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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/the-surprising-link-between-your-inner-voice-and-your-heart-rate/" 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 surprising link between your inner voice and your heart rate</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 11th 2025, 08:00</div>
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<p><p>A new study published in <a href="https://doi.org/10.1111/psyp.70138"><em>Psychophysiology</em></a> has found that silently speaking to oneself in an emotional way – whether positively or negatively – can cause an increase in heart rate, even in the absence of physical movement.</p>
<p>For decades, psychologists have known that inner speech, which is the silent dialogue individuals have with themselves, is a common cognitive activity. Inner dialogue plays a role in regulating attention, motivation and emotion. However, limited research had been conducted on whether this internal voice could directly affect our body’s physiology.</p>
<p>Led by Mikkel Wallentin, researchers from Aarhus University in Denmark set out to explore this connection and investigate how emotional inner speech could influence heart rate compared to non-emotional speech, as well as the outcomes of positive compared to negative speech.</p>
<p>The research team conducted two experiments involving a total of 90 participants aged 18-75 years. Each person lay on a bed, wore heart rate monitors, and was asked to engage in one of three types of inner speech: positive self-encouragement, negative self-criticism, or neutral counting of numbers. Heart rate was continuously monitored and motion tracking ensured that any changes in heart rate were not attributable to physical movement.</p>
<p>In the first experiment, each trial lasted three minutes. In the second, the researchers shortened and varied the trial lengths to make the timing less predictable. Across both studies, the researchers observed that emotional inner speech – regardless of whether it was positive or negative – resulted in a statistically significant increase in heart rate compared to the neutral counting condition. However, no significant difference was found between the effects of positive and negative inner speech.</p>
<p>The heart rate increase typically began about 10 seconds after the start of the inner speech and tapered off over time. The researchers also ruled out other possible explanations, such as subtle body movements or changes in breathing. Even after filtering out these effects, the link between emotional inner speech and heart rate remained.</p>
<p>Interestingly, the study found no connection between these heart rate changes and participants’ scores on depression or rumination surveys. Most participants reported that they regularly talked to themselves and found the task easy to perform.</p>
<p>While the findings are compelling, the researchers caution that the effects were modest – less than one beat per minute on average – and may not reflect what happens in everyday life when people are walking, talking, or dealing with real emotional stress. Additionally, the study relied on participants to follow instructions about what kind of inner speech to use, without direct verification.</p>
<p>The study, “<a href="https://doi.org/10.1111/psyp.70138">Heart Talk: Emotional Inner Speech Increases Heart Rate</a>,” was authored by Mikkel Wallentin, Line Kruse, Xinyi Yan, Paula Samide, Anja Feibel Meerwald, David Trøst Fjendbo, and Johanne S. K. Nedergaard.</p></p>
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<td><a href="https://www.psypost.org/from-tango-to-starcraft-creative-activities-linked-to-slower-brain-aging-according-to-new-neuroscience-research/" 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;">From tango to StarCraft: Creative activities linked to slower brain aging, according to new neuroscience research</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 11th 2025, 06:00</div>
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<p><p>Engaging in creative activities such as music, dance, drawing, and even certain types of video games may support healthier brain aging, according to a large international study published in <em><a href="https://doi.org/10.1038/s41467-025-64173-9" target="_blank">Nature Communications</a></em>. The research provides new evidence that people who frequently engage in creative pursuits tend to have brain patterns associated with younger biological age. The study suggests that these activities may have measurable benefits for brain health, regardless of whether someone is a professional artist or simply a casual participant.</p>
<p>The idea that creative experiences might promote well-being has received increasing attention. Prior studies have connected artistic engagement to emotional regulation, social connection, and cognitive benefits. However, most of this research has relied on self-reported outcomes or general cognitive measures. While these studies offer support for the potential benefits of creativity, they leave open the question of whether these effects are also reflected in the biological processes of brain aging.</p>
<p>To explore this, researchers turned to the concept of “brain clocks.” These models use brain data to estimate a person’s biological brain age, which may differ from their actual chronological age. A younger-than-expected brain age is considered a potential indicator of healthier brain function, while an older-than-expected brain age has been linked to neurodegenerative and psychiatric conditions. </p>
<p>The researchers hypothesized that engaging in creative activities over time may be linked to patterns in brain activity associated with delayed aging. They also aimed to explore the underlying mechanisms of this relationship by analyzing brain network properties and testing whether short-term creative learning could produce similar, if smaller, effects.</p>
<p>“I’ve always been a musician. When I was younger, I played guitar while traveling across Europe, and music helped me cope with ADHD. It gave me focus, flow, and balance,” said senior author <a href="https://www.linkedin.com/in/agustin-ibanez-b727172b/" target="_blank">Agustin Ibanez</a>, a full professor at Universidad Adolfo Ibáñez, director of <a href="https://brainlat.uai.cl/autoridad/agustin-ibanez/" target="_blank">the Latin American Brain Health Institute (BrainLat)</a>, and professor at <a href="https://www.gbhi.org/profiles/agustin-ibanez" target="_blank">the Global Brain Health Institute (GBHI)</a> at Trinity College Dublin.</p>
<p>“Later, as a neuroscientist, I became fascinated by how creative experiences like music and dance can regulate stress and reshape brain dynamics. Yet, despite the growing evidence linking arts and health, we still lacked biological proof that creativity affects the brain. With this study, we wanted to close that gap by using brain clocks to measure whether creativity can literally slow brain aging.”</p>
<p>To examine this relationship, the researchers analyzed brain data from a large and diverse sample of 1,472 healthy adults across 13 countries. This included 1,240 participants whose brain scans were used to train machine learning models, and 232 participants whose creative experiences were analyzed in detail. The second group consisted of both creative experts—such as experienced tango dancers, musicians, visual artists, and strategy video game players—and individuals who completed a short-term learning program in gaming.</p>
<p>The team used a machine learning approach called support vector regression to create “brain clocks” based on resting-state brain activity recorded through electroencephalography (EEG) or magnetoencephalography (MEG). These tools measure the brain’s electrical signals and offer insight into how different areas of the brain are functionally connected. </p>
<p>By comparing each participant’s predicted brain age with their actual age, the researchers calculated a “brain age gap.” A negative gap, meaning the brain appeared younger than expected, was taken as a possible sign of healthier brain function.</p>
