By Joseph Correra Imagine you give a presentation and ten classmates praise you afterward. They mention how engaging, prepared, and intellectual you were. However, one person disagreed and said you seemed unsure of what you were talking about. As time passes, what comment are you ruminating on? Why can one negative comment outweigh ten positive comments? Why do we tend to remember the negative more deeply than the positive? Psychologists call this negativity bias: negative experiences carry more psychological weight and impact than positive ones, even when received at the same time. Although this might have been an important skill for our ancestors' survival, and no longer holds that importance today, it still influences how we process everyday interactions. But what exactly makes negative comments so difficult to forget? What makes them outshine positive comments so heavily? To better understand why negative comments stick with us longer, we first need to understand negativity bias. This psychological phenomenon describes our tendency to pay more attention to negative information than positive information. Research shows that negative experiences often influence emotions, judgments, and behavior more strongly than positive experiences (Baumeister et al., 2001). Well, why does this happen? One possible explanation comes from evolutionary history. In the past, recognizing a possible threat was necessary for survival, while recognizing positive experiences could provide opportunities for reward. This can lead to failing to recognize a dangerous situation and can result in consequences such as injury and death. As a result, paying closer attention to negative information may have provided an evolutionary advantage. Although we no longer face many of the same threats as our ancestors, this tendency still influences our everyday lives. For example, imagine one of our ancestors searching for food in the wilderness. Finding a food source would help, but failing to recognize a nearby predator could be life-threatening. As a result, paying more attention to potential dangers than to positive opportunities may have increased their chances of survival. Fewer people need to worry about this now, so our minds use this innate tendency for less important events, like getting a question wrong on an exam or someone saying they didn't like your presentation. Instead of focusing solely on physical dangers, we may pay more attention to criticism, rejection, or other negative social experiences. Negativity bias helps explain why criticism captures our attention, but what makes these comments so difficult to forget? Part of the answer comes from how our brains process and store emotional memories. The research suggests that negative emotions can strengthen our memory for certain details of an experience, making some negative events particularly memorable (Kensinger, 2007). Two important brain regions involved in this process are the amygdala and hippocampus. The amygdala processes emotionally significant information, while the hippocampus helps form and consolidate memories. Research shows that interactions between these regions can shape how we remember emotional experiences (Phelps, 2004). However, our brains are not the only reason negative comments stick with us. We may also find ourselves repeatedly thinking about criticism, replaying conversations, or wondering what we could have done differently. This process, known as rumination, can prolong negative emotions and make it harder to move past an experience (Nolen-Hoeksema et al., 2008). Another reason negative comments can affect us so strongly is our need for social acceptance. Humans naturally want to feel accepted and valued by those around us, so when someone criticizes or rejects us, it can threaten our sense of belonging and self-esteem (Williams, 2009). Interestingly, research has found that social rejection is associated with activity in brain regions involved in emotional distress. In one study, Eisenberger et al. (2003) examined how the brain responds to social exclusion and found that being excluded activated regions associated with distress. These findings help explain why rejection can feel so emotionally painful, even when there is no physical harm. This is especially relevant today, as social media has made it easier than ever to receive feedback from others. Whether it is a negative comment on a post or criticism from someone we care about, these experiences can leave us questioning how others perceive us. Although negativity bias is a common psychological tendency, we can change how we respond to negative feedback. One important step is recognizing when we are focusing too heavily on criticism. Research suggests that repeatedly thinking about negative experiences, known as rumination, can prolong emotional distress (Nolen-Hoeksema et al., 2008). Another strategy involves changing how we interpret negative experiences. Instead of assuming one critical comment reflects how everyone feels about us, we can consider the broader context. This process, known as cognitive reappraisal, is an emotion regulation strategy that involves changing how we think about an experience to influence our emotional response (Gross, 1998). Finally, give positive feedback the attention it deserves. While constructive criticism can help us learn and improve, one negative comment should not automatically outweigh several positive ones. By becoming more aware of how we process feedback, we can begin to develop a more balanced perspective on our experiences. The next time you catch yourself focusing on one negative comment amid a sea of positive ones, remind yourself that psychology explains why this happens. Negativity bias, emotional memory, and our need for social acceptance all influence how we process criticism. Although negative feedback is essential for improvement and learning, remember that criticism doesn't show the full picture. By acknowledging how we respond to negative experiences, we can start giving positive feedback the attention it deserves. Negative comments are no more justified than positive ones, just because people think about them for longer. Sources: Baumeister, R. F., Bratslavsky, E., Finkenauer, C., & Vohs, K. D. (2001). Bad is stronger than good. Review of General Psychology, 5(4), 323–370. https://doi.org/10.1037/1089-2680.5.4.323 Eisenberger, N. I., Lieberman, M. D., & Williams, K. D. (2003). Does rejection hurt? An fMRI study of social exclusion. Science, 302(5643), 290–292. https://doi.org/10.1126/science.1089134 Gross, J. J. (1998). The emerging field of emotion regulation: An integrative review. Review of General Psychology, 2(3), 271–299. https://doi.org/10.1037/1089-2680.2.3.271 Kensinger, E. A. (2007). Negative emotion enhances memory accuracy: Behavioral and neuroimaging evidence. Current Directions in Psychological Science, 16(4), 213–218. https://doi.org/10.1111/j.1467-8721.2007.00506.x Nolen-Hoeksema, S., Wisco, B. E., & Lyubomirsky, S. (2008). Rethinking rumination. Perspectives on Psychological Science, 3(5), 400–424. https://doi.org/10.1111/j.1745-6924.2008.00088.x Phelps, E. A. (2004). Human emotion and memory: Interactions of the amygdala and hippocampal complex. Current Opinion in Neurobiology, 14(2), 198–202. https://doi.org/10.1016/j.conb.2004.03.015 Williams, K. D. (2009). Ostracism: A temporal need-threat model. In M. P. Zanna (Ed.), Advances in experimental social psychology (Vol. 41, pp. 275–314). Academic Press. https://doi.org/10.1016/S0065-2601(08)00406-1

Written by: Nitya Thakur Ask someone what loneliness feels like and they will usually describe an absence: an empty apartment, a phone that doesn't buzz, a Friday night with nowhere to be. What they are less likely to describe is what loneliness is doing, quietly in the background, to the way their brain processes the world. Loneliness is not simply the felt experience of missing people. It is a measurable neurobiological state, one that changes how the brain allocates attention, how it interprets ambiguous social cues, how hard its stress-response system works, and even how its architecture is organized at rest. And, somewhat cruelly, it is a state that tends to reinforce itself. That last part is the reason this topic is worth taking seriously beyond the obvious "loneliness is bad for you" framing everyone already accepts. The interesting, slightly uncomfortable finding across decades of research is that loneliness does not just make people want connection more. It is also linked, in specific and identifiable ways, to a brain that struggles more at successfully pursuing it. Understanding why requires understanding loneliness the way neuroscience increasingly does: not as an emotion to be soothed, but as an alarm system, doing exactly what it evolved to do, in a modern environment it was never built for. Why Loneliness Exists At All: The Evolutionary Case To understand what the lonely brain is doing, it helps to start with why it does it. The most influential framework here comes from psychologist John Cacioppo and his collaborator Louise Hawkley, whose Evolutionary Theory of Loneliness (ETL) reframes loneliness as something closer to hunger or thirst than to a mood. Cacioppo and Hawkley argue that because humans are a fundamentally social species who depend on a secure social environment to survive and thrive, the perception of social isolation triggers heightened vigilance for threat and a stronger drive to reconnect (Hawkley & Cacioppo, 2010). The logic tracks evolutionarily: for most of human history, being cut off from the group was not just emotionally unpleasant; it was dangerous. No group meant no shared vigilance against predators, no shared resources, and no collective defense. So a brain that treated social disconnection as an urgent, threat-adjacent signal, rather than a neutral fact, would have had a survival edge. Loneliness, in this view, is doing its job when it makes you uncomfortable. Discomfort is the point. It is supposed to motivate you to go fix the problem. Here is where it gets more interesting, and where the theory earns its place in a literature review rather than a self-help book. Cacioppo and colleagues argue that the same evolutionary logic that makes loneliness aversive also makes it biased. Because the perceived cost of missing a real social threat was historically higher than the cost of missing a social opportunity, the theory holds that lonely individuals develop an implicit hypervigilance to negative social cues, cues that get coded as possible signs of rejection or exclusion, even when they are ambiguous or entirely benign (Cacioppo & Cacioppo, 2018). A slightly delayed text reply. A flat tone in an email. A friend who seems distracted during a conversation. For someone who isn't lonely, these register as noise. For someone who is chronically lonely, the brain is primed to treat them as a signal. This is not a metaphor. It has been tested directly. In an eye-tracking study of eighty-five young adults, participants in the upper quartile of loneliness showed measurable visual vigilance toward socially threatening information within two seconds of its appearing on screen, a bias that tracked with loneliness scores in a quadratic pattern rather than a simple straight line (Bangee et al., 2014). The lonely brain, quite literally, looks toward the threat, and it does so fast. Figure 1. The self-reinforcing spiral proposed by the Evolutionary Theory of Loneliness: perceived social isolation motivates a drive to reconnect, which triggers hypervigilance to social threats, which biases attention, interpretation, and memory toward confirming that threat, producing more negative social displays and interactions, which in turn are behaviourally confirmed by others, deepening the isolation that started the cycle. And this is the trap. The theory proposes a self-reinforcing loop: hypervigilance for social threat makes ambiguous interactions feel more dangerous, which produces avoidant or defensive behavior, which can in turn elicit exactly the kind of distant or unwelcoming responses from others that confirm the original fear, deepening the loneliness that produced the vigilance in the first place (Cacioppo & Hawkley, 2009). Loneliness does not just make people want to reconnect. It can make reconnection harder, using the very mechanism designed to protect them. The Body Keeps Score: Loneliness and the Stress-Response System If hypervigilance is the cognitive signature of loneliness, its physiological signature runs through the hypothalamic–pituitary–adrenal (HPA) axis, the same stress-response system that shows up repeatedly in research on early attachment and emotion dysregulation. This is not a coincidence. A brain treating social disconnection as a threat should, in theory, recruit the same biological machinery it uses for any other threat. The data broadly support this. Lonely adolescents and adults show an elevated cortisol awakening response, the spike in cortisol that normally occurs shortly after waking, compared to their non-lonely counterparts, and lonely adults have also been found to show higher average cortisol levels across the day (Hawkley & Cacioppo, 2010). There is even a temporal, almost dose-response relationship in some findings: elevated loneliness on one day has been linked to a larger cortisol awakening response the following morning in older adults, suggesting the stress system is tracking loneliness in something close to real time. Where it gets messier, and worth flagging honestly rather than glossing over, is what happens when researchers try to catch this stress response in the act, during an actual social challenge in the lab. Results here have been inconsistent: some studies find no difference in cortisol reactivity between lonely and non-lonely adults after mental stress tasks or simulated social encounters, such as meeting a stranger or giving a speech. So the clearest physiological fingerprint of loneliness may be a chronic, low-grade elevation in baseline stress hormones rather than an exaggerated spike during any single social event. That distinction matters clinically. It suggests loneliness functions less like a jump scare and more like a system that never fully powers down. What the Resting Brain Looks Like When It's Lonely Everything above describes what loneliness does to other systems: attention and stress physiology. But one of the more genuinely interesting developments in this literature over the past several years is the attempt to find loneliness inside the brain's own resting architecture, using resting-state functional MRI to look at how brain regions talk to each other when a person isn't doing anything in particular. The most conceptually elegant of these findings comes from Spreng and colleagues, who analyzed structural and functional MRI data from roughly 38,700 middle-aged and older adults in the UK Biobank and linked perceived social isolation to properties of the brain's default network, the set of interconnected regions most active when the mind is turned inward: remembering, imagining, reflecting on oneself, and thinking about other people's mental states (Spreng et al., 2020). The logic the researchers themselves offered is worth sitting with: in the absence of the social interaction a person actually wants, a lonely brain may lean more heavily on internally generated substitutes, reminiscing, imagining social scenarios, mentally rehearsing conversations, all of which are exactly the cognitive functions the default network supports. Loneliness, on this account, may not be just a gap in social contact but a redirection of the very network responsible for constructing a sense of self and other, turned inward because the outward version isn't available. A finding this size and this widely cited is worth pausing on for three specific reasons. First, loneliness here was captured with a single self-report item, whether a participant reported often feeling lonely, rather than the twenty-item UCLA Loneliness Scale used in the eye-tracking study described earlier (Bangee et al., 2014); a binary self-report is a coarser instrument than a validated multi-item scale, and it isn't obvious the two are capturing identical experiences. Second, in a sample of roughly 38,700 people, a statistically significant connectivity difference can be a very small one; the study establishes direction of effect convincingly, but not necessarily clinical magnitude. Third, the design is cross-sectional: it cannot establish that loneliness caused the default-network differences observed rather than the reverse, that a particular resting-state architecture predisposed someone toward feeling isolated in the first place, or that a shared third factor, such as early-life stress, shaped both independently. None of this undercuts the finding; it just means loneliness is associated with these network differences, not shown to cause them. Figure 2. Core regions of the default mode network, shown from medial (top) and lateral (bottom) views: the medial prefrontal cortex, posterior cingulate cortex, and inferior parietal lobule. This network supports self-referential thought, autobiographical memory, and mentalizing about others. Loneliness research has repeatedly implicated this same network, rather than a separate "loneliness center," suggesting isolation is processed through machinery built for constructing the self. This connects directly to a related finding: in a sample of 942 adults, greater loneliness was associated with denser, less modular connectivity between the default network and other systems involved in attention and cognitive control, a pattern that also tracked with a diminished sense of meaning in life (Mwilambwe-Tshilobo et al., 2019). Less modularity is a subtle finding but a meaningful one; brain networks generally function best when they are specialized and somewhat separable, and a blurring of those boundaries has been linked elsewhere to less efficient cognitive processing. Separately, a resting-state study of 1,336 young adults found that loneliness was positively associated with functional connectivity between the inferior frontal gyrus and regions involved in visual attention, including the supplementary motor area, precentral gyrus, and superior parietal lobule, a pattern the researchers interpreted as consistent with the broader hypervigilance account: stronger connectivity in visual-attention circuitry lining up neatly with a brain that has been theorized, for over a decade, to be scanning more intently for social threat (Brilliant T. et al., 2022). It is a rare case where a resting-state connectivity finding and a decades-old behavioral theory actually converge on the same story. There is also a structural angle, older but