Tom Homan pushes Border Patrol out of Minneapolis in sweeping shake-up as Trump's'little Napoleon' Greg Bovino faces humiliating exit Insiders reveal the REAL misstep that got Kristi Noem humiliatingly sidelined by Trump... and the weak excuse she's peddling to try and save her own skin Insidious secret life of promiscuous neurosurgeon found dead in his $2.5m mansion'He has no loyalty': The bitter secret fallout between One Direction star Harry Styles and his former bandmates - as insiders reveal for the first time what really happened at Liam Payne's funeral The $1 supplement that will protect you from winter viruses... including new'super flu' Is Angelina Jolie quitting America? Private struggles emerge... as actress weighs major lifestyle that threatens to rupture her family Young single mother's selfless final act after finding out she had just weeks to live Seven dead in private jet crash as audio reveals voice said'Let there be light' seconds before tragedy at snowy Maine airport Gisele Bundchen relaxes with new husband and baby on boat after ex Tom Brady admits divorce'took a lot out of me' Defiant Trump dismisses Alzheimer's fears as he struggles to recall name of disease in interview America's best and worst states to retire revealed - and why Florida is no longer the obvious winner I'm a neurologist... here are three simple tricks to help you kick any bad habit Furious family hit out at Kate Hudson's'abominable' Oscar nomination amid toxic feud NFL's'scripted' conspiracy theory resurfaces as fans find five-month old post hinting at Super Bowl 60 matchup I'm a neurologist... here are three simple tricks to help you kick any bad habit Some bad habits are small. But over time, they add up, and suddenly you're wondering how you ended up here. Now, a neurologist says three simple tricks can help break the cycles that quietly take over our lives. Dr Arif Khan, a pediatric neurologist, outlined'cue shift,' the'one-step rule' and'reward rewrite' as practical tools to stop negative patterns in their tracks.

Scientists Thought Parkinson's Was in Our Genes. New ideas about chronic illness could revolutionize treatment, if we take the research seriously. Amy Lindberg spent 26 years in the Navy and she still walked like it--with intention, like her chin had someplace to be. But around 2017, her right foot stopped following orders. Lindberg and her husband Brad were five years into their retirement. After moving 10 times for Uncle Sam, they'd bought their dream house near the North Carolina coast. They had a backyard that spilled out onto wetlands. From the kitchen, you could see cranes hunting. They kept bees and played pickleball and watched their children grow. But now Lindberg's right foot was out of rhythm. She worked hard to ignore it, but she couldn't disregard the tremors.

We present Ask WhAI, a systems-level framework for inspecting and perturbing belief states in multi-agent interactions. The framework records and replays agent interactions, supports out-of-band queries into each agent's beliefs and rationale, and enables counterfactual evidence injection to test how belief structures respond to new information. We apply the framework to a medical case simulator notable for its multi-agent shared memory (a time-stamped electronic medical record, or EMR) and an oracle agent (the LabAgent) that holds ground truth lab results revealed only when explicitly queried. We stress-test the system on a multi-specialty diagnostic journey for a child with an abrupt-onset neuropsychiatric presentation. Large language model agents, each primed with strong role-specific priors ("act like a neurologist", "act like an infectious disease specialist"), write to a shared medical record and interact with a moderator across sequential or parallel encounters. Breakpoints at key diagnostic moments enable pre- and post-event belief queries, allowing us to distinguish entrenched priors from reasoning or evidence-integration effects. The simulation reveals that agent beliefs often mirror real-world disciplinary stances, including overreliance on canonical studies and resistance to counterevidence, and that these beliefs can be traced and interrogated in ways not possible with human experts. By making such dynamics visible and testable, Ask WhAI offers a reproducible way to study belief formation and epistemic silos in multi-agent scientific reasoning.

Electroencephalography (EEG) plays a crucial role in the diagnosis of various neurological disorders. However, small hospitals and clinics often lack advanced EEG signal analysis systems and are prone to misinterpretation in manual EEG reading. This study proposes an innovative hybrid artificial intelligence (AI) system for automatic interpretation of EEG background activity and report generation. The system combines deep learning models for posterior dominant rhythm (PDR) prediction, unsupervised artifact removal, and expert-designed algorithms for abnormality detection. For PDR prediction, 1530 labeled EEGs were used, and the best ensemble model achieved a mean absolute error (MAE) of 0.237, a root mean square error (RMSE) of 0.359, an accuracy of 91.8% within a 0.6Hz error, and an accuracy of 99% within a 1.2Hz error. The AI system significantly outperformed neurologists in detecting generalized background slowing (p = 0.02; F1: AI 0.93, neurologists 0.82) and demonstrated improved focal abnormality detection, although not statistically significant (p = 0.79; F1: AI 0.71, neurologists 0.55). Validation on both an internal dataset and the Temple University Abnormal EEG Corpus showed consistent performance (F1: 0.884 and 0.835, respectively; p = 0.66), demonstrating generalizability. The use of large language models (LLMs) for report generation demonstrated 100% accuracy, verified by three other independent LLMs. This hybrid AI system provides an easily scalable and accurate solution for EEG interpretation in resource-limited settings, assisting neurologists in improving diagnostic accuracy and reducing misdiagnosis rates.