<p>After developing their brain clock model, the researchers used it to estimate the brain age of participants in two groups. The first included experts and matched non-experts across four creative domains: dance, music, visual arts, and video games. The second group included individuals who underwent 30 hours of training in a real-time strategy video game (StarCraft II) to assess whether learning a new creative skill could affect brain age.</p>
<p>The findings were consistent across creative disciplines. Experts in each domain had lower brain age gaps compared to non-experts, suggesting that their brains appeared younger than their actual age. This effect was seen in tango dancers, musicians, visual artists, and gamers. On average, experts had brain ages that were five to seven years younger than their chronological age. The researchers also found that the greater the level of creative expertise—as measured by years of experience or training—the larger the delay in brain age.</p>
<p>In the learning group, participants who completed the short-term gaming program also showed a modest reduction in brain age, although the effects were smaller than those seen in experts. The study included an active control group trained in a non-creative video game (Hearthstone), which did not show any significant brain age changes. This comparison helped isolate the role of creativity from other factors such as general cognitive engagement or game mechanics.</p>
<p>Ibanez was surprised by the consistency of the findings: “Across multiple creative domains, creative engagement was linked to delayed brain aging—an impact comparable to, and sometimes stronger than, the benefits reported for physical exercise or diet. This suggests that creativity should be seen as a true lifestyle factor.”</p>
<p>The brain regions showing the largest differences in connectivity tended to be those most affected by age in the general population, particularly areas involved in attention, motor coordination, and visual processing. These regions also showed increased functional connectivity in participants with more creative experience.</p>
<p>In addition to estimating brain age, the researchers examined how creativity might influence the brain’s efficiency. They used tools from network science to study how well different brain areas communicate with one another. People with more creative experience showed higher levels of local and global efficiency, meaning their brains were better organized for processing information. These patterns were linked to lower brain age gaps.</p>
<p>“In many studies, we see accelerated brain aging linked to inequality, pollution, or disease. Here, we saw the opposite: creativity delayed aging. That was both surprising and inspiring.”</p>
<p>Finally, the researchers used a computational model to simulate brain activity and assess the strength of connections between regions. They found that individuals with more creative expertise had stronger inter-regional coupling, which reflects more coordinated communication across the brain. This effect was specific to long-term expertise and did not appear in the short-term learning group.</p>
<p>“Creativity can help your health. Our results show that engaging in creative activities like music, dance, visual arts, or even gaming is associated with younger, healthier brain patterns. You don’t need to be a professional artist; creative play, learning, or expression in any form can contribute to brain vitality and emotional well-being.”</p>
<p>Although the findings suggest a link between creativity and delayed brain aging, the study does not establish cause and effect. It is possible that people with younger brains are more likely to engage in creative activities, or that other factors—such as education, lifestyle, or socioeconomic status—contribute to both creativity and brain health. </p>
<p>The researchers took steps to control for variables such as age, sex, and education, and they included matched control groups to strengthen their conclusions. Still, more research is needed to rule out alternative explanations.</p>
<p>“These are correlational findings, not causal. Creative engagement recruits systems for attention, emotion, memory, and movement; and these are the very networks that decline with age. So even if it’s not the sole cause, it’s part of the protective process. More research is need to establish causal effects.”</p>
<p>The study also relied on a combination of existing datasets, with each creative domain examined separately. While the results were consistent across domains, larger studies with more diverse forms of creativity—such as writing, acting, or crafts—could help expand the findings. </p>
<p>Future research could also examine whether creative engagement leads to improvements in other aspects of aging, such as memory, mood, or physical health. The researchers plan to extend their work by combining brain clock models with molecular aging markers and applying them in populations with neurodegenerative conditions. They are also developing studies that look at how lifestyle and cultural factors interact with creativity to influence the aging process.</p>
<p>“We’re expanding this research to include other lifestyle and cultural factors like exercise and multilingualism, integrating brain imaging with molecular aging clocks. Together with Daisy Fancourt and other colleagues, we’ve just launched the GRACE-Epi Project, funded by Wellcome Trust (starting in 2026), to test whether arts and cultural engagement influence biological aging markers such as metabolomics, proteomics, and epigenetics.” </p>
<p>“In parallel, I’m contributing to the forthcoming Lancet Series on Arts and Health, which will give global visibility to the biological and social mechanisms linking arts and well-being. We also plan to extend this work to neurodegenerative conditions, exploring how creativity and artistic engagement might foster resilience and slow disease progression.”</p>
<p>The study, “<a href="https://doi.org/10.1038/s41467-025-64173-9" target="_blank">Creative experiences and brain clocks</a>,” was authored by Carlos Coronel-Oliveros, Joaquin Migeot, Fernando Lehue, Lucia Amoruso, Natalia Kowalczyk-Grębska, Natalia Jakubowska, Kanad N. Mandke, Joana Pereira Seabra, Patricio Orio, Dominic Campbell, Raul Gonzalez-Gomez, Pavel Prado, Jhosmary Cuadros, Enzo Tagliazucchi, Josephine Cruzat, Agustina Legaz, Vicente Medel, Hernan Hernandez, Sol Fittipaldi, Florencia Altschuler, Sebastian Moguilner, Sandra Baez, Hernando Santamaria-Garcia, Alfredis González-Hernández, Jasmin Bonilla-Santos, Bahar Güntekin, Claudio Babiloni, Daniel Abasolo, Gaetano Di Caterina, Görsev G. Yener, Javier Escudero, John Fredy Ochoa-Gómez, Marcio Soto-Añari, Martin A. Bruno, Pedro A. Valdes-Sosa, Renato Anghinah, Rodrigo A. Gonzalez-Montealegre, Ruaridh A. Clark, Adolfo M. García, Laura Kaltwasser, Martin Schürmann, Jil M. Meier, Aneta Brzezicka, Robert Whelan, Brian Lawlor, Ian H. Robertson, Christopher Bailey, Lucia Melloni, Nisha Sajnani, and Agustin Ibanez.</p></p>
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<td><a href="https://www.psypost.org/artificial-intelligence-exhibits-human-like-cognitive-errors-in-medical-reasoning/" 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;">Artificial intelligence exhibits human-like cognitive errors in medical reasoning</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 10th 2025, 18:00</div>
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<p><p>A new study suggests that advanced artificial intelligence models, increasingly used in medicine, can exhibit human-like errors in reasoning when making clinical recommendations. The research found these AI models were susceptible to cognitive biases, and in many cases, the magnitude of these biases was even greater than that observed in practicing human doctors. The findings were published in <em><a href="https://ai.nejm.org/doi/10.1056/AIcs2400639" target="_blank">NEJM AI</a></em>.</p>
<p>The use of generative artificial intelligence in healthcare has expanded rapidly. These AI models, often called large language models, can draft medical histories, suggest diagnoses, and even pass medical licensing exams. They develop these abilities by processing immense quantities of text from the internet, including everything from scientific articles to popular media. The sheer volume and variety of this training data, however, means it is not always neutral or factual, and it can contain the same ingrained patterns of thought that affect human judgment.</p>
