Grey Matters VC Issue 7 by greymattersjournalvc

FEATURING Leaping Into The Research Potential Of Xenopus Frogs From Computational To Cognitive: ChatGPT And Natural Language Models Making Waves: The Neural Activity Of The Dying Brain

FALL 2023

@greymattersjournalvc GREY MATTERS JOURNAL AT VASSAR COLLEGE | ISSUE 7greymattersjournalvc.org

GREY MATTERS JOURNAL AT VASSAR COLLEGE | ISSUE 7

TABLE OF CONTENTS 6

24 FROM COMPUTATIONAL TO COGNITIVE: FEATURED ARTICLE

FEATURED ARTICLE

BRAIN SMOG: HOW POLLUTION DAMAGES THE BRAIN

MAKING WAVES: THE NEURAL ACTIVITY OF THE DYING BRAIN

by Madilyn Sandy/ art by Katie Hieb

by Anoushka Bhatt/ art by Jane Stempien

THE GENDER GAP IN 18 MISS-DIAGNOSED: ASD DIAGNOSIS by Kristen Carroll/ art by Michelle Shaffer

38 MOLDING THE PLASTIC BRAIN:

NANOPLASTICS IN THE AGE OF CLIMATE CHANGE by Alex Kaye/ art by Iris Li

BILINGUAL BRAIN: LEARNING, 21 THE LANGUAGE, LONGEVITY by Alexis Earp and Erin Kaufman/ art by Abigail Schoenecker

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ISSUE NOTES ON THE COVER

LEARN MORE

Art by Alexandra Adsit

Check out our website to read our blog, find out how to get involved, and more at greymattersjournalvc.org.

LET US KNOW If you have any questions or comments regarding this Issue 7, please write a letter to the editor at brainstorm.vassar@gmail.com.

GREY MATTERS JOURNAL AT VASSAR COLLEGE | ISSUE 7

PRODUCTION STAFF

SHAWN BABITSKY Editor-in-Chief

FREDERICA VON SIEMENS Senior Editor, General Editing

RILEIGH CHINN Senior Editor, Scientific Review

SOFIA BALICH Social Media Coordinator

Co-Senior Managing Editor

FRANK RYAN

Co-Senior Managing Editor & Production Manager

ANSHUMAN DAS

JACLYN NARLESKI

EVE ANDERSEN

JULIA VITALE

AINSLEY SMITH

Treasurer & Senior Editor, Lay Review

IONA DUNCAN

Senior Editor, Lay Review

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Senior Editor, General Editing

Senior Editor, Scientific Review

SOPHIA SKLAR

Art Executive

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GREY MATTERS JOURNAL AT VASSAR COLLEGE | ISSUE 7

ARTISTS

AUTHORS

FACULTY ADVISORS

Kishi Oyagi Anna Bishop Katie Hieb Iris Li Madelyn Turner Alexandra Adsit Jane Stempien Michelle Shaffer Abigail Schoenecker

Hannah Koolpe Jadon-Sean Sobejana Madilyn Sandy Alex Kaye Wolff Gilligan Jack Matter Kristen Carroll Anoushka Bhatt Alexis Earp Erin Kaufman

Dr. Bojana Zupan PhD Dr. Evan Howard PhD

SCIENTIFIC REVIEW

LAY REVIEW

GENERAL EDITING

Paloma Oteiza Maleigha Vietti Matthew Peeples Margot Vaughan Maria Cusick Lilah Lichtman Alyssa Gu Emilee Busby Kate Billow Isabel McGuire Gordon Zhang Eli Kanetsky

Juliana Ishimine Quincey Dern Laurel Obermueller Susanna Osborne Nico Silverman-Lloyd Grace Speranza Owen Raiche Kenza Squali-Houssaini Duncan Beauchamp Kaitlin Raskin Jolie Walker Jonathan Eccher-Mullally Arden Spehar Anna Conway Martine Schwan Emma San Filippo Charlotte Bowman Jaya Moorjani Chloe Lucas Abigail Wang Madeleine Stewart

Dhriti Seth Talia Roman Chloe Mengden Evelynn Bagade Kevin Li Maxx Martinez Daniel Wunschel Lily Brigman Hailey Brigger Autumn Cullinan Eden Lanham Yichen Ding

SPECIAL THANKS Olivia Pocat - Layout Sophia Greene - Social Media Brooke Berbeco - Social Media

GREY MATTERS JOURNAL AT VASSAR COLLEGE | ISSUE 7

EDITOR’S NOTE Regardless of what subject one studies, we are all driven by one common trait: a deep curiosity for the unknown. As our work on issue seven of Grey Matters Journal VC culminates, I am most grateful for the team’s dedication to improving their understanding of novel topics in neuroscience. Our goal at Grey Matters is to explore topics that were previously unfamiliar and enhance the accessibility of scientific literature to the public. Deconstructing barriers in science to help bring the public closer to the world of neuroscience is an ambitious undertaking — one that would not be possible without the dedicated efforts of our team. As I navigate my new role as Editor in Chief, I am grateful for the opportunity to lead a team of such inquisitive, talented, and passionate people. Creating this publication would not be possible without the effort of our authors, the persistence of our editors, and the creativity of our illustrators. Without our dedicated team, this journal would not be where it is today. The seventh issue of Grey Matters VC features articles from a wide assortment of disciplines. We invite you to explore the neural underpinnings and emerging therapies of a frightening brain cancer in “Battle for the Brain: Glioblastoma’s Invasion and the Immunotherapy Counterattack,” to jump into an exciting experimental model for studying neural development in “Leaping Into the Research Potential of Xenopus Frogs,” and to digest the neurotoxic effects of plastic in “Molding the Plastic Brain: Nanoplastics in the Age of Climate Change.” We hope these pieces, as well as the other fascinating articles produced in this issue, feed your curiosity and spark the same excitement in you that they do in us. Finally, I would like to thank you — our readers. Your support is integral to the success of the journal and we hope that this issue continues to engage your scientific curiosity. Cheers,

Shawn Babitsky Editor-in-Chief

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SECTION TITLE

FEATURED

LEAPING INTO THE RESEARCH POTENTIAL OF XENOPUS FROGS 6

by Jadon-Sean Sobejana art by Anna Bishop

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XENOPUS

icture a mother frog laying her eggs in a tranquil pond, a scene we often see in nature documentaries. Now, picture a human mother cradling her newborn baby. At first glance, it may be difficult to spot any similarities between the two scenes. However, the process of development from a single cell to an embryo and finally to a developed organism is strikingly similar for tadpoles and human infants [1]. One species of frog that exhibits this type of development is a western clawed frog species named Xenopus tropicalis, a promising new model for studying human neural development and associated disorders [1, 2, 3]. When it comes to researching the genetic basis of neurodevelopmental disorders, animal research models are especially useful [4, 5]. The majority of current research utilizes rodent models, but X. tropicalis — a unique biological model — offers new perspectives on the brain and genetic changes linked to conditions such as autism spectrum disorder (ASD) [1, 6]. ASD — a neurodevelopmental disorder — impacts how people communicate and socialize, varying from person to person [7, 8, 9]. Many potential genetic markers have been associated with ASD, but there is still a limited understanding of the disorder’s causes [7, 10, 11, 12]. Utilizing the unique biological model of X. tropicalis presents a new approach to understanding the genetic basis of ASD [1, 13].

RIBBETING DISCOVERIES IN DEVELOPMENT & GENETIC ENGINEERING Important similarities between X. tropicalis frogs and humans are evident during embryonic cell development, the journey by which a single cell becomes a fully functioning being. In its embryonic stage, an organism forms and develops through the division and differentiation of cells [14]. All cells created through cell division have an identical genetic code to the first cell to divide, but cellular differentiation allows individual cells to express distinct genes and

perform specific functions [14, 15]. Despite these similarities, X. tropicalis’ embryonic cell development differs from that of humans in a particularly intriguing way: when the first X. tropicalis cell divides into two, one new cell continues dividing to form the right side of the frog’s body, while the other cell continues dividing to form the left side of the frog’s body [6]. For most other animals, including humans, the entire body of the organism develops from the initial cell rather than the left and right sides developing independently [1, 14]. X. tropicalis’ unique pattern of embryonic development means that one side of the frog can be genetically modified during cellular division while the other side is left untouched [6]. Genetic engineering is employed to investigate the roles of specific genes by creating genetic modifications at the cellular level and observing how modifications affect an organism [16, 17]. Standard genetic engineering experiments modify genes in an embryo that will develop into an animal with the genetic mutation expressed in all of its cells [17]. Then, a group of modified organisms is compared to a group of organisms whose genes have not been altered [17]. Observing differences between the two groups allows us to determine the extent to which specific genes underlie a certain function [17]. A popular method of genetic engineering is CRISPR — a technology that involves injecting embryonic cells with a modified gene of interest [18]. After injection, cells continue to divide, with newly divided cells carrying the mutation [18]. Genetic modification of X. tropicalis utilizes CRISPR to edit one of the two cells that form when the first embryonic cell divides [18]. If the modified gene is injected into the cell that will form the left side of the frog’s body, all cells on the left side of the frog’s body and brain will express the injected gene, but cells on the right side of the frog’s body will not [1]. If we think of the process of gene editing as making a pizza, genetic modifications are like adding or removing toppings. In most animal models, genetic alterations would have to be made to the entire organism, which could be akin to adding toppings to an entire pizza.

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XENOPUS Each individual comparison in a research study would then require multiple animals: some with modified genes and some unmodified as a control [19, 20]. In the X. tropicalis model, genetic alterations can be made to just one-half of its body, similar to adding toppings to half of a pizza. With the advantage of genetically altering only one-half of its body, the X. tropicalis model itself acts as its own control. Since the same individual is used for comparison, there is no need to control for genetic variation between organisms. The unique embryonic development in X. tropicalis, in tandem with cutting-edge genetic engineering methods, allows us to utilize new technology to explore the complex neurology of ASD [2, 3].

NEURAL FROG-GENITOR CELLS: THE ALTERED X. TROPICALIS BRAIN Though the exact origins of ASD remain uncertain, a disruption in the formation of connections between nerve cells, or neurons, may be connected to the development of the disorder. This correlation is observable in both X. tropicalis and human brains [3, 21, 22, 23]. Neurons perform a variety of roles and form connections with each other. Before neurons are assigned a specific role, they are called neural progenitor cells (NPCs), which we can think of as ‘blank slates’ that eventually specialize to perform many different functions in the brain [21, 22]. ASD in humans is associated with abnormally increased numbers of NPCs in the brain and a disruption in

the specialization and differentiation of these NPCs, which can manifest as an increase in brain size [22, 24, 25, 26]. The combination of increased NPC numbers and disrupted differentiation has been theorized to contribute to the behavioral symptoms of ASD in humans [25]. In addition to X. tropicalis’ ability to act as their own control, X. tropicalis brains develop similarly to human brains, which means that we can use this model to study neurodevelopmental abnormalities displayed by some humans with ASD [1]. When animals are genetically modified to express genetic differences commonly associated with ASD, abnormal brain growth and a disproportionate number of NPCs are evident, both of which are also observed in humans with ASD [22, 27, 28]. In order to insert genes linked to ASD, CRISPR is to modify X. tropicalis by selectively altering the DNA in one of the two cells formed after the first embryonic cell splits [23]. The abnormally large development observed in one hemisphere of the brain serves as a biomarker for the successful application of CRISPR gene-editing techniques [23]. CRISPR has been used across several studies to implement different ASD-linked genes. In all instances, an increase in NPCs was observed, strengthening the correlation between ASD-linked genes and disruptions of NPC specialization [23, 29]. With X. tropicalis, we can control for variation between individual organisms by comparing brain size since the half of the brain that develops from the modified cell may be a different size than the half of the brain that develops from the unmodified cell [29]. The presence of similar abnormalities in the brains of genetically altered X. tropicalis and the brains of humans with ASD supports the utility of X. tropicalis as a neurological model to study ASD [6].