foundational: differences in loneliness have been linked to measurable differences in the volume of brain regions tied to social perception and, separately in older adults, to variation in regions supporting cognitive processing and emotion regulation, even after accounting for how large a person's actual social network is (Kanai et al., 2012; Düzel et al., 2019). That last detail matters: it means loneliness is doing something to the brain that isn't fully explained by simply having fewer people around. It is tracking the subjective experience, not just the objective headcount. It's worth noting that every study discussed so far, Spreng et al.'s, is cross-sectional, so the same caution about direction of effect applies across the board: none of them can establish that loneliness caused the patterns observed rather than the reverse. above, cross-sectional studies, so the same caution about direction of effect applies across all four: none of them can establish that loneliness caused the patterns observed rather than the reverse. And because this field has increasingly asked not just "Where does loneliness show up?" But "can we predict it?" Feng and colleagues used connectome-based predictive modeling, an approach that scans whole-brain connectivity patterns without preselecting a region of interest, to individually predict loneliness scores from resting-state connectivity data alone (Feng et al., 2019). This matters methodologically because it sidesteps a common criticism of neuroimaging research on emotional states, that researchers go looking in a handful of favorite regions and inevitably find something. A connectome-wide, unbiased approach that can still predict loneliness is a stronger kind of evidence. A Complication Worth Keeping: Age Changes the Picture No literature review is honest if it only reports the studies that lines up neatly, so here is one that didn't. A study using the Lifespan Human Connectome Project Aging dataset applied graph theory, a method for quantifying how efficiently and modularly a brain network is organized, to a wide age range of adults and found no overall main effect of loneliness on network organization once age and demographic factors were accounted for (Chen & Canli, 2026). What it did find was an interaction: loneliness related differently to the local interconnectivity of the default and frontoparietal networks depending on a person's age, while older age on its own was associated with lower functional connectivity, reduced global efficiency, and less modular network organization overall. The study also reconfirmed a demographic pattern seen elsewhere: higher loneliness was associated with younger age, being male, unmarried, and living alone, which complicates the popular finding of loneliness as primarily an older person's condition. This is a useful finding to sit with rather than smooth over, because it suggests the "lonely brain" is not one fixed signature. A twenty-two-year-old's loneliness, likely more situational, tied to a life transition, a new cityor a breakup, may look neurally different from a chronic decades-long loneliness in a seventy-year-old. The field is still working out what those differences actually are. Why This Matters Beyond the Scan The clinical stakes here are not subtle. Loneliness has been linked to poorer psychological well-being and elevated risk of mental disorders across the lifespan, and in older adults specifically, accelerated cognitive decline and heightened risk of dementia and Alzheimer's disease (Hawkley & Cacioppo, 2010). Given everything above, that is no longer a surprising correlation. A stress system running slightly hot around the clock, an attention system biased toward scanning for rejection, and a default network increasingly oriented inward rather than outward is not a recipe for good long-term brain health under anyone's model. But the same framework that explains why loneliness is dangerous also points toward why it is not fixed, and here the intervention literature actually backs up the theory rather than just gesturing at hope. A meta-analysis pooling twenty randomized controlled trials of loneliness-reduction interventions, sorted into four strategies: improving social skills, enhancing social support, increasing opportunities for social contact, and addressing maladaptive social cognition, found that interventions targeting social cognition directly were markedly more effective than the other three approaches, which produced comparatively small effects on their own (Masi et al., 2011). In other words: simply engineering more chances for contact was, on its own, one of the weaker levers available. What worked was intervening on the biased perception itself. It is worth being precise about what this does and doesn't show: the meta-analysis demonstrates that interventions targeting maladaptive social cognition broadly outperform other strategies, not that they specifically target the threat-detection or hypervigilance mechanism ETL proposes. Cognitive distortions about loneliness, such as overgeneralized beliefs that others are unavailable or uninterested, are a broader category than hypervigilance to social threat cues specifically, so the finding is consistent with the general shape of the ETL account without confirming its precise proposed mechanism. Even with that caveat, it reframes the goal of intervention. It is not primarily about forcing more interaction. It is about addressing the biased social cognition that, however understandably, learned to see connection as a risk. The brain that grew hypervigilant in the service of self-protection can, under the right conditions, learn a different read of the room. That is, in the end, the same quiet thesis running under all of this research: the systems shaped by disconnection are not sentenced to stay that way. References Bangee, M., Harris, R. A., Bridges, N., Rotenberg, K. J., & Qualter, P. (2014). Loneliness and attention to social threat in young adults: Findings from an eye tracker study. Personality and Individual Differences, 63, 16–23. https://doi.org/10.1016/j.paid.2014.01.039 Brilliant T., D., Takeuchi, H., Nouchi, R., Yokoyama, R., Kotozaki, Y., Nakagawa, S., Hanawa, S., Sekiguchi, A., Ikeda, S., Sakaki, K., Kawata, K. H. dos S., Nozawa, T., Yokota, S., Magistro, D., & Kawashima, R. (2022). Loneliness inside of the brain: Evidence from a large dataset of resting-state fMRI in young adults. Scientific Reports, 12, 7856. https://doi.org/10.1038/s41598-022-11724-5 Cacioppo, S., & Cacioppo, J. T. (2018). Loneliness in the modern age: An evolutionary theory of loneliness (ETL). Advances in Experimental Social Psychology, 58, 127–197. https://doi.org/10.1016/bs.aesp.2018.03.003 Cacioppo, J. T., & Hawkley, L. C. (2009). Perceived social isolation and cognition. Trends in Cognitive Sciences, 13(10), 447–454. https://doi.org/10.1016/j.tics.2009.06.005 Chen, Y.-W., & Canli, T. (2026). Social disconnection in the brain: Loneliness and age across networks using graph theory. Oxford Open Neuroscience. https://doi.org/10.64898/2026.02.03.703621 Düzel, S., Drewelies, J., Gerstorf, D., Demuth, I., Steinhagen-Thiessen, E., Lindenberger, U., & Kühn, S. (2019). Structural brain correlates of loneliness among older adults. Scientific Reports, 9, 13569. https://doi.org/10.1038/s41598-019-49888-2 Feng, C., Wang, L., Li, T., & Xu, P. (2019). Connectome-based individualized prediction of loneliness. Social Cognitive and Affective Neuroscience, 14(4), 353–365. https://doi.org/10.1093/scan/nsz020 Hawkley, L. C., & Cacioppo, J. T. (2010). Loneliness matters: A theoretical and empirical review of consequences and mechanisms. Annals of Behavioral Medicine, 40(2), 218–227. https://doi.org/10.1007/s12160-010-9210-8 Kanai, R., Bahrami, B., Duchaine, B., Janik, A., Banissy, M. J., & Rees, G. (2012). Brain structure links loneliness to social perception. Current Biology, 22(20), 1975–1979. https://doi.org/10.1016/j.cub.2012.08.045 Masi, C. M., Chen, H.-Y., Hawkley, L. C., & Cacioppo, J. T. (2011). A meta-analysis of interventions to reduce loneliness. Personality and Social Psychology Review, 15(3), 219–266. https://doi.org/10.1177/1088868310377394 Mwilambwe-Tshilobo, L., Ge, T., Chong, M., Ferguson, M. A., Misic, B., Burrow, A. L., Leahy, R. M., & Spreng, R. N. (2019). Loneliness and meaning in life are reflected in the intrinsic network architecture of the brain. Social Cognitive and Affective Neuroscience, 14(4), 423–433. https://doi.org/10.1093/scan/nsz021 Spreng, R. N., Dimas, E., Mwilambwe-Tshilobo, L., Dagher, A., Koellinger, P., Nave, G., Ong, A., Kernbach, J. M., Wiecki, T. V., & Ge, T. (2020). The default network of the human brain is associated with perceived social isolation. Nature Communications, 11, 6393. https://doi.org/10.1038/s41467-020-20039-w

Autumn Augustus Most people assume that confessions are the most reliable form of evidence. It feels intuitive to believe that only guilty people would admit to crimes they didn't commit. However, decades of psychological research challenge this assumption. Innocent people confess far more often than the public realizes, and these confessions are not random or irrational. They emerge from identifiable psychological processes that occur under specific conditions. False confessions represent a serious problem within the criminal justice system. They have contributed to wrongful convictions, contaminated investigations, and long-term harm to individuals who were never guilty. Psychology offers critical insight into why this happens by examining how stress, cognition, memory, and authority interact during police interrogations. Understanding false confessions requires moving beyond the idea that confessions reflect truth. Instead, they must be understood as behavioral responses shaped by pressure, vulnerability, and situational control. What Is a False Confession? Psychological research has identified three primary types of false confessions, each rooted in different motivations and mental processes. Voluntary false confessions occur when individuals admit to crimes they did not commit without direct police coercion. These confessions may arise from psychological distress, a desire for attention, or an attempt to protect someone else. Although they seem puzzling, they often reflect underlying emotional or cognitive struggles rather than guilt. Coerced compliant confessions occur when individuals knowingly confess to crimes they did not commit to escape an intolerable interrogation. Prolonged questioning, isolation, and intense psychological pressure can lead suspects to view confession as the fastest way to end the situation, even if they understand the long-term consequences. Coerced internalized confessions are the most psychologically disturbing. In these cases, individuals come to believe they may have committed the crime after being presented with false evidence or repeatedly told their memory is unreliable. Over time, distrust of memory can lead innocent people to internalize guilt despite a lack of factual involvement. Although these categories differ, they share a common thread: confessions are shaped by psychological pressure, not by truth alone. What Happens to the Mind Under Interrogation Interrogations place individuals under intense psychological strain. Stress plays a central role in impairing judgment and decision-making. Isolation, uncertainty, and repeated accusations elevate anxiety and reduce a person’s ability to think clearly. Under stress, individuals become less capable of evaluating long-term consequences and more focused on immediate relief. Fatigue further weakens resistance. Many interrogations last for hours, sometimes extending late into the night. As cognitive resources are depleted, individuals experience reduced problem-solving ability and diminished emotional regulation. At this stage, compliance becomes more likely, not because the person is guilty, but because they are exhausted. Memory distrust is another powerful mechanism. When interrogators challenge a suspect’s recollection or present false evidence, individuals may begin to question the accuracy of their own memory. Repeated doubt can destabilize a person’s sense of reality, leading them to accept narratives proposed by authority figures. Suggestibility and compliance also increase vulnerability. Some individuals are more inclined to accept misleading information or defer to authority, especially when under pressure. In these situations, confession becomes a coping response rather than a factual statement. Together, stress, fatigue, memory disruption, and suggestibility create conditions in which false confessions become psychologically understandable. Interrogation Tactics That Increase False Confessions Certain interrogation techniques are consistently associated with higher rates of false confessions. Isolation is a foundational tactic in many interrogation methods. Suspects are often placed alone in small, confined rooms for extended periods. This heightens anxiety, reinforces power imbalances, and reduces psychological resilience. Prolonged interrogations significantly increase susceptibility. Research shows that interrogations lasting more than six hours are associated with a greater likelihood of false confessions, particularly among vulnerable individuals. Minimization and maximization strategies are also widely used. Maximization involves exaggerating the seriousness of the situation, often through threats or overstated claims about evidence. This increases fear, and confessions appear inevitable. Minimization, in contrast, downplays the severity of the offense or implies leniency, creating a false sense of safety that encourages compliance. The presentation of false evidence is especially harmful. When suspects are told that incriminating evidence exists, even when it does not, they are far more likely to confess. This tactic can push individuals toward both compliant and internalized false confessions by undermining trust in their own memory. These methods rely on psychological manipulation rather than factual investigation, raising serious ethical and scientific concerns. Who Is Most Vulnerable? While anyone can be affected by interrogation pressure, certain populations face heightened risk. Adolescents are particularly vulnerable due to ongoing brain development. They tend to prioritize immediate relief over long-term consequences and are more sensitive to authority figures. This makes them more likely to confess simply to end discomfort. Individuals with intellectual disabilities or cognitive impairments may struggle to fully understand their rights or the implications of confession. They are also more likely to comply with authority figures and accept misleading information. Mental health conditions such as anxiety, depression, trauma, or psychosis further increase vulnerability. These conditions can impair concentration, heighten fear, and reduce emotional regulation, making individuals more susceptible to coercive pressure. Recognizing vulnerability is essential to prevent false confessions and to protect individuals less equipped to withstand psychological manipulation. Why False Confessions Are So Dangerous Once a confession is obtained, it fundamentally alters the course of an investigation. Confessions create confirmation bias, leading law enforcement to interpret ambiguous evidence as proof of guilt. Alternative suspects may be ignored, and contradictory evidence may be dismissed. False confessions also influence legal outcomes. Many innocent individuals plead guilty after confessing because they believe they cannot overcome the weight of their own statement in court. This leads to wrongful convictions that often never reach trial. The consequences extend beyond the individual. False confessions undermine public trust in the justice system and allow actual perpetrators to remain free. What Psychology Says We Should Change Psychological science has driven calls for reform aimed at reducing false confessions. Mandatory recording of interrogations creates transparency and allows courts to evaluate the conditions under which confessions are obtained. This provides critical context that written statements alone cannot capture. Expert testimony from psychologists helps jurors understand how stress, fatigue, memory distortion, and coercive tactics influence behavior. Without this context, confessions are often misinterpreted as voluntary and reliable. Limiting the length of interrogation and restricting deceptive practices can reduce cognitive exhaustion and emotional overwhelm. Alternative interrogation models, such as the PEACE model, emphasize information gathering rather than confrontation and have shown promise in reducing coercion. These reforms reflect a growing recognition that justice must be informed by psychological science. Final Thoughts False confessions do not occur because innocent people are irrational or dishonest. They happen because the human mind has limits. Under sufficient pressure, stress, and manipulation, cognition can break down in predictable ways. Psychology reveals that confession is not a simple reflection of truth. It is a behavior shaped by environment, vulnerability, and authority. Recognizing this reality is essential for creating a justice system that values accuracy, fairness, and human dignity. Understanding false confessions is not about excusing crime. It is about ensuring that truth is not lost when the mind is pushed beyond its breaking point. References Conti, R. P. (1999). The psychology of false confessions. The Journal of Credibility Assessment and Witness Psychology, 2(1), 14–36. Gudjonsson, G. H. (2018). The psychology of false confessions: Forty years of science and practice. John Wiley & Sons. Houston, K. A., Meissner, C. A., & Evans, J. R. (2014). Psychological processes underlying true and false confessions. In R. Bull (Ed.), Investigative interviewing (pp. 19–34). Springer. Kassin, S. M. (2014). False confessions: Causes, consequences, and implications for reform. Policy Insights from the Behavioral and Brain Sciences, 1(1), 112–121. https://doi.org/10.1177/2372732214548678 Kassin, S. M. (2015). The social psychology of false confessions. Social Issues and Policy Review, 9(1), 25–51. https://doi.org/10.1111/sipr.12009 Kassin, S. M. (2017). False confessions. Wiley Interdisciplinary Reviews: Cognitive Science, 8(6), e1439. https://doi.org/10.1002/wcs.1439 Leo, R. A. (2009). False confessions: Causes, consequences, and implications. Journal of the American Academy of Psychiatry and the Law, 37(3), 332–343. Ofshe, R. J., & Leo, R. A. (1997). The social psychology of police interrogation: The theory and classification of true and false confessions. Studies in Law, Politics, and Society, 16, 189–251.