<p>Cognitive biases are systematic patterns of deviation from rational judgment. For example, the “framing effect” describes how the presentation of information can influence a decision. A surgical procedure described as having a 90 percent survival rate may seem more appealing than the same procedure described as having a 10 percent mortality rate, even though the outcomes are identical. </p>
<p>Researchers Jonathan Wang and Donald A. Redelmeier, from research institutes in Toronto, hypothesized that AI models, trained on data reflecting these human tendencies, might reproduce similar biases in their medical recommendations.</p>
<p>To test this idea, the researchers selected 10 well-documented cognitive biases relevant to medical decision-making. For each bias, they created a short, text-based clinical scenario, known as a vignette. Each vignette was written in two slightly different versions. While both versions contained the exact same clinical facts, one was phrased in a way designed to trigger a specific cognitive bias, while the other was phrased neutrally.</p>
<p>The researchers then tested two leading AI models: GPT-4, developed by OpenAI, and Gemini-1.0-Pro, from Google. They prompted the models to act as “synthetic respondents,” adopting the personas of 500 different clinicians. These personas were given a unique combination of characteristics, including medical specialty, years of experience, gender, and practice location. Each of these 500 synthetic clinicians was presented with both versions of all 10 vignettes, and the AI’s open-ended recommendations were recorded.</p>
<p>The results for GPT-4 showed a strong susceptibility to bias in nine of the ten scenarios tested. A particularly clear example was the framing effect. When a lung cancer surgery was described using survival statistics, 75 percent of the AI responses recommended the procedure. When the identical surgery was described using mortality statistics, only 12 percent of the responses recommended it. This 63-percentage-point difference was significantly larger than the 34-point difference observed in previous studies of human physicians presented with a similar scenario.</p>
<p>Another prominent bias was the “primacy effect,” where information presented first has an outsized influence. When a patient vignette began with the symptom of coughing up blood, the AI included pulmonary embolism in its potential diagnosis 100 percent of the time. </p>
<p>When the vignette began by mentioning the patient’s history of chronic obstructive pulmonary disease, pulmonary embolism was mentioned only 26 percent of the time. Hindsight bias was also extremely pronounced; a treatment was rated as inappropriate in 85 percent of cases when the patient outcome was negative, but in zero percent of cases when the outcome was positive.</p>
<p>In one notable exception, GPT-4 demonstrated a clear superiority to human reasoning. The model showed almost no “base-rate neglect,” a common human error of ignoring the overall prevalence of a disease when interpreting a screening test. The AI correctly calculated the probability of disease in both high-prevalence and low-prevalence scenarios with near-perfect accuracy (94 percent versus 93 percent). In contrast, prior studies show human clinicians struggle significantly with this type of statistical reasoning.</p>
<p>The researchers also explored whether the characteristics of the synthetic clinician personas affected the AI’s susceptibility to bias. While there were minor variations, with family physician personas showing slightly more bias and geriatrician personas slightly less, these differences were not statistically significant. No single characteristic, such as years of experience or practice location, appeared to protect the AI model from making biased recommendations.</p>
<p>A separate analysis was conducted using the Gemini-1.0-Pro model to see if the findings would be replicated. This model also displayed significant biases, but its patterns were different from both GPT-4 and human doctors. For example, Gemini did not exhibit the framing effect in the lung cancer scenario. In other tests, it showed biases in the opposite direction of what is typically seen in humans. </p>
<p>When testing for a bias related to capitulating to pressure, Gemini was less likely to order a requested test, not more. These results suggest that different AI models may have their own unique and unpredictable patterns of error.</p>
<p>The authors acknowledge certain limitations to their study. The AI models tested are constantly being updated, and future versions may incorporate safeguards against these biases. Detecting and correcting these ingrained reasoning flaws, however, is far more complex than filtering out obviously false or inappropriate content. The biases are often subtle and woven into the very medical literature used to train the models.</p>
<p>Another caveat is that the study used simulated clinical scenarios and personas, not real-world patient interactions. The research measured the frequency of biased recommendations but did not assess how these biases might translate into actual patient outcomes, costs, or other real-world impacts. The study was also limited to 10 specific cognitive pitfalls, and many other forms of bias may exist within these complex systems.</p>
<p>The findings suggest that simply deploying AI in medicine is not a guaranteed path to more rational decision-making. The models are not detached, purely logical agents; they are reflections of the vast and imperfect human data they were trained on. The authors propose that an awareness of these potential AI biases is a necessary first step. For these powerful tools to be used safely, clinicians will need to maintain their critical reasoning skills and learn to appraise AI-generated advice with a healthy degree of skepticism.</p>
<p>The study, “<a href="https://ai.nejm.org/doi/10.1056/AIcs2400639" target="_blank">Cognitive Biases and Artificial Intelligence</a>,” was authored by Jonathan Wang and Donald A. Redelmeier.</p></p>
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<td><a href="https://www.psypost.org/a-multi-scale-view-of-the-brain-uncovers-the-blueprint-of-intelligence/" 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;">A multi-scale view of the brain uncovers the blueprint of intelligence</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 10th 2025, 16:00</div>
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<p><p>A new analysis of the brain’s intrinsic communication networks suggests a layered organization behind human intelligence. The research, published in <em><a href="https://doi.org/10.1016/j.neuroimage.2025.121094" target="_blank">NeuroImage</a></em>, indicates that while efficient information flow within certain brain systems is associated with higher cognitive ability, excessive communication from networks involved in internal thought may hinder performance. These findings paint a more nuanced picture of how the brain’s architecture supports both general and specific intellectual skills.</p>
<p>The human brain is an extraordinarily complex network, and understanding how its structure and function give rise to cognitive abilities like intelligence remains a central challenge in neuroscience. Intelligence is not a single entity; it is often described as a hierarchy, with a general factor influencing broad abilities such as problem-solving, verbal understanding, and processing speed. </p>
<p>Researchers have long sought to identify the neural signatures that correspond to these different layers of cognition. A team of scientists from the G. D’Annunzio University of Chieti-Pescara in Italy designed a study to investigate this relationship by examining the brain’s functional connectivity, which maps the synchronized activity between different brain regions.</p>
<p>The researchers conceptualized the brain as a collection of specialized modules, or communities of brain regions that communicate intensely with one another. To get a comprehensive view, they analyzed this modular structure at multiple scales, or resolutions. </p>
