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<td><span style="font-family:Helvetica, sans-serif; font-size:20px;font-weight:bold;">PsyPost – Psychology News</span></td>
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<td><a href="https://www.psypost.org/nanoparticle-therapy-restores-brain-function-in-mice-with-alzheimers-disease/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Nanoparticle therapy restores brain function in mice with Alzheimer’s disease</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 1st 2025, 10:00</div>
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<p><p>An international team of scientists has developed specialized nanoparticles that can reverse the cognitive symptoms of Alzheimer’s disease in mice. The treatment works by repairing the brain’s natural filtration and waste-removal system, leading to the rapid clearance of toxic proteins and long-lasting cognitive recovery. This research, published in <em><a href="https://www.nature.com/articles/s41392-025-02426-1" target="_blank">Signal Transduction and Targeted Therapy</a></em>, suggests that targeting the brain’s protective barrier could be a powerful new strategy for combating neurodegenerative diseases.</p>
<p>Alzheimer’s disease is a progressive brain disorder that gradually erodes memory and thinking skills. At its core, the disease is associated with the abnormal buildup of a protein called amyloid-beta. In a healthy brain, amyloid-beta is regularly cleared away, but in Alzheimer’s patients, it clumps together to form sticky plaques that disrupt communication between brain cells and trigger inflammation. For decades, researchers have searched for ways to remove these plaques or prevent their formation.</p>
<p>A key player in this process is the blood-brain barrier, a dense network of cells that acts as a highly selective gatekeeper for the brain. It allows essential nutrients to enter while blocking out harmful substances. This barrier also plays an active role in pushing waste products, including amyloid-beta, out of the brain and into the bloodstream for disposal. In Alzheimer’s disease, this barrier becomes dysfunctional, trapping amyloid-beta inside the brain and contributing to its accumulation. The scientists behind this new study focused on repairing this fundamental breakdown in the brain’s maintenance system.</p>
<p>The researchers engineered tiny, hollow spheres made of polymers, known as polymersomes. These are not simply vehicles to carry a drug; they are designed to be the therapeutic agent itself. The surface of each nanoparticle was decorated with a specific number of molecules that act like keys, designed to interact with a particular protein on the blood-brain barrier called LRP1. This protein, LRP1, is a primary transporter responsible for moving amyloid-beta out of the brain.</p>
<p>The team’s design was based on a sophisticated understanding of how LRP1 works. When LRP1 binds to large, sticky clumps of amyloid-beta, the binding is very strong. This strong interaction signals the cell to destroy the LRP1 protein, reducing the brain’s ability to clear amyloid. The researchers engineered their nanoparticles to have a “medium-strength” attraction to LRP1. This moderate binding doesn’t trigger the protein’s destruction. Instead, it encourages the LRP1 protein to efficiently shuttle its cargo out of the brain and then return to the barrier to repeat the process.</p>
<p>To test their design, the team used a well-established mouse model of Alzheimer’s disease. These mice are genetically programmed to develop amyloid plaques and cognitive decline similar to that seen in humans. The researchers administered just three intravenous injections of the nanoparticles to 12-month-old mice, an age when significant pathology is already present. The results were immediate and striking.</p>
<p>Within just two hours of a single injection, the amount of amyloid-beta in the brains of the treated mice dropped by nearly 45 percent. Concurrently, levels of amyloid-beta in their bloodstream increased by eight times. This provided clear evidence that the nanoparticles were successfully helping the blood-brain barrier transport the toxic protein out of the brain and into general circulation, where it could be disposed of by the body.</p>
<p>To visualize these effects in living animals, the team used positron emission tomography, a type of brain imaging that can detect amyloid plaques. Scans taken before the treatment showed significant amyloid buildup in the Alzheimer’s model mice. Just 12 hours after receiving the nanoparticles, new scans revealed a sharp decrease in the amyloid signal. Further analysis using advanced 3D imaging of the entire brain confirmed an overall reduction in amyloid volume of approximately 41 percent.</p>
<p>The treatment did more than just remove amyloid; it appeared to restore the health of the blood-brain barrier itself. The researchers found that after treatment, the levels of the LRP1 transport protein on the barrier cells returned to normal. They also observed changes in other cellular proteins, indicating that the nanoparticles had successfully shifted the barrier’s transport mechanism from a destructive pathway to a protective and efficient one.</p>
<p>The most significant finding was the effect on cognition and behavior. To measure this, the scientists used the Morris water maze, a standard test of spatial learning and memory in rodents. In this test, mice must learn the location of a hidden platform in a small pool of water. Before treatment, the Alzheimer’s mice struggled with this task. After receiving the nanoparticles, their performance improved dramatically, becoming nearly indistinguishable from that of healthy, non-diseased mice.</p>
<p>Remarkably, this cognitive recovery was long-lasting. The researchers tested the same mice again six months after the initial three-dose treatment. Even after this extended period, the treated mice retained their improved cognitive abilities, performing just as well as healthy mice of the same age. Additional behavioral tests, such as those measuring nest-building ability and preference for sweet tastes, further supported the conclusion that the treatment had restored not only memory but also the animals’ overall quality of life.</p>
<p>While these results offer a promising new avenue for Alzheimer’s research, the study has limitations. The experiments were conducted in mice, and animal models, though valuable, do not perfectly replicate the complexity of human Alzheimer’s disease. The path from a successful mouse study to a safe and effective human therapy is a long one that will require extensive further investigation.</p>
<p>Future research will likely focus on confirming these mechanisms in more complex models and eventually testing the safety and efficacy of such a strategy in humans. The study also opens the door to applying this concept of “barrier repair” to other neurological disorders where blood-brain barrier dysfunction is a known factor, such as Parkinson’s disease or amyotrophic lateral sclerosis. By shifting the focus from simply targeting a disease’s symptoms to repairing the underlying biological systems, this work establishes a new and potentially powerful approach in the fight against neurodegeneration.</p>
<p>The study, “<a href="https://www.nature.com/articles/s41392-025-02426-1" target="_blank">Rapid amyloid-β clearance and cognitive recovery through multivalent modulation of blood–brain barrier transport</a>,” was authored by Junyang Chen, Pan Xiang, Aroa Duro-Castano, Huawei Cai, Bin Guo, Xiqin Liu, Yifan Yu, Su Lui, Kui Luo, Bowen Ke, Lorena Ruiz-Pérez, Qiyong Gong, Xiaohe Tian & Giuseppe Battaglia.</p></p>