By Kayden Costantino One of the largest areas of research in social psychology is intergroup relations, where researchers aim to build theory and develop interventions to improve the ways that different groups of people interact in a diverse world. Within this area, researchers have taken various perspectives to better understand group interactions and attitude formation. One approach that scientists interested in social cognition take is to study “mentalizing.” Mentalizing, defined as “the ability to attribute mental states (e.g., knowledge, intentions, emotions, perception) to self and others,” has historically been used to study differences in clinical populations (i.e., individuals with autism spectrum disorder and anxiety disorders). More recently, however, mentalizing has helped psychologists understand how individuals attribute mental states and human qualities to others (Quesque et al., 2024). With many questions still to be addressed in the field, mentalizing provides a compelling route for examining the cognitive mechanisms behind assumptions about outgroup members and the prejudice or empathy directed toward them. Defining and Measuring Mentalizing Mentalizing is a topic that has been around for many years in cognitive science, but it has gone by many names over time. Quesque et al. (2024) acknowledge the lack of clarity in the terms used to describe this process, noting that mentalizing has been referred to as theory of mind, mental state attribution, cognitive empathy, and more. This lack of consistency has burdened the progress of the research area. Without concrete terms and definitions, it becomes difficult to build upon existing knowledge, especially for early-career researchers (Quesque et al., 2024). Nonetheless, researchers who use the term “mentalizing” to capture this process seem to agree that they are describing the process of thinking about the mental states of others, which is a core ability for building human connection and fostering effective social interactions. Another issue that researchers encounter when they set out to study mentalizing is that the process itself occurs in the perceiver’s brain and is entirely invisible. Still, cognitive researchers have thought outside the box and developed creative solutions for creating ecologically valid mentalizing tasks. Measures such as the faux pas task (Baron-Cohen et al., 1997) and the Strange Stories task (Happé, 1994) assess mentalizing through items that require respondents to take another person’s perspective and infer why they did or said something, indirectly tapping into how the respondent perceives that person’s thoughts and feelings. Other tasks use visual stimuli, such as the Movie for the Assessment of Social Cognition (Dziobek et al., 2006), while others assess the ability to represent another person’s visual experience (Piaget & Inhelder, 1956). Importantly, not all mentalizing tasks are created equal. Successful performance on some mentalizing tasks can be explained by more parsimonious processes, such as self-projection or emotional ascription (Quesque & Rossetti, 2020). With these considerations in mind, and following guidelines for creating ecologically valid mentalizing tasks, researchers should continue experimenting with new ways to tap into this phenomenon in psychological research. How Mentalizing Relates to Group Processes One important consideration, as mentioned above, is that when people mentalize about others, they may rely heavily on self-projection (Todd & Tamir, 2024). While this concern is relevant for some applications, it may be less problematic when studying how people mentalize about outgroup members. Individuals appear to rely less on self-information when a target is perceived as less similar to them, such as when the target is perceived as belonging to a different group (Todd & Tamir, 2024). Without the influence of self-projection or related response biases, measures of mentalizing about outgroups may therefore capture information more directly related to the outgroup member’s attributes or the perceiver’s implicit attitudes toward them. This makes mentalizing especially compelling for understanding outgroup perception. Additionally, previous research suggests that individuals mentalize less about people from other groups than about members of their own groups (Gönültaş et al., 2020), even when those groups are completely arbitrary and artificially created (McClung & Stephen, 2000). Other experiments show that when mental states are attributed to groups of people, groups perceived as more similar in composition are more likely to be attributed a “group mind,” while individuals within those groups are attributed less agency or intentionality (Waytz & Young, 2012). These findings have implications for how perceivers attribute mental states to outgroups, especially to members who violate expected stereotypes, potentially leading differential perception compared to prototypical members of their groups. Future Directions for Mentalizing So much is still unknown about mentalizing, especially in the context of improving intergroup relations and reducing prejudice. One area that researchers should focus on is identifying the factors that increase people’s tendency to mentalize, with the expectation that greater mentalizing during social interactions may help people better understand the perspectives, emotions, and intentions of others. For example, Tiv et al. (2021) found that being in a linguistically diverse environment is associated with greater mentalizing. Similar work could build on this finding by examining how exposure to diverse social environments influences mentalizing about people from different groups. Understanding when and why people engage in mentalizing may ultimately help researchers identify ways to encourage more thoughtful and nuanced perceptions of others. The future of mentalizing research is still unclear, but its ability to illuminate otherwise invisible processes of person perception and the attribution of human qualities makes it a productive area of focus for social scientists. Mentalizing can help us understand not only how people navigate everyday social interactions, but also how they think about groups and categories of people on a larger scale. In doing so, it may provide insight into the cognitive processes through which prejudice, stereotyping, and dehumanization emerge. As the field continues to develop, researchers should look beyond the traditional use of mentalizing in clinical assessment and consider how it can help us understand intergroup relations. For everyone else, mentalizing offers a useful way to reflect on our own social perceptions: How much do we think about the minds of people who are similar to us, and how does that change when someone belongs to a group different from our own? While mentalizing alone is unlikely to provide a simple solution to intergroup prejudice, understanding when and how we engage in it may be an important step toward understanding how people of all backgrounds perceive and ultimately relate to one another. Works Cited Baron-Cohen, S., Jolliffe, T., Mortimore, C., & Robertson, M. (1997). Another advanced test of theory of mind: Evidence from very high functioning adults with autism or Asperger syndrome. Journal of Child Psychology and Psychiatry, 38, 813–822. Dziobek, I., Fleck, S., Kalbe, E., Rogers, K., Hassenstab, J., Brand, M., . . . Convit, A. (2006). Introducing MASC: A movie for the assessment of social cognition. Journal of Autism and Developmental Disorders, 36, 623–636. Gönültaş, S., Selcuk, B., Slaughter, V., Hunter, J., & Ruffman, T. (2020). The Capricious Nature of Theory of Mind: Does Mental State Understanding Depend on the Characteristics of the Target? Child Development, 91(2), e280–e298. https://doi.org/10.1111/cdev.13223 Happé, F. G. (1994). An advanced test of theory of mind: Understanding of story characters’ thoughts and feelings by able autistic, mentally handicapped, and normal children and adults. Journal of Autism and Developmental Disorders, 24, 129–154. McClung, J., & Stephen, R. (2000). Representing other minds: Mental state reference is moderated by group membership. Journal of Experimental Social Psychology, 76, 385–392. Piaget, J., & Inhelder, B. (1956). The child’s conception of space. London, England: Routledge & Kegan Paul. Quesque, F., Apperly, I., Baillargeon, R., Baron-Cohen, S., Becchio, C., Bekkering, H., Bernstein, D., Bertoux, M., Bird, G., Bukowski, H., Burgmer, P., Carruthers, P., Catmur, C., Dziobek, I., Epley, N., Erle, T. M., Frith, C., Frith, U., Galang, C. M., Gallese, V., … Brass, M. (2024). Defining key concepts for mental state attribution. Communications psychology, 2(1), 29. https://doi.org/10.1038/s44271-024-00077-6 Quesque, F., & Rossetti, Y. (2020). What Do Theory-of-Mind Tasks Actually Measure? Theory and Practice. Perspectives on Psychological Science, 15(2), 384–396. https://doi.org/10.1177/1745691619896607 Tiv, M., O’Regan, E., & Titone, D. (2021). In a bilingual state of mind: Investigating the continuous relationship between bilingual language experience and mentalizing. Bilingualism: Language and Cognition, 24, 918–931. https://doi.org/10.1017/S1366728921000225 Todd, A., & Tamir, D. (2024). Factors that amplify and attenuate egocentric mentalizing. Nature. https://doi.org/10.1038/s44159-024-00277-1 Waytz, A., & Young, L. (2012). The Group-Member Mind Trade-Off: Attributing Mind to Groups Versus Group Members. Psychological Science, 23(1), 77–85. https://doi.org/10.1177/0956797611423546

Author: Alyssa Lee Editor: Martha W Introduction Parenting is filled with facing many decisions and choices, each having impacts on their child’s development. Parents want to make sure their choices are aimed at optimising this growth. One of the most crucial decisions a parent must make, particularly those who have ‘summer born’ children (April-August), is deciding when to enter their child in formal education. In the UK, most children start Reception after their fourth birthday. However, this is not compulsory and parents can decide to defer their child’s entry until January or April in the same academic year, to send them part time, or to skip Reception entirely and have their child start in Year 1. For ‘summer born’ children, parents have the additional option to request their child start Reception at age five, effectively delaying their progression by a year (UK Government). In the wake of all these options, what does child psychology say about the developmental effects of delaying school entry? Emotional Development, Mental Health and Self-Regulation Research consistently links delayed school entry to higher emotional and self-regulation skills. A Danish study using a large-scale survey of Danish children (the Danish National Birth Cohort or DNBC) found that delaying entry into kindergarten by one year (age six) significantly improved the child’s mental health. Specifically, the researchers found a reduction in inattention and hyperactivity at age seven. These benefits were still exhibited at a later age (eleven years old) and noted that the presence of such self-regulation in children strongly correlates to an improvement in academic performance (Dee & Sievertsen, 2018). Consistent to these findings, a longitudinal study by Simpson et al. (2026) investigated the impact of deferring school entry on executive function and self-regulation. By comparing first-year students to those who remained in preschool for an additional year, they found the deferred group exhibited better baseline verbal working memory and inhibitory control, alongside improved parent-reported socio-emotional outcomes. However, these differences partially diminished by the end of the academic year, suggesting the initial developmental advantage may level out over time. Overall, research has established the benefits of delaying school-entry by a year for cognitive and emotional development. Why might this be? Both studies postulate that it may provide children more time to develop self-regulatory and emotional understanding in informal settings. Unstructured play is vital for the healthy development of children; it allows children to express their feelings, problem-solve, get creative and navigate social scenarios in a safe and engaging environment. By delaying school-entry, children are given more time to play, helping them master behavioural regulation before facing the demands of a formal classroom (Nijhof et al., 2018). Academic Performance Additionally, delaying school-entry positively correlates with early academic advantages. Educational tracking data and academic assessment comparisons has shown that older entrants initially perform better in math and language assessments (Attar & Cohen-Zada, 2018; Dhuey et al., 2019) and demonstrate a higher competency in receptive vocabulary (Görlitz et al., 2022). However, later in education (high school), this academic gap closes, indicating school starting age has little long-term effect on standard test scores (Dhuey et al., 2019; Görlitz et al., 2022). Conversely, some research suggests that delaying school entry has no significant impact on certain academic milestones. For instance, Sucena et al. (2020) demonstrated that a delayed start does not affect a child's success in reading acquisition. Instead, socioeconomic status is a much stronger predictor of success. They found that children from higher socioeconomic backgrounds consistently performed better in phonemic awareness and letter-sound knowledge, highlighting the crucial role of early educational interventions in closing this socioeconomic gap. Conclusions and Limitations of The Research Current research suggests delaying school-entry offers clear benefits, though the duration and strength of the research varies. Emotional and social advantages, such as improved self-regulation, tend to have longer-lasting effects, while the initial academic leverage diminishes early on (~1 year) or may not have even existed. Despite the advancements in the child development field this research has provided, it is important to note their limitations. There is a lack of longitudinal studies tracking children from school entry through adulthood, making it difficult to fully understand the lifelong impacts of delayed entry. Furthermore, no two children are identical; what benefits one child may not suit another and their socioeconomic background may play a larger factor on their academic performance rather than the age they entered school. Ultimately, while scientific research supports giving children extra time to develop foundational skills through play, the decision cannot be made relying wholly on this information. Parents must weigh the empirical evidence against their personal circumstances and the unique needs of their child. References Attar, I., & Cohen-Zada, D. (2018). The effect of school entrance age on educational outcomes: Evidence using multiple cutoff dates and exact date of birth. Journal of Economic Behavior & Organization, 153, 38–57. https://doi.org/https://doi.org/10.1016/j.jebo.2018.06.007 Dee, T. S., & Sievertsen, H. H. (2018). The gift of time? School starting age and mental health. Health economics., 27(5), 781–802. https://doi.org/10.1002/hec.3638 Dhuey, E., Figlio, D., Karbownik, K., & Roth, J. (2019). School Starting Age and Cognitive Development. Journal of Policy Analysis and Management, 38(3), 538–578. https://doi.org/https://doi.org/10.1002/pam.22135 Görlitz, K., Penny, M., & Tamm, M. (2022). The long-term effect of age at school entry on cognitive competencies in adulthood. Journal of Economic Behavior & Organization, 194, 91–104. https://doi.org/https://doi.org/10.1016/j.jebo.2021.12.009 Nijhof, S. L., Vinkers, C. H., van Geelen, S. M., Duijff, S. N., Achterberg, E. J. M., van der Net, J., Veltkamp, R. C., Grootenhuis, M. A., van de Putte, E. M., Hillegers, M. H. J., van der Brug, A. W., Wierenga, C. J., Benders, M. J. N. L., Engels, R. C. M. E., van der Ent, C. K., Vanderschuren, L. J. M. J., & Lesscher, H. M. B. (2018). Healthy play, better coping: The importance of play for the development of children in health and disease. Neuroscience & Biobehavioral Reviews, 95, 421–429. https://doi.org/https://doi.org/10.1016/j.neubiorev.2018.09.024 Simpson, H. G., Chevalier, N., & Booth, J. N. (2026). School Entry Deferral: Effects of Age and Schooling on Children's Longitudinal Executive Function Development and Parent-Reported Socio-Emotional and Regulatory Outcomes. Developmental Science, 29(5), e70257. https://doi.org/https://doi.org/10.1111/desc.70257 Sucena, A., Marques, C., Silva, A. F., Garrido, C., & Pimenta, R. (2020). Is Redshirting Beneficial for Reading Acquisition Success? Front Psychol, 11, 1165. https://doi.org/10.3389/fpsyg.2020.01165 UK Government. School admissions: School starting age. Retrieved 18 August from https://www.gov.uk/schools-admissions/school-starting-age