JUMPING ON THE XENOPUS BANDWAGON When it comes to studying brain development, rodents are commonly used because their DNA closely resembles that of humans; however, the rodent model has specific disadvantages that the X. tropicalis model does not [6, 19, 20]. For one, due to the difficulty of directly editing genes in rodent embryos, two rodents carrying the mutation of interest are bred to produce offspring [6, 30]. An average litter of rodents consists of eight offspring, and of that litter, only a small number will carry the genetically modified trait of interest, requiring labs to breed more rodents to create a

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XENOPUS workable sample size [6]. On the other hand, a female X. tropicalis frog can lay thousands of eggs at once. We can also directly edit the genes of the tadpole embryos so that all modified X. tropicalis offspring will carry the trait of interest [18]. Another disadvantage of the rodent model is that rodents must be euthanized prior to investigating their pathology, as their brain structure can only be studied after death. To examine rodent brain pathology at multiple moments in development, we need brain samples from multiple rodents euthanized at different ages. Alternatively, X. tropicalis brains can be imaged while the frog is still alive [3, 6]. An advanced technique called two-photon imaging allows us to inject a light-sensitive dye into the live animal, which illuminates brain structures, making them visible through the frog’s transparent skin [31, 32]. Unlike in rodent models, the same individual X. tropicalis brain can be imaged throughout multiple stages of development [32]. Due to the ease of observation, X. tropicalis models augment our research toolbox by allowing for more efficient conduction of certain studies, such as longitudinal studies that follow subjects over time.

IT’S NOT EASY BEING GREEN: THE LIMITATIONS OF XENOPUS While the possibilities afforded by X. tropicalis as a tool for genetic research are enticing, the model still has some drawbacks [1, 3, 16]. The diagnostic criteria for ASD are based on behavioral changes observed in humans; ASD is a strictly human disorder, and no model organism can successfully replicate ASD’s behavioral symptoms [33, 34]. Although rodent behavior does not manifest in the same way as human behavior, rodent models allow us to test variables such as anxiety-like behavior [35, 36]. For example, rodents that carry a specific genetic mutation associated with ASD display more anxious

behavior than rodents without this mutation, as measured by hyperactivity in an open arena [35, 37, 38]. In frogs, standardized behavioral tests to measure anxiety-like behavior and memory do not exist [3, 6]. Therefore, research involving the X. tropicalis model is limited to the potential pathological and genetic mechanisms that are associated with ASD. The inability of X. tropicalis to reflect behavioral symptoms may seem like a significant drawback, but the opportunities presented by the model for augmenting neurological research cannot be understated.

A HOPPORTUNITY FOR THE FUTURE Over the last decade, our understanding of ASD has improved significantly thanks to new scientific discoveries [39, 40]. Imaging techniques and technology have advanced and revealed specific neural structures and activities correlated with ASD [40, 41, 42]. We have been able to use advanced technology to identify genes that potentially contribute to the development of ASD [40, 43]. However, despite recent advancements, there is a lot left to discover. In an attempt to fill in the gaps in our knowledge, alternative methods and models are being employed [1, 2]. The ability of a single X. tropicalis subject to act as both a mutated subject and a control subject presents exciting possibilities for neurodevelopmental research [6]. Though they are starkly different from humans, X. tropicalis frogs offer an innovative and promising way to study the links between genes and neurodevelopmental disorders such as ASD [1, 2, 23]. X. tropicalis might not immediately answer all of our questions about ASD, but it allows us to leap toward greater advancements in the future. References on page 42.

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GLIOBLASTOMA

BATTLE FOR THE BRAIN: GLIOBLASTOMA’S INVASION AND THE IMMUNOTHERAPY COUNTERATTACK by Jack Matter/ art by Alexandra Adsit

silent adversary creeps through the corners of the brain, gathering strength before emerging to wreak havoc. While the body puts up a valiant defense against cancer, it often fails to defeat this enemy, which enlists unsuspecting nervous system cells and evades the body’s immune system defenses [1, 2, 3]. Glioblastoma, often called one of the most lethal brain cancers, is an enemy that comes from within [4, 5, 6]. Conventional treatment options do little to change the outcome of the battle [6, 7, 8]. However, immunotherapies offer hope, aiming to reengineer the body to fight back against the enemy [2, 6, 9]. While substantial challenges complicate the path toward a cure, innovative treatment methods may provide the prospect of a breakthrough in the fight against glioblastoma [1, 2, 9].

CANCER VS. THE IMMUNE SYSTEM Cancer is a group of diseases typically characterized by several distinct hallmarks, or traits that are crucial to understanding its nature [1, 10]. One of these hallmarks is cancer’s ability to multiply rapidly. While healthy cells contain quality control checkpoints and limits on how often they can replicate themselves, cancerous cells do not; therefore, they can replicate indefinitely. Cancerous cells begin as healthy cells, but genetic misfires transform them into an uncontrollable, aggressive force [1, 10]. Cancer operates like an invading army composed of one’s own rebelling cells that the body’s defense force cannot keep in check [12]. Usually that defense force, the immune system, can effectively prevent the formation of cancerous cells by recognizing unhealthy cells as harmful and sending immune cells to destroy them [12]. Some unhealthy cells, however, can escape immune detection by concealing or shedding the molecules that flag them as harmful [1, 10]. The ability to avoid immune detection — another

one of cancer’s hallmarks — makes these cells dangerous and ultimately allows them to become cancerous [1, 2, 10]. After proliferation, cancerous cells often form solid masses called tumors, but it is important to note that not all tumors are cancerous [13, 14, 15]. Tumors are cancerous when they have the ability to send cells, like soldiers, to invade their surroundings and even other parts of the body [13].

WHY GLIOBLASTOMA? Glioblastoma is a cancer originating in the brain or spinal cord, which together comprise the central nervous system (CNS) [16, 17]. Glioblastoma is a mass of unhealthy versions of a type of cell called glial cells.

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GLIOBLASTOMA But what exactly are healthy glial cells, and what do they have to do with the functions of the nervous system? Glial cells and neurons comprise the brain and spinal cord, working together to perform complex duties. Glial cells regulate a wide range of functions by supervising and modifying the connections between neurons. Neurons create and spread signals throughout the CNS, allowing the brain to communicate with the rest of the body. Think of neurons as the wires that carry electrical signals from one place to another within the CNS. Glial cells are the electricians that help create and maintain the connections between these wires. Without glial cells, the electrical grid fizzles out, and all grid functions are compromised [16, 17].

Glial cells are major players in our brain’s ability to adapt its structure and function in response to new information throughout life, a process called plasticity [18, 19, 20, 21]. One type of glial cell, the astrocyte, plays a critical role in this plasticity [22]. Astrocytes clean up existing connections, remove wires that are no longer used, and form new wiring [22]. Astrocytes also play a key part in creating and regulating blood vessels in the brain, acting as guides for the cells that construct new vessels [23]. When random genetic mutations or DNA modifications alter astrocytes, they can become precursors to glioblastoma cells [24]. Precursor cells that survive long enough to replicate may ultimately turn into glioblastoma tumor cells [24]. As tumor cells, cancerous astrocytes can use existing blood vessels or create new ones that behave like supply lines from other parts of the body [25]. The tumor cells hijack blood supply lines to feed themselves and expand into healthy tissues [25].

HIJACKING PLASTICITY: HOW GLIOBLASTOMA SPREADS While glioblastoma seldom spreads outside the brain, it spreads rapidly and aggressively throughout near and distant neural regions by taking advantage of neuron activity, plasticity, and other healthy cells [26, 27, 28]. Individual neurons can stimulate and connect to other healthy brain cells, forming communication circuits [29]. However, neuronal stimulation is a double-edged sword: two-way signaling between healthy neurons and brain tumor cells increases the activity of tumor cells, unintentionally promoting rapid cancer growth [27, 30]. Since tumor cells can also increase healthy neuronal activity, this can trigger a cycle of neuron-tumor stimulation [30, 31]. Glioblastoma tumors further cement the neuron-tumor stimulation cycle and exhibit plasticity, modifying the activity of surrounding healthy neurons and remodeling their connections [3, 30, 31, 32, 33]. In addition to maintaining this growth cycle, glioblastoma can enlist healthy astrocytes to its cause [3]. As unwitting allies, tumor-associated astrocytes release signals that suppress the immune system’s ability to detect and eliminate tumor cells [3]. Glioblastoma can also ‘steal’ mitochondria — the cellular power plants responsible for creating the chemical energy that drives cell processes — from healthy CNS cells [34]. Non-cancerous astrocytes can transfer functional mitochondria to glioblastoma cells through direct cell-to-cell contact, which may help explain how glioblastoma cells proliferate so rapidly and why they are so difficult to eliminate [34]. Hijacking neuron activity, utilizing plasticity, recruiting tumor-associated astrocytes, and stealing mitochondria are characteristics that fuel the aggressiveness of glioblastoma [32, 35, 36]. Glioblastoma is often referred to as the most lethal brain cancer, defined by its nearly unstoppable hostility and current incurability [4, 5, 6, 37]. Despite modern treatment options and periods of remission where glioblastoma is undetectable, around 95% of people do not survive five years post-diagnosis, and 90% of people diagnosed with glioblastoma relapse within two years of diagnosis [6, 7, 8, 38, 39]. Although the outlook for those suffering from glioblastoma remains grim, some hope can be found in new forms of treatment that leverage immunotherapies to target the cancer [6].

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GLIOBLASTOMA

THE NEW FRONTLINE: IMMUNOTHERAPIES FOR GLIOBLASTOMA Current treatments act as a frontline defense against glioblastoma and may prolong a person’s life, but most treatments fall short in some regard due to either intense side effects, minimal success in permanently curing the cancer, or both [40, 41, 42]. Neurosurgery is highly invasive and fails to remove all cancer cells, leading to poor survival outcomes even after surgical intervention, as the surviving cancer cells replicate again [43]. Glioblastoma is also highly resistant to chemotherapies and radiation therapies due to the presence of protective barriers in the brain and the tumor’s ability to repair its damaged DNA [43]. Further, chemotherapies and radiation therapies cannot selectively target tumor cells, so healthy cells inevitably get caught in the crossfire [41, 44]. Damage to healthy cells often has extensive, debilitating side effects, including a severely weakened immune system [41, 44]. Healthy cells that replicate rapidly — such as hair and intestinal cells — are disproportionately damaged or killed by chemotherapies, which cause side effects like loss of hair, appetite, and weight [40, 41, 44, 45, 46]. Despite current treatment limitations and cancer’s methods of evading the immune system, new ther-

apies that boost immune responses show promise [1, 2, 9, 10]. The idea of using the immune system to fight disease has existed for around 2,000 years but has only been consistently applied to fighting cancer since the 1980s [47, 48]. This movement towards harnessing the immune system has led to the development of immunotherapies. These treatments recruit an individual’s immune cells to directly target and kill only cancer cells, avoiding the harmful side effects of more traditional treatments [48]. Immunotherapies retrain immune cells to detect and remember cancerous cells based on identifying molecules called antigens, which are substances that the immune system can typically identify as foreign threats and target with antibodies [12]. Antibodies are molecules produced by some immune cells that bind to specific antigens, activating an immune cell response that can locate and destroy the threat [12, 49]. One type of immune response activates T cells, an umbrella term for a large body of immune cells, including the immune system’s killer cells [12]. To recruit T cells, the immune system must first be able to recognize antigens found on tumors [50]. However, since cancer cells can either hide or shed their antigens, it is difficult for immune cells to detect and destroy the cells [1]. Therefore, by manipulating immune cells, like T cells, immunotherapies could improve their defensive functions against cancerous cells [50]. T-cell therapies are an example of recent antigen-based immunotherapies that have been approved to treat several blood cancers and may be useful in treating glioblastoma [51, 52]. T-cell therapies involve collecting a person’s T cells and re-engineering them in a laboratory so they can bind to distinct cancer cell antigens [53]. Specifically, collected T cells are modified to have chimeric antigen receptors, or CARs, on their cell surface. CARs eliminate middleman cells often involved in T cell activation and allow T cells to bind directly to specific antigens on the surface of cancer cells [53]. Therefore, these receptors give T cells a new capability, combining the specificity of an antibody with the existing lethality of a T cell [54]. Immunotherapies are customized for each person’s specific cancer using their individual T cells [51, 53]. However, even with customization, CAR T-cell therapy is still limited in effectiveness for glioblastoma treatment in particular [55]. Since glioblastoma cells are incredibly diverse in terms of potential target antigens, it is challenging to develop CAR T-cells that can effectively bind to and combat every subtype of glioblastoma cell for extended periods, leading to the potential for relapse [55].

