Scientists are developing biomarkers to objectively measure pain, addressing a fundamental medical challenge that has contributed to the opioid crisis and led to consistent underestimation of pain in women and minorities.

Four research teams funded by the Department of Health and Human Services are developing technologies to quantify pain like other vital signs. Their approaches include a blood test for endometriosis pain, a device measuring nerve response through pupil dilation, microneedle patches sampling interstitial fluid, and a wearable sensor detecting pain markers in sweat.

"When patients are told that the pain is all in their head, the implication is that it's imagined, but the irony is that's sort of right," said Adam Kepecs, a neuroscience professor at Washington University. "The pain only exists in your brain. It's neural activity, which is why it's invisible and uniquely personal. But it's still real." These innovations could transform treatment for the nearly 25% of Americans suffering from chronic pain, while potentially saving billions in healthcare costs.

PhD students trained in small research groups are more likely to remain in academia than those from larger labs, according to a comprehensive analysis published in Nature Human Behaviour.

The study, which examined 1.5 million scientists and 1.8 million mentorships across chemistry, physics and neuroscience, found that trainees from large research groups had 38-48% lower "survival rates" in academia between the 1980s and 1995 compared to their small-group counterparts.

However, researchers from larger labs who do stay in academia tend to achieve greater career success, publishing papers with higher citation rates and more frequently ranking among the most-cited scientists.

The research team, led by social-data scientist Roberta Sinatra from the University of Copenhagen, discovered that successful large-group scientists typically published more first-author papers with their mentors as last authors, suggesting they received substantial attention despite the group size.

An anonymous reader quotes an opinion piece from The Guardian, written by Prof Mark Pallen and Dr Aimee Parker (Quadram Institute, Norwich), Prof Nick Loman (University of Birmingham), Prof Alan Walker (University of Aberdeen): Contrary to what is implied in [this article], the weight of expert opinion in medical microbiology rejects the existence of a "brain microbiome" in the sense of a resident microbial community in healthy human brains. While pathogenic microbes -- such as Borrelia burgdorferi, which causes Lyme disease, or fungal pathogens like Cryptococcus neoformans -- can invade the brain and cause neurological symptoms, these are examples of infections, not evidence of a native microbial community.

Similarly, cognitive benefits of vaccines can be explained by their role in preventing infections or modulating immune responses and inflammation, rather than any impact on a "brain microbiome." Furthermore, the studies cited in the article have not undergone independent validation, nor do they provide any kind of consistent picture. This mirrors controversies around other supposed microbiomes -- such as that of the placenta -- which have failed to withstand independent scrutiny. Over a decade of research indicates that contamination, typically from laboratory reagents, is the most plausible explanation for such findings, particularly when even supposedly ultrapure water has been shown to harbor DNA signatures and culturable microbes.

If diverse microbes are truly abundant in the brain, why have they not been repeatedly and consistently cultured in over a century and a half of medical microbiology? Why have they not been observed in numerous microscopy studies of human brain tissue? Efforts to explore overlooked roles of microbes in neurological conditions are welcome, but they must be grounded in robust and reproducible science -- not speculative discussion of a "brain microbiome." In the article mentioned above, author Amy Fleming discusses the emerging research connecting infections such as Borrelia, Cryptococcus, and herpes viruses to reversible dementia, challenging the long-held belief that the brain is sterile.

She highlights the Alzheimer's Pathobiome Initiative, which investigates how brain infections may contribute to diseases like Alzheimer's, with the goal of developing new diagnostic tools and treatments. Vaccines like BCG and zoster have shown protective effects, while good hygiene, oral health, and a healthy lifestyle can help reduce risks.

"It's that time again," writes USA Today, noting that Sunday morning millions of Americans (along with millions more in Canada, Europe, parts of Australia, and Chile) "will set their clocks back an hour, and many will renew their twice-yearly calls to put an end to the practice altogether..." Experts say the time changes are detrimental to health and safety, but agree that the answer isn't permanent DST. "The medical and scientific communities are unified ... that permanent standard time is better for human health," said Erik Herzog, a professor of biology and neuroscience at Washington University in St. Louis and the former president of the Society for Research on Biological Rhythms...

Springing forward an hour in March is harder on us than falling back in November. The shift in spring is associated with an increase in heart attacks, and car accident rates also go up for a few days after, he said. But the answer isn't permanent daylight saving time, according to Herzog, who said that could be even worse for human health than the twice-yearly changes. By looking at studies of people who live at the easternmost edge of time zones (whose experience is closest to standard time) and people who live at the westernmost edge (more like daylight saving time), scientists can tell that health impacts of earlier sunrises and sunsets are much better. Waking up naturally with the sun is far better for our bodies than having to rely on alarm clocks to wake up in the dark, he said.