<p>A low-resolution view reveals large, sprawling networks, while a high-resolution view pinpoints smaller, more specialized circuits. They hypothesized that this multi-scale brain organization would mirror the hierarchical structure of intelligence itself, with general abilities relating to large-scale networks and specific skills relating to finer, more localized modules.</p>
<p>To test this, the team conducted three separate studies, each using a different set of cognitive assessments and participants. The first study involved 33 university students who underwent resting-state functional magnetic resonance imaging (fMRI), a technique that measures brain activity while a person is at rest. </p>
<p>This provides a snapshot of the brain’s baseline communication patterns. Participants also completed the Wechsler Adult Intelligence Scale (WAIS-IV), a comprehensive test that yields a full-scale intelligence quotient (IQ) score as well as indices for perceptual reasoning, processing speed, working memory, and verbal comprehension.</p>
<p>The analysis focused on two key network properties. The first was nodal efficiency, a measure of how effectively a brain region can exchange information with the rest of the network. The second was the participation coefficient, which quantifies how much a region communicates across different modules, acting as a bridge between specialized communities. </p>
<p>The results showed that higher full-scale IQ scores were associated with greater nodal efficiency in a widespread “task-positive” network, which includes regions in the frontal, parietal, and occipital lobes that are typically active during goal-oriented tasks. This connection was most evident at a low-resolution scale, suggesting that general intelligence relies on the efficient functioning of a large, integrated brain system.</p>
<p>When the researchers examined the brain’s network at a higher resolution, they found that specific cognitive abilities were linked to the efficiency of smaller, more specialized modules. Better processing speed, for example, was associated with greater efficiency within the visual network. Superior perceptual reasoning was connected to efficiency in sensorimotor and fronto-parietal networks, which are involved in integrating sensory information and executing cognitive control. These findings support the idea that the brain encodes intelligence hierarchically, with broad networks supporting general ability and distinct subsystems supporting specialized skills.</p>
<p>The study also produced another consistent pattern. Higher scores on full-scale IQ, perceptual reasoning, and processing speed were all associated with lower participation coefficients in the default mode and subcortical networks. These networks are typically active during inward-focused thought, such as daydreaming or recalling memories. The negative association suggests that better cognitive performance is linked to less cross-talk from these internally oriented systems. This may reflect an ability to shield task-focused processing from irrelevant internal distractions.</p>
<p>The second study aimed to replicate and extend these findings using a larger, publicly available dataset from the Human Connectome Project, which included 500 participants. These individuals completed a different set of cognitive tests. The researchers first grouped the test scores into four broad cognitive categories: Language, Executive Functions, Fluid Reasoning, and Memory. They then performed the same brain network analysis on the participants’ resting-state fMRI scans.</p>
<p>The results from this larger dataset were consistent with the first study. Higher scores in fluid reasoning, which involves abstract problem-solving, were linked to greater nodal efficiency in sensorimotor and fronto-parietal networks. Better memory performance was associated with higher efficiency in the visual network. </p>
<p>The analysis again revealed that better performance in language and memory was linked to a lower degree of cross-network communication from the default mode network. The convergence of these findings across two independent datasets and different cognitive tests strengthens the conclusions.</p>
<p>For the third study, the research team recruited another group of 37 university students. These participants completed the Raven’s Progressive Matrices, a well-known test designed specifically to measure fluid reasoning. The analysis of their brain scans confirmed the patterns observed in the previous two studies. Higher fluid reasoning scores were associated with greater efficiency in sensorimotor and fronto-parietal executive networks. At the same time, better performance was linked to reduced cross-network communication involving subcortical, limbic, and fronto-parietal modules.</p>
<p>Taken together, the three studies build a cohesive account of how the brain’s intrinsic network architecture relates to intelligence. The efficiency of information processing within key sensory and executive networks appears to be a positive contributor to cognitive ability. Conversely, the degree to which internally focused networks integrate with the rest of the brain seems to have a negative influence on performance, possibly by creating interference. The multi-resolution approach also provides novel evidence for a parallel between the brain’s layered network organization and the hierarchical structure of human intelligence.</p>
<p>The study is not without its limitations. Because three independent groups of participants were used, a direct, one-to-one comparison of the results from the different cognitive tests was not possible. Another point of consideration is the use of resting-state fMRI, which measures the brain’s baseline activity rather than its activity during an active cognitive task. </p>
<p>Future research could explore how these brain networks reconfigure when individuals are actively engaged in problem-solving. Such studies could help clarify the dynamic interplay between network efficiency and segregation during real-time cognitive processing. Future work might also investigate whether interventions aimed at modulating these network properties could lead to changes in cognitive performance.</p>
<p>The study, “<a href="https://doi.org/10.1016/j.neuroimage.2025.121094" target="_blank">Intrinsic brain mapping of cognitive abilities: A multiple-dataset study on intelligence and its components</a>,” was authored by Simone Di Plinio, Mauro Gianni Perrucci, Grazia Ferrara, Maria Rita Sergi, Marco Tommasi, Mariavittoria Martino, Aristide Saggino, and Sjoerd JH Ebisch.</p></p>
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<td><a href="https://www.psypost.org/cognitive-disability-might-be-on-the-rise-in-the-u-s-particularly-among-younger-adults/" 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;">Cognitive disability might be on the rise in the U.S., particularly among younger adults</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 10th 2025, 14:00</div>
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<p><p>A retrospective analysis of data from the Centers for Disease Control and Prevention’s Disability and Health Data System revealed that the prevalence of self-reported cognitive disability rose from 5.3% in 2013 to 7.4% in 2023. The increase was most pronounced among young adults, rising from 5.1% in 2013 to 9.7% in 2023 for this group. The research was published in <em>Neurology</em>.</p>
<p>Cognitive disability refers to a significant limitation in a person’s ability to think, learn, remember, or solve problems compared to what is typical for their age. It can affect skills such as reasoning, understanding language, attention, and the ability to plan or make decisions.</p>
<p>Some people are born with cognitive disabilities due to genetic or developmental conditions like Down syndrome or fragile X syndrome. Others may acquire them later in life through brain injury, stroke, or illnesses that damage the nervous system. Cognitive disabilities often co-occur with other challenges, such as motor or sensory impairments, but they can also exist on their own.</p>
<p>In the United States, more than 70 million adults are affected by some form of disability. That is 1 in 4 individuals. Among these, cognitive disability is the first or the second most reported type of disability (depending on the source of the report).</p>
