SNL season finale cold open sees ghost of Jeffrey Epstein played by Will Ferrell'haunt' Trump as dark jokes leave viewers shocked Jordon Hudson blasts double standards over Mike Vrabel and Dianna Russini'affair' scandal: 'What is going on?' No one wants to hang out with her': Why Meghan and Harry have been ditched by A-list friends as insiders reveal Oprah's merciless snub, why the Clooneys now want nothing to do with them - and how SHE'S the problem Truth about Kate Middleton's past before Prince William... we Americans see this for what it is: KENNEDY Kim Kardashian roasted over'ridiculous' outfit at Gucci show as she sits front row with Anna Wintour and Mariah Carey I was on track to make $1 million... then I quit my job and moved into an off-grid tiny home with no running water or electricity Professional tasters decide best and worst fast food cheeseburger - do you agree? Hamptons cancer cluster: Rates are spiking in summer enclave of New York's wealthy elite... and doctors think they know the tragic reason why Disturbing trove of images woke Los Angeles mayor Karen Bass doesn't want you to see: Filthy truth is so much worse than people think... Taylor Swift dazzles in glittering gown as she and Travis Kelce steal the spotlight at friend's wedding in NYC Golf star becomes instant fan favorite after stopping to smoke a cigarette with crowd in the middle of the PGA Championship: 'Man of the people' New kind of penis enlargement surgery will add inches, claims the doctor set to offer it... but there is a gruesome detail that may make some think twice She was every bit the adoring mother... then a leaked video exposed a'sadistic' secret even cops said'will bring tears to your eyes' I saw a 40-year-old middle-class mom in a psychiatric ward after a single hit of this drug. Her symptoms were terrifying but it's so common now... here's what you must know: DR MAX PEMBERTON Expert reveals the best way to cut the bread - and why you should never leave a'hinge' 'I saw things I can never unsee': Man who snuck into Air India crash morgue reveals what he saw... why it could blow apart the pilot suicide theory... and what happened when we visited the lone survivor Many people have reported near-death experiences, but in some cases, survivors appeared to bring back something far more unsettling than memories. Some survivors claimed they saw and heard things that should have been impossible while they were clinically dead, including conversations in operating rooms and objects located far outside their hospital beds. Several of the most famous cases involved patients whose brains allegedly showed little or no measurable activity at the time of their experiences.

Foundation models of brain activity promise a new frontier for in silico neuroscience by emulating neural responses to complex stimuli across tasks and modalities. A natural next step is to ask whether these models can also be used in reverse. Can we recover a stimulus or its properties from synthetic brain activity? We study this question in a proof-of-concept setting using TRIBEv2. We pair the brain emulator with large language models (LLMs) that generate news headlines from linguistic parameters such as valence, arousal, and dominance. We then use simulation-based inference to learn a probabilistic mapping from brain maps to latent stimulus parameters. Our results show that these parameters can be recovered from predicted brain maps, validating the quality of neural encodings. They also show that LLMs can serve as controllable stimulus generators for simulated experiments. Together, these findings provide a step toward decoding and inverse design with foundation brain models.

People with brain or spinal cord-related paralysis often need to rely on others for basic tasks, limiting their independence. A potential solution is brain-machine interfaces (BMIs), which could allow them to voluntarily control external devices (e.g., robotic arm) by decoding brain activity to movement commands. In the past decade, deep-learning decoders have achieved state-of-the-art results in most BMI applications, ranging from speech production to finger control. However, the'black-box' nature of deep-learning decoders could lead to unexpected behaviors, resulting in major safety concerns in real-world physical control scenarios. In these applications, explainable but lower-performing decoders, such as the Kalman filter (KF), remain the norm. In this study, we designed a BMI decoder based on KalmanNet, an extension of the KF that augments its operation with recurrent neural networks to compute the Kalman gain.

Our visual experience in daily life are dominated by dynamic change. Decoding such dynamic information from brain activity can enhance the understanding of the brain's visual processing system. However, previous studies predominately focus on reconstructing static visual stimuli. In this paper, we explore to decode dynamic visual perception from electroencephalography (EEG), a neuroimaging technique able to record brain activity with high temporal resolution (1000 Hz) for capturing rapid changes in brains. Our contributions are threefold: Firstly, we develop a large dataset recording signals from 20 subjects while they were watching 1400 dynamic video clips of 40 concepts.

This paper presents a probabilistic-graphical model that can be used to infer characteristics of instantaneous brain activity by jointly analyzing spatial and temporal dependencies observed in electroencephalograms (EEG). Specifically, we describe a factor-graph-based model with customized factor-functions defined based on domain knowledge, to infer pathologic brain activity with the goal of identifying seizure-generating brain regions in epilepsy patients. We utilize an inference technique based on the graph-cut algorithm to exactly solve graph inference in polynomial time. We validate the model by using clinically collected intracranial EEG data from 29 epilepsy patients to show that the model correctly identifies seizure-generating brain regions. Our results indicate that our model outperforms two conventional approaches used for seizure-onset localization (5-7% better AUC: 0.72, 0.67, 0.65) and that the proposed inference technique provides 3-10% gain in AUC (0.72, 0.62, 0.69) compared to sampling-based alternatives.

Prolonged grief disorder affects around 1 in 20 people, and we're starting to understand the neuroscience behind it For most people, the intense sting of grief eases with time. For some, however, persistent and painful grief remains, developing into prolonged grief disorder. A new review of the condition, which affects around 5 per cent of bereaved people, sheds light on how it develops. This could help doctors predict which recently bereaved people will benefit from extra support. The decision to include prolonged grief disorder (PGD) in the American Psychiatric Association's diagnostic manual in 2022 sparked intense debate over whether it was pathologising a normal human response to loss and imposing an arbitrary timeline on what constitutes "normal" grief.

Our visual experience in daily life are dominated by dynamic change.

However, in practice, new reconstruction methods could improve performance for at least three other reasons: learning more about the distribution of stimuli, becoming better at reconstructing text or images in general, or exploiting weaknesses in current image and/or text evaluation metrics. Here we disentangle how much of the reconstruction is due to these other factors vs. productively using the neural recordings.