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<td><a href="https://www.psypost.org/googles-ai-co-scientist-just-solved-a-biological-mystery-that-took-humans-a-decade/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Google’s AI co-scientist just solved a biological mystery that took humans a decade</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 1st 2025, 08:00</div>
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<p><p>Can artificial intelligence function as a partner in scientific discovery, capable of generating novel, testable hypotheses that rival those of human experts? Two recent studies highlight how a specialized AI developed by Google not only identified drug candidates that showed significant anti-fibrotic activity in a laboratory model of chronic liver disease but also independently deduced a complex mechanism of bacterial gene transfer that had taken human scientists years to solve.</p>
<p>The process of scientific discovery has traditionally relied on human ingenuity, combining deep expertise with creative insight to formulate new questions and design experiments. However, the sheer volume of published research makes it challenging for any single scientist to connect disparate ideas across different fields. A new wave of artificial intelligence tools aims to address this challenge by augmenting, and accelerating, human-led research.</p>
<p>One such tool is Google’s AI co-scientist, which its developers hope will significantly alter the landscape of biomedical research. Recent studies published in <em>Advanced Science</em> and <em>Cell </em>provide early evidence of this potential, showing the system’s ability to not only sift through vast datasets but also to engage in a reasoning process that can lead to high-impact discoveries.</p>
<h2>Google’s AI Co-scientist: A Multi-Agent System for Discovery</h2>
<p><a href="https://research.google/blog/accelerating-scientific-breakthroughs-with-an-ai-co-scientist/">Google’s AI co-scientist</a> is a multi-agent system built upon the Gemini 2.0 large language model, designed to mirror the iterative process of the scientific method. It operates not as a single entity, but as a team of specialized AI agents working together. This structure is intended to help scientists generate new research ideas, create detailed proposals, and plan experiments.</p>
<p>The system operates through a “scientist-in-the-loop” model, where human experts can provide initial research goals, offer feedback, and guide the AI’s exploration using natural language. The specialized agents each handle a distinct part of the scientific reasoning process. The Generation Agent acts as a brainstormer, exploring scientific literature and engaging in simulated debates to produce initial ideas. The Reflection Agent serves as a peer reviewer, critically assessing these ideas for quality, novelty, and plausibility.</p>
<p>Other agents contribute to refining the output. The Ranking Agent runs an Elo-based tournament, similar to chess rankings, to prioritize the most promising hypotheses. The Evolution Agent works to improve top-ranked ideas by combining concepts or thinking in unconventional ways. A Meta-review Agent synthesizes all the feedback to improve the performance of the other agents over time. This collaborative, self-improving cycle is designed to produce increasingly novel and high-quality scientific insights.</p>
<h2>AI Pinpoints New Drug Candidates for Liver Fibrosis</h2>
<p>In the study published in <em>Advanced Science</em>, researchers partnered with Google to explore new ways of treating liver fibrosis, a progressive condition marked by excessive scarring in the liver. Current treatment options are extremely limited, in part because existing models for studying the disease do not accurately replicate how fibrosis develops in the human liver. These limitations have hindered drug development for years.</p>
<p>To address this gap, the research team asked the AI co-scientist to generate new, testable hypotheses for treating liver fibrosis. Specifically, they tasked the AI with exploring how epigenomic mechanisms—chemical changes that influence gene activity without altering the DNA sequence—might be targeted to reduce or reverse fibrosis.</p>
<p>“For the data used in the paper, we provided a single prompt and received a response from AI co-scientist, which are shown in supplemental data file 1,” explained <a href="https://peltzlab.stanford.edu/team/gary-peltz/" target="_blank" rel="noopener">Gary Peltz</a>, a professor at Stanford University School of Medicine. “The prompt was carefully prepared, providing the area (epigenomic effects in liver fibrosis) and experimental methods (use of our hepatic organoids) to focus on. However, in most cases, it is important to iteratively engage with an AI in order to better define the question and enable it to provide a more complete answer.”</p>
<p>The AI system scanned the scientific literature and proposed that three classes of epigenomic regulators could be promising targets for anti-fibrotic therapy: histone deacetylases (HDACs), DNA methyltransferase 1 (DNMT1), and bromodomain protein 4 (BRD4). It also outlined experimental techniques for testing these ideas, such as single-cell RNA sequencing to track how the drugs might affect different cell populations. The researchers incorporated these suggestions into their experimental design.</p>
<p>To test the AI’s proposals, the team used a laboratory system based on human hepatic organoids—three-dimensional cell cultures derived from stem cells that resemble key features of the human liver. These mini-organs contain a mix of liver cell types and can model fibrosis when exposed to fibrotic triggers like TGF-beta, a molecule known to promote scarring. The organoid system allowed researchers to assess not just whether a drug could reduce fibrosis, but also whether it would be toxic or promote regeneration of liver tissue.</p>
<p>The findings provided evidence that two of the drug classes proposed by AI (HDAC inhibitors and BRD4 inhibitors) showed strong anti-fibrotic effects. One of the tested compounds, Vorinostat, is an FDA-approved cancer drug. In the organoid model, it not only suppressed fibrosis but also appeared to stimulate the growth of healthy liver cells.</p>
<p>“Since I was working on the text for a grant submission in this area, I was surprised by the AI co-scientist output,” Peltz told PsyPost.</p>
<p>In particular, Peltz was struck by how little prior research had explored this potential. After checking PubMed, he found over 180,000 papers on liver fibrosis in general, but only seven that mentioned Vorinostat in this context. Of those, four turned out to be unrelated to fibrosis, and another only referenced the drug in a data table without actually testing it. That left just two studies directly investigating Vorinostat for liver fibrosis.</p>
<p>While the HDAC and BRD4 inhibitors showed promising effects, the third AI-recommended class, DNMT1 inhibitors, did not. One compound in this category was too toxic to the organoids to be considered viable for further study.</p>
<p>To evaluate the AI’s performance, Peltz also selected two additional drug targets for comparison based on existing literature. These were chosen precisely because they had more published support suggesting they might work against fibrosis.</p>
<p>But when tested in the same organoid system, the inhibitors targeting those well-supported pathways did not reduce fibrosis. This outcome suggested that the AI was able to surface potentially effective treatments that human researchers might have missed, despite extensive literature reviews.</p>
<p>Looking ahead, Peltz said his team is “developing additional data with our liver organoid system to determine if Vorinostat can be effective for reducing an established fibrosis, and we are talking with some organizations and drug companies about the potential for Vorinostat being tested as an anti-fibrotic agent.”</p>