<p>Study author Ka-Ho Wong and his colleagues wanted to explore how cognitive disability prevalence changed over the past decade in the U.S. and whether associations between cognitive disability and race, age, and other social determinants of health persist over time.</p>
<p>They analyzed data from the Centers for Disease Control and Prevention’s Disability and Health Data System, which integrates nationally representative responses from US adults. They analyzed a total of 4,507,061 responses collected between 2013 and 2023.</p>
<p>Cognitive disability was identified using the following question: “Because of a physical, mental, or emotional condition, do you have serious difficulty concentrating, remembering, or making decisions?”. Participants who answered with a “Yes” to this question were considered to have self-reported having a cognitive disability.</p>
<p>Participants also self-reported depression by answering the question “Have you ever been told by a doctor, nurse, or other health professional that you have a depressive disorder, including depression, major depression, dysthymia, or minor depression?” and reported various other health-related and demographic data.</p>
<p>The researchers excluded individuals from their analysis who had also reported ever being diagnosed with a depressive disorder. This was done to better isolate cognitive challenges not attributable to depression.</p>
<p>Results showed that the prevalence of self-reported cognitive disability increased steadily during the study period, rising from 5.3% in 2013 to 7.4% in 2023. Young adults, aged 18 to 39, exhibited the largest increases of any age group, with prevalence nearly doubling from 5.1% in 2013 to 9.7% in 2023.</p>
<p>In contrast, the prevalence among individuals aged 70 or older slightly decreased from 7.3% in 2013 to 6.6% in 2023. Examining different racial and ethnic groups, the prevalence of cognitive disability in 2023 was highest among American Indian and Alaska Native individuals (11.2%) and Hispanic individuals (9.9%). The prevalence was lowest among Asian individuals (4.8% in 2023). There were no substantial gender differences.</p>
<p>“The disproportionate growth in cognitive disability among younger adults seems to be the primary driver of the overall national trend. These findings warrant further investigation, given their potential long-term implications for population health, workforce productivity, and health care systems,” the study authors concluded.</p>
<p>The study sheds light on trends in self-reported cognitive health in the U.S. over the past decade. However, it should be noted that cognitive disability in this study was measured with a single, self-reported question, which means reporting bias could have affected the results.</p>
<p>The paper, “<a href="https://doi.org/10.1212/WNL.0000000000214226">Rising Cognitive Disability as a Public Health Concern Among US Adults,</a>” was authored by Ka-Ho Wong, Christopher D. Anderson, Cecilia Peterson, Erin Bouldin, Lauren Littig, Neeharika Krothapalli, Trieste Francis, Yvonne Kim, Giselle Cucufate, Jonathan Rosand, Kevin Navin Sheth, and Adam de Havenon.</p></p>
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<td><a href="https://www.psypost.org/for-individuals-with-depressive-symptoms-birdsong-may-offer-unique-physiological-benefits/" 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;">For individuals with depressive symptoms, birdsong may offer unique physiological benefits</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 10th 2025, 12:00</div>
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<p><p>A recent investigation suggests that listening to birdsong can alleviate sadness about as effectively as a guided mindful breathing exercise. The study also indicates that for individuals with depressive symptoms, the natural sounds of birds may offer unique benefits for physiological recovery. These findings were published in the journal <em><a href="https://doi.org/10.1111/aphw.70081" target="_blank">Applied Psychology: Health and Well-Being</a></em>.</p>
<p>The sounds of nature are increasingly being recognized as a potential resource for supporting mental health. While visual exposure to natural scenery has been widely studied, the auditory components of the environment are now receiving more focused attention. Researchers are exploring how sounds like flowing water, wind, and birdsong can influence human emotion and physiology.</p>
<p>Among these natural sounds, birdsong is particularly noteworthy. It is often considered a restorative element in soundscapes and has been shown in some studies to be more effective at reducing stress than other natural sounds. Birdsong is also highly accessible, present even in urban parks and backyards, making it a practical option for widespread exposure. </p>
<p>A team of researchers from the Department of Psychology and Behavioral Sciences and the College of Biomedical Engineering and Instrument Science at Zhejiang University in China sought to understand its specific effects on sadness, a core emotion associated with depression.</p>
<p>Past research frequently bundled birdsong with other nature sounds or compared it only to silence or urban noise. This made it difficult to isolate the effects of the birdsong itself. To create a more rigorous test, the Zhejiang University team decided to compare it against a well-established technique for emotion regulation: audio-guided mindfulness. </p>
<p>Mindfulness practices require active, controlled attention to one’s thoughts and physical sensations. Listening to birdsong, in contrast, is a more passive experience, engaging the senses with a lower cognitive demand. The researchers hypothesized that this passive quality might make birdsong an especially suitable intervention for individuals with depressive symptoms, who can sometimes find cognitively demanding tasks difficult to engage with.</p>
<p>To measure the body’s response in addition to self-reported feelings, the scientists used a technique called heart rate variability analysis. Heart rate variability is a measure of the small variations in time between each heartbeat. It provides a window into the nervous system’s ability to adapt to stress and regulate emotions. Generally, greater variability is associated with better emotional and physiological resilience, while reduced variability is often observed in individuals experiencing sadness or depression.</p>
<p>The research team recruited 187 university students for their study. Participants were first assessed for depressive symptoms using two standardized questionnaires. Based on their scores, they were categorized as either having depressive symptoms or not. This created two large pools of participants. Each pool was then randomly split, with half assigned to a birdsong intervention and the other half to a mindful breathing intervention. This resulted in four distinct groups for comparison.</p>
<p>The experiment for each participant proceeded in three main stages, each lasting six minutes. First, a baseline resting phase allowed researchers to record initial emotional and physiological states. Next, participants underwent a sadness induction phase where they watched emotionally charged clips from one of two sad films. This step was designed to produce a consistent feeling of sadness across all groups.</p>
<p>Following the sadness induction, the intervention phase began. Participants listened to one of two audio recordings. The birdsong group heard a high-quality recording of mixed birdsongs, selected in consultation with ornithology experts. The mindfulness group listened to an audio track guiding them through a typical mindful breathing exercise, which involved focusing on breath and bodily sensations. </p>
<p>Throughout all three phases, researchers collected electrocardiogram signals to measure heart rate variability and asked participants to rate their emotional state on three dimensions: valence (the pleasantness of the feeling), arousal (the intensity of the feeling), and dominance (the sense of being in control of the feeling).</p>
<p>The analysis of the data produced several key observations. The sadness induction procedure was effective for all participants. After watching the film clips, individuals across all four groups reported feeling significantly less pleasant, more emotionally aroused, and less in control. Their physiological data showed corresponding changes, including a decrease in heart rate and an increase in short-term heart rate fluctuations, a pattern consistent with an acute state of sadness.</p>