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GLIOBLASTOMA Tumor vaccines are another promising type of immunotherapy in fighting glioblastoma [56, 59]. Tumor vaccines rely on the same principle as traditional vaccines — that viruses have antigens that differentiate them from healthy cells — and adapt that principle to battle cancer [57]. For example, a flu virus has specific foreign antigens not ordinarily present in the body [58]. By injecting a harmless version of a flu virus, the immune system can be trained to remember flu virus antigens and mobilize to neutralize the threat in the future [58]. A tumor vaccine functions similarly, with the knowledge that cancer cells possess antigens that differentiate them from healthy cells [57]. In this case, oncolytic viruses that only target and infect cancer cells are used [57]. Once infected, the cancer cell becomes a host for virus replication and reaches the maximum threshold of virus that it can hold [55, 57]. When a cancer cell reaches this threshold, it bursts like an overfilled water balloon and dies, flooding the environment with its antigens [55, 57]. This phenomenon then stimulates the immune system to activate and kill other cancer cells: popping a cancer cell and releasing its antigens sounds like an emergency alarm urging the immune system to act [56]. Glioblastoma cells are particularly vulnerable to viral infection, and oncolytic virus-based vaccines can simultaneously activate the immune system and train its memory, which is essential in establishing long-term immune resistance to the cancer [56, 59].

Despite the potential success of oncolytic virus-based therapies in treating glioblastoma, there is a difficult challenge to circumvent: the blood-brain barrier (BBB) [56]. The BBB is a selective, tight-knit layer of cells and fluids that prevents harmful substances from entering the brain [60]. Most often, therapies are injected into the veins or taken orally, which can reduce how much of the treatment makes it across this barrier [61]. Substances typically must cross the BBB through the slow process of diffusion, which is the gradual movement of molecules from areas of high concentration to low concentration [56, 60]. However, some virus-based therapies cannot even cross the BBB by diffusion, and these combined obstacles make it challenging to get oncolytic virus therapies to reach a deeply embedded brain tumor [56]. One potential solution to bypass this delivery challenge is convection-enhanced delivery, which allows for direct injection of therapeutics into the brain via the fluid-filled spaces between CNS cells [62, 63]. This method relies on the force of convection — the inherent movement of fluid from high to low pressure — to influence fluid motion at the injection site, creating a high-to-low pressure gradient from the point of injection to the tumor that can overcome the forces of diffusion [62, 63, 64]. A small hole is made in the skull to allow access to the target area of the brain based on the tumor’s location [62, 63]. A syringe containing the immunotherapy is then placed inside the hole; the pressure of the fluid at the syringe tip is greater than the pressure in the brain, which pushes the fluid through the BBB and into the tumor [62, 63]. Glioblastoma, infamous for its relentless aggression, manipulates the brain’s complex circuitry to evade the immune system’s defenses, harnesses the power of plasticity, and subverts healthy astrocytes to fuel its attack [2, 6, 33]. Immunotherapies represent a potential paradigm shift from existing treatments that are often inadequate at completely treating glioblastoma [2, 6, 38]. New treatments, such as T-cell therapies and tumor vaccines, may successfully reprogram the immune system to target cancer cells directly [12, 50, 56]. While the road ahead may contain obstacles, a more effective and less taxing path to victory in the battle against glioblastoma is a goal worth fighting for. References on page 44.

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POLLUTION

BRAIN SMOG: HOW POLLUTION DAMAGES THE BRAIN by Madilyn Sandy/ art by Katie Hieb

Disclaimer: This article uses descriptors to refer to populations of color that reflect descriptors utilized in cited literature. The journal wishes to recognize that these terms may not be inclusive or representative of all racial or ethnic identities and communities referred to in this article. uring the summer of 2023, phones across Dtification: the East Coast lit up with an alarming noan air quality warning [1]. The raging

wildfires of Quebec, Canada created an eerie, apocalyptic-looking haze as smoke drifted across the continent, polluting everything in its wake. In New York, the Air Quality Index classified the air as ‘very unhealthy,’ indicating that those exposed to the air could experience adverse health effects [1]. Depending on the severity of the pollution, people were strongly encouraged to remain indoors, especially those with weak respiratory systems. Air quality warnings were partially due to high levels of fine particulate matter in the atmosphere, known as PM2.5. PM2.5 can be various substances — dust and soil particles, exhaust smoke, metals, and chemical compounds — and comes from many natural and human-induced sources [2]. Although the effects of PM2.5 on some bodily systems, such as the cardiovascular and respiratory systems, are relatively well-studied, the long-term neurological effects of PM2.5 exposure require further research [3, 4, 5]. Understanding the full impact of PM2.5 also raises environmental justice and health equity concerns: populations of color and those of low-income status are disproportionately exposed to PM2.5 and its negative side effects, as these groups are more likely to live and work in close proximity to PM2.5 sources [6, 7].

HOLY SMOKES! WHERE DOES PM2.5 COME FROM? The origins of PM2.5 are diverse. From natural causes such as wildfire smoke and dust storms to human-induced sources like traffic-related air pollution and fossil fuel combustion, PM2.5

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POLLUTION levels are inextricably linked to weather patterns and global energy consumption [2, 8, 9]. Global fossil fuel consumption — the use of finite energy sources including coal, oil, and natural gas — was responsible for 27.3% of global PM2.5 emissions in 2017 [9]. Fossil fuel emissions come from various origins, such as transportation, industrial, and residential sectors [9, 10]. During your morning commute, cars in the long line of traffic expel PM2.5 through gasoline and diesel emissions [10]. Local factories that rely on coal for energy production release PM2.5 through smoke stacks [9]. The majority of American homes expel PM2.5 by using natural gas, propane, and fuel oil for electricity [11]. As all these aforementioned examples illustrate, our reliance on fossil fuels constantly exposes us to PM2.5. Although global PM2.5 concentrations have declined due to many nations’ efforts to implement renewable energy, levels are still on the rise in some industrializing countries [12]. Beyond human-induced sources, climate change causes extreme weather events such as wildfires, dust storms, and tornadoes to become increasingly common and release extra PM2.5 into the atmosphere [13, 14]. Even though climate change impacts everyone, concentrations of PM2.5 are not distributed equally around the world: some areas experience lower levels of the pollutant, while others contain higher levels [15]. For example, living or working in close proximity to oil and gas plants, coal mines, or industrial facilities increases one’s exposure to PM2.5 [6, 7]. People of color and lower-income groups tend to be disproportionately exposed to air pollution because they are systemically segregated to areas with more environmental hazards and threats [6]. Exposure to PM2.5 is linked to various life-threatening conditions that contribute to almost four million global deaths annually [2].

PM2.5: A SMALL BUT MIGHTY INTRUDER PM2.5 can access the brain through multiple pathways due to its tiny size [3, 16, 17, 18]. As indicated by its name, PM2.5 molecules are 2.5 micrometers or less in diameter — much too small to be seen by the human eye [19]. To put this into perspective, the tip of one strand of human hair is around 60 micrometers, 24 times larger than a PM2.5 molecule [20]. While the human body recognizes PM2.5’s bigger and bulkier cousin PM10 as an invader and prevents it from entering, PM2.5’s size makes it stealthy enough to enter the body [19]. One of the ways PM2.5 can enter the body and reach the brain is through the nose [21]. PM2.5 inhalation is unavoidable — these particles are everywhere, suspended in the vast network of air that we breathe. Imagine you are walking down a crowded city street, watching cars slowly creep by, trailing plumes of PM2.5 molecules into the surrounding air. You pass by a café and take a deep breath, relishing in the smell of freshly brewed espresso. As you inhale the heavily polluted city air, PM2.5 molecules, along with gasses like nitrogen and oxygen, rush into your nose. From there, these molecules are able to travel directly to your brain through the olfactory nerve, a communication network that bridges the nose and the brain, allowing us to smell [16]. In the nose, a bundle of tiny nerve fibers extends from smell receptors and enters the brain through small openings in the skull, which PM2.5 can sneak through [17]. Alternatively, PM2.5 molecules may travel through the nose to the lungs, thereby entering the bloodstream and making their way up to the brain. PM2.5 can penetrate the blood-brain barrier (BBB), a tight-knit system of cells that prevents unwanted molecules from passing into and throughout the brain [3]. Prolonged exposure of the brain to high concentrations of PM2.5 can weaken the security system of the BBB by chemically disrupting tight junctions, or proteins that are integral in keeping this tightly-knit structure sealed [3, 18]. PM2.5 is able to breach the BBB by compromising this tight seal, much like widening a gap in a fence. In addition to inhalation, PM2.5 can enter the blood via digestion before it reaches the brain [22].

NEUROINFLAMMATION: THE DOUBLE AGENT OF THE BRAIN Regardless of the path it takes through the body, contact with PM2.5 initiates an immune response that can ultimately damage the brain [22, 23]. The brain needs a controlled chemical environment to function properly, which requires specific levels of oxygen and acidity,

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POLLUTION

DEATH BY A THOUSAND PARTICLES

amongst other conditions [24]. However, the brain’s chemical balance is easily disrupted by invaders like bacteria or foreign molecules [25]. When PM2.5 enters, it is recognized as an invader, resulting in an immune response that fights to maintain the brain’s chemical stability [25]. The PM2.5-provoked immune response and subsequent neuroinflammation is an essential protective mechanism of the brain that can become detrimental if it continues chronically [26, 27]. One mechanism of neuroinflammation is the activation of microglia — cells that function as the ‘immune’ system of the brain — which scan the environment for pathogens and activate in response to potential dangers. While microglia activation can be beneficial in the short-term by protecting the brain from damage, long-term activation of microglia can be harmful [28]. Prolonged neuroinflammation is also correlated with heightened levels of certain immune signaling molecules that can initiate cell death and extend inflammation [27, 29]. In addition, the BBB becomes more penetrable after PM2.5 enters the brain, which allows for greater movement of immune cells into the brain from other parts of the body and subsequent neuron damage and death [30]. Due to these immune responses, individuals continuously exposed to PM2.5 throughout their lifetime may experience chronic neuroinflammation and, consequently, its adverse effects [27]. Communities of color and low-income populations who are pushed into residential areas with greater pollution levels are at higher risk for continuous PM2.5 exposure and the resulting neurological consequences associated with chronic neuroinflammation [6, 7].