<h2>An AI Recapitulates a Decade-Long Discovery in Days</h2>
<p>In a separate demonstration of its reasoning power, the AI co-scientist was challenged to solve a biological mystery that had taken a team at Imperial College London over a decade to unravel. The research, published in <em>Cell</em>, focused on a peculiar family of mobile genetic elements in bacteria known as capsid-forming phage-inducible chromosomal islands, or cf-PICIs.</p>
<p>Scientists were puzzled by how identical cf-PICIs were found in many different species of bacteria. This was unexpected because these elements rely on viruses called phages to spread, and phages typically have a very narrow host range, often infecting only a single species or strain. The human research team had already solved the puzzle through years of complex experiments, but their findings were not yet public.</p>
<p>They had discovered a novel mechanism they termed “tail piracy,” where cf-PICIs produce their own DNA-filled “heads” (capsids) but lack tails. These tailless particles are then released and can hijack tails from a wide variety of other phages infecting different bacterial species, creating chimeric infectious particles that can inject the cf-PICI’s genetic material into a new host.</p>
<p>To test the AI co-scientist, the researchers provided it only with publicly available information from before their discovery was made and posed the same question: how do identical cf-PICIs spread across different bacterial species?</p>
<p>The AI co-scientist generated five ranked hypotheses. Its top-ranked suggestion was that cf-PICIs achieve their broad host range through “capsid-tail interactions,” proposing that the cf-PICI heads could interact with a wide range of phage tails. This hypothesis almost perfectly mirrored the “tail piracy” mechanism the human team had spent years discovering.</p>
<p>The AI, unburdened by the researchers’ initial assumptions and biases from existing scientific models, arrived at the core of the discovery in a matter of days. When the researchers benchmarked this result, they found that other leading AI models were not able to produce the same correct hypothesis, suggesting a more advanced reasoning capability in the AI co-scientist system.</p>
<h2>Limitations and the Path Forward</h2>
<p>Despite these promising results, researchers involved in the work caution that significant limitations remain. The performance of the AI co-scientist has so far been evaluated on a small number of specific biological problems. More testing is needed to determine if this capability can be generalized across other scientific domains. The AI’s reasoning is also dependent on the quality and completeness of the publicly available data it analyzes, which may contain its own biases or gaps in knowledge.</p>
<p>Perhaps most importantly, human expertise remains essential. While an AI can generate a large volume of plausible hypotheses, it lacks the deep contextual judgment that comes from years of hands-on experience. An experienced scientist is still needed to evaluate which ideas are truly worth pursuing and to design the precise experiments required for validation. The challenge of how to prioritize AI-generated ideas is substantial, as traditional experimental pipelines are not fast or inexpensive enough to test every promising lead.</p>
<p>“Generally, AI output must be evaluated by people with knowledge in the area; and AI output is most valuable to those with domain-specific expertise because they are best positioned to assess it and to make use of it,” Peltz told PsyPost.</p>
<p>Nevertheless, these two studies provide evidence that AI systems are evolving from helpful assistants into true collaborative partners in the scientific process. By generating novel and experimentally verifiable hypotheses, tools like the AI co-scientist have the potential to supercharge human intuition and accelerate the pace of scientific and biomedical breakthroughs.</p>
<p>“I believe that AI will dramatically accelerate the pace of discovery for many biomedical areas and will soon be used to improve patient care,” Peltz said. “My lab is currently using it for genetic discovery and for drug re-purposing, but there are many other areas of bioscience that will soon be impacted. At present, I believe that AI co-scientist is the best in this area, but this is a rapidly advancing field.”</p>
<p>The study, “<a href="https://doi.org/10.1002/advs.202508751">AI-Assisted Drug Re-Purposing for Human Liver Fibrosis</a>,” was authored by Yuan Guan, Lu Cui, Jakkapong Inchai, Zhuoqing Fang, Jacky Law, Alberto Alonzo Garcia Brito, Annalisa Pawlosky, Juraj Gottweis, Alexander Daryin, Artiom Myaskovsky, Lakshmi Ramakrishnan, Anil Palepu, Kavita Kulkarni, Wei-Hung Weng, Zhuanfen Cheng, Vivek Natarajan, Alan Karthikesalingam, Keran Rong, Yunhan Xu, Tao Tu, and Gary Peltz.</p>
<p>The study, “<a href="https://doi.org/10.1016/j.cell.2025.08.019" target="_blank" rel="noopener">Chimeric infective particles expand species boundaries in phage-inducible chromosomal island mobilization</a>,” was authored by Lingchen He, Jonasz B. Patkowski, Jinlong Wang, Laura Miguel-Romero, Christopher H.S. Aylett, Alfred Fillol-Salom, Tiago R.D. Costa, and José R. Penadés.</p>
<p>The study, “<a href="https://doi.org/10.1016/j.cell.2025.08.018" target="_blank" rel="noopener">AI mirrors experimental science to uncover a mechanism of gene transfer crucial to bacterial evolution</a>,” was authored by José R. Penadés, Juraj Gottweis, Lingchen He, Jonasz B. Patkowski, Alexander Daryin, Wei-Hung Weng, Tao Tu, Anil Palepu, Artiom Myaskovsky, Annalisa Pawlosky, Vivek Natarajan, Alan Karthikesalingam, and Tiago R.D. Costa.</p></p>
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<td><a href="https://www.psypost.org/in-neuroscience-breakthrough-scientists-identify-key-component-of-how-exercise-triggers-neurogenesis/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">In neuroscience breakthrough, scientists identify key component of how exercise triggers neurogenesis</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 1st 2025, 06:00</div>
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<p><p>A recent study suggests that some of exercise’s brain-enhancing benefits can be transferred through tiny particles found in the blood. Researchers discovered that injecting these particles, called extracellular vesicles, from exercising mice into sedentary mice promoted the growth of new neurons in the hippocampus, a brain region important for learning and memory. The findings were published in the journal <em><a href="https://doi.org/10.1016/j.brainres.2025.150003" target="_blank">Brain Research</a></em>.</p>
<p>Aerobic exercise can enhance cognitive functions, in part by stimulating the birth of new neurons in the hippocampus. This process, known as adult neurogenesis, is linked to improved learning, memory, and mood regulation. Understanding the specific mechanisms connecting physical activity to brain health could lead to new therapies for age-related cognitive decline and other neurological conditions.</p>
<p>However, the exact biological conversation between active muscles and the distant brain has remained largely a mystery. A leading hypothesis suggests that exercise releases specific factors into the bloodstream that travel to the brain and initiate these changes. Previous work has shown that blood plasma from exercising animals can be transferred to sedentary ones, resulting in cognitive benefits. This observation has narrowed the search for the responsible agents.</p>