<p>Both the birdsong and the mindful breathing interventions successfully counteracted these effects. After the six-minute listening period, participants in both intervention types reported a significant increase in pleasant feelings, a decrease in emotional intensity, and a restored sense of control. This confirmed that both methods were effective at reducing the subjective experience of sadness.</p>
<p>Subtle differences emerged between the two interventions. The mindful breathing exercise resulted in a slightly greater improvement in emotional pleasantness compared to the birdsong recording. Participants who practiced mindful breathing were also more likely to select positive emotion labels to describe their state after the intervention.</p>
<p>The physiological data revealed another layer of complexity. For individuals without depressive symptoms, the mindful breathing practice was more effective at stabilizing short-term fluctuations in heart rate. For participants with depressive symptoms, however, listening to birdsong produced a more pronounced stabilizing effect on these same heart rate fluctuations. This finding suggests that the passive, low-effort nature of listening to birdsong may be particularly beneficial for promoting physiological regulation in individuals experiencing depressive symptoms.</p>
<p>The study provides a nuanced look at how different auditory experiences can aid in emotion regulation, but the researchers acknowledge some limitations. The experiment was conducted in a controlled laboratory setting, and its effects were measured only immediately after the intervention. The long-term benefits of repeated exposure to birdsong remain an open question for future investigation.</p>
<p>The participants were all university students, a relatively narrow demographic. Studies involving a more diverse population could help determine if these effects are consistent across different age groups and cultural backgrounds. Researchers also suggest that future work could explore which specific acoustic characteristics of birdsong, such as its pitch, rhythm, or complexity, are most responsible for its positive effects. Including a neutral sound condition, like white noise, could also help clarify the mechanisms at play.</p>
<p>This research contributes to a growing understanding of how simple, accessible elements of the natural world can be harnessed to support mental well-being. It suggests that while active practices like mindfulness are powerful tools, passive engagement with natural soundscapes may offer a complementary and sometimes more suitable path toward emotional balance, especially for those navigating the challenges of a low mood.</p>
<p>The study, “<a href="https://doi.org/10.1111/aphw.70081" target="_blank">Birdsongs and audio-guided mindful breathing: Comparable sadness-reducing effects in the lab</a>,” was authored by Xuanyi Wang, Tian Lu, Wanlin Chen, Jing Zheng, Hang Chen, and Shulin Chen.</p></p>
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<td><a href="https://www.psypost.org/mind-captioning-this-scientist-just-used-ai-to-translate-brain-activity-into-text/" 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;">Mind captioning: This scientist just used AI to translate brain activity into text</a>
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<p><p>A new study published in <a href="https://doi.org/10.1126/sciadv.adw1464"><em>Science Advances</em></a> presents a method that converts human brain activity into coherent, descriptive text—even when the brain is not actively processing language. Instead of decoding words or sentences directly, the method interprets the nonverbal representations that occur before thoughts are put into words.</p>
<p>The study suggests that even when individuals are only watching or recalling silent video clips, their brain activity contains enough structured information to generate accurate descriptions of the scenes. Using functional MRI and advanced language models, <a href="https://www.kecl.ntt.co.jp/people/tomoyasu.horikawa.hp/">Tomoyasu Horikawa</a>, a distinguished researcher at NTT’s Communication Science Laboratories in Japan, was able to produce natural-language captions that closely matched both the objective content of the videos and the participants’ subjective recollections.</p>
<p>The motivation behind this work stemmed from a long-standing challenge in neuroscience: how to decode and interpret the rich, internal content of the human mind. While previous studies have shown some success in mapping brain activity to language, these efforts often rely on participants actively thinking in words, such as by speaking, reading, or listening. Such approaches limit the scope of decoding because not all mental experiences are verbal, and not all individuals have equal access to language, particularly those with conditions like aphasia.</p>
<p>Human thoughts often involve visual scenes, events, and abstract concepts that are not immediately translated into words. These mental representations can be detailed and structured, incorporating relationships between objects, actions, and environments. However, most decoding methods fall short of capturing this complexity, especially when relying on models that either imitate existing language structures or depend on hand-crafted databases of descriptions.</p>
<p>The researcher aimed to bridge this gap by developing a method that could interpret nonverbal mental representations—those formed during perception or memory—into coherent and meaningful text. The goal was not to read minds in the traditional sense, but to provide an interpretive interface that reflects what the brain is representing during an experience.</p>
<p>“I’ve long been fascinated by how the brain generates and represents content associated with our subjective conscious experiences, such as mental imagery and dreaming,” Horikawa told PsyPost. “I believe that brain decoding technology can help us investigate these questions while providing clear and intuitive interpretations of the information encoded in the brain.”</p>
<p>“Developing more sophisticated decoding methods could therefore advance our understanding of the neural bases of conscious experience — and, in the long run, help people whose difficulties might be relieved or overcome through direct information readout from the brain. The idea of mind captioning grew out of this effort — to better understand how such internal representations can be translated into language and shared meaningfully.”</p>
<p>Horikawa designed a decoding method called “mind captioning.” The approach involves two main steps: first, translating brain activity into semantic features using a deep language model; and second, generating natural language descriptions that align with those semantic features.</p>
<p>The study involved six adult participants, all native Japanese speakers with varying levels of English proficiency. They were shown thousands of short video clips depicting a wide range of visual content, including objects, actions, and social interactions. These videos were silent and shown without accompanying language. Functional MRI scans captured the participants’ brain activity during both the viewing of the videos and subsequent mental recall of the same clips.</p>
<p>The researcher trained a set of linear decoding models to map patterns of brain activity to semantic features extracted from captions written about each video. These semantic features were derived using a language model known as DeBERTa, which is designed to represent the meaning of text in a high-dimensional space.</p>
<p>After learning this mapping, the decoder was applied to new brain activity from both perception and recall conditions. The resulting semantic features were then used to generate text using another language model (RoBERTa) optimized for filling in missing words in a sentence. Through an iterative process of guessing, testing, and replacing words, the system gradually produced full sentences that reflected the brain’s decoded representations.</p>