The neuroinflammatory response caused by PM2.5 exposure physiologically alters the brain and is associated with the progressive loss of neurons, a process called neurodegeneration [31]. Upon entering the brain, PM2.5 is recognized as a foreign particle by the brain’s immune cells, which respond by producing proteins that induce neuroinflammation [3, 32, 33]. In this case, neuroinflammation is meant to mitigate damage and get rid of foreign invaders like infectious bacteria [34]. However, the brain’s immune system is unable to directly expel foreign molecules like PM2.5. As a result, immune cells release more inflammatory molecules in an attempt to neutralize the threat of PM2.5, which perpetuates a state of chronic inflammation [3, 33]. This PM2.5-mediated neuroinflammatory state may contribute to increased rates of neuron death, cognitive impairments, and abnormal accumulations of a type of protein in the brain called tau [3, 33]. Abnormal tau protein accumulation is associated with neurodegenerative diseases like Alzheimer’s and Parkinson’s; therefore, it is possible that the tau accumulation following PM2.5 exposure could be implicated in these diseases [3, 35]. However, further research is needed to solidify this connection [3]. Apart from the neuroinflammatory response, exposure to PM2.5 is also associated with decreased volume in a type of brain tissue called gray matter, which, when deteriorated, is correlated with neurological deficits [36]. Gray matter is primarily composed of cell bodies of neurons, and plays a key role in processes such as the integration of information, memory, emotions, and cognition [37]. A disruption in these vital processes is catastrophic, exacerbating cognitive decline [8, 38]. PM2.5 exposure is associated with decreased gray matter volume in the prefrontal cortex, an area correlated with higher-level cognitive functions such as working memory and long-term memory retrieval [36]. A further decrease in gray matter due to PM2.5 exposure is also observed in the cerebellum, which results in a loss in coordination and balance, and in the basal ganglia, which can cause an interruption of coordination and motor control [8, 38, 39, 40]. Some PM2.5 particles, such as heavy metals from fuel combustion and industrial processes, damage the basal ganglia by degrading the connections between neurons [38, 39]. Although a loss of gray matter is detrimental for all,

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POLLUTION children are particularly vulnerable to adverse neurological consequences of PM2.5 exposure since their brains are still developing [39]. PM2.5 activates the brain’s primary means of protecting itself, impacting key neurological structures and impairing a broad array of cognitive processes [8, 38, 39].

THINKING BEYOND THE SKULL While the molecular implications of cell death are frightening, their significance is only fully grasped when contextualized by the overlying social risk factors that dictate who is affected by PM2.5 [6]. The risk of high PM2.5 exposure may vary significantly between different communities, demonstrating systemic socioeconomic and racial inequalities [6, 7]. Those of a lower socioeconomic status are more likely to live and work near hazardous sites, such as coal plants, oil refineries, trash incinerators, and natural gas facilities [41]. Additionally, in urban settings, lower-income residents are more likely to live and work farther away from grassy areas, trees, and parks, all of which mitigate PM2.5 pollution through the absorption and decomposition of the particles [42, 43]. Even though people may be aware of the negative health concerns associated with air pollution, many people subject to environmental threats cannot afford to relocate due to financial constraints, among other logistical factors [44]. In the United States, racial minorities are also more likely to be exposed to sites that emit PM2.5, due to gentrification [45]. Black and Hispanic populations in the U.S. are more likely to be pushed into living in industrial areas with less exposure to green spaces [43, 46, 47]. In the United States, on average, Black populations are exposed to 21% more PM2.5 than the overall population [7]. Comparatively, Hispanic populations are exposed to 12% higher PM2.5 concentrations compared to the national average. White populations, on the other hand, face a 7% lower than average exposure to PM2.5, demonstrating clear health inequities [7]. Air pollution is a persistent problem that poses health risks and potential neurological issues, distributed unequally as certain regions of the globe have higher levels of PM2.5 than others [5, 6, 31]. According to the World Health Organization’s guide-

lines, the annual mean concentration of PM2.5 should not exceed five micrograms in a cubic meter of air [48]. Unfortunately, 95% of countries surpass this threshold [49]. For example, the United States is among the countries with the lowest average PM2.5 concentrations but still has an average of 7.7 micrograms per cubic meter of pollutant [50]. However, PM2.5 exposure varies significantly within the U.S. and is often less concentrated in rural areas [51]. In 2015, China had an average PM2.5 concentration of 50.0 micrograms per cubic meter of pollutant, and in 2019, India had an average PM2.5 concentration of 91.7 micrograms per cubic meter of pollutant [52, 53]. Notably, higher-income countries, including the United States, tend to have lower levels of PM2.5 exposure [41]. This trend is in part because many lower-income countries rely on industries that burn a lot of fossil fuels to increase economic development [54]. PM2.5 pollution is a potent global threat; therefore, lowering its emission rates is imperative to improving public health, especially for vulnerable populations. Emerging research on PM2.5 demonstrates profound negative neurological consequences resulting from exposure [36, 39]. Given the uncertainty surrounding the long term effects of air pollution on cognitive function, it is crucial to continue reducing PM2.5 in the atmosphere and studying its broad effects to support human and ecological health. References on page 46.

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ASD

MISS-DIAGNOSED: THE GENDER GAP IN ASD DIAGNOSIS by Kristen Carroll/ art by Michelle Shaffer

Disclaimer: This article uses both identity-first and person-first language surrounding autism spectrum disorder, as well as male and female-gendered language to refer to sex assigned at birth. This choice was made because cited surveys indicate an even split in preference among autistic individuals [1]. Additionally, cited literature uses gendered language to refer to sex assigned at birth. The journal wishes to respect different preferences for person-first versus identity-first language and recognizes that gender is independent of sex assigned at birth. n movies and TV shows like Rain Man Itrum and The Good Doctor, autism specdisorder (ASD) is depicted from

the perspective of male characters, portraying their common symptoms as the standard for ASD. Autism spectrum disorder is a neurodevelopmental condition characterized by behavioral markers such as differences in communication and sensory processing [2, 3, 4]. Autism exists on a spectrum, with symptoms and needs varying case by case [2, 5, 6]. Representation of autism in the media has brought more attention to the disorder, but not to every aspect of it: the female perspective is often underrepresented. Much like in film and television, the diagnostic criteria for ASD in real life fails to consider how autistic females may present differently than males, leading to misdiagnosis or a complete lack of diagnosis for females [7, 8, 9, 10].

ASD DIAGNOSIS: THEN AND NOW ASD remained unidentified until Leo Kanner, an Austrian-American psychiatrist, conducted a study on the disorder in 1938 [11]. In his investigation, Kanner considered the traits of eleven young children: eight males and three females. After studying the children’s common behaviors, speech, and motor development, Kanner conceptualized a new disorder called autism [11]. Despite his significant contribution to our understanding of autism, Kanner’s study was flawed due to its small and skewed sample.

The bias towards male populations in ASD research continues to hinder the ability of autistic females to receive diagnoses today, as the traits females display are often inconsistent with the traits displayed by autistic males [7, 8, 9, 10]. ASD has become more common in recent years, with an estimated 75-fold increase in the rate of ASD diagnosis over a twenty-year period [12]. Despite its increasing prevalence in society, the ratio of male to female ASD diagnoses has remained relatively stable; for every three to four males with ASD, only one female is diagnosed [10].

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ASD An ASD diagnosis can be beneficial or even life-changing for a person with autism, as a diagnosis opens up doors for additional support to improve well-being [13]. Once diagnosed, doctors can offer patients various tools to improve their quality of life, including mental health services and accommodations to navigate education services better. Education accommodations include extended time for testing, flexible deadlines, and distraction-free test zones [13]. Unfortunately, these accommodations are not accessible to everyone, especially those who don’t receive an official diagnosis. Since diagnostic criteria for autism are heavily influenced by the common signs and symptoms found in males, autistic females are less likely to be diagnosed than males, preventing them from receiving proper interventions [7, 8, 9, 10].

CHECKING OUT THE CHECKLIST To be diagnosed with ASD, a person must meet specific diagnostic criteria early in life, including communication difficulties, social impairment, and restrictive, repetitive behaviors and interests [14]. The first criterion to be diagnosed with ASD is that individuals must show multiple deficits in their social interactions. Examples may include difficulty interpreting nonverbal social cues and maintaining friendships, struggling with verbal communication, or having trouble understanding the feelings and needs of others. Signs and symptoms typically become evident in late infancy as communication skills develop and become essential to navigating social interactions. A second criterion for diagnosis is that one must have restrictive and repetitive behaviors and interests (RRBIs) [14]. Common restrictive patterns include strong attachments to a limited range of activities or interests, such as continuously listening to the same song or following the same schedule daily [15]. Repetitive patterns can also include body movements such as flapping one’s hands or rocking back and forth, a behavior classified as ‘stimming’ [16]. Autistic people perform ‘stimming’ — either consciously or unconsciously — in an attempt to regulate their emotions [16]. Altogether, these symptoms must significantly impede a person’s daily functioning for them to receive an ASD diagnosis [14]. Society is shaped around neurotypical individuals — whose needs often differ from autistic individuals — and so daily activities like attending class or socializing with others can be exhausting for people with autism [17, 18]. An autistic person who has difficulty

communicating with peers or managing sensory stimuli can become overwhelmed at school or work, leading to involuntary emotional and physical reactions characterized as ‘meltdowns’ [19]. Picture each daily task or stressor as one shake of a Coca-Cola can. As stress and anxiety build up throughout the day, causing more and more shakes of the can, the pressure builds up. Eventually, the can explodes. In an autistic individual, this explosion is known as a ‘meltdown’ [19]. Beyond communication, social, and other behavioral criteria, an autism diagnosis requires a physician’s documentation of symptoms in early childhood [14]. However, autistic people are often not assessed for the disorder as children, which can lead to underdiagnosis or misdiagnosis, both of which frequently delay one’s ability to receive treatment [20]. Support from schools or doctors falls short when an autistic individual does not have an official ASD diagnosis. Furthermore, both underdiagnosis and misdiagnosis are thought to be more prevalent in autistic females than in autistic males during their first psychiatric evaluations because present-day criteria do not account for autistic females’ symptoms [20, 21].

WHY DO AUTISTIC FEMALES FALL THROUGH THE CRACKS? Like Kanner’s first study on ASD, later studies on behavioral markers of autism predominantly recruited males [22, 11, 32, 7, 10, 8]. However, females with ASD often present with different traits than autistic males [24]. For example, autistic females often exhibit different restrictive and repetitive behaviors and interests (RRBIs) than autistic males [25]. Autistic individuals tend to have specific interests, sometimes termed ‘restricted interests,’ that are repetitive or limited to a narrow range that holds the individual’s attention [26]. Autistic males’ restricted interests tend to be in math, science, sports, and technology, which are more stereotypically associated with autism. However, autistic

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ASD females usually have restricted interests in different categories, such as dancing, music, arts and crafts, and fantasy tales [27, 28, 29, 25]. As a result, behaviors can fly under the radar of teachers, parents, and even doctors [24, 27]. Females, therefore, do not appear to meet the criterion for RRBIs, leading to a decrease in diagnosis for autistic females [25]. Another way autistic females often differ in presentation from autistic males is through their social relationships [24]. Females with autism often exhibit a higher social motivation and a stronger inclination towards forming social connections than males with autism. They usually have a stronger desire to develop friendships, potentially making them appear more neurotypical [24]. As a result, autistic females are less likely to meet the social impairment criterion, which can reduce their likelihood of receiving an autism diagnosis. While autistic females often seek social interactions, they frequently find these interactions strenuous because they try to reduce their expression of autistic traits through a behavior known as ‘masking’ [24, 30]. ‘Masking’ entails forcing oneself to maintain eye contact, mimic facial expressions, and create a ‘script’ for social situations [24]. The motivation to mask can stem from a desire to fit in with neurotypical society to avoid rejection, discrimination, or bullying [30, 31]. Autistic females exhibit this behavior more often than autistic males, which may be the result of societal pressure stemming from common gender norms [24, 32, 34, 35]. For example, females are often perceived as more extroverted than males and, as a result, are expected to thrive in social situations [32]. Therefore, autistic females are forced to mask more often, disguising autistic traits and further contributing to underdiagnosis [32].

CLOSING THE GAP IN ASD DIAGNOSIS Despite differences in the presentation of autism spectrum disorder between males and females, the diagnostic criteria are based predominantly on the male presentation, thereby excluding females [24]. Symptoms observed in autistic females often do not align with the established criteria, leading to a delay in or lack of diagnosis [25, 8, 24]. Delayed diagnoses can lead to delayed care and, subsequently, more difficulties experienced by autistic people [36, 37]. Without a timely diagnosis, autistic individuals are less likely to receive early intervention, an approach starting from early childhood that has been shown to improve ASD-related symptoms [38]. Receiving early intervention can help to mitigate difficulties faced by individuals with ASD sooner [38]. Increasing funding to ensure females are included in research and redefining current diagnostic criteria may eventually close this gap between the sexes, improving the rates of accurate diagnosis and the quality of life for autistic females [8, 39]. References on page 49.