Author: Malleeka Suy Editor: Tarati Papisetty It’s a mermaid! It’s a fish! It’s a…technique that maps thousands of gene transcripts within a tissue sample?! Okay, so maybe this doesn’t have anything to do with sealife or serene pastimes, but it does have something to do with how we can “map” our brains using single-cell spatial transcriptomics, a fancy-sounding method that measures gene expression while keeping the location of the cells and tissue intact. This is a lot to unpack, so let’s go MERFISH-ing! To understand how MERFISH works, we first need to understand what RNA is. Different genes are made up of different DNA sequences. When a gene undergoes transcription, a process that reads DNA, it produces strands of code called ribonucleic acid, or RNA. Quantifying RNA transcripts in a specific cell or region allows researchers to characterize cellular activity, model diseases, and tailor treatments. RNA sequencing is a common technique used in research labs to sequence the RNA molecules in cells. The RNA is isolated from a cell or tissue sample, converted to DNA, and sequenced, allowing researchers to identify the RNA molecules present in the sample. (National Human Genome Research Institute). But what if you not only wanted to profile the RNA transcripts in a cell or tissue, but also visualize where different RNA transcripts are across regions in a larger sample? Or what if you didn’t want to destroy the sample completely? For instance, instead of focusing on a region of the brain such as the prefrontal cortex, could you profile RNA transcripts within cells across the entire brain? No technique at the time could achieve these results on such a large scale until MERFISH was introduced in 2015. At Harvard University, Dr. Xiaowei Zhuang and her lab collaborated with the biotechnology company Vizgen to develop multiplexed error-robust fluorescence in situ hybridization, or MERFISH. Zhuang’s paper introducing MERFISH, “Spatially resolved, highly multiplexed RNA profiling in single cells”, was published in Science and has been cited over 4,500 times in academic literature (Chen et. al., 2015). But what exactly is MERFISH, and why has it been so important? Surprisingly, it has nothing to do with fish. MERFISH is a method that images thousands of RNAs in individual cells. More specifically, it can visualize and count up to a billion RNA transcripts from up to 10,000 different genes in each cell, all within a 1-by-2-centimeter tissue slice (Vizgen)! This allows researchers to spatially localize transcripts with single-molecule accuracy and map gene expression and cellular composition, all to a nanometer-scale resolution. How It Works: A rough pipeline of MERFISH. a) A brain is procured, frozen, and sectioned into regions. The regions are fixed and sliced onto a slide. b) Different reagents are added to treat the sample, with varying incubation times. c) The sample is imaged using the MERSCOPE (not pictured). d) Data and imaging from the MERSCOPE are analyzed and visualized into cell-type-specific clusters, preserving their position in the brain region. Made in BioRender. MERFISH is actually an expansion on an existing spatial transcriptomic technique: single-molecule FISH, or smFISH. First, you start with a list of genes and the corresponding RNA transcripts they produce. In smFISH, fluorescent probes bind to specific RNA targets in the tissue and produce fluorescent signals. The MERSCOPE, which is the Vizgen platform used to image the sample, then counts the different fluorescent spots to quantify RNA expression. smFISH works well in counting and mapping RNA, but it can only focus on a couple of genes and RNA transcripts, limiting its spatial capabilities. MERFISH introduces three different features to smFISH to allow for more expansive and accurate counting and imaging (Vizgen). First, the probes in the staining now contain a unique barcode corresponding to a target gene. Secondly, there are multiple rounds of imaging to allow the MERSCOPE to read the barcode bit by bit. Each round determines whether a fluorescent signal is present, revealing another bit of the barcode. Third, error robust barcoding catches any errors in reading the barcode. For example, if some background fluorescence in the sample causes a reading error, the MERFISH system assigns what’s been transcribed to the nearest correct barcode (Vizgen). MERFISH-ing The Brain With billions of cells and even more transcripts produced in the brain, MERFISH has made a splash in neuroscience research. Millions of dollars have been invested in compiling data packages and brain cell atlases using MERFISH. For instance, the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) was launched by the NIH as part of an effort to deepen the understanding of the human brain and how we can treat, prevent, and cure brain disorders. Since 2014, BRAIN has awarded $2.4 billion in research awards and grants, including a $126 million grant to the Salk Institute of Biological Sciences in efforts to map the aging human brain under BICAN (Salk Institute). The BRAIN Initiative Cell Atlas Network (BICAN) is a collaborative effort of many labs and tools across the United States to create an atlas of the brains of different species, such as mice and humans (BRAIN Initiative Cell Atlas Network). Data from this effort are publicly available on the Brain Knowledge Platform for reference, analysis, and experimental use. Through this effort, a study published in Nature has imaged more than 1,100 genes across 10 million cells in the adult mouse brain using MERFISH (Zhang et. al., 2023). This comprehensive characterization of the mouse brain has allowed scientists to infer cell-cell communication and molecular interactions, such as possible ligand-receptor binding (Zhang et. al., 2023). In 2025, MERFISH revealed the molecular and cellular development of the human fetal cortex, producing an atlas comprised of over 18 million cells across eight cortical areas (Qian et. al., 2025). The study shed light on human cortical development, demonstrating how invaluable such high-resolution imaging and characterization have been for research. What a Catch! Through the engineering feat of Dr. Zhuang and her collaborators at Harvard and Vizgen, neurodevelopment, neuroepigenetics, and many other areas of neuroscience have benefited from MERFISH. Precisely characterizing cells in the brain while also preserving and visualizing where they are has proven paramount to how we understand brain formation and development. This fuels the race towards a cure for common neurological diseases and disorders such as Alzheimer’s or Huntington’s disease. By creating a “baseline” brain model, scientists can pinpoint specific cells or pathways to research, opening up avenues of discovery and innovation. What a catch! Bibliography Allen Institute for Brain Science. (n.d.). BRAIN Initiative Cell Atlas Network (BICAN). Brain Knowledge Platform. Retrieved August 25, 2026, from https://knowledge.brain-map.org/program/bican Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015). Spatially resolved, highly multiplexed RNA profiling in single cells. Science, 348(6233), aaa6090. https://doi.org/10.1126/science.aaa6090 McManus, A. (2023, October 13). Building a brain cell atlas with MERSCOPE. Vizgen. https://vizgen.com/building-a-brain-cell-atlas-with-merscope/ National Human Genome Research Institute. (2024, May 24). Ribonucleic acid (RNA) fact sheet. https://www.genome.gov/about-genomics/educational-resources/fact-sheets/ribonucleic-acid-fact-sheet National Human Genome Research Institute. (n.d.). RNA-seq (RNA sequencing). In Talking glossary of genomic and genetic terms. Retrieved August 25, 2026, from https://www.genome.gov/genetics-glossary/RNA-seq Qian, X., Coleman, K., Jiang, S., Kriz, A. J., Marciano, J. H., Luo, C., Cai, C., Manam, M. D., Caglayan, E., Lai, A., Exposito-Alonso, D., Otani, A., Ghosh, U., Shao, D. D., Andersen, R. E., Neil, J. E., Johnson, R., LeFevre, A., Hecht, J. L., … Walsh, C. A. (2025). Spatial transcriptomics reveals human cortical layer and area specification. Nature, 644(8075), 153–163. https://doi.org/10.1038/s41586-025-09010-1 Salk Institute for Biological Studies. (2022, September 22). Salk Institute to lead $126 million effort to map the aging human brain. https://www.salk.edu/news-release/salk-institute-to-lead-126-million-effort-to-map-the-aging-human-brain/ Vizgen. (n.d.-a). How MERFISH technology works. Retrieved August 25, 2026, from https://vizgen.com/resources/how-merfish-technology-works/ Vizgen. (n.d.-b). MERFISH: Spatially resolved transcriptomics. Retrieved August 25, 2026, from https://vizgen.com/technology/merfish-spatially-resolved-transcriptomics/ Wilson, S. (2023, April 25). Leading the global expansion of spatial transcriptomics. Vizgen. https://vizgen.com/vizgen-leading-the-global-expansion-of-spatial-transcriptomics/ Zhang, M., Pan, X., Jung, W., Halpern, A. R., Eichhorn, S. W., Lei, Z., Cohen, L., Smith, K. A., Tasic, B., Yao, Z., Zeng, H., & Zhuang, X. (2023). Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature, 624(7991), 343–354. https://doi.org/10.1038/s41586-023-06808-9

Written By: McKenzie McFall Edited By: Keoni Andrews Everyone on the planet needs to sleep. Outside of food and water, sleep is one of the most important things humans do to make sure we do not get exhausted and have enough energy to complete daily activities. Sleep is an incredibly important matter to humans. It is recommended that humans get at least 8 hours of sleep per night in order to do their daily functions, like work, school, hobbies, etc. But other times, some humans get less sleep for an extended amount of time, leading to drowsiness, exhaustion, and increased fatigue. Although fatigue is very common, it is a constant state of being extremely tired. Waking up becomes difficult and doing the activities you want to do, when you have the overwhelming urge to sleep. Some of the most common symptoms of fatigue are lack of motivation, having trouble concentrating, increased feelings of irritability, depressive feelings, weakened muscles and joints (Cleveland Clinic, 2023). Fatigue not only can mentally impact you, but physically impact you too. Fatigue impacts people because some individuals work in settings, where they might have to work over twelve hour periods or work at unusual times (i.e nightshift jobs). Fatigue is the body’s signal for when someone either needs to take a break or a nap. When sleep is delayed or not given at all, it affects the body’s circadian rhythm. Circadian rhythm is your body’s sleep and wake cycle, which signals the body when it is time to sleep. When the body disrupts its circadian rhythm, stress levels and fatigue increases, along with decreased concentration. Worker’s fatigue is a specific type of fatigue, which pertains to workers who work longer hours and get less consistent sleep. Worker’s fatigue increases the risk of accidents and injuries by 18% during evening shifts and 30% during night shifts in comparison to day shifts. (OSHA). Neuromuscular fatigue involves physiological processes that occur in structures, like motor cortex or muscle contractile proteins. Motor units in the body are one of the basic elements in the CNS (Central Nervous System) and muscles produce movement in the body. It comprises motor neurons in the ventral horn of the spinal cord, axons, and muscle fibers innervated by the axons. The CNS controls muscle force by modifying the activity of motor units in the muscle. Central and peripheral mechanisms of fatigue have been examined during isolated muscle contractions. Including, maximal and submaximal voluntary contractions torques. Even though some central fatigue has been reported to supraspinal mechanisms, testing of motor neuron excitability during fatiguing contractions shows slower firing rates not solely due to decreased excitatory input (Abd-Elfattah, 2015). Neurological fatigue, often referred as central or brain fatigue, is classified as a state of exhaustion originating in the CNS. It is characterized by lack of energy and inability to sustain cognitive effort. Neurological fatigue differs significantly from tiredness because it is persistent that is not relieved by a full night’s sleep. It can severely impact a person’s ability to work, relationships, and perform daily tasks. Neurological fatigue is the brain's failed ability to do an optimal performance. As it can manifest cognitive impairment, including weakened attention span, difficulties with concertation, and memory and executive functioning problems (Science Sights, 2025). Neurological fatigue is one of the most common symptoms across psychiatric and chronic conditions. There are multiple casualties to this fatigue subtype, which presents itself in numerous ways (e.g..) Brain-to-muscle fatigue means that the brain struggles to efficiently communicate with the muscles. Metabolic reasons could be at play which means the cells in the human body lack sufficient energy or when nerve signals fail to transmit properly. It results in the body feeling too heavy or weak. Central fatigue originates in changes to the brain itself. It particularly happens when chronic inflammation is present. Immune system activity produces cytokines (inflammatory chemicals) that travel into the brain. It triggers sickness symptoms such as brain fog, exhaustion, and mental withdrawal. In neurological diseases, this response becomes a chronic condition. Sometimes there could be a neurotransmitter imbalance. When dopamine and serotonin levels are disrupted, it can result in energy crashes that result in fatigue. Without dopamine, there is no drive to start or sustain an activity and without serotonin, sometimes a small task can feel extremely difficult. Areas of the brain, like the frontal lobe, hypothalamus, and basal ganglia help the body receive and sustain energy. When those areas are affected by neurological disease, the brain can not function properly (Denis, 2025). One of the main drives of neurological fatigue is neuroinflammation, which activates the brain’s immune system in response to injuries or illness. Neuroinflammation activates microglia and astrocytes, which can lead to cellular dysfunction and oxidative stress. The damage causes bioenergetic decline that limits the brain’s energy supply. Reduced energy can impair the brain’s ability to do cognitive processes. Additionally, it involves dysregulation of neurotransmitters. Dopamine pathways in the basal ganglia are implicated, which can lead to a lack of drive and feelings of exhaustion. Since dopamine is involved in reward-seeking behavior and motivation. Altered levels of serotonin that influence arousal and effort perception contribute to subjective overwhelming fatigue (Science Sights, 2025). Neurological fatigue can be one of the main debilitating symptoms of any chronic condition. But, there are some ways to manage it. Taking small breaks or breaking down tasks can help with energy conversation and decrease feelings of fatigue. Some diets, stress management, and certain medications can help address inflammation that can worsen immune overactivity that causes more fatigue. Some supplements like Coenzyme 10 help improve cellular energy production and support mitochondrial function. Building consistent sleep routines and rest time help treat sleep disorders and lessen fatigue. Fatigue can be challenging to overcome, even if a person gets sleep consistently. Sometimes individuals can not avoid it because of their occupation or socioeconomic status. But fatigue can help explain certain behaviors of chronic conditions or neurological fatigue. In turn giving additional insight on what kind of factors cause fatigue and what kind of research can give better insight on to lessen fatigue. References Abd-Elfattah, H. M., Abdelazeim, F. H., & Elshennawy, S. (2015). Physical and cognitive consequences of fatigue: A review. Journal of advanced research, 6(3), 351–358. https://doi.org/10.1016/j.jare.2015.01.011 “Fatigue.” Cleveland Clinic, https://my.clevelandclinic.org/health/symptoms/21206-fatigue. Accessed 24 Aug. 2026. Long Work Hours, Extended or Irregular Shifts, and Workers Fatigue . Occupational Safety and Health Administration. “What Causes Neurological Fatigue and How to Manage It.” ScienceInsights, 20 Nov. 2025, https://scienceinsights.org/what-causes-neurological-fatigue-and-how-to-manage-it/. Why Fatigue Is So Common in Neurological Conditions. https://www.neurahealth.co/blog/why-fatigue-is-so-common-in-neurological-conditions. Accessed 24 Aug. 2026.