<p>Among these potential messengers are extracellular vesicles, which are minuscule sacs released by cells that carry a diverse cargo of proteins, lipids, and genetic material. During exercise, tissues like muscle and liver release these vesicles into circulation at an increased rate. Because these particles are capable of crossing the protective blood-brain barrier, researchers proposed they might be a key vehicle for delivering exercise’s benefits directly to the brain. </p>
<p>“The hippocampus is critical for learning and memory and atrophy of the hippocampus is associated with common mental health problems like depression, anxiety,, PTST, epilepsy, Alzheimer’s disease and normal aging. So figuring out ways of increasing the integrity of the hippocampus is an avenue to pursue for addressing these problems,” explained study author <a href="https://rhodeslab.beckman.illinois.edu/" target="_blank">Justin Rhodes</a>, a professor at the University of Illinois, Urbana-Champaign.</p>
<p>“It is known that exercise increases the formation of new neurons in the hippocampus, and is a natural way to combat all the aforementioned mental health problems. It is further known that there are factors released into the blood that contribute to adult hippocampal neurogenesis. But most likely a mixture of factors rather than one magic chemical. The idea that extracellular vesicles containing lots of different kinds of molecules could communicate with complex chemical signatures from the blood to the brain and contribute to neurogenesis was not known until our study.”</p>
<p>To examine this, the researchers used two groups of mice. One group had unlimited access to running wheels for four weeks, while the other group was housed in cages with locked wheels, serving as a sedentary control. As expected, the running mice showed a significant increase in new brain cells in their own hippocampi, confirming the effectiveness of the exercise regimen.</p>
<p>After four weeks, the team collected blood from both the exercising and sedentary donor mice. From this blood, they isolated the extracellular vesicles using a filtration method that separates particles by size. This process yielded two distinct batches of vesicles: one from the exercising mice and one from the sedentary mice.</p>
<p>The team then administered these vesicles to a new set of sedentary recipient mice over a four-week period. These recipients were divided into three groups. One group received injections of vesicles from the exercising donors, a second group received vesicles from the sedentary donors, and a third group received a simple phosphate-buffered saline solution as a placebo.</p>
<p>To track the creation of new cells in the recipients’ brains, the mice were given injections of a labeling compound known as BrdU. This substance is incorporated into the DNA of dividing cells, effectively tagging them so they can be identified and counted later under a microscope. To ensure the reliability of their results, the entire experiment was conducted twice with two independent groups of mice.</p>
<p>The researchers found that mice that received vesicles from the exercising donors exhibited an approximately 50 percent increase in the number of new, BrdU-labeled cells in the hippocampus compared to mice that received vesicles from sedentary donors or the placebo solution. The findings were consistent across both independent cohorts, strengthening the conclusion that something within the exercise-derived vesicles was promoting cell proliferation.</p>
<p>“I was surprised that the vesicles were sufficient to increase neurogenesis,” Rhodes told PsyPost. “That is because when you exercise, there is not only the contribution of blood factors, but things going on in the brain like large amounts of neuronal activity in the hippocampus that I thought would be necessary for neurogenesis to occur. The results suggest apparently not, the vesicles alone without the other physiological components of actual exercise, are sufficient to increase neurogenesis to a degree, not the full degree, but to a degree.” </p>
<p>The researchers also examined the identity of these new cells to determine what they were becoming. Using fluorescent markers, they identified that, across all groups, about 89 percent of the new cells developed into neurons. A smaller fraction, about 6 percent, became a type of support cell called an astrocyte. This indicates that the vesicles from exercising mice increased the quantity of new neurons being formed, rather than changing what type of cells they became.</p>
<p>Finally, the team assessed whether the treatment affected the density of blood vessels in the hippocampus, as exercise is also known to promote changes in brain vasculature. By staining for a protein found in blood vessel walls, they measured the total vascular area. They found no significant differences in vascular coverage among the three groups, suggesting that the neurogenesis-promoting effect of the vesicles may be independent of vascular remodeling.</p>
<p>“One of the reasons exercise improves mental health is that it stimulates new neurons to form in an area of your brain that is important for learning and memory and for inhibiting stress, and now we know a big piece of the puzzle as to how exercise does this,” Rhodes said. “Exercise causes tissues like muscle and liver to secrete vesicles (sacs that contain lots of different kinds of chemicals) that reach the brain and stimulate neurogenesis.”</p>
<p>“Those vesicles can be taken from an animal that exercises and placed into an animal that is not exercising, and it can increase neurogenesis, not to the full level of that exercise does, but significantly increase it. That strongly suggests the vesicles themselves are carrying critical information. One can imagine a therapy in the future where either vesicles are harvested from exercising humans from their blood and introduced into individuals or synthetic vesicles are made that carry the unique mixture of chemicals that are identified in the exercise vesicles.” </p>
<p>While the findings point to extracellular vesicles as key players in exercise-induced brain plasticity, the study also highlights several areas for future inquiry. A primary question is whether the vesicles directly act on the brain or if their effects are mediated by peripheral organs. It is not yet known what fraction of the injected vesicles crossed the blood-brain barrier. The vesicles could potentially trigger signals in other parts of the body that then influence the brain.</p>
<p>The specific molecular cargo within the vesicles responsible for the neurogenic effect also needs to be identified. A companion study by the same research group found that vesicles from exercising mice were enriched with proteins related to brain plasticity, antioxidant defense, and cellular signaling. Future work will be needed to pinpoint which of these molecules, or combination of molecules, is responsible for the observed increase in new neurons.</p>
<p>“I think there are a lot of ways this could go,” Rhodes told PsyPost. “First, it is a pretty big black box between injecting the animals with vesicles and neurogenesis happening in the hippocampus. How many of the extracellular vesicles make it to the brain? Are they acting in the brain or in the periphery, i.e., maybe via peripheral nerves, mesenteric nervous system, immune activation or other ways we didn’t think of yet.” </p>
<p>“If they are reaching the brain, how do they merge with brain cells, do they reach astrocytes first? How do the vesicles get taken up by the brain cells is it through phagocytosis? What do the chemical signals do to the brain cells that causes increased neurogenesis? Do they act directly on neural progenitor cells, or astrocytes or mature neurons? What are the signaling mechanisms involved in the communication from the extracellular vesicles to the neurons/astrocytes/progenitor cells that causes neurogenesis to occur?”</p>