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BILINGUAL

THE BILINGUAL BRAIN: LEARNING, LANGUAGE, LONGEVITY by Alexis Earp and Erin Kaufman/ art by Abigail Schoenecker

t is almost impossible for a person living in the United States to go about their dayto-day life without encountering a variety of non-English words and phrases. A sign at the pharmacy may include the Spanish word farmacia. Care tags that advise machine-washing a sweater in cold water might include the Mandarin phrase 冷水机洗. A party host could serve hors-d’œuvres. However, for many Americans, exposure to a non-English language is more than just a passing moment: it is a vital method of communication. In the U.S. alone, 67.8 million residents speak a language other than English at home — a number roughly equivalent to the population of France [1, 2]. Since 1980, the percentage of bilinguals in the U.S. has almost doubled [3]. Globally, more than half of the world’s population is estimated to be bilingual [3]. However, the impact of bilingualism reaches far beyond census data: compared to monolinguals, bilinguals demonstrate physiological differences in their brains [4]. Although most bilinguals do not learn two languages in an attempt to reap these physiological benefits, a fascinating aspect of bilingualism is that it seems to aid in preserving certain brain functions as one ages [4, 5].

AGING IS INEVITABLE: C’EST LA VIE! The natural and inevitable process of aging may be accompanied by a deterioration of certain brain functions, such as the ability to make decisions and process new information [6]. For example, an avid reader may be able to grow their vocabulary, but the speed at which they can learn new words slows as they age [6]. This phenomenon, called cognitive decline, is a natural result of aging that is accompanied by structural changes in the brain [6]. Neural atrophy, a process through which brain volume and connections

between brain cells deteriorate over time, can interfere with cognitive processes associated with language production and memory [6, 7, 8]. Although age-related cognitive decline is normal and inevitable, not everyone experiences it to the same degree [6, 9]. The ability to resist the physiological effects of aging, also known as cognitive reserve, varies among

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BILINGUAL individuals [10]. While cognitive reserve may in part be influenced by genetics, lifestyle practices — such as reading regularly, completing brain teasers, or learning a second language — can help people better resist the effects of cognitive decline and improve cognitive reserve by strengthening the adaptability, or plasticity, of their brain [10, 11, 12].

AUDIO, VIDEO, DISCO: EXERCISING THE BRAIN THROUGH LEARNING Neuroplasticity is the nervous system’s ability to modify connections between neurons in response to a person’s life experiences, such as injury, aging, and learning [13]. Imagine you are going to the gym for the first time: in the beginning, you may only be able to lift light weights or run a short distance. As you continuously adapt to an exercise routine, your heart and muscles grow stronger, improving not only your ability to perform specific exercises at the gym, but also your ability to successfully tackle new forms of physical activity, like playing volleyball. A similar process happens in the brain: as you use specific pathways in your brain, connections between neurons are reinforced, strengthening these pathways [14]. Completing brain teasers and puzzles on a regular basis has been suggested to strengthen cognitive reserve because the brain is challenged with tasks that reinforce previously created pathways [15]. Strengthening these pathways makes the brain more resistant to neural atrophy [6, 16]. Similarly to performing brain teasers, speaking two languages may protect against cognitive decline by increasing neuroplasticity [4].

WHITE MATTER: A CERTAIN JE NE SAIS QUOI Distinct structural and functional differences at the neuronal level in the bilingual brain may be indicative of increased neuroplasticity [4, 17, 18]. The part of the neuron that carries electrical signals from one neuron to another, called an axon, is structurally different in bilinguals than in monolinguals [18, 19, 20]. Axons are surrounded by a layer of fatty tissue called myelin, and these myelinated axons make up a type of tissue called white matter. Axon density and the integrity of white matter in the brain can be measured via fractional anisotropy (FA), where a high FA value indicates greater axon integrity. [19, 21]. Older bilinguals tend to have higher neuronal levels of FA compared to older monolinguals in areas associated with language processing, suggesting greater axon strength or insulation in bilinguals [19, 20, 22, 23]. Older bilinguals have also been found to have a greater amount of white matter in the brain, particularly in their corpus callosum, a structure that bridges the two hemispheres of the brain [21, 22, 24]. Brain regions associated with language processing span both hemispheres, so increased white matter volume in the corpus callosum may aid in the exchange of information between neural regions [22, 25, 26]. The higher FA values and greater white matter volume observed in the corpus callosum are also observed in the arcuate fasciculus — a bundle of axons that connect regions of the brain associated with language — of bilinguals when compared to monolinguals [22, 23, 27]. An increase in FA, and therefore an increase in white matter integrity, may be correlated with enhanced neuroplasticity in bilinguals [17, 22].

BONA FIDE BENEFITS: BILINGUALISM PRESERVING THE BRAIN In addition to enhanced neuroplasticity, bilinguals demonstrate greater preservation of language-associated brain regions as they age [28]. The superior longitudinal fasciculus (SLF), a collection of fibers in the brain that connects several of the brain’s lobes, is thought to play a role in language, memory, and attention control [29]. Bilinguals display higher levels of myelination of axons in the SLF than monolinguals: myelination may help protect important neuronal connections from age-related degradation [23, 29]. The preservation of myelinated axons may be linked to the preservation of cognitive functions in bilinguals [23, 29]. Additionally, there is differing preservation of gray matter, or neuronal cell bodies, between aging bilinguals and monolinguals in a region of the brain

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BILINGUAL involved with speech production known as Broca’s area [30, 31]. Although Broca’s area is primarily known for its role in language production, it also regulates working memory, memory processing, and action perception and execution [32]. The greater preservation of gray matter in Broca’s area may allow bilinguals to easily switch between multiple languages [22, 32, 33, 34]. The conservation of white and gray matter within language-associated regions in the brain supports the hypothesis that bilinguals have a greater cognitive reserve [22, 33, 34].

LIFELONG BENEFITS: SAY HOLA TO COGNITIVE RESERVE In addition to observed structural changes in the bilingual brain, older bilingual adults seem to exhibit improved cognitive functions, including memory and attention preservation [11, 22, 35]. Bilinguals demonstrate a greater ability to switch their attention between multiple tasks, outperforming monolinguals on auditory attention-related tasks [20,36]. While performance in attention-related tasks declines as all individuals age, bilinguals seem to continue to outperform monolinguals in these tasks, suggesting greater retention of memory and attention [36]. Furthermore, bilinguals exhibit higher gray matter volume in the superior temporal gyrus — a brain region associated with auditory attention — and this increase in gray matter may underlie improved attention abilities in bilingual individuals [20, 22, 37]. In addition to attention tasks, older bilinguals also seem to perform better than older monolinguals on tasks related to working memory, which refers to the brain’s ability to retain newly learned information and apply it to a current task [38, 39]. This heightened performance may also be connected to the functioning of the SLF because of its involvement in language, memory, and attention [40]. Furthermore, there is also a correlation between increased myelination in the SLF and improved working memory, which could lead to bilinguals performing better on working memory tasks than monolinguals [29]. Physiological differences between older bilinguals and monolinguals support the conclusion that there are long-term benefits and improved cognitive reserve in bilinguals [6, 29].

HAKUNA MATATA: IT’S NEVER TOO LATE Bilingualism appears to be associated with physiological changes in the brain that aid in preserving cognitive functions [20]. Though more research is needed to solidify these findings, learning a second language could be a promising way to delay not only cognitive decline but also age-related cognitive conditions such as some neurodegenerative diseases [41]. Due to the potential long-term cognitive benefits, bilingualism is now being studied as a possible means of cognitive therapy for older populations [41, 42]. While early language learning has been linked with greater neuroplasticity, language learning at any age will allow one to reap some of the physiological benefits of bilingualism [20, 35]. Research on bilingualism as a form of cognitive therapy is critical because as the aging population grows, the strain of cognitive decline on families, healthcare systems, and economies will only become more pronounced [41]. A rise in bilingualism could lead to improved cognitive reserve in the global population [20, 41]. The benefits of bilingualism span beyond communication: they build pathways to healthier people, healthier communities, and healthier futures. References on page 51.

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NLP

FEATURED

FROM COMPUTATIONAL TO COGNITIVE: CHATGPT AND NATURAL LANGUAGE MODELS

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by Wolff Gilligan/ art by Maddie Turner

NLP

ave you ever wondered if ChatGPT can really think and speak like you do? The science behind it seems to raise more questions than answers. In order to start answering those questions, we must explore how current research is attempting to enable computers to comprehend and generate human language [1, 2, 3]. With the advent of OpenAI’s ChatGPT, it may seem like natural-sounding computer-generated language is finally within reach. ChatGPT, while not flawless, demonstrates an impressive use of language, engaging users with responses that are often remarkably human-like. Despite these advances, there is still a long road ahead to enabling language processing models that fully grasp the intricacies of human language. One key issue has emerged: the division between the engineering-based development of these systems and the academic study of how humans comprehend and use language. Separating the two onceclose disciplines has led to a world where computer models like ChatGPT often interpret and respond to language in ways that, while sophisticated, lack the depth of human cognition [2, 4, 5]. Investigating the current limitations of natural language processing and the ways in which we can take a more multidisciplinary approach to the field has the power to close the linguistic gap between humans and computational models.

NODES, NEURAL NETWORKS, AND NUANCE. OH MY! Language is both an integral tool for communication and a vastly complicated one, which makes breaking it down for computers seem like an impossible challenge. However, this is precisely the endeavor of Natural Language Processing (NLP): the field dedicated to enabling computers to understand human language for use in a myriad of tasks [6]. One common task for NLP involves analyzing sections of text or individual words to determine whether they convey a positive, negative, or neutral sentiment — a process known as sentiment prediction [7]. Given the word ‘melancholic,’ a sentiment prediction model might guess that the sentiment is 0.4 on a scale from ‘very negative’ (0) to ‘very positive’ (1), and it might give the word ‘happy’ a 0.9. The ability to efficiently separate positive from negative across large volumes of online data, such as product reviews or social media posts, makes sentiment prediction models valuable tools [7]. Sentiment prediction models are the ancestors of the modern ChatGPT, but their techniques and frameworks are still used today [8].

Sentiment prediction models and other language processing systems are considered miraculous; however, to understand how they are implemented, we must look under the hood. At the core of NLP models lies a fundamental computational building block: the simple neural network [9, 10, 11]. Put plainly, a neural network is a system of connected processing units called nodes. The connections between nodes are mathematically fine-tuned by an algorithm to achieve a desired output when presented with a given input. For example, a sentiment prediction model would have words as the input, and the predicted sentiment of those words as the output. The nodes are structured into three distinct ‘layers:’ the input layer (which receives the raw data), the hidden layer (where the computations take place), and the output layer (which presents the final results of the computations). The network is fed data (that has been generated by groups of people and digitized) in successive rounds, a process called ‘training’ [7]. In each round, the network extrapolates patterns from the input and then comes up with a guess of what the output is, representing what it believes the real answer to be. The network then checks its work, and, if needed, refines its connections to become more precise, then tries again. Over time, the model ‘learns’ patterns based on the data it receives in order to produce more accurate outputs. The learning process takes place in the hidden layer, which is virtually impossible for humans to directly interpret. A well-constructed neural network is designed to eventually be able to accurately predict the correct output, solely based on the input [9, 10, 11]. Predicting outputs for a specific problem begins with designing a new neural network model. For example, to perform the aforementioned sentiment prediction, we would first need a dataset of words paired with numbers representing their sentiments on a scale of 0 to 1 (suggesting very negative to very positive); for instance, ‘melancholic = 0.4’ or ‘happy = 0.9.’ To train our model, we feed it words from our dataset, which make up the input layer, and the model then guesses the value of the sentiment, the output [11]. Based on the connections made in the hidden layer, the model generates its first guess. The guess might be very wrong, for example, a prediction of 0.8 (fairly positive) for the word ‘melancholic.’ At this point, the model checks its work by comparing its guess to the human-generated answer, and it adjusts the connections in its hidden layer using the algorithm [11]. Through thousands of guesses and recalibrations, the hidden layer’s connections become increasingly accurate at predicting the sentiment of a given word from the dataset [9, 10, 11].

