Research into whether drugs like ayahuasca can mitigate the effects of traumatic brain injury is in its infancy. Pro athletes like the Buffalo Bills' Jordan Poyer are forging ahead anyway. Roam the wide-open halls and cavernous showrooms of the Colorado Convention Center during Psychedelic Science, the world's largest psychedelics conference, and you'll see exhibitors hawking everything from mushroom jewelry, to chewable gummies containing extracts of the psychoactive succulent plant kanna, to broad flat-brim baseball caps emblazoned with "MDMA" and "IBOGA." Booths publicize organizations such as the Ketamine Taskforce and the Psychedelic Parenthood Community, and even, a live-action feature film looking to attract investors. It's a motley, multifarious symposium where indigenous-plant-medicine healers mingle with lanyard-clad pharma-bros, legendary underground LSD chemists, and workaday stoners tottering around in massive red and white toadstool hats that make them look like that cute little mushroom guy from . And yet, oddest among such oddities may be the sight of enormously burly NFL tough guys talking candidly about their feelings.

The first time Conor Gormally got a concussion, he felt as if he were standing on a ship at sea. A high school soccer player, he had decided to try out something new during his off-season: wrestling. His very first opponent caught him off guard, with a headbutt to the temple. "I stood up, then my horizon tilted to a 40-degree angle and I fell to the ground," Gormally told me years later. He felt the room tip and roll. "I was sobbing and saying, 'I don't know why I'm crying. I don't know what's happening here,' " Gormally recalled. After examining Gormally, the school athletic trainer told him to go home and rest. Gormally's primary care provider said the same thing, adding that he shouldn't return to school or practice until his symptoms resolved.

Over the past decade, the intricacies of sports-related concussions among female athletes have become readily apparent. Traditional clinical methods for diagnosing concussions suffer limitations when applied to female athletes, often failing to capture subtle changes in brain structure and function. Advanced neuroinformatics techniques and machine learning models have become invaluable assets in this endeavor. While these technologies have been extensively employed in understanding concussion in male athletes, there remains a significant gap in our comprehension of their effectiveness for female athletes. With its remarkable data analysis capacity, machine learning offers a promising avenue to bridge this deficit. By harnessing the power of machine learning, researchers can link observed phenotypic neuroimaging data to sex-specific biological mechanisms, unraveling the mysteries of concussions in female athletes. Furthermore, embedding methods within machine learning enable examining brain architecture and its alterations beyond the conventional anatomical reference frame. In turn, allows researchers to gain deeper insights into the dynamics of concussions, treatment responses, and recovery processes. To guarantee that female athletes receive the optimal care they deserve, researchers must employ advanced neuroimaging techniques and sophisticated machine-learning models. These tools enable an in-depth investigation of the underlying mechanisms responsible for concussion symptoms stemming from neuronal dysfunction in female athletes. This paper endeavors to address the crucial issue of sex differences in multimodal neuroimaging experimental design and machine learning approaches within female athlete populations, ultimately ensuring that they receive the tailored care they require when facing the challenges of concussions.

The sport-related concussion (SRC) battery relies heavily upon subjective symptom reporting in order to determine the diagnosis of a concussion. Unfortunately, athletes with SRC may return-to-play (RTP) too soon if they are untruthful of their symptoms. It is critical to provide accurate assessments that can overcome underreporting to prevent further injury. To lower the risk of injury, a more robust and precise method for detecting concussion is needed to produce reliable and objective results. In this paper, we propose a novel approach to detect SRC using long short-term memory (LSTM) recurrent neural network (RNN) architectures from oculomotor data. In particular, we propose a new error metric that incorporates mean squared error in different proportions. The experimental results on the smooth pursuit test of the VR-VOMS dataset suggest that the proposed approach can predict concussion symptoms with higher accuracy compared to symptom provocation on the vestibular ocular motor screening (VOMS).