<p>The study, “<a href="https://doi.org/10.1016/j.brainres.2025.150003" target="_blank">Exercise-induced plasma-derived extracellular vesicles increase adult hippocampal neurogenesis</a>,” was authored by Meghan G. Connolly, Alexander M. Fliflet, Prithika Ravi, Dan I. Rosu, Marni D. Boppart, and Justin S. Rhodes.</p></p>
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<td><a href="https://www.psypost.org/scientists-question-caffeines-power-to-shield-the-brain-from-junk-food/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Scientists question caffeine’s power to shield the brain from junk food</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Oct 31st 2025, 18:00</div>
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<p><p>A recent study provides evidence that while a diet high in fat and sugar is associated with memory impairment, habitual caffeine consumption is unlikely to offer protection against these negative effects. These findings, which come from two related experiments, help clarify the complex interplay between diet, stimulants, and cognitive health in humans. The findings were published in <em><a href="https://doi.org/10.1016/j.physbeh.2025.115078" target="_blank" rel="noopener">Physiology & Behavior</a></em>.</p>
<p>Researchers have become increasingly interested in the connection between nutrition and brain function. A growing body of scientific work, primarily from animal studies, has shown that diets rich in fat and sugar can impair memory, particularly functions related to the hippocampus, a brain region vital for learning and recall.</p>
<p>Human studies have started to align with these findings, linking high-fat, high-sugar consumption with poorer performance on memory tasks and with more self-reported memory failures. Given these associations, scientists are searching for potential protective factors that might lessen the cognitive impact of a poor diet.</p>
<p>Caffeine is one of the most widely consumed psychoactive substances in the world, and its effects on cognition have been studied extensively. While caffeine is known to improve alertness and reaction time, its impact on memory has been less clear. Some research in animal models has suggested that caffeine could have neuroprotective properties, potentially guarding against the memory deficits induced by a high-fat, high-sugar diet. These animal studies hinted that caffeine might work by reducing inflammation or through other brain-protective mechanisms. However, this potential protective effect had not been thoroughly investigated in human populations, a gap this new research aimed to address.</p>
<p>To explore this relationship, the researchers conducted two experiments. In the first experiment, they recruited 1,000 healthy volunteers between the ages of 18 and 45. Participants completed a series of online questionnaires designed to assess their dietary habits, memory, and caffeine intake. Their consumption of fat and sugar was measured using the Dietary Fat and free Sugar questionnaire, which asks about the frequency of eating various foods over the past year.</p>
<p>To gauge memory, participants filled out the Everyday Memory Questionnaire, a self-report measure where they rated how often they experience common memory lapses, such as forgetting names or misplacing items. Finally, they reported their daily caffeine consumption from various sources like coffee, tea, and soda.</p>
<p>The results from this first experiment confirmed a link between diet and self-perceived memory. Individuals who reported eating a diet higher in fat and sugar also reported experiencing more frequent everyday memory failures. The researchers then analyzed whether caffeine consumption altered this relationship. The analysis suggested a potential, though not statistically strong, moderating effect.</p>
<p>When the researchers specifically isolated the fat component of the diet, they found that caffeine consumption did appear to weaken the association between high fat intake and self-reported memory problems. At low levels of caffeine intake, a high-fat diet was strongly linked to memory complaints, but this link was not present for those with high caffeine intake. This provided preliminary evidence that caffeine might offer some benefit.</p>
<p>The second experiment was designed to build upon the initial findings with a more robust assessment of memory. This study involved 699 healthy volunteers, again aged 18 to 45, who completed the same questionnaires on diet, memory failures, and caffeine use. The key addition in this experiment was an objective measure of memory called the Verbal Paired Associates task. In this task, participants were shown pairs of words and were later asked to recall the second word of a pair when shown the first. This test provides a direct measure of episodic memory, which is the ability to recall specific events and experiences.</p>
<p>The findings from the second experiment once again showed a clear association between diet and memory. A higher intake of fat and sugar was linked to more self-reported memory failures, replicating the results of the first experiment. The diet was also associated with poorer performance on the objective Verbal Paired Associates task, providing stronger evidence that a high-fat, high-sugar diet is connected to actual memory impairment, not just the perception of it.</p>
<p>When the researchers examined the role of caffeine in this second experiment, the results were different from the first. This time, caffeine consumption did not moderate the relationship between a high-fat, high-sugar diet and either of the memory measures. In other words, individuals who consumed high amounts of caffeine were just as likely to show diet-related memory deficits as those who consumed little or no caffeine.</p>
<p>This lack of a protective effect was consistent for both self-reported memory failures and performance on the objective word-pair task. The findings from this more comprehensive experiment did not support the initial suggestion that caffeine could shield memory from the effects of a poor diet.</p>
<p>The researchers acknowledge certain limitations in their study. The data on diet and caffeine consumption were based on self-reports, which can be subject to recall errors. The participants were also relatively young and generally healthy, and the effects of diet on memory might be more pronounced in older populations or those with pre-existing health conditions. Since the study was conducted online, it was not possible to control for participants’ caffeine intake right before they completed the memory tasks, which could have influenced performance.</p>
<p>For future research, the scientists suggest using more objective methods to track dietary intake. They also recommend studying different populations, such as older adults or individuals with obesity, where the links between diet, caffeine, and memory may be clearer. Including a wider array of cognitive tests could also help determine if caffeine has protective effects on other brain functions beyond episodic memory, such as attention or executive function. Despite the lack of a protective effect found here, the study adds to our understanding of how lifestyle factors interact to influence cognitive health.</p>
<p>The study, “<a href="https://doi.org/10.1016/j.physbeh.2025.115078" target="_blank" rel="noopener">Does habitual caffeine consumption moderate the association between a high fat and sugar diet and self-reported and episodic memory impairment in humans?</a>,” was authored by Tatum Sevenoaks and Martin Yeomans.</p></p>
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<td><a href="https://www.psypost.org/new-2-saliva-test-may-aid-in-psychiatric-diagnosis/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">New $2 saliva test may aid in psychiatric diagnosis</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Oct 31st 2025, 16:00</div>