Herzog said Florida, where [Senator Marco] Rubio has championed the Sunlight Protection Act, is much less impacted by the negative impacts of daylight saving time because it's as far east and south as you can get in the U.S., while people in a state like Minnesota would have much more time in the dark in the morning.
The article also reminds U.S. readers that "No state can adopt permanent daylight saving time unless U.S. Congress passes a law to authorize it first." Nevertheless... Oklahoma became the most recent state to pass a measure authorizing permanent daylight saving time, pending Congressional approval, in April. Nineteen other states have passed laws or resolutions to move toward daylight saving time year-round, if Congress were ever to allow it, according to the National Conference of State Legislatures...

Only two states and some territories never have to set their clocks forward or backward... [Hawaii and Arizona, except for the Navajo Nation.]

A new study reveals that the human sense of smell is far more sensitive than previously thought, capable of distinguishing odors and their sequences within just 60 milliseconds. CNN reports: In a single sniff, the human sense of smell can distinguish odors within a fraction of a second, working at a level of sensitivity that is "on par" with how our brains perceive color, "refuting the widely held belief that olfaction is our slow sense," a new study finds. Humans also can discern between various sequences of odors -- distinguishing a sequence of "A" before "B" from sequence "B" before "A" -- when the interval between odorant A and odorant B is merely 60 milliseconds, according to the study, published Monday in the journal Nature Human Behavior. [...]

The new findings challenge previous research in which the timing it took to discriminate between odor sequences was around 1,200 milliseconds, Dr. Dmitry Rinberg, a professor in the Department of Neuroscience and Physiology at NYU Langone Health in New York, wrote in an editorial accompanying the study in Nature Human Behavior. "The timing of individual notes in music is essential for conveying meaning and beauty in a melody, and the human ear is very sensitive to this. However, temporal sensitivity is not limited to hearing: our sense of smell can also perceive small temporal changes in odor presentations," he wrote. "Similar to how timing affects the perception of notes in a melody, the timing of individual components in a complex odor mixture that reaches the nose may be crucial for our perception of the olfactory world."

The ability to tell apart odors within a single sniff might be an important way in which animals detect both what a smell is and where it might be in space, said Dr. Sandeep Robert Datta, a professor in the Department of Neurobiology at Harvard Medical School, who was not involved in the new study. "The demonstration that humans can tell apart smells as they change within a sniff is a powerful demonstration that timing is important for smell across species, and therefore is a general principle underlying olfactory function. In addition, this study sheds important light on the mysterious mechanisms that support human odor perception," Datta wrote in an email. "The study of human olfaction has historically lagged that of vision and hearing, because as humans we think of ourselves as visual creatures that largely use speech to communicate," he said, adding that the new study helps "fill a critical gap in our understanding of how we as humans smell."

An anonymous reader quotes a report from Neuroscience News: Attention deficit/hyperactivity disorder -- also known as ADHD -- is typically thought of as a childhood condition. But more adults are realizing that their struggles with attention, focus and restlessness could in fact be undiagnosed ADHD, thanks in large part to trending social media videos racking up millions of views. A new national survey of 1,000 American adults commissioned by The Ohio State University Wexner Medical Center and College of Medicine finds that 25% of adults now suspect they may have undiagnosed ADHD. But what worries mental health experts is that only 13% of survey respondents have shared their suspicions with their doctor. That's raising concerns about the consequences of self-diagnosis leading to incorrect treatment.

"Anxiety, depression and ADHD -- all these things can look a lot alike, but the wrong treatment can make things worse instead of helping that person feel better and improving their functioning," said psychologist Justin Barterian, PhD, clinical assistant professor in Ohio State's Department of Psychiatry and Behavioral Health. An estimated 4.4% of people ages 18 to 44 have ADHD, and some people aren't diagnosed until they're older, Barterian said. "There's definitely more awareness of how it can continue to affect folks into adulthood and a lot of people who are realizing, once their kids have been diagnosed, that they fit these symptoms as well, given that it's a genetic disorder," Barterian said. The survey found that younger adults are more likely to believe they have undiagnosed ADHD than older generations, and they're also more likely to do something about it. Barterian said that should include seeing a medical professional, usually their primary care provider, to receive a referral to a mental health expert to be thoroughly evaluated, accurately diagnosed and effectively treated.

An anonymous reader quotes a report from Scientific American, written by science journalist Simon Makin: The persistence of memory is crucial to our sense of identity, and without it, there would be no learning, for us or any other animal. It's little wonder, then, that some researchers have called how the brain stores memories the most fundamental question in neuroscience. A milestone in the effort to answer this question came in the early 1970s, with the discovery of a phenomenon called long-term potentiation, or LTP. Scientists found that electrically stimulating a synapse that connects two neurons causes a long-lasting increase in how well that connection transmits signals. Scientists say simply that the "synaptic strength" has increased. This is widely believed to be the process underlying memory. Networks of neural connections of varying strengths are thought to be what memories are made of.