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NLP The process of training models — by adapting the hidden layer to achieve greater accuracy over time — is the same for more complex networks, such as those in the GPT family [12]. ChatGPT was trained on a dataset called the Common Crawl, which is an open source compilation of billions of pages across the Internet [12]. Due to its highly complex nature, the ChatGPT model features more intricate hidden layers and algorithms, enabling it to discern more nuanced patterns in language [9, 10, 11]. Additionally, with a more complex model, we can generate more complex outputs, like the ones produced by ChatGPT [12]. At all levels of complexity, pure pattern recognition is the engine of neural networks; the networks are limited by their algorithmic complexity and self-contained processes.

SYNTAX SORCERY: DIVINING THE BASICS OF COGNITIVE LINGUISTICS When we ‘talk’ with ChatGPT, it is easy to assume we are using a computer model that truly understands human language. Yet this assumption overlooks a crucial question: does ChatGPT process and generate language in the same way humans do? While NLP models are able to produce and read human language, that does not mean that they interpret and formulate words in the same manner as humans [2]. We need to consider the fact that computers are fundamentally different agents from humans, starting with their basic ‘hardware.’ Although both com-

puters and humans produce words, the neurons in the human brain and the computational neural network of computers are fundamentally not the same. Consequently, the ways in which computers and humans process language must also be different. Therefore, in order to grasp the mechanics of how we understand, produce, and interpret language, we need to explore the field of cognitive linguistics [13]. While the development of computer models may seem disjointed from the goal of human language understanding, the two are deeply intertwined [14]. The first algorithms used for NLP were developed back in the 1960s, although they were limited in practical usage due to the lack of computational power at the time [15]. As a result, research in the field of NLP was often restricted to a theoretical discussion of abstract ideas about language that could not yet be actualized [15]. Similar to cognitive linguists, early NLP researchers were exploring language at a conceptual level [2]. As processing power progressed, allowing for the large-scale application of pre-existing algorithms, NLP became less theoretical and more practical, leading to a separation between the fields [14]. While the fields have diverged, their goals remain the same; therefore, it is necessary to discuss cognitive linguistics and NLP in relation to each other. At the core of cognitive linguistics exists the basic grammatical units of language, such as form-meaning pairings [16]. Form-meaning pairings refer to the connection between how a linguistic element, such as a word, sounds and the idea it represents [16, 17]. For instance, consider the connection between the word ‘bucket’ (form) and the physical container it represents (meaning). Nearly all linguistic theories recognize that language is made up of form-meaning pairings, including the theory of construction grammar [17]. According to the general theory of construction grammar, form-meaning pairings apply to a variety of linguistic elements collectively called constructions, including words, idioms, and phrases [17]. Let’s revisit

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NLP our form-meaning pairing example from before, using the word ‘bucket.’ Think now about the phrase ‘kick the bucket:’ it does not mean to physically boot a pail, but is instead an idiom meaning ‘to die.’ Yet, notice the individual words ‘kick’ and ‘bucket.’ Despite their original meanings, these words have come together to form an entirely different meaning. Within the framework of construction grammar, the idiom ‘kick the bucket’ would be considered a construction since its form is paired with a meaning that differs from the literal translation of the individual words [16, 17]. Concepts like these are very useful for breaking down language; nonetheless, cognitive linguistics is not just about dissecting language into tiny components. The field helps us to understand the rich tapestry of connections, meanings, and experiences that language encompasses.

It is crucial to acknowledge the differences and potential synergies between traditional NLP models that utilize neural networks and systems inspired by cognitive linguistics. Neural networks used in NLP tasks often approach language as an array of patterns [10, 11]. Recall that sentiment analysis models achieve their prediction of ‘positive,’ ‘negative,’ or ‘neutral’ by recognizing patterns in large datasets and then applying this knowledge to analyze new inputs. Neural networks operate by adjusting their predicted output based on patterns they have previously seen, without any inherent understanding of what the patterns signify in human cognition [11]. As a result, the network output is only based on the form of language rather than its meaning. In contrast, cognitive linguistics is not only about patterns, but also the intricacies of meaning, context, and their relation to the human

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NLP

experience [2]. Traditional NLP models — like sentiment analysis neural networks — might tell you that a sentence is negative because they have seen similar patterns before. Alternatively, a cognitive linguistics-inspired model would be able to discern the negativity based on various other factors, possibly taking into account the underlying constructions, context, and human experiences associated with words [4]. While humans consider words and phrases within the context of their own lives and thoughts, neural networks reduce words and phrases to their form [2, 5, 16]. Going back to our ‘kick the bucket’ idiom, a neural network would solely focus on the form of the phrase and not take note of its morbid connotation [2, 16]. Neural networks may detach forms from the human experience of language, resulting in a loss or misinterpretation of the word’s meaning [2, 5, 14]. While the neural network cannot explain its answer, the predictions of a cognitive linguistics-inspired model are easier for humans to understand [4, 5]. Though a difference in explainability may seem insignificant, it is the first sign of how NLP models fail to capture the true essence of human language.

PARSING PREDICTABILITY: BRIDGING GAPS WITH COGNITIVE LINGUISTICS While the objective of both NLP and cognitive linguistics requires similar knowledge and research, the relationship between the two is not as strong

as one might assume [5, 14]. Despite NLP’s recent advancements with miracle models such as ChatGPT, future strides can only be made to address weaknesses in NLP research by better integrating cognitive linguistics concepts [5, 14]. Some of the issues associated with NLP systems are evident when examining a common language task: reading. Cognitive linguists theorize that when people read or process sentences, the level of effort required depends on how predictable each word or phrase is, based on the context [18]. For instance, the flowery and irregular writing of Hamlet may take more effort to read than the more commonplace vocabulary and sentence structure of Frog and Toad. While we know that predictability plays a role in reading, the way in which humans naturally anticipate what follows in a sentence remains unknown [19]. To mimic this behavior in computers, an algorithm that incorporates grammar and structure rules is utilized in order to predict the next word in a sentence [18]. An example of such an algorithm is the left corner parser, which is simply a method of computational language processing that uses a flowchart of grammatical rules that is examinable and understandable by humans. There is no hidden layer here! To figure out which method of language processing is more accurate, we can compare the performance of the left corner parser to a highly complex neural network model (GPT-2) and a baseline set of human data collected through observing people reading. While both systems performed worse than humans, the left corner parser achieved equivalent or superior results when compared to GPT-2 in predicting the next word [18]. The findings suggest that large neural language models are not as proficient at grasping more nuanced aspects of human language, which are more pronounced in more complex tasks like predicting the

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NLP next word in a sentence [19]. GPT-2, a large neural network model, underperformed in comparison to an algorithm designed using insights from cognitive linguistics [18]. Beyond GPT-2, systems that solve complex problems related to the understanding of human language are often based on cognitive linguistics research [4]. Despite advances in complex neural networks, these models often struggle with analyzing sentence structure, grammar, and meaning, leading to difficulties in performing complex tasks like inferencing [13, 20]. Inferences, which are conclusions drawn from context rather than explicit statements, require an understanding of nuanced interactions and sequences in language. Humans make inferences quite naturally, even with minimal context; for example, the statement ‘John dried the clothes’ implies that the clothes were wet to begin with and have transitioned to a dry state [21]. However, even with immense amounts of data — such as the Common Crawl, which ChatGPT was trained on — NLP models struggle with making these types of inferences [20, 22]. The difficulty of complex language processing for NLP models, along with the desire for improved performance, provides a major opportunity for the integration of cognitive linguistics into NLP. Integrated systems that are better able to incorporate insights from cognitive linguistics into NLP models have been developed to perform a more nuanced analysis of language [23]. An example of one such system is VerbNet, a cognitive linguistics-inspired tool that provides a comprehensive classification of verbs based on their meaning and grammar [21, 24, 25]. VerbNet is not a neural network, but a top-down system that uses explicitly-stated grammatical rules from human language, just like the left-corner parser from earlier. While a neural network reduces a verb to its form, VerbNet incorporates NLP and cognitive linguistics to consider both the form and meaning of a given verb. VerbNet doesn’t just look at the surface structure of sentences, but it also analyzes the different meanings that each verb can have in different contexts and how these meanings interact with each other. What makes VerbNet unique is that it pays particular attention to an aspect of verbs called diathesis alternations. A diathesis alternation is a change in a sentence that switches the focus of the verb from its subject to its object, or vice versa, which often involves rearranging the sentence structure [21, 24, 25]. For instance, a sentence like ‘John broke the window’ can be rephrased as ‘the window broke’

without losing its essential meaning. VerbNet’s capability to capture these nuances in verb meaning allows it to better understand and represent the changing relationships of verb subjects (John) and objects (the window) in sentence structure. This method of representing smaller components of sentences and their connected conditions gives a deeper insight into language meaning than neural network models that mainly focus on surface-level text analysis [21, 25]. So, how can this help advance NLP research? By integrating VerbNet’s nuanced, cognitive linguistics-driven approach to understanding verbs and their roles in events, NLP models can understand language more accurately [21, 25]. The ability of NLP systems to make inferences and understand subtle changes in meaning, which these systems currently struggle with, can then improve significantly.

KICKING THE AI BUCKET: THE FUTURE OF NLP Research into the field of NLP is incredibly advanced, as exemplified by models such as ChatGPT; however, research has room to grow. Inferencing tasks and other similarly nuanced language problems that current NLP models face are significant hurdles that can be overcome by more extensive integration of cognitive linguistic concepts into computational models [5, 14]. The current lack of integration arises from neural networks’ focus on pure pattern recognition, which is exacerbated by training neural networks on large datasets, neglecting the intricacies present in human language [2, 10, 11]. A crucial step towards developing more accurate and wide-ranging models is incorporating concrete, human-designed rulesets into the training of complex neural networks [4, 26]. Systems like VerbNet can be integrated with current neural network models. If properly integrated, cognitive linguistics-inspired enhancements could greatly improve the accuracy of NLP models, as well as expand our understanding of human language [21, 25]. A composite model would thus be able to perform nuanced tasks, such as inferencing, more precisely and efficiently. By harnessing the power of cognitive linguistics and using a more multidisciplinary and integrative approach, NLP models have the potential to become refined producers of human language [4]. References on page 53.

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SMELL & MEMORY

A Scent is Worth a Thousand Words: The Neuroscience of Smell and Memory by Hannah Koople/ art by Kishi Oyagi

t’s a brisk fall day. Leaves fall gently at your feet as you walk around your small college town, trying to find a cafe to study in. While you search, you find yourself strolling by a bakery when a warm, buttery, slightly sweet aroma fills your nose. You smile as you realize the delightful smell is coming from freshly baked bread. Suddenly, you are transported back in time: you are six years old again, standing in your grandmother’s kitchen as your small, flour-covered hands knead a ball of soft dough. You recall the joy you felt when sitting by the oven and watching the dough rise, the same sweet aroma filling your nose

as you wait for the bread to bake to a golden brown. A sense of nostalgia and warmth washes over you: you feel as though your grandmother is with you, giving you a hug as you enjoy the fresh bread you baked together. You feel immense happiness as you relive this sentimental moment. Most of us have experienced a smell that suddenly evokes an emotional memory. The powerful connection between the memory-associated areas of the brain and the olfactory system — the sensory network that controls our sense of smell — is what brings about this interesting phenomenon and allows us to relive moments from our pasts [1].