Written by: Olivia Mickles Edited by: Keoni Andrews ABSTRACT Premenstrual dysphoric disorder (PMDD) is a severe disorder associated with significant psychological and functional impairment. This literature review examines current research which discusses possible biological and environmental factors of PMDD, its relationship with suicidal ideation and behavior, and available treatment approaches. Research suggests that abnormal sensitivity to seemingly normal hormonal fluctuations, neurological processes, and genetic factors may contribute to PMDD, while also highlighting a concerning association between PMDD and suicidal ideation, plans, and attempts. Of the current treatments available, selective serotonin reuptake inhibitors (SSRIs) are particularly effective in reducing symptoms, while hormonal, psychological, and other nonmedical interventions have more varied results. Overall, the literature supports PMDD as a clinically significant condition and emphasizes the existing gaps in understanding surrounding its causes, identifying suicide risk, and developing effective long-term treatments. INTRODUCTION Premenstrual dysphoric disorder is a severe and debilitating condition characterized by recurring emotional, cognitive, behavioral, and physical symptoms that onset during the luteal phase of the menstrual cycle. Though premenstrual symptoms are common, PMDD is a distinguishable disorder due to the severity of its symptoms and how they interfere with daily activity and functioning. PMDD was officially identified as a psychiatric disorder in 2013 in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), signifying an important step toward recognizing severe premenstrual symptoms as a legitimate mental health concern. This diagnosis, however, remains controversial, with some researchers arguing that PMDD pathologizes “normal” female experiences of menstruation and distress (Browne, 2014). This debate has contributed to the uncertainty surrounding the disorder and may have limited the awareness, research, and clinical attention that would be otherwise possible if the disorder was given complete recognition. Despite these controversies, growing literature suggests that PMDD is associated with clinically significant psychological distress and may be associated with elevated risk for suicidal thoughts and behaviors. This concern is especially important because the DSM-5-TR provides a quite brief discussion of suicidality in relation to PMDD, despite research suggesting significantly high rates of suicidal ideation, suicide planning, and suicide attempts among individuals with the disorder (Wikman et al., 2022; Pilver et al., 2012, & Roy et al., 2025). Understanding this relationship is imperative because PMDD symptoms occur cyclically, meaning that periods of increased psychological distress and suicide risk may correspond with specific phases of the menstrual cycle. The potential relationship between PMDD and suicidality therefore represents an important area for further clinical research and screening. The purpose of this literature review is to examine the current research surrounding PMDD, with focuses on its possible causes, relationship with suicidal ideation and behavior, and available treatment options. First, research on the etiology of PMDD is examined, including evidence concerning sensitivity to ovarian hormone fluctuations, neurological and genetic factors, and the interaction between the hypothalamic-pituitary-gonadal and hypothalamic-pituitary-adrenal axes. Second, research is reviewed which examines the relationship between PMDD and suicidal ideation, plans, and attempts, with attention to methodological strengths and limitations across studies. Finally, current treatment approaches are evaluated, including selective serotonin reuptake inhibitors (SSRIs), hormonal interventions, cognitive-behavioral therapy, and non-medical interventions. Overall, this review aims to evaluate the current understanding of PMDD and identify important gaps that may contribute to improved diagnosis, suicide-risk screening, and treatment for individuals affected by the disorder. ETIOLOGY Since the Classical Period of Ancient Greece, physicians, philosophers, and scientists have noticed a link between menstruation, behavior, and the brain. In around 450 BC, the Greek physician Hippocrates described a condition he named “agitated blood” where excess blood travelled from women’s brains to be expelled by the uterus. Then, in the 16th century, descriptions of depression, melancholia, and nervous excitement were linked to menstruation; and in the 1930s gynecologist Robert Tilden Frank wrote the first modern description of “premenstrual tension” that recognized that “normal” women experience premenstrual symptoms, but for some women those symptoms are debilitating physically and/or psychologically. Finally, in the late 20th century, late luteal phase dysphoric disorder was included in the DSM-III-R under “Proposed Diagnostic Categories in Need of Further Research”, and was later officially added to the DSM-V in 2013 under the title premenstrual dysphoric disorder (PMDD) (Epperson, 2013). This addition to the diagnostic manual is quite controversial for various reasons that will be discussed later in this review, but it is the result of centuries of documentation of a significant change in women’s behavior and emotionality before menstruation. Baller et al. (2013) used positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) scans on women with and without PMDD diagnoses while they completed the n-back task. There were three hormone conditions which include ovarian suppression by leuprolide acetate, a gonadotropin-releasing hormone (Gnrh) agonist, suppression by leuprolide and estradiol, and suppression by leuprolide progesterone. A main effect was observed in both the PET and fMRI studies where women with PMDD, under controlled hormone conditions, had higher prefrontal and dorsolateral prefrontal cortex activation than controls who underwent the same hormone changes. As a whole, this suggests that in PMDD there is abnormal working memory activation in the dorsolateral prefrontal cortex which is related to the disorder’s severity, symptoms, lower Global Assessment of Functioning scores, and early age at onset. Therefore, dysfunction in the dorsolateral prefrontal cortex could be an underlying foundation for risk of developing PMDD rather than hormone differences alone. Other studies, however, have found that estrogen still does have a significant effect on the development of PMDD. Huo et al. (2009) performed haplotype analyses of estrogen receptors alpha and beta in women with and without PMDD to examine the possible genetic sources of affective dysregulation. The findings suggest a link between a genetic variation in the estrogen receptor alpha gene (ESR1) and a higher risk for PMDD due to ESR1 impacting numerous elements of Central Nervous System (CNS) development and function, playing a large role in arousal (Garey et al., 2003), being involved in serotonin function and subsequent mood regulation (Rubinow et al., 1998), and being a hormone which individuals with the disorder display an abnormal behavioral response to even at normal levels (Schmidt et al., 1998). According to the authors, this is the first positive genetic finding surrounding PMDD. Another potential contributor to the disorder could be CNS sensitivity to reproductive hormones and genetic and psychosocial factors (i.e. stress). As previously mentioned, women with PMDD do not differ from asymptomatic women regarding ovarian hormone levels, so they may be simply experiencing an abnormal response to normal hormone levels. Hantsoo and Epperson (2015) suggest this response could be due to CNS sensitivity to fluctuations in estrogen and progesterone. Estradiol has strong effects on neurotransmitter systems involved in mood regulation, cognition, eating, sleep, and other behaviors, so the effects of it on serotonin are important to understand when studying the pathophysiology of PMDD. Symptoms of PMDD include depressed mood, food cravings, and impaired cognitive performance during the luteal phase, and all of which are cognitive-affective features that may be influenced by serotonin levels. If considering the CNS sensitivity model, it could then be suggested that women with the disorder are more specifically sensitive to the effects of estrogen on serotonin, which therefore explains the serotonin-related abnormalities in cognition and behavior during the luteal phase that dissipate when estrogen levels rise again after the luteal phase ends. Another hormone, progesterone, has levels rise during the luteal phase and then rapidly drop during menses, creating a whiplash of exposure that could also be a critical factor in the etiology of PMDD. A recent animal model found that rats in withdrawal from doses of progesterone presented with anhedonia, social withdrawal, and an increase in anxious behavior, all of which are common symptoms of PMDD. It was suggested that this fluctuation can alter GABA-A receptor function, leading to progesterone not being blocked from converting to allopregnanolone (ALLO), a neurosteroid derived from progesterone, and negative effects of progesterone surfacing (Smith et al., 2006 & Li et al., 2012). Based on this research, PMDD may therefore be better conceptualized as a disorder of CNS sensitivity to hormonal fluctuations rather than abnormal hormone production. The hypothalamic-pituitary-adrenal (HPA) axis is a communication system between the hypothalamus and pituitary and adrenal glands that is responsible for releasing hormones into the blood in response to a stress reaction in the body. The hypothalamic-pituitary-gonadal (HPG) axis, on the other hand, regulates reproductive function by releasing GnRH which leads to the production of estrogen and progesterone in females. Multiple studies have found results that suggest dysregulation of the HPA axis in women with PMDD where their baseline cortisol levels are significantly lower compared to healthy controls. Low basal cortisol can lead to symptoms such as fatigue, mood disturbances, difficulty concentrating, and an inability to tolerate minor stressors (Girdler et al., 1998), all of which are recognized as possible symptoms of PMDD. Hantsoo et al. (2023) highlighted the main finding of multiple animal studies that suggests that a relationship exists between the HPA and HPG axes and changes in progesterone and estradiol. One hypothesis of how that interaction affects those with PMDD is that lower cortisol during the luteal phase can lead to decreased inhibition of the HPA axis, further increasing cortisol levels during stress, leading to symptoms of the disorder such as impaired concentration and mood imbalances like anxiety, depression, and irritability. Hormonal contraception, a common medication taken by women for various reasons, including the treatment of PMDD, is another possible explanation for PMDD regarding changes in the HPA axis due to the combination of progestin and estrogen increasing baseline cortisol levels and subsequently decreasing one’s capacity for increases in cortisol following a stressor (Hertel et al., 2017). This concept, however, requires further research to become a confident claim. Finally, incorporating multiple aforementioned theories together, Crowley and Girdler (2014) suggested that dysregulated ovarian neuroactive steroid control of the HPA axis could be a key contributor to PMDD as neuroactive steroids regulate CNS activity and include ovarian hormones, such as allopregnanolone, as metabolites. Allopregnanolone is active at the GABA-A receptor and regulates the HPA axis in response to stress. Their results showed that individuals with PMDD had both lower basal cortisol levels and higher allopregnanolone levels than control subjects, highlighting the possibility that the interaction of the HPA and HPG axes is a key factor in PMDD pathophysiology. Aside from hormonal and neurological paths to understanding the etiology of PMDD, it is also necessary to consider environmental and genetic factors. According to Johns Hopkins Medicine (2026), PMDD is a “much more severe” form of premenstrual syndrome (PMS), and, while anyone can develop the disorder, those with a family history of PMS, PMDD, depression, postpartum depression, or other mood disorders are at higher risk, possibly due to higher genetic risk for abnormal reactions to normal hormonal changes or serotonin deficiencies. Those also suggested to be at risk are individuals with less access to education about the management and treatment of PMDD, or less access to healthcare in general, and cigarette smokers. In addition, the DSM-5-TR identifies possible environmental factors, such as stress, history of interpersonal trauma, and sociocultural beliefs regarding female sexual behavior and female gender roles, and genetic factors like the most stable premenstrual symptoms being an estimated 50% heritable. Considering family genetics, uncontrollable environmental disparities, and cultural differences can put young women at risk for developing more severe PMS symptoms and even PMDD, there is a need for a call to action to educate all people, and medical professionals, about the risk factors that can lead to the development and exacerbation of PMDD. Despite its inclusion in the DSM-5-TR, PMDD remains a heavily debated disorder within the literature. Some researchers believe in its severity as an understudied, unrepresented mental health condition, while others, like Browne (2014), see it as a socially constructed display of female distress. Browne argues that PMDD pathologizes a normal monthly biological response to the menstrual cycle, yet changes in “normal” functioning do not immediately signal a pathological concern. Even though that statement is not untrue, within the already scarce pre-existing literature on the subject, there is a troubling amount of research with significant findings that demonstrates how PMDD can lead to suicidal ideation and suicide attempts. Even within the DSM-5-TR includes a one sentence warning under the section Association With Suicidal Thoughts or Behavior that reads, “The premenstrual phase has been considered by some to be a risk period for suicide”. Browne makes a point when stating that distress can be significant without having to be pathologized and that the premenstrual phase can come with a “normal” amount of anger and distress that does not fit the criteria for a mental disorder; however, advocates of the diagnosis argue that PMDD is distinguished from average premenstrual experiences by the severity of symptoms, degree of functional impairment, and evidence suggesting changes in sensitivity to normal hormonal fluctuations. Another critique presented is that trauma and the social environment can cause biological changes that lead to monthly distress, so therefore a psychiatrist diagnosis is unreasonable. Although this point does not directly contradict the idea that trauma is a risk factor for PMDD, it reframes trauma-induced biological changes as evidence against PMDD being a discrete disorder, which reflects an alternative interpretation of the relationship between environmental influences and biological mechanisms. However, contemporary models of psychopathology recognize that environmental experiences and biological mechanisms interact, so past trauma originating the path to distress does not invalidate PMDD as a legitimate, significant condition. RELATION TO SUICIDAL IDEATION Regardless of ongoing debate surrounding how to classify PMDD, there is a growing amount of empirical literature suggesting that the disorder is associated with a significant relation to suicidal ideation, planning, and attempts. The research demonstrates that the severity and functional impairment that comes with PMDD extends beyond standard premenstrual experiences and warrants serious clinical attention. Wikman et al. (2022) aimed to gather more information on the prevalence of PMDD and to examine the correlate of suicidal ideation in the late luteal phase in women with expected and confirmed PMDD. 110 women participated in the study and, utilizing the Montgomery–Åsberg Depression Rating Scale self-rated version (MADRS-S), 75.5% reported previous drug treatment for the disorder, 31.8% reported a history of depression, and 41.8% and 18.2% reported moderate and severe luteal phase depressive symptoms. Suicidal ideation was reported by 39.1%, or 43, of the women, and higher depressive symptoms and previous psychological treatment for the disorder were positively associated with current suicidal ideation, while high ratings of self-rated health were associated with decreased risk of suicidal ideation. Regarding depressive symptoms, according to ratings on the Daily Record of Severity of Problems (DRSP) over the past two menstrual cycles, feelings of hopelessness, worthlessness, guilt, and feeling easily hurt were all significantly higher for women with PMDD and suicidal ideation than women with PMDD without suicidal ideation. A strength of this study is the real-time monitoring of women’s symptoms throughout the menstrual cycle to confirm their PMDD, which reinforces the validity of the diagnosis, and a weakness of is the limited sample size which limits generalizability and increases the chance for sampling errors to occur (Wikman et al., 2022). As a whole, these are concerning findings that suggest that, given a random population of women with PMDD, a significant proportion of them would be experiencing suicidal ideation during the late luteal phase. Though some women in this population sought psychological treatment for PMDD, there is still a lot of misunderstanding and stigma surrounding the condition that can be contributing to the lack of research and implementation of interventions in clinical settings. Meanwhile, many women are feeling extreme levels of depression and anxiety during their luteal phase that has lead some individuals to suicidal behavior. Pilver et al. (2012) examined whether PMDD status was associated with suicidal ideation, plans, and attempts, unrelated to sociodemographic factors or other comorbid psychiatric disorders. 