In the search for molecules that enable LTP, two main contenders emerged. One, called PKMzeta (protein kinase Mzeta), made a big splash when a 2006 study showed that blocking it erased memories for places in rats. If obstructing a molecule erases memories, researchers reasoned, that event must be essential to the process the brain uses to maintain memories. A flurry of research into the so-called memory molecule followed, and numerous experiments appeared to show that it was necessary and sufficient for maintaining numerous types of memory. The theory had a couple of holes, though. First, PKMzeta is short-lived. "Those proteins only last in synapses for a couple of hours, and in neurons, probably a couple of days," says Todd Sacktor, a neurologist at SUNY Downstate Health Sciences University, who was co-senior author of the 2006 study. "Yet our memories can last 90 years, so how do you explain this difference?" Second, PKMzeta is created in cells as needed, but then it has to find the right synapses. Each neuron has around 10,000 synapses, only a few percent of which are strengthened, says neuroscientist Andre Fenton, the other co-senior author of the 2006 study, who is now at New York University. The strengthening of some synapses and not others is how this mechanism stores information, but how PKMzeta molecules accomplish this was unknown.

A new study published in Science Advances by Sacktor, Fenton and their colleagues plugs these holes. The research suggests that PKMzeta works alongside another molecule, called KIBRA (kidney and brain expressed adaptor protein), which attaches to synapses activated during learning, effectively "tagging" them. KIBRA couples with PKMzeta, which then keeps the tagged synapses strengthened. Experiments show that blocking the interaction between these two molecules abolishes LTP in neurons and disrupts spatial memories in mice. Both molecules are short-lived, but their interaction persists. "It's not PKMzeta that's required for maintaining a memory, it's the continual interaction between PKMzeta and this targeting molecule, called KIBRA," Sacktor says. "If you block KIBRA from PKMzeta, you'll erase a memory that's a month old." The specific molecules will have been replaced many times during that month, he adds. But, once established, the interaction maintains memories over the long term as individual molecules are continually replenished. [...] "What seems clear is that there is no single 'memory molecule,'" concludes Scientific American. "Regardless of any competing candidate, PKMzeta needs a second molecule to maintain long-term memories, and there is another that can substitute in a pinch."

"There are also some types of memory, such as the association of a location with fear, that do not depend on PKMzeta," the report adds. "Nobody knows what molecules are involved in those cases, and PKMzeta is clearly not the whole story."

A recent study reveals that 94% of spreadsheets used in business decision-making contain errors, highlighting significant risks of financial and operational mistakes. Phys.org reports: Errors in spreadsheets can lead to poor decisions, resulting in financial losses, pricing mistakes, and operational problems in fields like health care and nuclear operations. "These mistakes can cause major issues in various sectors," adds Prof. Pak-Lok Poon, the lead author of the study. Spreadsheets are crucial tools in many fields, such as linear programming and neuroscience. However, with more people creating their own spreadsheets without formal training, the number of faulty spreadsheets has increased. "Many end-users lack proper software development training, leading to more errors," explains Prof. Poon.

The research team reviewed studies from the past 35.5 years for journal articles and 10.5 years for conference papers, focusing on spreadsheet quality and related techniques across different fields. The study found that most research focuses on testing and fixing spreadsheets after they are created, rather than on early development stages like planning and design. This approach can be more costly and risky. Prof. Poon emphasizes the need for more focus on the early stages of spreadsheet development to prevent errors. The study suggests that adopting a life cycle approach to spreadsheet quality can help reduce errors. Addressing quality from the beginning can help businesses lower risks and improve the reliability of their decision-making tools. The study has been published in the journal Frontiers of Computer Science.

OpenAI CEO Sam Altman and businesswoman Arianna Huffington have announced they're working on an "AI health coach" via Thrive AI Health. According to a Time magazine op-ed, the two executives said that the bot will be trained on "the best peer-reviewed science" alongside "the personal biometric, lab, and other medical data you've chosen to share with it." The Verge reports: The company tapped DeCarlos Love, a former Google executive who previously worked on Fitbit and other wearables, to be CEO. Thrive AI Health also established research partnerships with several academic institutions and medical centers like Stanford Medicine, the Rockefeller Neuroscience Institute at West Virginia University, and the Alice L. Walton School of Medicine. (The Alice L. Walton Foundation is also a strategic investor in Thrive AI Health.) Thrive AI Health's goal is to provide powerful insights to those who otherwise wouldn't have access -- like a single mother looking for quick meal ideas for her gluten-free child or an immunocompromised person in need of instant advice in between doctor's appointments. [...]