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<p><p>A team of researchers in Brazil has engineered an inexpensive, disposable sensor that can detect a key protein linked to mental health conditions using a drop of saliva. Published in the journal <em><a href="https://doi.org/10.1021/acspolymersau.5c00038" target="_blank" rel="noopener">ACS Polymers Au</a></em>, the device could one day offer a rapid, non-invasive tool to help in the diagnosis and monitoring of disorders like depression and schizophrenia. The results are available in under an hour, offering a significant departure from current lab-based methods.</p>
<p>Diagnosing and managing psychiatric disorders currently relies heavily on clinical interviews and patient-reported symptoms, which can be subjective. Scientists have been searching for objective biological markers, and a protein called brain-derived neurotrophic factor, or BDNF, has emerged as a promising candidate. Lower-than-normal levels of BDNF, which supports the health and growth of neurons, have been consistently associated with conditions like major depression, bipolar disorder, and schizophrenia.</p>
<p>Existing methods for measuring BDNF typically involve blood draws and rely on complex, time-consuming laboratory procedures like the enzyme-linked immunosorbent assay. These techniques are often expensive and require specialized equipment and personnel, making them impractical for routine clinical use or for monitoring patient progress outside of a dedicated lab. The researchers sought to develop a fast, affordable, and non-invasive alternative that could be used at the point of care, motivated by the global increase in mental health conditions.</p>
<p>The foundation of the device is a small, flexible strip of polyester, similar to a piece of plastic film. Using a screen-printing technique, the scientists printed three electrodes onto this strip using carbon- and silver-based inks. This fabrication method is common in electronics and allows for inexpensive, mass production of the sensor strips.</p>
<p>To make the sensor specific to BDNF, the team modified the surface of the main working electrode in a multi-step process. First, they coated it with a layer of microscopic carbon spheres, which are synthesized from a simple glucose solution. This creates a large, textured surface area that is ideal for anchoring other molecules and enhances the sensor’s electrical sensitivity.</p>
<p>Next, they added a sequence of chemical layers that act as a sticky foundation for the biological components. Onto this foundation, they attached specialized proteins called antibodies. These anti-BDNF antibodies are engineered to recognize and bind exclusively to the BDNF protein, much like a key fits into a specific lock. A final chemical layer was added to block any remaining empty spots on the surface, which prevents other molecules in saliva from interfering with the measurement.</p>
<p>When a drop of saliva is applied to the sensor, any BDNF protein present is captured by the antibodies on the electrode. This binding event physically alters the electrode’s surface, creating a minute barrier that impedes the flow of electrons. The device then measures this change by sending a small electrical signal through the electrode and recording its resistance to that signal.</p>
<p>A greater amount of captured BDNF creates a larger barrier, resulting in a higher resistance, which can be precisely quantified. The entire process, from sample application to result, can be completed in about 35 minutes. The data is captured by a portable analyzer that can communicate wirelessly with a device like a smartphone, allowing for real-time analysis.</p>
<p>The research team demonstrated that their biosensor was remarkably sensitive. It could reliably detect BDNF across a vast concentration range, from incredibly minute amounts (as low as 10⁻²⁰ grams per milliliter) up to levels typically seen in healthy individuals.</p>
<p>This wide detection range is significant because it means the device could potentially identify the very low BDNF levels that may signal a disorder. It could also track the increase in BDNF levels as a patient responds positively to treatment, such as antidepressants, offering an objective measure of therapeutic success.</p>
<p>The sensor also proved to be highly selective. When tested against a variety of other substances commonly found in saliva, including glucose, uric acid, paracetamol, and even the spike protein from the SARS-CoV-2 virus, the device did not produce a false signal. It responded specifically to BDNF, confirming the effectiveness of its design.</p>
<p>Furthermore, tests using human saliva samples that were supplemented with known quantities of the protein showed that the sensor could accurately measure BDNF levels even within this complex biological fluid. The researchers estimated the cost of the materials for a single disposable strip to be around $2.19, positioning it as a potentially accessible diagnostic tool.</p>
<p>The current study was a proof-of-concept and has certain limitations. The experiments were conducted with a limited number of saliva samples from a single volunteer, which were then modified in the lab to contain varying concentrations of the target protein.</p>
<p>The next essential step will be to test the biosensor with a large and diverse group of patients diagnosed with various psychiatric conditions to validate its accuracy and reliability in a real-world clinical setting. Such studies would be needed to establish clear thresholds for what constitutes healthy versus potentially pathological BDNF levels in saliva. The researchers also plan to secure a patent for their technology and refine the device for potential commercial production. Future work could also explore integrating sensors for other biomarkers onto the same strip, allowing for a more comprehensive health assessment from a single saliva sample.</p>
<p>The study, “<a href="https://doi.org/10.1021/acspolymersau.5c00038" target="_blank" rel="noopener">Low-Cost, Disposable Biosensor for Detection of the Brain-Derived Neurotrophic Factor Biomarker in Noninvasively Collected Saliva toward Diagnosis of Mental Disorders</a>,” was authored by Nathalia O. Gomes, Marcelo L. Calegaro, Luiz Henrique C. Mattoso, Sergio A. S. Machado, Osvaldo N. Oliveira Jr., and Paulo A. Raymundo-Pereira.</p></p>
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<td><a href="https://www.psypost.org/the-secret-to-sustainable-ai-may-have-been-in-our-brains-all-along/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">The secret to sustainable AI may have been in our brains all along</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Oct 31st 2025, 14:00</div>
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<p><p>Researchers have developed a new method for training artificial intelligence that dramatically improves its speed and energy efficiency by mimicking the structured wiring of the human brain. The approach, detailed in the journal <em><a href="https://doi.org/10.1016/j.neucom.2025.131740" target="_blank" rel="noopener">Neurocomputing</a></em>, creates AI models that can match or even exceed the accuracy of conventional networks while using a small fraction of the computational resources.</p>
<p>The study was motivated by a growing challenge in the field of artificial intelligence: sustainability. Modern AI systems, such as the large language models that power generative AI, have become enormous. They are built with billions of connections, and training them can require vast amounts of electricity and cost tens of millions of dollars. As these models continue to expand, their financial and environmental costs are becoming a significant concern.</p>