<p>These generated sentences were evaluated in several ways. First, they were compared to human-written captions for accuracy and similarity using standard natural language evaluation metrics like BLEU, ROUGE, and BERTScore. The results showed that the machine-generated descriptions were highly discriminative: they could distinguish between different videos with strong reliability, even among 100 options.</p>
<p>The decoding method, when applied to participants’ brain activity, could identify the correct video with nearly 50% accuracy—a substantial improvement over the 1% expected by chance.</p>
<p>Notably, the method also generated quality descriptions from brain activity during the recall phase, though performance was not as high as for direct viewing. This indicates that the method could verbalize remembered experiences without requiring external stimuli. In some cases, the decoder performed well even on single instances of mental imagery.</p>
<p>“When I first tested the text generation algorithm after coming up with the approach, I was genuinely surprised to see how the original text corresponding to the extracted semantic features was progressively built up — step by step — into a coherent structure,” Horikawa said. “It felt as if I were hearing the faint voice of the brain seeping through the noise of the data, which made me confident that the approach could work.”</p>
<p>One of the key findings is that these descriptions included more than just lists of objects. They captured interactions and relationships, such as who did what to whom, or how different elements were arranged in space. When the word order of the generated sentences was shuffled, their similarity to the reference captions dropped sharply, showing that the original structure conveyed relational meaning, not just vocabulary.</p>
<p>“Another impressive finding came from the neuroscientific analysis <a href="https://www.science.org/doi/10.1126/sciadv.adw1464#F4" target="_blank" rel="noopener">shown in Figure 4E</a>, where we examined how perception-trained decoders generalized to mental imagery using different types of feature representations (visual, visuo-semantic, and semantic),” Horikawa told PsyPost. “Although this trend was conceptually expected, we observed a remarkably clear gradient of generalizability across these levels, with semantic representations showing the strongest ability to bridge neural patterns between perception and recall.”</p>
<p>The study also found that descriptions could be generated without relying on activity in the brain’s traditional language areas. Even when these regions were excluded from analysis, the system still produced intelligible and structured descriptions. This suggests that meaningful semantic information is distributed across brain regions that process visual and contextual information, not just language.</p>
<p>“The study shows that it’s possible to generate coherent, meaningful text from brain activity — not by decoding language itself, but by interpreting the nonverbal representations that come before language,” Horikawa explained. “This may suggest that our thoughts are organized in a way that already carries structural information even before we put them into words, offering a new window into how the brain transforms experience into expression.”</p>
<p>“In the future, if we can learn to express ourselves more freely or interact with machines directly through our own brain activity, as in brain–machine interfaces, we may be able to unlock more of the brain’s potential.”</p>
<p>Although the study presents a promising approach, it comes with several limitations. The sample size was small, involving only six participants, all of whom underwent extensive training and scanning. However, each subject contributed many hours of data, which helped improve the decoding model’s reliability.</p>
<p>“Although our study included a relatively small number of participants, each contributed a substantial amount of data (about 17 hours of brain scanning), which allowed us to establish strong and reliable effects within individuals,” Horikawa said. “For example, the model achieved around 50% accuracy in a 100-alternative video identification task for each participant (see supplementary) — highly reliable performance given the difficulty of the problem (chance = 1%).”</p>
<p>“Importantly, these robust within-subject effects were consistently observed across all six participants, suggesting that the findings are practically significant despite the limited number of participants.”</p>
<p>Another limitation lies in the nature of the stimuli. The videos used in the study reflected common, real-world scenarios. It’s unclear whether the method would work as well for abstract concepts, atypical scenes, or highly personal mental content like dreams.</p>
<p>“As our method generates text from brain activity, it may be misinterpreted as a form of language decoding or reconstruction,” Horikawa noted. “However, this is not actually decoding of language information in the brain, but rather a linguistic interpretation of non-linguistic mental representations. Our method leverages the universal and versatile nature of natural language to provide intelligible interpretations of the information represented in the brain.”</p>
<p>There are also concerns about privacy. The idea of interpreting mental content raises ethical questions about autonomy and consent. While the current method requires large amounts of data from cooperative individuals, future advances may reduce this barrier.</p>
<p>“Some people may worry that this technology poses risks to mental privacy,” Horikawa told PsyPost. “In reality, the current approach cannot easily read a person’s private thoughts — it requires substantial data collection from highly cooperative participants, and its accuracy remains limited, with outputs affected by bias and noise. At present, the risks appear to be not high, though the ethical and social implications should continue to be discussed carefully as the technology develops.”</p>
<p>“What is important is not only to develop these technologies responsibly, but also to reflect on how we handle the information decoded from brain activity. We should avoid immediately treating the outputs as someone’s ‘true thoughts,’ and instead ensure that individuals retain autonomy in deciding whether and how to regard or present such outputs as their own intentions.”</p>
<p>Looking ahead, the approach could be extended to other types of mental content, such as auditory experiences, emotions, or internal narratives. It may also help in designing communication systems for individuals who cannot use speech or writing. By treating language as a bridge rather than the source, the method opens new possibilities for exploring how the brain generates and organizes meaning before it is expressed.</p>
<p>“My long-term goal is to understand the neural mechanisms underlying our subjective conscious experiences, and to help humans more fully realize the potential of the brain through scientific and technological advances,” Horikawa explained. “We plan to continue improving brain decoding approaches to access the information encoded in the brain more accurately and in greater detail, while ensuring that these technologies remain both scientifically valuable for understanding the brain and beneficial for people.”</p>
<p>The study, “<a href="https://doi.org/10.1126/sciadv.adw1464">Mind captioning: Evolving descriptive text of mental content from human brain activity</a>,” was authored by Tomoyasu Horikawa.</p></p>
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<td><a href="https://www.psypost.org/brain-imaging-study-reveals-how-different-parts-of-the-brain-fall-asleep-at-different-times/" 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;">Brain imaging study reveals how different parts of the brain “fall asleep” at different times</a>
<div style="font-family:Helvetica, sans-serif; text-align:left;color:#999;font-size:11px;font-weight:bold;line-height:15px;">Nov 10th 2025, 10:00</div>
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<p><p>As people drift into sleep, their brains do not shut down all at once. Instead, different brain regions change their activity and energy use at different times and in distinct ways. A new study published in <em><a href="https://doi.org/10.1038/s41467-025-64414-x" target="_blank">Nature Communications</a></em> provides detailed evidence that sensory and cognitive regions of the brain follow separate paths into sleep, with important implications for how the brain remains responsive to the environment even while unconscious.</p>