The bot is still in its early stages, adopting an Atomic Habits approach. Its goal is to gently encourage small changes in five key areas of your life: sleep, nutrition, fitness, stress management, and social connection. By making minor adjustments, such as suggesting a 10-minute walk after picking up your child from school, Thrive AI Health aims to positively impact people with chronic conditions like heart disease. It doesn't claim to be ready to provide real diagnosis like a doctor would but instead aims to guide users into a healthier lifestyle. "AI is already greatly accelerating the rate of scientific progress in medicine -- offering breakthroughs in drug development, diagnoses, and increasing the rate of scientific progress around diseases like cancer," the op-ed read.

An anonymous reader quotes a report from the BBC: A vote against using MDMA as part of therapy for PTSD has provoked a powerful backlash among researchers who study psychedelic drugs. Some 13 million Americans struggle with post-traumatic stress disorder (PTSD). Existing therapies only bring relief for a fraction of patients, and new treatments are sorely needed, according to psychiatrists wrestling with the scale of the problem. So, there was distinct disappointment when an advisory committee at the US Food and Drug Administration (FDA) voted earlier this month against a therapy that many had hoped could offer the first new treatment for PTSD in 25 years. A number of experts who study psychedelics have since spoken out in support of MDMA-assisted therapy for PTSD and have sharply criticized the recommendations of the FDA's Psychopharmacological Drugs Advisory Committee. But some are still optimistic that the treatment might be approved when the FDA delivers its final decision in August.

While MDMA, also commonly known as ecstasy or molly, is listed as a Schedule 1 controlled substance in the US and so is illegal to use outside research, there has been a growing number of studies suggesting that when used with psychotherapy it could have potential for treating PTSD and some other mental health conditions. Ahead of the meeting, FDA approval of MDMA-assisted therapy for PTSD seemed likely, says Sandeep Nayak, an assistant professor of psychiatry at Johns Hopkins University, who investigates psychedelics as treatments for substance use and mood disorders. About two-thirds of people who received three sessions of MDMA and talk therapy no longer qualified for a PTSD diagnosis at the end of two Phase 3 clinical trials. It's an outcome that is "almost double that of existing medications", says Gul Dolen, a neuroscientist at the University of California, Berkeley, who researches the mechanisms of how psychedelics achieve therapeutic effects. "What's more, [the treatment] led to durable improvements in these patients lasting at least six months."

About half of people who enroll in current gold standard PTSD treatments drop out, which is "absurd," says Loree Sutton, a psychiatrist and retired Brigadier General in the US Army. She says new treatments are essential. "We have to do better." "Even if there are risks, we've got to figure this out, because we cannot not let this treatment be available," adds Rachel Yehuda, a professor of psychiatry and neuroscience at the Icahn School of Medicine at Mount Sinai who has conducted studies on the effects of MDMA-assisted therapy for PTSD. "Without it, we're just leaving too many people in suffering that they don't need to be in, and that is not right." The FDA is currently considering an application from California-based drug company Lykos Therapeutics for using MDMA capsules taken in conjunction with therapy in the treatment of PTSD. In the recent FDA advisory meeting, committee members cited apparent flaws in study design and data collection. The nine-hour hearing concluded with committee members voting 9-2 that the available data do not show "that the drug is effective" for PTSD, and voting 10-1 that the benefits of MDMA do not outweigh the risks.

An anonymous reader quotes a report from Popular Science: As detailed in the new issue of the journal Cell Stem Cell, University of Wisconsin-Madison researchers have developed a novel 3D-printing approach for creating cultures that grow and operate similar to brain tissue. While traditional 3D-printing involves layering "bio-ink" vertically like a cake, the team instead tasked their machine to print horizontally, as if playing dominoes. As New Atlas explains, researchers placed neurons grown from pluripotent stem cells (those capable of becoming multiple different cell types) within a new bio-ink gel made with fibrinogen and thrombin, biomaterials involved in blood clotting. Adding other hydrogels then helped loosen the bio-ink to solve for the 3 encountered during previous 3D-printed tissue experiments. According to Su-Chun Zhang, a research lead and UW-Madison professor of neuroscience and neurology, the resultant tissue is resilient enough to maintain its structure, but also sufficiently malleable to permit adequate levels of oxygen and nutrient intake for the neurons. "The tissue still has enough structure to hold together but it is soft enough to allow the neurons to grow into each other and start talking to each other," Zhang explains in a recent university profile.