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SMELL & MEMORY

THE JOURNEY OF THE ODOR MOLECULE When you inhale the aroma of the bread, odor molecules travel up your nose, first stimulating sensory receptors in the upper area of your nasal cavity [1]. In your nasal cavity, these odor molecules fit into corresponding olfactory sensory receptors. Just as a key must fit into its matching lock to open, odor molecules must bind to specific olfactory receptors to be detected. [1]. Although an odor molecule can only bind to one specific receptor, this selectivity does not apply to the receptors themselves, which can be ‘unlocked’ by a number of different molecules [2]. The human olfactory system is able to detect at least one trillion distinct odors despite having only 400 olfactory receptors [2, 3]. To identify the smell of bread, the brain must receive an electrical signal associated with that scent. As odor molecules attach to olfactory receptors, they act as a trigger for this electrical signal to travel to the olfactory bulb in the brain via the olfactory nerve [4]. The olfactory bulb acts as an intermediary between the brain and the outside world. From the olfactory bulb, the electrical signal travels further into the brain to the olfactory cortex; the cortex consolidates different electrical signals encoding unique scents, like tangy yeast and warm butter, into the rich sensory experience of smelling bread [4].

SIGNATURE SMELLS: HOW ODORS ARE STORED WITH MEMORIES AND EMOTION After the electrical signal is analyzed in the olfactory cortex, it travels to higher brain regions that will encode the odor signal into a meaningful memory [4, 5]. One of these brain regions is the hippocampus, which is responsible for storing recollections of our experiences, or our autobiographical memories [5]. Olfactory input is uniquely processed in the hippocampus, whereas most sensory stimuli like touch or vision are first received by and processed in the brain’s sensory relay station: the thalamus [6, 7]. The olfactory signal bypasses the thalamus and travels straight to the hippocampus, which acts as a large filing cabinet, processing and storing memory files

for us to access later [5]. Memories can be connected to specific odors, so when we inhale a familiar scent, the associated memory file is retrieved by the hippocampus [8]. The olfactory cortex and hippocampus are connected to each other through a large network of neurons; without that connection, we wouldn’t form odor-related memories or remember a previously encountered smell [8]. Even when we are not actively receiving odor stimuli, the olfactory cortex interacts with the hippocampus via these neuronal networks, more so than other sensory systems [9]. Due to this strong neuronal connection, the autobiographical memory becomes associated with an odor. From then on, that particular scent will create a powerful and vivid recollection of the memory, one that is stronger than a recollection caused by a visual cue [10]. Think about your reaction to a picture of someone baking bread versus the experience of actually smelling bread. The actual smell might fully transport you back to our grandmother’s kitchen and covered in flour as a warm, golden loaf was pulled out of the oven; merely seeing a picture of someone baking bread might only cue a faint recollection of the childhood memory. The interplay between the olfactory system and the hippocampus is not just involved in storing memories, but also works with a brain region called the amygdala to assign an emotional context to odor-related memories [11]. The amygdala — our brain’s attention grabbing machine — is involved in the interpretation and processing of emotional stimuli such as fear or joy [12]. Picture an event from childhood: maybe you used crayons to create a homemade birthday card or roasted marshmallows over a crackling campfire. Moments that evoke strong feelings like excitement or happiness are more likely to be remembered [13]. When an experience arouses strong emotions, the amygdala is activated and promotes the storage of the emotional memory in the hippocampus [14]. The amygdala and hippocampus work together to link a memory with its emotional significance, such as the nostalgia evoked by the smell of bread [14].

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SMELL & MEMORY

THE POWER OF SCENT: WHY ODOR MEMORIES ARE IMPORTANT Memories associated with a specific odor are often evoked before we are even consciously aware of the smell [15]. Imagine sitting by the oven with your children, watching a fresh loaf of bread rise and bake. The smell fills the house with a warm, comforting aroma as the childhood memory of baking with your grandmother is again evoked. Involuntary memories can arise from the subconscious connection of smells to particular people, experiences, or

places [16]. Memories conjured by odors are instinctual, so we can recall these kinds of memories faster [15]. Smell was the first scent that animals evolved as it was necessary to engage in mating behaviors, interact with other animals, and detect potentially harmful substances [4, 17]. Therefore, having strong smell-associated memories is a survival instinct, allowing us to recognize and learn what certain things smell like and then decide whether we should interact with them [4, 17]. We also utilize scent for more complex interactions with our environment, such as remembering past events or information [15, 18].

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SMELL & MEMORY For example, students who smelled a scent while studying for an exam and then smell it again while taking their exam perform better [19]. The scent students smell acts as a cue to locate and retrieve the correct memory ‘file’ associated with exam content. Additionally, using odors to cue context-related memories may have therapeutic benefits for people who struggle to recall long-term memories, such as individuals with dementia [15, 18]. For example, people with Alzheimer’s who are exposed to odor stimuli are able to recall memories quicker and frequently report experiencing a sense of ‘traveling back in time’ to the place where they originally experienced recalled memories [15, 18]. The ability of odors to cue our past enables us to not only recall relevant information, but to revisit incredible moments in our lives.

Olfactory memories, whether we realize it or not, play an important role in our lives. As odor molecules travel up our nasal cavity to trigger olfactory receptors, the complex processes of sensory perception, memory formation, and recollection ensue. The connection between the olfactory system, the hippocampus, and the amygdala allows us to remember meaningful smells that elicit strong emotions. Smell-memory processes first evolved to assist our survival, but have since become an integral part of our memory-recollection and emotional processing systems. We can use smells to recall memories or information and to re-experience the special feelings that each memory evokes. So, the next time the smell of crayons or Elmer’s glue reminds you of your childhood, let it also remind you of the intricate systems within your brain that make that recollection possible. References on page 54.

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DEATH

FEATURED

MAKING WAVES: THE NEURAL ACTIVITY OF THE DYING BRAIN by Anoushka Bhatt/ art by Jane Stempien

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DEATH

eath is an inescapable part of life. But what really happens as we die? Although everyone eventually dies, very little is actually known about the brain activity associated with death. In the past, death was defined as the termination of cardiac activity and lung functioning; however, this definition has since evolved. A person is now considered dead — both clinically and legally — when blood flow to the brain stops and neural activity ceases, which is known as brain death [1]. While brain death is sometimes preceded by a halt in cardiac activity, a person is not considered officially dead until their brain activity ceases completely [2]. Since previous neurological data had shown a decrease in brain activity after cardiac arrest, it was widely accepted that brain activity typically decreased as a person is dying [3, 4]. However, a 2022 case study — in which a man’s brain activity was monitored as his heart stopped beating — displayed a relative increase in certain brain waves leading up to and following the cardiac arrest that led to his death [5]. This case lends us a new perspective into the inner workings of a person’s brain as they approach death [5, 6]. Although the neural activity of the dying brain has been monitored before, this case study was the first time that any heightened activity was observed leading up to death, raising new questions about the experience of dying [6].

[11]. To monitor his seizures, John’s doctors hook him up to an EEG by placing small electrodes on his head [5]. Each electrode measures electrical activity in different regions of John’s brain and the EEG generates a corresponding image that depicts brain waves as multiple lines on a screen [12]. The frequency of brain waves, measured by the number of wave peaks per second, denotes the type of wave [13]. Each type of brain wave contributes to different states of brain activity, like sleeping or problem-solving. [13, 14]. As John experiences intermittent seizures, his doctors observe shifts in electrical activity on the EEG [5].

SETTING THE SCENE: JOHN DOE ARRIVES IN THE ER An 87-year-old man — John Doe — arrives at the emergency room after a fall [5]. He has sustained a head injury, but his neurological status appears to be normal. He passes the basic tests of neurological functioning and demonstrates intact reflexes as well as a normal pupillary response to light [5]. Suddenly, his neurological status deteriorates: his pupil sizes distort and his eyes no longer respond to light, a sign of neurological damage [7]. Random bursts of electricity in John’s brain disrupt normal electrical patterns and cause epileptic seizures, during which his movements, sensations, and consciousness are impaired [8]. During each seizure, which can last from a few seconds to a couple of minutes, John temporarily loses control of his body [9]. His complex network of brain cells, or neurons, begins to glitch like a malfunctioning computer. Billions of neurons in the brain comprise this complex network and send electrical messages throughout the body and allow a person to do everything from breathing to thinking [10]. When neurons fire, they produce electrical signals that can be picked up by a machine called an electroencephalogram, or EEG

DIRECTLY PRECEDING CARDIAC ARREST: JOHN DOE IN CRITICAL CONDITION John Doe’s condition deteriorates further as blood begins pooling on the surface of his brain, pressing against and damaging brain tissue [5, 15]. Unexpectedly, John goes into cardiac arrest: his heart stops pumping blood [5, 16]. As blood flow to John’s brain and the rest of his body comes to a standstill, the EEG continues to collect data on the ongoing electrical patterns in his brain [5]. The machine detects two distinct types of brain waves: gamma and alpha. On the EEG, gamma wave activity increases directly before John’s cardiac arrest [5]. Typically associated with complex mental processes like attention, language, learning, and memory, gamma waves are recognized by their high frequency and are primarily observed during periods of high alertness and consciousness [17, 18]. Therefore, the increase in gamma waves just before John’s cardiac arrest was highly perplexing, considering he had lost consciousness.

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DEATH Alpha waves, which were also observed on the EEG, have a lower frequency than gamma waves and are generally observed when people feel relaxed: when the brain is in an idle state [13, 19]. Thus, it is surprising that both alpha and gamma waves were simultaneously observed in John’s brain activity. The case study proposes cross-frequency coupling as a possible mechanism to elucidate the puzzling presence of two contradictory waves. Through cross-frequency coupling, alpha waves are able to modulate the activity of gamma waves [5]. Alpha waves are typically inhibitory, meaning that they can regulate other brain waves by suppressing their activity [20]. Since the activity of alpha waves is decreasing in this particular study, they fail to inhibit gamma activity, leading to an increase in gamma waves [20]. However, it is not quite possible to discern whether cross-frequency coupling is responsible for the increase in gamma wave activity. Overall, the most prominent activity seen in John’s brain is gamma activity. Gamma waves are often correlated with activity in the visual cortex — the part of the brain that processes visual information — and are also involved in memory recall [5, 21, 22]. The observed increase in gamma wave activity before cardiac arrest invites inquiry into what, if anything, is experienced visually in the moments leading up to death.

AFTER CARDIAC ARREST: QUESTIONS AND CONCERNS After John goes into cardiac arrest, blood flow to his brain decreases and his brain exhibits less overall activity [5]. However, this case showed both an increase in gamma activity leading up to cardiac arrest and a relative increase in gamma activity in the moments following cardiac arrest. Although John’s brain activity decreased overall due to reduced blood flow, the relative levels of gamma activity — in comparison to the levels of other brain waves — were higher in the moments following cardiac arrest than during the time between seizures, the interictal period [5]. A common method of approximating normal brain activity during seizures, the interictal period was used as a baseline for comparison in this case, as it was the closest representation of resting brain activity for John Doe [5, 6, 23]. The relative increase of gamma waves in John’s brain following cardiac arrest is not consistent with the generally accepted idea that brain activity slows in the moments leading up to death [5, 6]. In prior cases, EEGs from four patients were collected during the withdrawal of life support, subsequent brain death, and for 30 minutes after death [4]. Leading up to brain death, the four EEGs showed varying levels of activity; however, they demonstrated complete inactivity for at least one and a half minutes directly preceding the stopping of the heart and no activity afterward. These findings are at odds with what was observed in John’s case, and may be explained by the fact that John’s death was a natural result of cardiac arrest, whereas the other four deaths were ‘slower,’ a consequence of withdrawing life support [4]. These findings raise many questions. Is the observed activity indicative of consciousness or merely a random firing of neurons? What do these findings mean in relation to the various cultural definitions of death [24]? Should doctors change the way they treat people in their last moments before death? As more questions arise, these findings may shape our understanding of what occurs during our final moments. While more information is necessary to paint a more comprehensive picture of brain activity during death, there are ethical concerns when it comes to conducting research on a dying person [4, 25, 26]. Attaching electrodes to the heads of people who are dying, for instance, may

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DEATH compromise their end-of-life comfort [25]. Another concern with end-of-life research is the issue of obtaining informed consent from people who are already incapacitated and unable to provide consent [26]. As this area of research grows, ethical considerations will be of continued importance.