<p>“Training many of today’s popular large AI models can consume over a million kilowatt-hours of electricity, which is equivalent to the annual use of more than a hundred US homes, and cost tens of millions of dollars,” said Roman Bauer, a senior lecturer at the University of Surrey and a supervisor on the project. “That simply isn’t sustainable at the rate AI continues to grow. Our work shows that intelligent systems can be built far more efficiently, cutting energy demands without sacrificing performance.”</p>
<p>To find a more efficient design, the research team looked to the human brain. While many artificial neural networks are “dense,” meaning every neuron in one layer is connected to every neuron in the next, the brain operates differently. Its connectivity is highly sparse and structured. For instance, in the visual system, neurons in the retina form localized and orderly connections to process information, creating what are known as topographical maps. This design is exceptionally efficient, avoiding the need for redundant wiring. The brain also refines its connections during development, pruning away unnecessary pathways to optimize its structure.</p>
<p>Inspired by these biological principles, the researchers developed a new framework called Topographical Sparse Mapping, or TSM. Instead of building a dense network, TSM configures the input layer of an artificial neural network with a sparse, structured pattern from the very beginning. Each input feature, such as a pixel in an image, is connected to only one neuron in the following layer in an organized, sequential manner. This method immediately reduces the number of connections, known as parameters, which the model must manage.</p>
<p>The team then developed an enhanced version of the framework, named Enhanced Topographical Sparse Mapping, or ETSM. This version introduces a second brain-inspired process. After the network trains for a short period, it undergoes a dynamic pruning stage. During this phase, the model identifies and removes the least important connections throughout its layers, based on their magnitude. This process is analogous to the synaptic pruning that occurs in the brain as it learns and matures, resulting in an even leaner and more refined network.</p>
<p>To evaluate their approach, the scientists built and trained a type of network known as a multilayer perceptron. They tested its ability to perform image classification tasks using several standard benchmark datasets, including MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100. This setup allowed for a direct comparison of the TSM and ETSM models against both conventional dense networks and other leading techniques designed to create sparse, efficient AI.</p>
<p>The results showed a remarkable balance of efficiency and performance. The ETSM model was able to achieve extreme levels of sparsity, in some cases removing up to 99 percent of the connections found in a standard network. Despite this massive reduction in complexity, the sparse models performed just as well as, and sometimes better than, their dense counterparts. For the more difficult CIFAR-100 dataset, the ETSM model achieved a 14 percent improvement in accuracy over the next best sparse method while using far fewer connections.</p>
<p>“The brain achieves remarkable efficiency through its structure, with each neuron forming connections that are spatially well-organised,” said Mohsen Kamelian Rad, a PhD student at the University of Surrey and the study’s lead author. “When we mirror this topographical design, we can train AI systems that learn faster, use less energy and perform just as accurately. It’s a new way of thinking about neural networks, built on the same biological principles that make natural intelligence so effective.”</p>
<p>The efficiency gains were substantial. Because the network starts with a sparse structure and does not require complex phases of adding back connections, it trains much more quickly. The researchers’ analysis of computational costs revealed that their method consumed less than one percent of the energy and used significantly less memory than a conventional dense model. This combination of speed, low energy use, and high accuracy sets it apart from many existing methods that often trade performance for efficiency.</p>
<p>A key part of the investigation was to confirm the importance of the orderly, topographical wiring. The team compared their models to networks that had a similar number of sparse connections but were arranged randomly. The results demonstrated that the brain-inspired topographical structure consistently produced more stable training and higher accuracy, indicating that the specific pattern of connectivity is a vital component of its success.</p>
<p>The researchers acknowledge that their current framework applies the topographical mapping only to the model’s input layer. A potential direction for future work is to extend this structured design to deeper layers within the network, which could lead to even greater gains in efficiency. The team is also exploring how the approach could be applied to other AI architectures, such as the large models used for natural language processing, where the efficiency improvements could have a profound impact.</p>
<p>The study, “<a href="https://doi.org/10.1016/j.neucom.2025.131740" target="_blank" rel="noopener">Topographical sparse mapping: A neuro-inspired sparse training framework for deep learning models</a>,” was authored by Mohsen Kamelian Rad, Ferrante Neri, Sotiris Moschoyiannis, and Roman Bauer.</p></p>
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<td><a href="https://www.psypost.org/vulnerability-to-stress-magnifies-how-a-racing-mind-disrupts-sleep/" style="font-family:Helvetica, sans-serif; letter-spacing:-1px;margin:0;padding:0 0 2px;font-weight: bold;font-size: 19px;line-height: 20px;color:#222;">Vulnerability to stress magnifies how a racing mind disrupts sleep</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Oct 31st 2025, 12:00</div>
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<p><p>A new study provides evidence that a person’s innate vulnerability to stress-induced sleep problems can intensify how much a racing mind disrupts their sleep over time. While daily stress affects everyone’s sleep to some degree, this trait appears to make some people more susceptible to fragmented sleep. The findings were published in the <em><a href="https://doi.org/10.1111/jsr.70220" target="_blank">Journal of Sleep Research</a></em>.</p>
<p>Scientists have long understood that stress can be detrimental to sleep. One of the primary ways this occurs is through pre-sleep arousal, a state of heightened mental or physical activity just before bedtime. Researchers have also identified a trait known as sleep reactivity, which describes how susceptible a person’s sleep is to disruption from stress. Some individuals have high sleep reactivity, meaning their sleep is easily disturbed by stressors, while others have low reactivity and can sleep soundly even under pressure.</p>
<p>Despite knowing these factors are related, the precise way they interact on a daily basis was not well understood. Most previous studies relied on infrequent, retrospective reports or focused on major life events rather than common, everyday stressors. The research team behind this new study sought to get a more detailed picture. They aimed to understand how sleep reactivity might alter the connection between daily stress, pre-sleep arousal, and objectively measured sleep patterns in a natural setting.</p>
<p>“Sleep reactivity refers to an individual’s tendency to experience heightened sleep disturbances when faced with stress. Those with high sleep reactivity tend to show increased pre-sleep arousal during stressful periods and are at greater risk of developing both acute and chronic insomnia,” explained study authors <a href="https://sleepcognition-lab.org/" target="_blank">Ju Lynn Ong and Stijn Massar</a>, who are both research assistant professors at the National University of Singapore Yong Loo Lin School of Medicine.</p>