Because of their horizontal construction, the new tissue cells formed connections not only within each layer, but across them, as well -- much like human neurons. The new structures could interact thanks to producing neurotransmitters, and even created support cell networks within the 3D-printed tissue. In these experiments, the team printed both cerebral cortex and striatum cultures. Although responsible for very different functions -- the former associated with thought, language, and voluntary movement; the latter tied to visual information -- the two 3D-printed tissues could still communicate, "in a very special and specific way," Zhang said. Researchers believe their technique isn't limited to creating just those two types of cultures, but hypothetically "pretty much any type of neurons [sic] at any time," according to Zhang. This means the 3D-printing method could eventually help study how healthy portions of the brain interact with parts affected by Alzheimers, examining cell signal pathways in Downs syndrome, as well as use tissue to test new drugs. "Our brain operates in networks," Zhang explained. "We want to print brain tissue this way because cells do not operate by themselves. They talk to each other. This is how our brain works and it has to be studied all together like this to truly understand it."

Dan Rockmore, the director of the Neukom Institute for Computational Sciences at Dartmouth College, writing for The New Yorker: Recently, statistical modelling has taken on a new kind of importance as the engine of artificial intelligence -- specifically in the form of the deep neural networks that power, among other things, large language models, such as OpenAI's G.P.T.s. These systems sift vast corpora of text to create a statistical model of written expression, realized as the likelihood of given words occurring in particular contexts. Rather than trying to encode a principled theory of how we produce writing, they are a vertiginous form of curve fitting; the largest models find the best ways to connect hundreds of thousands of simple mathematical neurons, using trillions of parameters.They create a vast data structure akin to a tangle of Christmas lights whose on-off patterns attempt to capture a chunk of historical word usage. The neurons derive from mathematical models of biological neurons originally formulated by Warren S. McCulloch and Walter Pitts, in a landmark 1943 paper, titled "A Logical Calculus of the Ideas Immanent in Nervous Activity." McCulloch and Pitts argued that brain activity could be reduced to a model of simple, interconnected processing units, receiving and sending zeros and ones among themselves based on relatively simple rules of activation and deactivation.

The McCulloch-Pitts model was intended as a foundational step in a larger project, spearheaded by McCulloch, to uncover a biological foundation of psychiatry. McCulloch and Pitts never imagined that their cartoon neurons could be trained, using data, so that their on-off states linked to certain properties in that data. But others saw this possibility, and early machine-learning researchers experimented with small networks of mathematical neurons, effectively creating mathematical models of the neural architecture of simple brains, not to do psychiatry but to categorize data. The results were a good deal less than astonishing. It wasn't until vast amounts of good data -- like text -- became readily available that computer scientists discovered how powerful their models could be when implemented on vast scales. The predictive and generative abilities of these models in many contexts is beyond remarkable. Unfortunately, it comes at the expense of understanding just how they do what they do. A new field, called interpretability (or X-A.I., for "explainable" A.I.), is effectively the neuroscience of artificial neural networks.

This is an instructive origin story for a field of research. The field begins with a focus on a basic and well-defined underlying mechanism -- the activity of a single neuron. Then, as the technology scales, it grows in opacity; as the scope of the field's success widens, so does the ambition of its claims. The contrast with climate modelling is telling. Climate models have expanded in scale and reach, but at each step the models must hew to a ground truth of historical, measurable fact. Even models of covid or elections need to be measured against external data. The success of deep learning is different. Trillions of parameters are fine-tuned on larger and larger corpora that uncover more and more correlations across a range of phenomena. The success of this data-driven approach isn't without danger. We run the risk of conflating success on well-defined tasks with an understanding of the underlying phenomenon -- thought -- that motivated the models in the first place.

Part of the problem is that, in many cases, we actually want to use models as replacements for thinking. That's the raison detre of modelling -- substitution. It's useful to recall the story of Icarus. If only he had just done his flying well below the sun. The fact that his wings worked near sea level didn't mean they were a good design for the upper atmosphere. If we don't understand how a model works, then we aren't in a good position to know its limitations until something goes wrong. By then it might be too late. Eugene Wigner, the physicist who noted the "unreasonable effectiveness of mathematics," restricted his awe and wonder to its ability to describe the inanimate world. Mathematics proceeds according to its own internal logic, and so it's striking that its conclusions apply to the physical universe; at the same time, how they play out varies more the further that we stray from physics. Math can help us shine a light on dark worlds, but we should look critically, always asking why the math is so effective, recognizing where it isn't, and pushing on the places in between.

Traumatic memories had their own neural mechanism, brain scans showed, which may help explain their vivid and intrusive nature. From a report: At the root of post-traumatic stress disorder, or PTSD, is a memory that cannot be controlled. It may intrude on everyday activity, thrusting a person into the middle of a horrifying event, or surface as night terrors or flashbacks. Decades of treatment of military veterans and sexual assault survivors have left little doubt that traumatic memories function differently from other memories. A group of researchers at Yale University and the Icahn School of Medicine at Mount Sinai set out to find empirical evidence of those differences.