FILLING THE GAPS: WHERE RESEARCH CAN TAKE US Despite its impact, John Doe’s case study has been criticized due to concerns with the study’s data analysis methods [6]. A primary concern in John Doe’s case study is the lack of healthy baseline activity recorded in John’s brain, since a baseline of normal neural activity was not recorded when John arrived at the ER with head trauma [1, 6]. As a result, interictal period activity between John’s seizures was compared to the observed brain activity preceding his death, which failed to provide a clear picture of John’s brain activity under normal circumstances [1]. Therefore, we cannot be certain that the observed brain activity is unusual, as it may appear. It is also important to note that muscle contractions — such as those corresponding with John’s sei-

zures — produce electrical signals themselves and are known to contaminate EEG readings [1, 27]. Therefore, John’s true level of brain activity may have been misrepresented due to muscle contractions [1]. While John Doe’s case study offers novel insight into brain activity at the end of life, it is still only one case study, and additional studies should be conducted to better understand the brain activity associated with cardiac arrest and traumatic brain injury [4]. Continued awareness of the variability in brain activity in each instance of death is vital, as each person exhibits physiological and neurological differences which can influence the way that end-of-life treatment is carried out. While death cannot be examined in a lab under controlled conditions, further research can provide us with a greater understanding of brain activity during our final moments. References on page 55.

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NANOPLASTICS

MOLDING THE PLASTIC BRAIN: NANOPLASTICS IN THE AGE OF CLIMATE CHANGE by Alex Kaye/ art by Iris Li

y the time you finish reading this sentence, around 100,000 plastic water bottles will have been purchased across the globe [1]. Let’s follow one of these bottles: once used, it is tossed into a trash bin and taken to a landfill, where heavy rainfall carries it off into a nearby stream before ferrying it into the ocean [2]. From here, ocean currents deposit it into an oceanic garbage patch — in this case, the Great Pacific Garbage Patch [3, 4]. While floating in this 1,600,000 cubic kilometer garbage patch, solar radiation and the physical forces of the tides cause the bottle to slowly break apart into millions of mi-

croscopic pieces of plastic called microplastics [5]. These pieces continue to degrade down to the nanoscopic scale, where they will appropriately be called nanoplastics. The size of one nanoplastic compared to a plastic bottle is akin to a single grain of sand in an Olympic-sized swimming pool [5]. It is hard to grasp this scale — one bottle cap could break down into one hundred quadrillion (100,000,000,000,000,000) nanoplastics, a quantity similar to the number of pennies you would need to pay off the total global debt [5, 6]. Though plastics degrade into smaller pieces, a reduction in size does not mean a reduction in harm; due

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NANOPLASTICS to their minuscule size, nanoplastics have unique properties that pose an active threat to organisms [7, 8, 9]. While the health effects of nanoplastics are still being studied, the landscape of research over the past few years has painted a stark image: nanoplastics are filling the stomachs, brains, and cells of countless animals, causing tissue damage, abnormal behaviors, and a reduced ability to adjust to rapidly changing environmental conditions [10, 11, 12].

MEET THE PLASTICS: NANOPLASTICS IN THE FOOD CHAIN So, what happens to these plastics? Plastic is often referred to as a ‘forever chemical’ because it takes hundreds of years to chemically transform into naturally occurring molecules, such as carbon dioxide [7]. Across oceanic garbage patches, most plastics float at the surface of the water. As they break apart, they often sink to the bottom of the ocean like a noxious snowfall [13]. Microscopic organisms called zooplankton mistake these nanoplastics for food and consume them, and because most animals cannot digest nanoplastics, plastic slowly accumulates inside their bodies [13, 14. 15]. When fish and other predators consume nanoplastic-filled zooplankton, they ingest a highly concentrated reservoir of plastic; nanoplastics are funneled into the fish, accumulating in their cells, tissues, and bodily fluids [13, 14, 16]. As these fish are eaten by even larger fish, the concentration of nanoplastics inside the larger fish increases exponentially [13. 17. 18]. Plastic pollution turns food chains into a plastic pyramid scheme, with those at the top of the pyramid carrying a disproportionately large share of plastics [13]. It is worth noting that humans are at the top of this pyramid — on average, we consume up to one credit card worth of plastic every week [19. 20]. Not even someone with a vegetarian diet and a strong water filter can avoid nanoplastic ingestion; nanoplastics are able to infiltrate plants, ensuring that all organisms are doomed to a life full of plastic [21, 22, 23].

“TOXIC” FT. BRITNEY SPEARS & NANOPLASTICS: INFLAMMATORY EFFECTS OF NANOPLASTICS Once inside the body, nanoplastics exhibit a litany of toxic effects: they damage cell membranes, provoke inflammation, and cause genetic damage [10, 24, 25]. All cells are surrounded by a protective membrane that allows vital nutrients to enter and keeps harmful substances out [24]. Nanoplastics can stick to these membranes, physically deforming them and encouraging the entry of nanoplastics into the cell [24]. Nanoplastics may even be able to

embed themselves inside cell membranes, blocking the passage of important molecules into and out of the cell [25]. The deformation and blockage of cell membranes prevents the cell from carrying out essential functions — such as the movement of materials across membranes — decreasing the cell’s ability to survive and potentially causing cell death [25]. Furthermore, the physical structure of nanoplastics allows them to slip through the cell membrane with ease, granting them access to critical parts of the cell, such as mitochondria, the structures that provide the cell with energy [26, 27]. Nanoplastics have a propensity to accumulate inside mitochondria, where they cause damage and stress, disrupt energy production, and produce molecules called reactive oxygen species [28, 29, 30]. Reactive oxygen species (ROS) can cause further damage to the cell by initiating harmful chemical reactions [26, 31].

The immune system is swift, decisive, and bold, providing the body with strong defenses that protect it from infections and harmful substances. Such boldness typically serves the body well, but in some cases, activation of the immune system can cause more harm than good [32, 33, 34]. The body’s immune cells are capable of recognizing ROS and can respond aggressively, resulting in a cascade of immune responses that causes destruction and widespread health effects [26, 35, 36]. ROS production causes inflammation, which — when coupled with chemical energy reserves that have been depleted due to the damaged mitochondria — results in increased numbers of senescent cells [37]. Senescent cells are cells that have reached the end of their life span but do not actually die. Instead, senescent cells stick around, becoming dysfunctional and continuously secreting molecules that further provoke the immune system and lead to a vicious cycle of inflammatory damage [37, 38, 39].

ONE FISH, TWO FISH, DEAD FISH, RUDE FISH: NEUROPLASTIC ADAPTATION IN A CHANGING WORLD The term ‘plastic’ refers to something highly malleable and easily shaped. Much like the plastic straws and water bottles we use in everyday life, the brain is also easily remolded. Neuroplasticity — the ability of the brain and body to dynamically adapt to the world

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NANOPLASTICS around it through the rewiring of neural connections — is key to an organism’s resilience [40, 41, 42]. Think of a pool party on a warm summer day: though seemingly innocuous, you may need to fan yourself off to prevent overheating or dodge unruly partygoers to avoid being thrown into the pool. You were not born with these pool party survival skills, but instead learned them at some point in your life. If this ability to learn from the environment had been inhibited, the summer heat would be a lot more brutal, and pool parties a lot less fun. [43, 44, 45]. Nanoplastics cause inflammation and cellular damage that may interfere with our ability to learn and respond to the outside world. Because of their small size, nanoplastics can easily infiltrate the brain, where they damage neurons and increase inflammation [8, 11, 46]. This type of damage frequently occurs in the hippocampus, a structure of the brain involved in memory consolidation and learning [47]. Nanoplastics can also disrupt cell growth and cause

mitochondrial dysfunction, impairing the production of new neurons in the hippocampus [26, 48, 49]. Hippocampal function is a critical component of neuroplasticity, making damage to this part of the brain particularly disruptive to animal behavior [48, 50]. Nanoplastics have also been shown to modify the production and recognition of molecules related to learning and memory, which are both critical to an organism’s ability to adjust to changing environments [51, 52, 53]. Nanoplastics can inhibit molecules that break down acetylcholine (ACh) — a neurotransmitter important in learning, memory, and muscle movement — and their presence is correlated with an excess of acetylcholine levels in muscles and the brain [11, 54, 55, 56, 57]. Altered ACh levels may explain some of the behavioral and locomotor abnormalities observed in organisms exposed to nanoplastics, such as impaired swimming in fish and hyperactivity in roundworms [8, 9, 58]. Moreover, the molecule that breaks down ACh is itself important for the brain’s ability to rewire itself — this molecule has been shown to promote the formation of connections between neurons [57, 59, 60]. However, ACh is just one small piece of the puzzle. Nanoplastics have been shown to impact levels of a variety of other neurotransmitters central to learning and memory [52]. Untangling the causes of these alterations is complex; alterations of neurotransmitter levels may stem from neuronal damage, genetic damage, direct interactions between nanoplastics and neurotransmitter-degrading molecules, or some combination thereof. However, one thing is clear: nanoplastics exert a range of toxic effects on the nervous system which ultimately impair neuroplasticity [8, 9, 52]. The impact of nanoplastics on neuroplasticity and the brain is illustrated by the abnormal behaviors seen in animals with high concentrations of nanoplastics: this accumulation has been correlated with aggression, impaired predator avoidance, and disrupted circadian rhythms in zebrafish [8, 61]. The importance of neuroplasticity is particularly salient in the context of the changing world imparted by climate change [44, 62]. These new landscapes present a host of new challenges, including changing temperatures, upended food chains, and altered resource availability [44, 49, 63]. Neuroplasticity permits

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organisms to adapt to these changes, whether by adjusting to new temperatures or learning to consume a new source of food [44, 64]. Impaired neuroplasticity stifles this sort of short-term adaptation, accelerating the ecological effects of climate change and the rate of species extinction [44, 65, 66].

LIFE IN PLASTIC, IT’S (NOT) FANTASTIC: ADDRESSING THE IMPACT OF PLASTIC POLLUTION While the effects of nanoplastics on organisms and ecosystems are already concerning, the dangers of plastic pollution stand out even more in the context of climate change. Nanoplastics elevate animals’ sensitivity to shifts in the environment, and unfortunately for life on Earth, such environmental shifts in the twenty-first century are widespread [61, 67, 68, 69]. Urbanization, for example, rapidly removes natural habitats from animals and forces them to either adjust to a new environment, move to a different location, or die [70, 71, 72]. In effect, impaired neuroplasticity means that a vulnerable species may fade into history, unable to keep up with the changing world [44, 65, 66]. This slide into history can be combated via the active removal of nanoplastics from waterways, firm restrictions on plastic production, and a transition toward the use of biodegradable plastics [73, 74]. Bioplastics — which can be made from plant fibers, fungi, and starches — are a promising alternative to the enduring, highly toxic

plastics we rely on today. [74, 75]. Promising new innovations aim to use bacteria to ‘upcycle’ plastics into biodegradable forms, which would transform plastic pollution into environmentally friendly forms of plastic [76, 77, 78]. Regardless of the path forward, without prompt action to stop the manufacturing of plastics, plastic pollution will only continue to rise globally, and the associated health effects of climate change and nanoplastics will only continue to compound [79, 80, 81]. Because nanoplastics impair animals’ ability to adjust to climate change, this trend threatens to accelerate the rate of species’ extinction worldwide [12, 66, 79]. Curbing plastic manufacturing and opting for environmentally friendly alternatives are thus necessary steps to combat the degradation of our brains and ecosystems globally [82, 83]. So, the next time you find yourself in the midst of a record-breaking heat wave, perhaps opt for a reusable water bottle over the hundred-quadrillion nanoplastics waiting to be unleashed from a single disposable plastic bottle. References on page 56.

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MISS-DIAGNOSED: THE GENDER GAP IN ASD DIAGNOSIS 1.

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THE BILINGUAL BRAIN: LEARNING, LANGUAGE, LONGEVITY 1.

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