3,965 women from the United States were assessed using the Collaborative Psychiatric Epidemiology Survey, with the dependent variables measured being suicidal ideation, suicide plans, and suicide attempt(s) and the independent variable being PMDD status (PMDD, moderate/severe PMS, no premenstrual symptoms). The results of the questioning show the prevalence of non-fatal suicidal behaviors increasing in a graded fashion according to individual PMDD status, where women with diagnosed PMDD were at the highest risk for experiencing suicidal ideation (37.4%), plans (19.1%), and attempts (16.2%) compared to women with only PMS or who were asymptomatic. Regarding psychiatric comorbidity, 22% of women with PMDD met the criteria for Substance Use Disorder, 40% met the criteria for Major Depressive Disorder, and 70% met the criteria for Anxiety Disorder. Although a substantial proportion of women with PMDD also met the criteria for Major Depressive Disorder (40%), Pilver et al. found that PMDD remained independently associated with suicidal ideation, plans, and attempts even after controlling for MDD and other psychiatric disorders. This finding supports the argument that PMDD is not simply a premenstrual manifestation of depression, but rather a distinct disorder with its own contribution to suicide risk. The authors further suggest that abnormalities in serotonin functioning may partially explain this relationship, as both PMDD and suicidal behavior have been linked to reduced serotonin activity during the luteal phase. A strength of this study is its use of a nationally representative sample while controlling for numerous demographic and psychiatric confounds, allowing for the isolation of the effect of PMDD on suicidal behavior. However, because PMDD diagnoses were based on retrospective survey data rather than prospective daily symptom ratings, the findings should be interpreted with some caution. An additional team of researchers conducted a cross-sectional study among Bangladeshi university students with the goal to assess the association between PMDD and depression, anxiety, stress, suicidal ideation, and suicidal attempts. Roy et al. (2025) utilized a questionnaire comprising demographic information, the Depression, Anxiety, and Stress Scale (DASS-21), and the Premenstrual Symptoms Screening Tool (PSST), which is a screening tool for premenstrual symptoms, were administered to measure the aforementioned variables. Consistent with Pilver et al. (2012), women with PMDD reported a significantly higher prevalence of past-year suicidal ideation (38.8%) and suicide attempts (28.6%) than participants with no premenstrual symptoms, along with a significant relationship between moderate/severe PMS and PMDD and a higher probability that suicidal ideation will be reported. After controlling for other variables, such as age at menarche, family history of PMS, presence of chronic disease, and physical exercise, participants with PMDD still remained more than five times as likely to report suicidal ideation than students without premenstrual symptoms, exhibiting that the relationship between PMDD and suicidality existed even after accounting for additional risk factors. Furthermore, depression, anxiety, and stress were all significant predictors of suicidal ideation, and the relationship between PMDD and suicidality became even stronger when these symptoms were present. These findings, consistent with the aforementioned studies, suggest that while PMDD itself is an important risk factor for suicidal ideation, comorbid psychological distress may increase the likelihood of suicidal thoughts and behaviors. The results also highlight the importance of implicating mental and menstrual health therapy for women with PMDD, severe PMS, and those with PMDD or severe PMS with comorbid depression, anxiety, and stress to regularly screen for signs of suicidal thoughts or behaviors. A limitation of this study is once again the retrospective data as well as a lack of clinician interviews which cannot ensure accurate diagnoses of PMDD, PMS, depression, anxiety, and stress. Future studies should use more precise diagnostic measurements and instruments to accurately assess participants. A major strength of this study, however, was its account of demographic and psychiatric variables as possible mediating factors which which was helpful in determining the impact of PMDD on suicidal thoughts and behaviors. Never before was such a diverse array of mediators assessed in this context. Key patterns are arising across the literature regarding the relationship between PMDD and suicidal ideation, with PMDD leading to significant increases in suicidal ideation, suicide planning, and suicide attempts among women in the luteal phase. It should also be noted that this pattern was seen among domestic and international samples. With such a large percentage of women struggling with this, it is necessary that proper intervention measures are implemented in medical and psychiatric settings. These measures could include screening tools like the DRSP, DASS-21, and PSST; and new cycle based risk assessments could be developed for even greater accuracy in screening someone with PMDD for suicide risk. Wikman et al. (2022) found that the relationship between women who sought prior psychological treatment for the disorder and suicidal ideation was positively associated, so, if some women with the disorder are seeking help for it, it would therefore be useful to have the proper diagnostic tools to give them the support they need. Also, with more visibility of the disorder curated by medical and psychiatric professionals, women can be made more aware of the disorder, its symptoms and effects, and how to get proper treatment, which in turn can help mitigate the effects of suicidal thoughts and behaviors put on by PMDD. TREATMENTS Though research has identified various promising biological mechanisms underlying PMDD, its exact pathophysiology still remains unknown. Therefore, treatment interventions tend to focus on symptom management rather than resolving the disorder. Current treatments fall into the categories of SSRIs, psychotherapy, hormone therapies, and lifestyle interventions. Current literature generally agrees that SSRIs are the most effective first-line treatment for decreasing PMDD symptoms (Hantsoo & Epperson, 2015; Rapkin et al., 2013; Johns Hopkins Medicine, 2026), yet there is less of a consensus regarding alternative approaches. Using SSRIs for PMDD is unusual because the medication can become effective hours to days after the first dose, meanwhile taking SSRIs for depression could take weeks for noticeable effects. The rapid onset is suggested to be because SSRIs convert 5α-dihydroprogesterone to allopregnanolone (ALLO) in minutes, rather than solely affecting serotonin transfer, boosting GABA-A receptors that induce mood regulating effects. This also allows intermittent dosing from ovulation to menstruation to be easily administered to reduce symptoms such as irritability and unpredictable mood swings; however, long term use of SSRIs would be necessary to reduce depressive and somatic symptoms (Hantsoo & Epperson, 2015 & Rapkin et al., 2013). Another positive of this treatment is that previous studies have shown that only a low dose (ex. 25-50 mg of sertraline) is needed to lower symptom severity, and the results were found in populations using fluoxetine (Steinberg et al., 2012), paroxetine (Yonkers et al,. 2006), citalopram (Ravindran et al., 2007), and escitalopram (Freeman et al,. 2005). Despite their effectiveness, SSRIs are not a perfect treatment and are associated with several limitations. They could cause adverse effects such as insomnia, nausea, headache, and possible sexual dysfunction, which may lead to medication discontinuation or prevent patients from using it in the first place. Further, treatment response rates are quite varied among individuals across studies, suggesting that SSRIs may not work for all women with PMDD (Hantsoo & Epperson, 2015 & Rapkin et al., 2013). Collectively, these findings not only support SSRIs as an effective initial therapy for women with PMDD, but also reinforce the theory that PMDD is a distinct neurobiological condition that differs from a form of major depressive disorder. Since PMDD symptom etiology appears to be linked to ovarian hormone fluctuations, researchers have also examined treatments that aim to stabilize hormone changes by suppressing the HPG axis or inhibit ovulation by controlling sex steroid fluctuations (Rapkin et al., 2013). Oral contraceptives (OCs) are commonly prescribed to treat PMS symptoms, but the evidence supporting their effectiveness for PMDD is weak. Although the number of large-scale studies on the effect of OCs on PMDD is minimal, there is a consensus among the research that OCs only produce mild symptom improvement, with the majority of the benefits deriving from a placebo effect (Hantsoo & Epperson, 2015, Rapkin et al., 2008, & Bäckström et al., 1992). Some research, however, has shown positive effects. For one, Bäckström et al. (1992) found evidence that OCs containing desogestrel, a synthetic progestin hormone, improved mood somewhat more effectively than levonorgestrel, commonly found in emergency contraception. Yet, the majority of findings still do not support OCs as a successful treatment. Joffe et al., (2003) reported that out of a group of women taking different forms of OCs, only 12.3% of participants reported improved mood; and Lopez et al. (2012) suggested that while certain OCs containing drospirenone and ethinyl estradiol slightly reduced PMDD symptoms, there was too large of a placebo effect to attribute the symptom reduction entirely to the hormonal treatment. Similarly, randomized, double-blind, placebo-controlled trials conducted by Freeman et al. (2012) and Halbreich et al. (2012) found that continuous dosing with levonorgestrel (90mcg) and ethinyl estradiol (20mcg) only slightly improved PMDD and PMS symptoms, also with a high placebo effect. A second form of hormonal treatment is GnRH agonists, which suppress ovulation by blocking reproductive hormone signals and generating postmenopausal levels of estradiol, progesterone, and ALLO. Using this intervention is usually only recommended to women with PMDD who find SSRI treatment ineffective, and it doubles as a “test” for how a woman would respond to an oophorectomy, or surgical menopause (Hantsoo & Epperson, 2015 & Rapkin et al., 2013). Common forms of GnRH agonists include goserelin, triptorelin, leuprolide, and histrelin. Some studies show that leuprolide acetate has been found to be successful in improving physical, emotional, and behavioral symptoms of PMDD (Pincus et al., 2011), while other research has found GnRH analogs to be successful in improving severe PMS symptoms and have adverse effects for those with premenstrual depression (Brown et al., 1994). Also, with GnRH agonists, there is an elevated risk for coronary artery disease and osteoporosis due to low estrogen levels. To counteract this side effect, estradiol add-back therapy, specifically with tibolone, has been found to improve PMDD and PMS symptoms and prevent bone loss (Wyatt et al., 2004 & Mitwally et al., 2002). Overall, GnRH agonists are seen as a “third-line therapy” that can be useful to confirm whether an individual's symptoms are due to PMDD or an affect disorder and to determine a possible outcome of an oophorectomy, yet their evidence of effectiveness is less extensive and less consistent than evidence for SSRIs. Further, considering hormonal treatment research findings as a whole still contain a significant amount of placebo effects, and not every woman responds to hormonal treatments, therefore other non-medical treatments must be implemented and improved to better treat PMDD. Though PMDD has a biological foundation, research has grown to recognize the value of psychological interventions in helping those affected cope with its symptoms. Kleinstäuber et al. (2012) found evidence suggesting that cognitive-behavioral therapy (CBT) improved premenstrual symptoms more effectively than SSRIs, which were found to be more beneficial in improving only the anxiety symptoms of PMDD, as it taught individuals how to cope with the disorder and shift their attribution of their symptoms. The same study also found that CBT produced longer treatment effects at follow-up than SSRIs like fluoxetine, and suggested that recommending SSRIs alone as a first-line treatment should be reexamined. Hunter et al. (2002) found that while fluoxetine reduced severe PMS, including PMDD, symptoms effectively, the use of CBT was associated with long-term improvement; and, multiple other researchers concur that PMDD treatment should advance beyond a medicinal approach toward an individualized, psychological and pharmacological therapy. Finally, an internet-based CBT intervention, a form of which has been reported to be promising for conditions such as postpartum depression, is proposed to be in development for PMDD as well (Kues et al., 2014). In comparison to pharmacological and hormonal treatments, non-medical interventions have significantly less empirical support regarding PMDD treatment. Nutritional supplements, dietary modifications, exercise, herbal treatments, and lifestyle interventions have all been studied, yet evidence of their effectiveness remains limited and is often based on PMS studies rather than PMDD studies. Dietary modification and calcium supplementation have been shown to have some minor benefits, particularly for treating PMS, with calcium supplements (1200 mg/day) having the strongest support due to randomized controlled trials that show it can improve both physical and mood symptoms (Thys-Jacobs et al., 1998 & Ghanbari et al., 2009). Vitamin B6, exercise, and other lifestyle modifications all have inconsistent findings with no clear positive effect. However, again, these studies are primarily focused on PMS than PMDD, which limits the generalizability of the aforementioned findings (Rapkin et al., 2013; Hantsoo & Epperson, 2015). Many herbal therapies have also shown some promise. A randomized control trial using chasteberry, or agnus castus, found significant positive effects on emotional and somatic symptoms of PMDD compared to a placebo (Schellenberg, R., 2001), and another study’s findings suggest that the response rate to chasteberry is equal to that of fluoxetine (Atmaca et al., 2003). St. John’s Wort was also shown to have a small beneficial effect in comparison with a placebo (Stevinson & Ernst, 2000; Dante & Faccinetti, 2011), but it should be used with caution given its known adverse effects when combined with SSRIs (risk