The team conducted brain scans of 28 people with PTSD while they listened to recorded narrations of their own memories. Some of the recorded memories were neutral, some were simply "sad," and some were traumatic. The brain scans found clear differences, the researchers reported in a paper published on Thursday in the journal Nature Neuroscience. The people listening to the sad memories, which often involved the death of a family member, showed consistently high engagement of the hippocampus, part of the brain that organizes and contextualizes memories. When the same people listened to their traumatic memories -- of sexual assaults, fires, school shootings and terrorist attacks -- the hippocampus was not involved.

[...] Indeed, the authors conclude in the paper, "traumatic memories are not experienced as memories as such," but as "fragments of prior events, subjugating the present moment." The traumatic memories appeared to engage a different area of the brain -- the posterior cingulate cortex, or P.C.C., which is usually involved in internally directed thought, like introspection or daydreaming. The more severe the person's PTSD symptoms were, the more activity appeared in the P.C.C. What is striking about this finding is that the P.C.C. is not known as a memory region, but one that is engaged with "processing of internal experience," Dr. Schiller said.

An anonymous reader quotes a report from Motherboard: [A] new tech startup, Prophetic, aims to bring lucid dreams to a much wider audience by developing a wearable device designed to spark the experience when desired. Prophetic is the brainchild of Eric Wollberg, its chief executive officer, and Wesley Louis Berry III, its chief technology officer. The pair co-founded the company earlier this year with the goal of combining technologies, such as ultrasound and machine learning models, "to detect when dreamers are in REM to induce and stabilize lucid dreams" with a device called the Halo according to the company's website. [...]

Prophetic does not make any medical claims about its forthcoming products -- Halo is tentatively slated for a 2025 release -- though Wollberg and Berry both expressed optimism about broader scientific research that suggests lucid dreams can reduce PTSD-related nightmares, promote mindfulness, and open new windows into the mysterious nature of consciousness. To explore those links further, Prophetic has partnered with the Donders Institute, a research center at Radboud University in the Netherlands that is focused on neuroscience and cognition, to generate the largest dataset of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) observations of lucid dreamers, according to the company. The collaboration will also explore one of central technologies behind Prophetic's vision, known as transcranial focused ultrasound (TUS). This non-invasive technique uses low-intensity ultrasound pulses to probe the brain, and interact with neural activity, with a depth and precision that cannot be achieved with previous methods, such as transcranial electrical stimulation or transcranial magnetic stimulation.

At this point, both the possibilities and limits of Prophetic's concept remain unclear. While ultrasound devices have been widely used in medicine for decades, the process of stimulating parts of the brain with TUS is a relatively new development. Within the past few years, scientists have shown that TUS "has the potential to be used both as a scientific instrument to investigate brain function and as a therapeutic modality to modulate brain activity," according to a 2019 study, and "could be a useful tool in the treatment of clinical disorders characterized by negative mood states, like depression and anxiety disorders," according to a 2020 study. What is not known, yet, is whether TUS can induce or stabilize lucid dreams, though the Prophetic team is banking on a positive answer to this open question. Its wearable headband prototype, the Halo, was developed with the company Card79 and can currently read EEG data of users. Over the next year, Prophetic aims to use the dataset from their partnership with the Donders Institute to train machine learning models that will stimulate targeted neural activity in users with ultrasound transducers as a means of inducing lucid dreams.

AI startup Anthropic, writing in a blog post: Neural networks are trained on data, not programmed to follow rules. With each step of training, millions or billions of parameters are updated to make the model better at tasks, and by the end, the model is capable of a dizzying array of behaviors. We understand the math of the trained network exactly -- each neuron in a neural network performs simple arithmetic -- but we don't understand why those mathematical operations result in the behaviors we see. This makes it hard to diagnose failure modes, hard to know how to fix them, and hard to certify that a model is truly safe. Neuroscientists face a similar problem with understanding the biological basis for human behavior. The neurons firing in a person's brain must somehow implement their thoughts, feelings, and decision-making. Decades of neuroscience research has revealed a lot about how the brain works, and enabled targeted treatments for diseases such as epilepsy, but much remains mysterious. Luckily for those of us trying to understand artificial neural networks, experiments are much, much easier to run. We can simultaneously record the activation of every neuron in the network, intervene by silencing or stimulating them, and test the network's response to any possible input.

Unfortunately, it turns out that the individual neurons do not have consistent relationships to network behavior. For example, a single neuron in a small language model is active in many unrelated contexts, including: academic citations, English dialogue, HTTP requests, and Korean text. In a classic vision model, a single neuron responds to faces of cats and fronts of cars. The activation of one neuron can mean different things in different contexts. In our latest paper, Towards Monosemanticity: Decomposing Language Models With Dictionary Learning , we outline evidence that there are better units of analysis than individual neurons, and we have built machinery that lets us find these units in small transformer models. These units, called features, correspond to patterns (linear combinations) of neuron activations. This provides a path to breaking down complex neural networks into parts we can understand, and builds on previous efforts to interpret high-dimensional systems in neuroscience, machine learning, and statistics. In a transformer language model, we decompose a layer with 512 neurons into more than 4000 features which separately represent things like DNA sequences, legal language, HTTP requests, Hebrew text, nutrition statements, and much, much more. Most of these model properties are invisible when looking at the activations of individual neurons in isolation.