<p>The findings provide new insight into the complex processes that govern sleep. They help explain how the brain can maintain a degree of responsiveness to sounds or other sensory inputs during light sleep, potentially allowing a person to wake up if something important happens nearby.</p>
<p>Although scientists have long known that sleep involves changes in brain activity and energy use, it has been less clear how these changes unfold across different parts of the brain. Earlier studies have suggested that sleep supports functions such as memory consolidation and waste removal. However, the coordination of various physiological processes like blood flow, metabolism, and electrical activity during sleep has not been well understood.</p>
<p>One challenge has been the limitations of previous brain imaging methods. While tools like functional magnetic resonance imaging (fMRI) can track blood flow changes, they do not directly measure energy use. Positron emission tomography (PET), which can assess glucose metabolism, typically lacks the time resolution to capture rapid changes. As a result, researchers have had a hard time observing how the brain’s energy use and activity fluctuate together during the transition from wakefulness to sleep.</p>
<p>To address this gap, the team behind the current study combined several imaging techniques to monitor the brain’s activity in real time as participants moved from being awake to asleep. Their goal was to better understand how different physiological systems—neural activity, blood flow, and glucose metabolism—interact during the sleep process.</p>
<p>“We were motivated by the expectation that different physiological processes in the brain—such as metabolism, blood flow, and neural activity—must be tightly coordinated during sleep. Yet most human studies have examined these processes separately, leaving the nature of their coordination largely unknown,” said study author <a href="https://jechenlab.com/" target="_blank">Jingyuan Chen</a>, an assistant investigator at Massachusetts General Brigham and an assistant professor of radiology at Harvard Medical School. </p>
<p>“Until recently, capturing multiple physiological processes simultaneously was technically difficult. Now, with advances in neuroimaging, we can use dynamic PET to track metabolic changes over time and combine it with EEG-fMRI to simultaneously measure neural activity and blood flow. These tools allow us to directly observe how these different systems interact during the transition from wakefulness to NREM sleep.”</p>
<p>Twenty-six healthy adults took part in the study, which took place during the afternoon after participants had slept only four hours the night before. Participants lay in a brain scanner with their eyes closed and were encouraged to fall asleep. </p>
<p>The researchers monitored their brain activity throughout the session. For those undergoing EEG, an expert later identified when participants were awake or in various stages of non-REM sleep. Others were evaluated based on behavioral signs, such as a keypress task that tracked responsiveness.</p>
<p>The data showed that as participants transitioned into NREM sleep, the brain exhibited a drop in glucose metabolism—its primary source of energy. At the same time, large, slow fluctuations in blood flow became more pronounced. These changes were not random. They followed a clear pattern that corresponded to the participant’s level of wakefulness, as measured by EEG signals or behavior.</p>
<p>“We were surprised to see much stronger oscillatory blood flow activity in sensory regions during NREM sleep compared to quiet wakefulness,” Chen told PsyPost. “One possible explanation is that during sleep, neural activity becomes highly synchronized, producing large-scale, rhythmic fluctuations, and these oscillations—around 0.02 Hz—are particularly effective at driving vascular responses, which may explain why we see such pronounced oscillations in blood flow during sleep.”</p>
<p>One of the key findings was that not all brain regions responded to sleep in the same way. The default mode network, which includes areas involved in introspective thought and memory processing, showed a marked drop in glucose metabolism during sleep. In contrast, sensory and motor areas remained more metabolically active and showed stronger blood flow fluctuations.</p>
<p>This means that while the brain as a whole becomes less active and consumes less energy during sleep, some parts stay relatively alert. These preserved dynamics in sensory regions may serve a protective function, allowing people to wake up in response to sounds or other stimuli even while most of the brain is at rest.</p>
<p>The researchers also observed that slow brain waves and other physiological patterns associated with sleep were tightly linked to the observed changes in metabolism and blood flow. For example, large fluctuations in blood flow in sensory regions tended to occur alongside infraslow oscillations in electrical activity. These patterns have been associated with varying degrees of arousal and may help explain how the brain remains periodically receptive to external inputs during sleep.</p>
<p>“Our main message is that the brain’s physiological processes—energy use, blood flow, and neural activity—interact in complex and dynamic ways as we fall asleep,” Chen explained. “We found that different brain regions ‘fall asleep’ at different times. Sensory regions remain active and metabolically dynamic during light and intermediate sleep, while higher-order cognitive regions are more strongly suppressed. This pattern may help the brain remain responsive to important sensory cues, facilitating awakenings during lighter and intermediate sleep stages.”</p>
<p>While the study provides evidence for region-specific changes in the brain during sleep, it also comes with some limitations. The sleep sessions took place in the afternoon and involved only partial sleep deprivation. This setup may have limited the amount of deep sleep achieved by participants, which means the results are mostly applicable to light and intermediate stages of NREM sleep. The loud environment of the MRI scanner may have also influenced the quality of sleep.</p>
<p>The imaging approach used in this study represents a significant advance in measuring real-time changes in the brain, but the tools are complex and not yet widely available. “Because imaging measures are inherently noisy, we need to average results across many participants to obtain statistically robust effects,” Chen noted. “However, since sleep has such a strong influence on brain activity and metabolism, clear effects can even be observed in some individual participants.” </p>
<p>Looking ahead, the researchers plan to explore how these processes are altered in people with sleep disorders or neurological diseases such as Alzheimer’s. Sleep is thought to play a role in clearing waste products from the brain, and disruptions in this process could contribute to disease progression. </p>
<p>“Our current findings in healthy young adults provide a kind of ‘blueprint’ for how physiological processes in the brain are coordinated during normal sleep,” Chen said. “Moving forward, we want to explore how these delicate interactions between metabolism, blood flow, and neural activity are disrupted in sleep and neurological disorders—such as Alzheimer’s disease. Understanding these disruptions could eventually help identify early markers of disease or guide interventions that restore healthy brain physiology during sleep.”</p>
<p>The study, “<a href="https://doi.org/10.1038/s41467-025-64414-x" target="_blank">Simultaneous EEG-PET-MRI identifies temporally coupled and spatially structured brain dynamics across wakefulness and NREM sleep</a>,” was authored by Jingyuan E. Chen, Laura D. Lewis, Sean E. Coursey, Ciprian Catana, Jonathan R. Polimeni, Jiawen Fan, Kyle S. Droppa, Rudra Patel, Hsiao-Ying Wey, Catie Chang, Dara S. Manoach, Julie C. Price, Christin Y. Sander, and Bruce R. Rosen.</p></p>
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<p><strong>Forwarded by:<br />
Michael Reeder LCPC<br />
Baltimore, MD</strong></p>
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