of serotonin excess) and other forms of medication (i.e. interference with efficacy of oral contraceptives). Interventions focused on circadian rhythm regulation, such as bright-light therapy and sleep deprivation, have also been explored based on evidence that women with PMDD have similar decreased melatonin secretion as major depressive disorder (Schechter et al., 2010). Sleep deprivation was suggested to correct dysregulated circadian rhythms by influencing the sleep-wake cycle, increasing the total time the individual sleeps at night, inducing more rapid eye movement sleep, and improving mood in women with PMDD. Further, Parry et al. (1989) and Lam et al. (1999) demonstrated that bright-light therapy can alleviate PMDD symptoms if administered early in the morning and/or in the evening. Overall, more research is required to determine the true effects of circadian rhythm therapies on PMDD. Unlike SSRIs and CBT, these non-medical treatments lack strong empirical support as the majority of evidence originates from PMS studies and studies that result in mild, adverse, or no effects. Consequently, researchers recommended these strategies only as alternatives to pharmacological and psychological interventions for women seeking additional reliefs for their PMDD, not as the main course of treatment. CONCLUSION The literature reviewed in this paper suggests that PMDD is a clinically significant disorder with symptoms that surpass the typical emotional and physical changes associated with the premenstrual phase. Although its exact pathophysiology remains unclear, research points toward a complex interaction between sensitivity to normal ovarian hormone fluctuations, neurological and genetic factors, and environmental influences such as stress and trauma. Results involving the CNS, estrogen and progesterone sensitivity, and interactions between the HPG and HPA axes suggest that PMDD cannot be explained by abnormal hormone levels alone. Rather, the disorder may involve an abnormal biological response to otherwise normal hormonal changes (Hantsoo & Epperson, 2015). The strongest concern identified across the literature is the relationship between PMDD and suicidal ideation and behavior. Studies using both nationally representative samples and clinical populations have found higher rates of suicidal ideation, suicide planning, and suicide attempts among individuals with PMDD, and this association has remained significant even after accounting for comorbidities such as depression and anxiety (Wikman et al., 2022; Pilver et al., 2012, & Roy et al., 2025). This suggests that PMDD may be an independent contributor to suicide risk. Future research may strengthen the evidence regarding this relationship by demonstrating high rates of suicidal ideation during the late luteal phase among women with confirmed PMDD. However, the majority of the existing research is limited by retrospective diagnoses, cross-sectional designs, small samples, and reliance on self-report measures. Future research should therefore prioritize cycle-based assessments of PMDD and suicidality using valid diagnostic and suicide-risk measures. Treatment research similarly contains both progress and limitations. SSRIs currently have the strongest evidence and remain the primarily recommended treatment, with the ability to produce therapeutic effects within days and to be taken intermittently during the luteal phase. However, adverse effects, varied response rates, and uncertainty regarding the long-term effectiveness of SSRIs suggest that they are not the best treatment for every individual (Hantsoo & Epperson, 2015 & Rapkin et al., 2013). Hormonal treatments, such as oral contraceptives and GnRH agonists, have also produced inconsistent results and demonstrate placebo effects (Hantsoo & Epperson, 2015, Rapkin et al., 2008, & Bäckström et al., 1992). Psychological interventions, particularly CBT, may provide lasting improvements and could be used alongside pharmacological treatment, while dietary, herbal, exercise, and circadian interventions currently have considerably less empirical support (Kleinstäuber et al., 2012; Hunter et al., 2002). Together, these findings suggest that treatment should be individualized according to symptom profile, response to treatment, and patient preferences to be most effective rather than relying solely on one intervention. In conclusion, the literature supports the need to take PMDD more seriously as a potential cause of significant psychological distress and suicidal behavior. The debate over whether PMDD is a distinct psychiatric disorder should not prevent researchers and clinicians from investigating individuals who experience severe psychological symptoms. 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Written by: Clarisse Kennedy Introduction Where did all your time go? Were you doing things, or thinking about doing those things. Both problematic social media use—the compulsive habit of consuming online media—and maladaptive daydreaming—a behaviour of excessive fantasizing—are serious disruptions to the balanced lifestyle. Especially emerging among the younger generation, these conditions quickly reach the intensity of addictions, and when they appear together, their symptoms augment, carrying an unresolving and detrimental impact on the individual. About Problematic Social Media Use The average Canadian teenager spends 4.2 hours a day on social media (Yakhnis et al., 2025), watching popular content, interacting with other users, and scrolling short form videos. In recent years, excessive daily consumption of short form videos, such as Instagram Reels and Tik Toks, has risen among youth (Mona et al., 2026). This problematic social media use has been linked to reduced productivity, disruptions to interpersonal relationships, poor self-perception, and mental disorders such as anxiety and depression (Yakhnis et al., 2025). So, why do we keep scrolling then? Why You Cannot Stop Scrolling Like nicotine, alcohol, and the slot machine, social media is addictive. Addiction is largely ruled by a particular brain chemical, the neurotransmitter dopamine. This chemical is responsible for the sensation of pleasure, and the brain absolutely loves it. If dopamine is released when engaging in an activity, making it a pleasurable experience, an individual will want to repeat the activity again to experience more pleasure. Additionally, in anticipation of doing an activity associated with pleasure, the brain will release more dopamine. This creates a feedback loop, encouraging the individual to repeat the activity over and over. The dopamine feedback loop is responsible for why you cannot eat only one potato chip—you know it will be pleasurable when you eat another! That is also how addiction happens; the brain’s want becomes a compulsive need, even if doing the activity culminates in negative consequences. In the case of social media addiction, watching videos, for example, is a dopamine-packed reward for the brain. Therefore, scrolling to new content brings the anticipation of getting another reward, and this prediction of pleasure further releases dopamine. Even if the next video is not interesting, the anticipation of reward from scrolling again keeps the activity engaging and pleasurable. On top of all that, social media has rewarding stimuli such as notifications, occurring randomly but frequently. As both receiving a notification and the anticipation of receiving one trigger a release of dopamine, individuals feel encouraged to check their phones often to get a quick dose of pleasure. Simply put, problematic social media use is cut out to become an addiction. While there are several rubrics to access addiction, the essential qualifications are as follows: a mood boost with consumption, a mood plummet from withdrawal, intense symptoms from craving, increased tolerance, immense difficulty with self-restraint, and most importantly, excessive use that intrudes on one’s personal, social, and occupational affairs (Macit et al., 2018). Problematic social media use easily meets this criteria. Individuals experience withdrawal symptoms such as restlessness and shortened attention span, and the longer and more frequent periods of consumption from built-up tolerance results in an excessive loss of time and focus in one’s day. The compulsive urge to consume online content makes self-moderation difficult, distressing many individuals who wish to abstain from the habit. About Maladaptive Daydreaming Maladaptive daydreaming is a behaviour characterized by vivid, immersive daydreaming that provides the individual with entertainment and escapism. The behaviour is considered maladaptive because it is fundamentally an unhealthy coping mechanism that is compulsive, excessive, and disrupting to one’s responsibilities. The daydreams themselves are described as complex, gratifying daydreams that often feature fantasy-based scenarios where the individual lives as an idealized or alternate version of themself. These daydreams provide the individual with immersion and enjoyment (Mansuklal et al., 2025), which makes the behaviour highly pleasurable, and highly absorbing of their time, impairing social, occupational, and personal life. Daydreaming as an Addiction The exact cause of maladaptive daydreaming is not known. It has been identified as a coping mechanism for trauma, but also appears in individuals without major trauma. Some studies show a correlation to neurodivergence and obsessive-compulsive disorder (Mansuklal et al., 2025). It can be assumed that maladaptive daydreaming involves dopamine release because of the immense pleasure and engagement individuals derive from their daydreams. In addition, exposure to music and media can heighten the daydreaming intensity, with music and media being triggers for the behaviour (Pietkiewicz et al., 2018) and supporting immersion in fantasizing (Somer, 2024). Maladaptive daydreaming can be categorized as a behavioural addiction as it meets the essential qualifications previously outlined. Of course, daydreaming is a mood booster, but disruption to the activity leaves individuals feeling irritable and impatient for another moment to resume daydreaming. Tolerance prolongs time spent daydreaming, and self-moderation is rendered difficult as the onset of daydreams becomes uncontrollable, with individuals slipping into daydreams unconsciously. The resulting self-isolation and neglect of responsibilities impacts the individual’s personal, social, and occupational affairs. Putting Two Together Interestingly, maladaptive daydreaming addiction manifests similarly to social media addiction. Beyond the obvious self-isolation and lifestyle disruption they both cause, the conditions have other unique features in common. Particularly of interest, both conditions often result in degraded esteem in their identity, as well as shame stemming from a sense of wasted potential. Furthermore, social media can enhance the immersion of maladaptive daydreaming. Due to these similarities, maladaptive daydreaming and problematic social media use can greatly augment each other’s severity when co-occurring. Both social media and daydreaming can alter the individual’s esteem in their identity. Individuals who daydream maladaptively often have fantasy, idealized versions of themselves that succeed in their imagined scenarios, as a way to cope with the individual’s real life sense of inferiority (Renzi et al., 2025). Similarly, online culture silently promotes hiding any imperfections from being shown in public posts, such as through careful image editing and curation. Consequently, individuals in online spaces often stray from their true identities in favour of displaying an idealized side of their life. With success in media revolving around factors such as likes, fame, and appearance—all features that are relevant solely online or that can be fabricated through hiding imperfections—individuals may find they are only praised online for aspects of themselves that do not exist in the real world (Macit et al., 2018), and will feel their true identity is inadequate. Narcissism, anxiety, and depression can emerge from this distorted self-esteem, with links to these three conditions noted for both maladaptive daydreaming (Mansuklal et al., 2025) and problematic social media use (Macit et al., 2018). Shame commonly occurs alongside addiction, usually due to the stigmatization of substance use and financially straining habits such as gambling. However, shame stemming from problematic social media use and maladaptive daydreaming does not typically arise from stigma, but rather from an individual’s feeling of wasted potential. Both conditions occupy significant time, causing individuals to neglect their personal, social, and occupational responsibilities. Individuals may feel shame from a sense that they have irreparably fallen behind because of their lack of productivity and social connection, especially in contrast to the productivity and socializing they view on social media or in their fantasies (Pietkiewicz et al., 2018). They may feel they are not good enough to achieve their goals or unequipped to socialize with others, a mentality which encourages further escapism and reduced motivation to end their addictions. While a daydream can commence on its own, in cases of individuals with co-occurring problematic social media use and maladaptive daydreaming, individuals report that consuming media such as movies, social media, pornography, and music, can help start a daydream and support their immersion throughout it. Theories behind this phenomenon could be that media provides creative, idealized scenarios that can be imagined (Pietkiewicz et al., 2018), and that music triggers visual mental imagery (Somer, 2024). For example, watching celebrity content could inspire daydreams about being famous, and high-energy music in the background of videos could trigger vivid, thrilling immersion. In a patient case-study, excessive internet usage emerged after excessive daydreaming commenced, as the many forms of media on the internet supported deeper engagement in the individual’s daydreams (Pietkiewicz et al., 2018). So, what does this mean? With this connection between media consumption and daydreaming, it is not uncommon for individuals to simultaneously experience problematic social media use and maladaptive daydreaming. Unfortunately, their co-occurrence can significantly worsen the outcomes brought on by both conditions. Their mutual influence on identity means poor self-esteem caused by maladaptive daydreaming can be further degraded by idealism in online culture, and shame from wasted potential derived from social media can be augmented by maladaptive daydreaming. Individuals trying to reduce or quit the addictions thus experience more difficulty in minimizing the intensity of their symptoms, and may see that a relapse for one addiction can easily trigger a relapse into the other. This begs the question of what can be done to address these interconnected behavioural addictions. Perhaps further research into the cause and treatment of maladaptive daydreaming could protect individuals who daydream from being predisposed to developing problematic social media use. Stricter rules could be established to prohibit features of social media that are designed to addict our brains. Evidently, awareness and empathy for individuals experiencing both addictions will bring us closer to definite solutions. This article was a ten minute read. Take a moment to know where your time went— to you, spent learning something valuable. Remember to log out, space in, and keep choosing the real world. References Macit, H. B., Macit, G., & Güngör, O. (2018). A research on social media addiction and dopamine driven feedback. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 5(3), 882–897. https://doi.org/10.30798/makuiibf.435845 Mansuklal, S. A., Pascoal, P. M., Somer, E., Costa, I. B., & Andersson, G. (2025). What is known about maladaptive daydreaming? A scoping review. Clinical Psychology & Psychotherapy, 32(4), e70104. Wiley Online Library. https://doi.org/10.1002/cpp.70104 Mona, A. E., Roshith, V., Peter, R., Roy, P., Hassan, A., Devika, M., & Trishala, M. (2026). Short video addiction and its impact on cognitive functioning in adolescents and youth: A systematic review. International Journal of Adolescence and Youth, 31(1). Taylor & Francis Online. https://doi.org/10.1080/02673843.2026.2623337 Pietkiewicz, I. J., Nęcki, S., Bańbura, A., & Tomalski, R. (2018). Maladaptive daydreaming as a new form of behavioral addiction. Journal of Behavioral Addictions, 7(3), 838–843. AKJournals. https://doi.org/10.1556/2006.7.2018.95 Renzi, A., & Mariani, R. (2025). What is the relationship between narcissism and maladaptive daydreaming? The role of defense mechanisms as mediators. Psychiatric Research and Clinical Practice, 7(3), 156–164. Wiley Online Library. https://doi.org/10.1176/appi.prcp.20250018 Somer, E. (2024). Calling the tune in maladaptive daydreaming: The impact of music on the experience of compulsive fantasizing. Psychology of Music, 52(6). https://doi.org/10.1177/03057356231222290 Yakhnis, I., & Cooper, M. (2025). Associations between social media use, personal screen time, and Mental Health Indicators among Canadian youth. https://static1.squarespace.com/static/5f31a311d93d0f2e28aaf04a/t/68e3ddec663c0934fc55798d/1759763948764/Social+Media+%26+Youth+Mental+Health.pdf