As dementia becomes more widespread, Mayo Clinic researchers believe that artificial intelligence is the key to enabling earlier and faster diagnoses. By pairing AI and EEG (electroencephalogram) tests, the team at the Mayo Clinic Neurology AI Program (NAIP) in Rochester, Minnesota, was able to identify specific types of dementia sooner than they would have through human analysis. Based on these findings, EEGs could eventually provide a more accessible, less expensive and less invasive way to assess brain health earlier, according to a hospital press release. The research was published last week in the journal Brain Communications. With an EEG, a technician attaches small metal electrodes to the patient's scalp, which measure electrical activity in the brain.

In this paper we analyze and compare different movement sensors: micro-chip gesture-ID, leap motion, noitom mocap, and specially developed sensor for tapping and foot motion analysis. The main goal is to evaluate the accu-racy of measurements provided by the sensors. This study presents rele-vance, for instance, in tremor/Parkinson disease analysis as well as no touch mechanisms for activation and control of devices. This scenario is especially interesting in COVID-19 scenario. Removing the need to touch a surface, the risk of contagion is reduced.

Acute stroke demands prompt diagnosis and treatment to achieve optimal patient outcomes. However, the intricate and irregular nature of clinical data associated with acute stroke, particularly blood pressure (BP) measurements, presents substantial obstacles to effective visual analytics and decision-making. Through a year-long collaboration with experienced neurologists, we developed PhenoFlow, a visual analytics system that leverages the collaboration between human and Large Language Models (LLMs) to analyze the extensive and complex data of acute ischemic stroke patients. PhenoFlow pioneers an innovative workflow, where the LLM serves as a data wrangler while neurologists explore and supervise the output using visualizations and natural language interactions. This approach enables neurologists to focus more on decision-making with reduced cognitive load. To protect sensitive patient information, PhenoFlow only utilizes metadata to make inferences and synthesize executable codes, without accessing raw patient data. This ensures that the results are both reproducible and interpretable while maintaining patient privacy. The system incorporates a slice-and-wrap design that employs temporal folding to create an overlaid circular visualization. Combined with a linear bar graph, this design aids in exploring meaningful patterns within irregularly measured BP data. Through case studies, PhenoFlow has demonstrated its capability to support iterative analysis of extensive clinical datasets, reducing cognitive load and enabling neurologists to make well-informed decisions. Grounded in long-term collaboration with domain experts, our research demonstrates the potential of utilizing LLMs to tackle current challenges in data-driven clinical decision-making for acute ischemic stroke patients.

The original version of this story appeared in Quanta Magazine. In recent decades, neuroscience has seen some stunning advances, and yet a critical part of the brain remains a mystery. I am referring to the cerebellum, so named for the Latin for "little brain," which is situated like a bun at the back of the brain. This is no small oversight: The cerebellum contains three-quarters of all the brain's neurons, which are organized in an almost crystalline arrangement, in contrast to the tangled thicket of neurons found elsewhere. Encyclopedia articles and textbooks underscore the fact that the cerebellum's function is to control body movement.

Researchers may have found a new way to target the sources of certain brain disorders. In a study led by scientists at Mass General Brigham, deep brain stimulation (DBS) was able to pinpoint dysfunctions in the brain that are responsible for four cognitive disorders: Parkinson's disease, dystonia (a muscle disorder condition that causes repetitive or twisting movements), obsessive compulsive disorder (OCD) and Tourette's syndrome. The discovery, published in Nature Neuroscience on Feb. 22, could potentially help doctors determine new treatments for these disorders. The study included 261 patients worldwide -- 70 had dystonia, 127 were Parkinson's disease patients, 50 had been diagnosed with OCD and 14 had Tourette's syndrome. The researchers implanted electrodes into the brains of each participant and used special software to determine which brain circuits were dysfunctional in each of the four disorders.

We present a web-based framework to screen for Parkinson's disease (PD) by allowing users to perform neurological tests in their homes. Our web framework guides the users to complete three tasks involving speech, facial expression, and finger movements. The task videos are analyzed to classify whether the users show signs of PD. We present the results in an easy-to-understand manner, along with personalized resources to further access to treatment and care. Our framework is accessible by any major web browser, improving global access to neurological care.