<p>“However, most prior research on stress, sleep, and sleep reactivity has relied on single, retrospective assessments, which may fail to capture the immediate and dynamic effects of daily stressors on sleep. Another limitation is that previous studies often examined either the cognitive or physiological components of pre-sleep arousal in isolation. Although these two forms of arousal are related, they may differ in their predictive value and underlying mechanisms, highlighting the importance of evaluating both concurrently.”</p>
<p>“To address these gaps, the current study investigated how day-to-day fluctuations in stress relate to sleep among university students over a two-week period and whether pre-sleep cognitive and physiological arousal mediate this relationship—particularly in individuals with high sleep reactivity.”</p>
<p>The research team began by recruiting a large group of full-time university students. They had the students complete a questionnaire called the Ford Insomnia Response to Stress Test, which is designed to measure an individual’s sleep reactivity. From this initial pool, the researchers selected two distinct groups for a more intensive two-week study: 30 students with the lowest scores, indicating low sleep reactivity, and 30 students with the highest scores, representing high sleep reactivity.</p>
<p>Over the following 14 days, these 60 participants were monitored using several methods. They wore an actigraphy watch on their wrist, which uses motion sensors to provide objective data on sleep patterns. This device measured their total sleep time, the amount of time it took them to fall asleep, and the time they spent awake after initially drifting off. Participants also wore an ŌURA ring, which recorded their pre-sleep heart rate as an objective indicator of physiological arousal.</p>
<p>Alongside these objective measures, participants completed daily surveys on their personal devices. Each evening before going to bed, they rated their perceived level of stress. Upon waking the next morning, they reported on their pre-sleep arousal from the previous night. These reports distinguished between cognitive arousal, such as having racing thoughts or worries, and somatic arousal, which includes physical symptoms like a pounding heart or muscle tension.</p>
<p>The first part of the analysis examined within-individual changes, which looks at how a person’s sleep on a high-stress day compared to their own personal average. The results showed that on days when participants felt more stressed than usual, they also experienced a greater degree of pre-sleep cognitive arousal. This increase in racing thoughts was, in turn, associated with getting less total sleep and taking longer to fall asleep that night. This pattern was observed in both the high and low sleep reactivity groups.</p>
<p>This finding suggests that experiencing a more stressful day than usual is likely to disrupt anyone’s sleep to some extent, regardless of their underlying reactivity. It appears to be a common human response for stress to activate the mind at bedtime, making sleep more difficult. The trait of sleep reactivity did not seem to alter this immediate, day-to-day effect.</p>
<p>“We were surprised to find that at the daily level, all participants did in fact exhibit a link between higher perceived stress and poorer sleep the following night, regardless of their level of sleep reactivity,” Ong and Massar told PsyPost. “This pattern may reflect sleep disturbances as a natural—and potentially adaptive—response to stress.”</p>
<p>The researchers then turned to between-individual differences, comparing the overall patterns of people in the high-reactivity group to those in the low-reactivity group across the entire two-week period. In this analysis, a key distinction became clear. Sleep reactivity did in fact play a moderating role, amplifying the negative effects of stress and arousal.</p>
<p>Individuals with high sleep reactivity showed a much stronger connection between their average stress levels, their average pre-sleep cognitive arousal, and their sleep quality. For these highly reactive individuals, having higher average levels of cognitive arousal was specifically linked to spending more time awake after initially falling asleep. In other words, their predisposition to stress-related sleep disturbance made their racing thoughts more disruptive to maintaining sleep throughout the night.</p>
<p>The researchers also tested whether physiological arousal played a similar role in connecting stress to poor sleep. They examined both the participants’ self-reports of physical tension and their objectively measured pre-sleep heart rate. Neither of these measures of physiological arousal appeared to be a significant middleman in the relationship between stress and sleep, for either group. The link between stress and sleep disruption in this study seemed to operate primarily through mental, not physical, arousal.</p>
<p>“On a day-to-day level, both groups exhibited heightened pre-sleep cognitive arousal and greater sleep disturbances in response to elevated daily stress,” the researchers explained. “However, when considering the study period as a whole, individuals with high sleep reactivity consistently reported higher average levels of stress and pre-sleep cognitive arousal, which in turn contributed to more severe sleep disruptions compared to low-reactive sleepers. Notably, these stress → pre-sleep arousal → sleep associations emerged only for cognitive arousal, not for somatic arousal—whether assessed through self-reports or objectively measured via pre-sleep heart rate.”</p>
<p>The researchers acknowledged some limitations of their work. The study sample consisted of young university students who were predominantly female and of Chinese descent, so the results may not be generalizable to other demographic groups or age ranges. Additionally, the study excluded individuals with diagnosed sleep disorders, meaning the findings might differ in a clinical population. The timing of the arousal survey, completed in the morning, also means it was a retrospective report that could have been influenced by the night’s sleep. It is also important to consider the practical size of these effects. </p>
<p>While statistically significant, the changes were modest: a day with stress levels 10 points higher than usual was linked to about 2.5 minutes less sleep, and the amplified effect in high-reactivity individuals amounted to about 1.2 additional minutes of wakefulness during the night for every 10-point increase in average stress.</p>
<p>Future research could build on these findings by exploring the same dynamics in more diverse populations. The study also highlights pre-sleep cognitive arousal as a potential target for intervention, especially for those with high sleep reactivity. Investigating whether therapies like cognitive-behavioral therapy for insomnia can reduce this mental activation could offer a path to preventing temporary, stress-induced sleep problems from developing into chronic conditions.</p>
<p>The study, “<a href="https://doi.org/10.1111/jsr.70220" target="_blank">Sleep Reactivity Amplifies the Impact of Pre-Sleep Cognitive Arousal on Sleep Disturbances</a>,” was authored by Noof Abdullah Saad Shaif, Julian Lim, Anthony N. Reffi, Michael W. L. Chee, Stijn A. A. Massar, and Ju Lynn Ong.</p></p>
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<p><strong>Forwarded by:<br />
Michael Reeder LCPC<br />
Baltimore, MD</strong></p>
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