is an emerging discipline, it has already shown great potential for treatments of neurodegenerative diseases. Further studies are needed to evaluate its efficacy, and to establish exact protocols for diverse patient populations based on baseline values for biomarkers of disease. As technologies like single-cell and spatial transcriptomics become more commercially available, they will be essential for further clinical trials. I anticipate a future where personalized medicine will be more integrated in clinics worldwide to improve outcomes for those suffering from neurodegenerative diseases. Sources Brown, R. C., Lockwood, A. H., & Sonawane, B. R. (2005). Neurodegenerative diseases: An overview of environmental risk factors. Environmental Health Perspectives , 113(9), 1250–1256. https://doi.org/10.1289/ehp.7567 Lamptey, R. N. L., Chaulagain, B., Trivedi, R., Gothwal, A., Layek, B., & Singh, J. (2022). A review of the common neurodegenerative disorders: Current therapeutic approaches and the potential role of nanotherapeutics. International Journal of Molecular Sciences , 23(3), 1851. https://doi.org/10.3390/ijms23031851 Yokoi, T. (2023). Alzheimer's disease is a disorder of consciousness. Gerontology and Geriatric Medicine , 9, 23337214231159759.   https://doi.org/10.1177/23337214231159759  Alzheimer's Association. (2023). 2023 Alzheimer's disease facts and figures. Alzheimer's &     Dementia , 19(4), 1598–1695.   https://doi.org/10.1002/alz.13016 Hampel, H., Toschi, N., Babiloni, C., Baldacci, F., Black, K. L., Bokde, A. L. W., ... & Vergallo, A. (2022). Precision medicine approach to Alzheimer's disease: Current state and future perspectives. Frontiers in Neuroscience , 16, 816144. Murdock, M. H., & Tsai, L.-H. (2023). Insights into Alzheimer's disease from single-cell genomic approaches. Nature Neuroscience , 26(2), 181–195.   https://doi.org/10.1038/s41593-022-01222-2 ​​He, Z., Chen, Q., Wang, K., Lin, J., Peng, Y., Zhang, J., Yan, X., & Jie, Y. (2024). Single-cell transcriptomics analysis of cellular heterogeneity and immune mechanisms in neurodegenerative diseases. European Journal of Neuroscience , 59(3), 333–357. https://doi.org/10.1111/ejn.16242 Nature Methods. (2021). Method of the Year 2020: spatially resolved transcriptomics. Nature Methods , 18(1), 1.   https://doi.org/10.1038/s41592-020-01042-x Balestrino, R., & Schapira, A. H. V. (2020). Parkinson disease. European Journal of Neurology , 27(1), 27–42.   https://doi.org/10.1111/ene.14108 Jakubiak, K., Paryani, F., Kannan, A., Lee, J., Madden, N., Li, J., Chen, D., Mahajan, A., Xia, S., Flowers, X., Menon, V., Sulzer, D., Goldman, J., Sims, P. A., & Al-Dalahmah, O. (2024). The spatial landscape of glial pathology and T-cell response in neurodegeneration . bioRxiv. https://doi.org/10.1101/2024.01.08.574736 Picture credit: https://knowledge.wharton.upenn.edu/article/precision-medicine-new-paradigms-risks-opportunities/