An anonymous reader quotes a report from The Guardian: A severely paralyzed woman has been able to speak through an avatar using technology that translated her brain signals into speech and facial expressions. The latest technology uses tiny electrodes implanted on the surface of the brain to detect electrical activity in the part of the brain that controls speech and face movements. These signals are translated directly into a digital avatar's speech and facial expressions including smiling, frowning or surprise. The patient, a 47-year-old woman, Ann, has been severely paralyzed since suffering a brainstem stroke more than 18 years ago. She cannot speak or type and normally communicates using movement-tracking technology that allows her to slowly select letters at up to 14 words a minute. She hopes the avatar technology could enable her to work as a counsellor in future.

The team implanted a paper-thin rectangle of 253 electrodes on to the surface of Ann's brain over a region critical for speech. The electrodes intercepted the brain signals that, if not for the stroke, would have controlled muscles in her tongue, jaw, larynx and face. After implantation, Ann worked with the team to train the system's AI algorithm to detect her unique brain signals for various speech sounds by repeating different phrases repeatedly. The computer learned 39 distinctive sounds and a Chat GPT-style language model was used to translate the signals into intelligible sentences. This was then used to control an avatar with a voice personalized to sound like Ann's voice before the injury, based on a recording of her speaking at her wedding.

The technology was not perfect, decoding words incorrectly 28% of the time in a test run involving more than 500 phrases, and it generated brain-to-text at a rate of 78 words a minute, compared with the 110-150 words typically spoken in natural conversation. However, scientists said the latest advances in accuracy, speed and sophistication suggest the technology is now at a point of being practically useful for patients. A crucial next step is to create a wireless version of the BCI that could be implanted beneath the skull. The findings have been published in the journal Nature.

There's an ongoing study (started in 2004) that examines the effects of alcohol (and other common substances) on mood, performance, and behavior. Started by Dr. Andrea King, a professor of behavioral neuroscience at the University of Chicago, its latest result is a study called "Holding your liquor: Comparison of alcohol-induced psychomotor impairment in drinkers with and without alcohol use disorder." They found that drinkers with alcohol use disorder (or AUD, traditionally known as alcoholism) displayed less impairment on fine motor and cognitive tasks than light or heavy social drinkers after consuming a standard intoxicating dose — equivalent to four to five drinks that produce breathalyzer readings of 0.08-0.09%, i.e., the threshold for drunk driving." Yet when those drinkers with AUD consumed a higher amount akin to their usual drinking habits — equivalent to seven to eight drinks and breathalyzer readings of 0.13% — they showed significant impairment on those same tasks, more than double their impairment at the standard intoxicating dose that did not return to baseline performance three hours after drinking.

"There's a lot of thinking that when experienced drinkers (those with AUD) consume alcohol, they are tolerant to its impairing effects," said Andrea King, PhD, Professor of Psychiatry and Behavioral Neuroscience at UChicago and senior author of the study. "We supported that a bit, but with a lot of nuances. When they drank alcohol in our study at a dose similar to their usual drinking pattern, we saw significant impairments on both the fine motor and cognitive tests that was even more impairment than a light drinker gets at the intoxicating dose..."

While they did show less overall alcohol impairment on the motor and cognitive tests, at the 30-minute interval they had similar slowing on the fine motor test as the light drinkers. They also recovered quicker to their baseline levels, supporting the notion that they had more tolerance and can "hold their liquor" better than people who don't drink as much. However, people with AUD do not often stop drinking at four or five drinks and engage in high intensity drinking. Thus, a subset of the drinkers with AUD in the study participated in a separate session where they drank a beverage more consistent with their regular drinking habits, equivalent to about seven or eight drinks. At this higher dose of alcohol, they showed more than double the amount of mental and motor impairment than after they had the standard intoxicating dose. They also never got back to their baseline level of performance, even after three hours. Their level of impairment even exceeded that of the light drinkers who consumed the standard dose, suggesting that the physical effects of the alcohol add up the more someone drinks, experienced or not.

"I was surprised at how much impairment that group had to that larger dose, because while it's 50% more than the first dose, we're seeing more than double the impairment," King said.
More than 140,000 people die from excessive alcohol use in the U.S. each year, according to figures from the Centers for Disease Control and Prevention — and 30% of traffic fatalities still involve alcohol intoxication. "I'm hoping we can educate people who are experienced high-intensity drinkers who think that they're holding their liquor or that they're tolerant and won't experience accidents or injury from drinking," said Dr. King.