Written by: Evelyn Castro Reviewed by: Vanessa Rosario Introduction Have you ever gotten so excited about a topic that you start to gabble and babble the sentences? Now imagine this happening to you every hour of the day all your life, except not because of excitement, but because of a speech impediment that affects how you communicate your thoughts to others. Cluttering makes an individual speak in an irregular, rapid, and uncontrollable way. They often omit pronunciation of syllables and sounds, as well as using an exaggerated amount of filling words while expressing ideas, such as “like”,“uhm”, and “so”. Just like the word “clutter”, patients feel like their thinking process feels pressured and disorganized, which creates a necessity to get them out as fast as possible, resulting in being misunderstood by the listeners (Sue et al., 2026). Unfortunately, many of the general and scientific audience do not know a lot about cluttering. Some patients are misdiagnosed with just stuttering while others underdiagnosed with having an anxiety speech (Sue et al.2026). The lack of awareness has also affected mental health in patients with cluttering, as many of them face different levels of social stigma while growing up. Even though this has affected research about the speech impediment, many scientists and speech pathologists stress the need to raise awareness and continue investigating more about the causes, effects, and treatment for this. Why Could Cluttering Happen While there is not a sufficient amount of cluttering research to define a specific causation for the disorder, there is evidence to believe that it can be related to the brain and genetics. About the Brain There are two small areas in the brain called the anterior cingulate cortex and the supplementary motor area. They both work together in detecting errors and making decisions, in this case with formulating words and phrases (Kelkar et al., 2023). We also have a neurotransmitter, which is a chemical messenger that passes from one neuron to another, called dopamine. Research has found an hyperactivity of this neurotransmitter in these two areas, which could potentially be one of the reasons why people with cluttering struggle to detect pauses between words and talk faster than someone without cluttering (Kelkar et al., 2023). This finding could be reinforced with other research in the basal ganglia, which is also known to have a role in decision making and work with the neurotransmitter dopamine. Scientists saw that people with cluttering also showed an increase of dopamine information between neurons in this area, which could help with explaining the mechanism of cluttering in the brain (Yvonne & Dario, 2022). About the Genetics While there is research that shows clutter being passed along families, the data is not enough to conclude anything yet(Kelkar et al.,2023). However, more twin studies and genetic research could help in reinforcing this hypothesis. Effects in Mental Health People with cluttering are often not aware of their fast and uncontrollable speech, causing much confusion on their listeners. This has caused them to become very aware of their body language, such as getting closer to them, squeezing their eyes, or nodding confusingly (Garret et al, 2026). This not only causes anxiety and exhaustion on them, but creates a negative conception of themselves. They often become extremely scared of talking to others, since they believe people would not take what they say seriously (Yvonne & Dario, 2022). While this normally starts in early adolescence, social isolation can continue into adulthood, affecting both their professional and personal life. Conclusion Cluttering continues to be a very unknown speech impediment in the world. Yet, it is crucial to continue investigating how it happens and the consequences it has in the patients. Educating others about speech impediments will not only allow them to understand how to help others with cluttering, but also help patients feel less in conflict with themselves. Research can also improve therapy techniques in the condition, improving patients’ speech faster than ever. References Garrett, S., Ballantyne, A., Gladman, T., & Swift, M. (2026). Exploring the personal impact of cluttering: A scoping review of current evidence. Journal of Fluency Disorders, 87, 106197. https://doi.org/10.1016/j.jfludis.2026.106197 Han, W., Zhou, L., Lu, J., & Pill, S. (2026). Cluttering in Children and Adolescents: Speech Motor Development, Neurocognitive Mechanisms, and Allied Health Implications. Children, 13(1), 97. https://doi.org/10.3390/children13010097 Hof, Yvonne & Strangis, Dario. (2022). An Adolescent Confronted With Cluttering: The Story of Johan. Perspectives of the ASHA Special Interest Groups. 7. 1357-1369. 10.1044/2022_PERSP-21-00267. Kelkar, P. (2023). Cluttering. In S. Maruthy & P. Kelkar (Eds.), Understanding and Managing Fluency Disorders (pp. 186–210). Routledge, London, UK.

Written by: Seinya Sam Have you noticed behavior in a young child that seems to fall into the category of clumsy? Have you noticed difficulty in walking, balancing, or struggling to roll over? These may be indicators in young children of a neuro-motor disorder. Dyspraxia, otherwise known as developmental coordination disorder (DCD), is a developmental disorder that hinders a child’s motor skills. Children with dyspraxia may have trouble executing fine and gross motor skills for their age range. These are often developmental delays found in these children. Moreover, conditions like this one have been found to be a result of impairments in genes during prenatal development rather than external factors. Genetic impairment often stands as a huge contributor to conditions such as dyspraxia. In the first genome-association wide study on dyspraxia (GWAS), researchers found that motor skill coordination derives from complicated genetic traits taking on varying forms. Across the numerous candidates who participated in their study, upon investigation, some of the nuances in their genes took on variations such as IQSEC1, which is a human gene on chromosome 3p252, which could have been a contributing factor in dyspraxia. This gene impairment is attributed to its role in neuronal network assembly and axon guidance, which are subfields of neurological development that deal with neuron development and processing. The findings of the researchers indicate that variations of genetics have influence on connectivity during early development of the brain, furthermore contributing to the argument that genetic impairment through variations has the ability to hinder brain development, which in turn can result in neurological-motor disorders such as dyslexia. This is because of disrupted developmental stages in the brain, which may later on show characteristics through signs of motor impairment in Dyspaxia. These characteristics may include poor hand-eye coordination, difficulty balancing, and trouble drawing, using scissors, or playing with toys. However, parents can be reassured that it is not something that they are doing wrong contributing to their child’s condition. But it is merely a result of the genetic lottery taking its course and contributing to a condition. Moreover, interference with motor neuron development is a significant contributor to conditions such as dyspraxia. Over time, dyspraxia has been found to correlate with disruptions in neurotransmitter processes. For motor functions to occur, signals are sent and processed through neurons and through the central nervous system in order to carry out executive functions. The neurological aspect of dyspraxia has been found to stem from delayed development during the early stage of neuron maturations those neurons make connections between the spinal cord and brain in order to carry out motor functions, followed by muscles' coordinated movements. If improper development takes place, it won’t provide beneficial conditions for optimal pathway creation, meaning the motor executive(cognitive neural system of the brain) of motor functions. The brain also has difficulty in sensory-data analysis in real time for individuals with dyspraxia, which can lead to other symptoms such as delayed motor function movements. For parents noticing these symptoms in their children, many may fear their symptoms arise from injuries during pregnancy, which is more than likely not the case for children. As numerous studies have shown previously, dyspraxia has been known to take on its form as a result of genes or motor neuron impairments. Meaning that those parents who may originally feel guilty don’t have to worry about that factor. Due to the fact of the matter being that their child’s condition has nothing to do with their fault. Although there is currently no cure for dyspraxia, there are numerous ways to alleviate some of the stress which many come with it for the individuals. This can be done through attempts such as cognitive therapy or physical therapy. With these treatments, children are able to utilize their fine and gross motor skills and can begin to improve upon their underdeveloped skills. It is a great way for parents to get involved and help make life quality better for their children with this condition. Moreover, their children may have an easier time improving their development through the implementation of exercise, games, and interactive play to improve their developmental standpoint. Overall, throughout the studies which had been made on the condition of dyspraxia, it is quite clear that there is more than meets the eye. While it was previously thought to be caused by injury, it has now been revealed that gene and neuron impairment contribute to the displayed effects of the condition. These are the results of impairment factors, which are contributors to the condition. However, parents, friends, mentors, and more should focus on how to help the individuals and look at their conditions in a positive light, rather than focusing on the negative. In this way, we can hold a more positive viewpoint towards neurological conditions, as well as give persons with them the resources to feel seen and overall heard despite their difficulties References Mountford, H. S., Hill, A., Barnett, A. L., & Newbury, D. F. (2021). Genome-wide association study of motor coordination. Frontiers in Human Neuroscience, 15. https://doi.org/10.3389/fnhum.2021.669902 Polatajko, H. J., & Cantin, N. (2005). Developmental coordination disorder (dyspraxia): An overview of the state of the art. Seminars in Pediatric Neurology, 12(4), 250–258. https://doi.org/10.1016/j.spen.2005.12.007 theAsianparent. (2025, March 11). Beware of Dyspraxia, Cause of Disrupted Motor Development in Children. theAsianparent. theAsianparent - Your Guide to Pregnancy, Baby & Raising Kids. https://sg.theasianparent.com/dyspraxia

Written by: Sophia Jeanne Edited by: Raquel Castro Introduction Would you believe me if I said people with paralysis could speak through computers? Well, this could be a reality for millions of people living with neurological conditions that impair motor function. People living with conditions like ALS, locked-in syndrome, Parkinson’s Disease (PD), and other neuromuscular disorders can have limited communication and movement, which means they may not be able to do certain things. Motor BCIs could enable communication, technology operation, and movement for people living with those conditions. BCI Overview & Explanation So, how do motor BCIs work? What even is a BCI? Well, I can assure you they do not read minds. First, let's define BCIs and motor BCIs. Brain-computer interfaces acquire brain signals, filter the signals, decode them, and translate them into computer commands (Mak & Wolpaw, 2010). Motor brain-computer interfaces are motor-imagery based, which is what the article will focus on. BCIs are separated into two categories: invasive and non-invasive. Invasive BCIs require surgical implantation via microelectrodes (Kubben, 2024). Noninvasive BCIs do not require surgical implantation and most commonly acquire signals via electroencephalography (EEG), which is a wearable cap. BCIs aim to increase the independence of those with loss of motor control and fill in the gap that current aids do not meet. Clinical Applications BCI training focuses on various motor tasks that address the upper and lower limbs separately. BCI upper-limb training has been shown to have significant immediate improvement in motor function in post-stroke patients (Bai et al., 2020). A majority of BCI research looks similar to this, especially research that addresses stroke recovery. Another recent trend is closed-loop deep brain stimulation, in which the system adapts in real time to improve treatment efficacy, also known as neuromodulation combotherapy (Leo et al., 2026). Combining BCIs with rehabilitation prioritizes neuroplasticity and creates personalized therapies for patients (Swarnakar, 2025). Challenges & Limitations The vast majority of BCI research is conducted on animals and on people without the disabilities that BCIs are intended to address. This is because a group of healthy participants can provide a standardized basis before moving to clinical trials. This especially hinders progress for invasive BCIs because there aren’t many people willing to get experimental brain surgery. Additionally, a large portion of BCI research revolves around upper-limb tasks, especially with left-hand vs. right-hand motor tasks. This type of research is foundational in the BCI field and is involved in calibrating motor BCI systems. But because many people are right-handed, it can lead to biased data. Conclusion What can we look forward to in the future of BCIs? Neurotechnology is not going anywhere, and many prototypes are being rigorously tested to move to human trials. The BCI development industry is currently focusing on restoring speech and aiding in movement in paralyzed patients and amputees. Currently, BCI use remains limited to laboratories, but breakthroughs are expected by 2030 (Herbert & Northoff, 2024). There are still many ethical and privacy concerns in the pathway to market release. References Bai, Z., Fong, K. N. K., Zhang, J. J., Chan, J., & Ting, K. H. (2020). Immediate and long-term effects of BCI-based rehabilitation of the upper extremity after stroke: a systematic review and meta-analysis. Journal of neuroengineering and rehabilitation, 17(1), 57. https://doi.org/10.1186/s12984-020-00686-2 Herbert, C., & Northoff, G. (2024). Editorial: Analyzing and computing humans - the role of language, culture, brain and health. Frontiers in human neuroscience, 18, 1439729. https://doi.org/10.3389/fnhum.2024.1439729 Kubben, P. (2024). Invasive Brain-Computer Interfaces: A Critical Assessment of Current Developments and Future Prospects. JMIR neurotechnology, 3, e60151. https://doi.org/10.2196/60151 Luo, Y., Liu, X., & Yang, M. (2026). Current status and future prospects of brain-computer interfaces in the field of neurological disease rehabilitation. Frontiers in rehabilitation sciences, 7, 1666530. https://doi.org/10.3389/fresc.2026.1666530 Mak, J. N., & Wolpaw, J. R. (2009). Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects. IEEE reviews in biomedical engineering, 2, 187–199. https://doi.org/10.1109/RBME.2009.2035356 Swarnakar R. (2025). Brain-Computer Interfaces in Rehabilitation: Implementation Models and Future Perspectives. Cureus, 17(7), e88873. https://doi.org/10.7759/cureus.88873