Automated decision support systems that are able to infer second opinions from experts can potentially facilitate a more efficient allocation of resources; they can help decide when and from whom to seek a second opinion. In this paper, we look at the design of this type of support systems from the perspective of counterfactual inference. We focus on a multiclass classification setting and first show that, if experts make predictions on their own, the underlying causal mechanism generating their predictions needs to satisfy a desirable set invariant property. Further, we show that, for any causal mechanism satisfying this property, there exists an equivalent mechanism where the predictions by each expert are generated by independent sub-mechanisms governed by a common noise. This motivates the design of a set invariant Gumbel-Max structural causal model where the structure of the noise governing the sub-mechanisms underpinning the model depends on an intuitive notion of similarity between experts which can be estimated from data. Experiments on both synthetic and real data show that our model can be used to infer second opinions more accurately than its non-causal counterpart.

Large language models can help you write code -- or rewrite adverts, so they look fresh. They can make it easier to quickly grasp the key points of a research paper or a news story by writing and answering questions. Or they can get things embarrassingly wrong. Large language models like GPT-3 are key to search engines like Google and Bing, as well as providing suggested replies in email and chat, trying to finish your sentence in Word and powering coding assistants like GitHub Copilot. Considerations of what harm they can do usually focus on what you get by learning from everything that's published on the web, which includes the less positive opinions held by some.

A highly concerning new study lays bare the danger of repeated head impacts for rugby players. After performing scans of 44 elite adult rugby players, experts found 23 per cent had abnormalities in brain structure, specifically in white matter and blood vessels of the brain. White matter mainly comprises the neural pathways, the long extensions of the nerve cells, and is crucial to our cognitive ability. The study also found 50 per cent of the rugby players had an unexpected reduction in brain volume. Non-profit the Drake Foundation, which backed the study, is now calling for immediate changes in rugby protocols to ensure long-term welfare of elite players.

"What AI does is it takes the commuting out of the hands of the human upfront and sort of says, 'well, you're telling me this is a concussed person and this is not, and here's some differences in variables that you may not be aware.' Of course it would be good to cross-validate that in another sample so you don't have something unique in this sample that is by chance but at least it gives you some clues so that you can have things that you can test prior but the AI can give you some hints of what you're not aware of," says Lecci.

In the world of tech startups, some say it's best to "fail fast, fail often"--and it's a mantra that WWE founder Vince McMahon might have had on his mind when he announced last week that he was bringing back the XFL. The gimmicky football league failed spectacularly when it first launched, flaming out in 2001 after only one season, and many are already predicting that McMahon's 2020 reboot will fail again. There are, however, nine other innovate startups on display this week, any number of which could have far more lasting impacts on the game of football. And in Minneapolis tomorrow, the day before the New England Patriots and Philadelphia Eagles square off in Super Bowl LII, these nine companies will compete across three categories in the National Football League's third annual "1st & Future" pitch competition, an event designed to spur new technologies that promote athlete safety and performance on the gridiron. One winner from each category will receive a US $50,000 check from the league, two tickets to Sunday's big game, and bragging rights for taking home what could be thought of as the "Heisman of Health-Tech."

School-aged football players are at an increased risk of long-term effects in the brain after just one season of play, according to new research. Two new studies conducted on children aged nine to 17 found changes in the area of the brain known as the default mode network (DMN), which is active while a person's mind is wandering and responsible for processing emotions. The effects were seen in both players with and without a history of concussion, but were even greater in those who had suffered at least one in the past. This study adds to the mounting evidence that concussions due to football lead to serious changes in the brain including multiple sclerosis, impaired emotions, depression and most popularly CTE, which was found in former professional football player Aaron Hernandez. The studies conducted at UT Southwestern Medical Center in Dallas, Texas, and Wake Forest University in Winston-Salem, North Carolina, used Head Impact Telemetry System (HITS) helmets on players with and without a history of concussion for an entire season.