"Their experience with alcohol only goes so far, and excessive drinkers account for most of the burden of alcohol-related accidents and injury in society. This is preventable with education and treatment."

Thanks to long-time Slashdot reader WankerWeasel for sharing the article.

"A little brain stimulation at night appears to help people remember what they learned the previous day," reports NPR — a finding that could one day help people with memory problems, sleeps issues, or depression: A study of 18 people with severe epilepsy found that they scored higher on a memory test if they got deep brain stimulation while they slept, a team reports in the journal Nature Neuroscience.

The stimulation was delivered during non-REM sleep, when the brain is thought to strengthen memories it expects to use in the future. It was designed to synchronize the activity in two brain areas involved in memory consolidation: the hippocampus and the prefrontal cortex.

"Some improved by 10% or 20%, some improved by 80%," depending on the level of synchrony, says Dr. Itzhak Fried, an author of the study and a professor of neurosurgery at the University of California, Los Angeles.

A study published in Nature Neuroscience suggests that brain signals can be used to detect the severity of chronic pain, potentially leading to the development of personalized therapies for individuals suffering from severe pain conditions. MIT Technology Review reports: Researchers from the University of California, San Francisco, implanted electrodes in the brains of four people with chronic pain. The patients then answered surveys about the severity of their pain multiple times a day over a period of three to six months. After they finished filling out each survey, they sat quietly for 30 seconds so the electrodes could record their brain activity. This helped the researchers identify biomarkers of chronic pain in the brain signal patterns, which were as unique to the individual as a fingerprint. Next, the researchers used machine learning to model the results of the surveys. They found they could successfully predict how the patients would score the severity of their pain by examining their brain activity, says Prasad Shirvalkar, one of the study's authors.

"The hope is that now that we know where these signals live, and now that we know what type of signals to look for, we could actually try to track them noninvasively," he says. "As we recruit more patients, or better characterize how these signals vary between people, maybe we can use it for diagnosis." The researchers also found they were able to distinguish a patient's chronic pain from acute pain deliberately inflicted using a thermal probe. The chronic-pain signals came from a different part of the brain, suggesting that it's not just a prolonged version of acute pain, but something else entirely.

From a Science magazine report, shared by schwit1: When neuropsychologist Bernhard Sabel put his new fake-paper detector to work, he was "shocked" by what it found. After screening some 5000 papers, he estimates up to 34% of neuroscience papers published in 2020 were likely made up or plagiarized; in medicine, the figure was 24%. Both numbers, which he and colleagues report in a medRxiv preprint posted on 8 May, are well above levels they calculated for 2010 -- and far larger than the 2% baseline estimated in a 2022 publishers' group report. "It is just too hard to believe" at first, says Sabel of Otto von Guericke University Magdeburg and editor-in-chief of Restorative Neurology and Neuroscience. It's as if "somebody tells you 30% of what you eat is toxic." His findings underscore what was widely suspected: Journals are awash in a rising tide of scientific manuscripts from paper mills -- secretive businesses that allow researchers to pad their publication records by paying for fake papers or undeserved authorship.

"Paper mills have made a fortune by basically attacking a system that has had no idea how to cope with this stuff," says Dorothy Bishop, a University of Oxford psychologist who studies fraudulent publishing practices. A 2 May announcement from the publisher Hindawi underlined the threat: It shut down four of its journals it found were "heavily compromised" by articles from paper mills. Sabel's tool relies on just two indicators -- authors who use private, noninstitutional email addresses, and those who list an affiliation with a hospital. It isn't a perfect solution, because of a high false-positive rate. Other developers of fake-paper detectors, who often reveal little about how their tools work, contend with similar issues. Still, the detectors raise hopes for gaining the advantage over paper mills, which churn out bogus manuscripts containing text, data, and images partly or wholly plagiarized or fabricated, often massaged by ghost writers.

Some papers are endorsed by unrigorous reviewers solicited by the authors. Such manuscripts threaten to corrupt the scientific literature, misleading readers and potentially distorting systematic reviews. The recent advent of artificial intelligence tools such as ChatGPT has amplified the concern. To fight back, the International Association of Scientific, Technical, and Medical Publishers (STM), representing 120 publishers, is leading an effort called the Integrity Hub to develop new tools. STM is not revealing much about the detection methods, to avoid tipping off paper mills. "There is a bit of an arms race," says Joris van Rossum, the Integrity Hub's product director. He did say one reliable sign of a fake is referencing many retracted papers; another involves manuscripts and reviews emailed from internet addresses crafted to look like those of legitimate institutions. Twenty publishers -- including the largest, such as Elsevier, Springer Nature, and Wiley -- are helping develop the Integrity Hub tools, and 10 of the publishers are expected to use a paper mill detector the group unveiled in April.