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Cutting-edge research has pulled back the curtain on the brain’s secret playbook for learning, unveiling rules that govern how we master new skills and knowledge—a discovery with profound implications for both education and artificial intelligence (AI). Scientists, backed by the U.S. National Institutes of Health (NIH), harnessed revolutionary synapse imaging technology to observe real-time changes among the brain’s neurons during learning, uncovering patterns that promise new understanding of how we become smarter—with practical lessons for schools in Thailand and emerging AI technologies worldwide SciTechDaily.

A new AI-driven study sheds light on why some people stick to exercise while others abandon routines. Using data from nearly 12,000 individuals, researchers found three strong predictors of meeting weekly activity guidelines: daily sedentary time, gender, and educational attainment. The results suggest tailored health advice and smarter public campaigns could help people stay active, including in Thailand.

Thailand faces rising health risks from obesity, heart disease, and diabetes. Urban life and demanding work schedules can limit time for movement, while long commutes and crowded cities encourage sedentary habits. Thailand’s Health Promotion Foundation reports that only about 40% of Thais exercise regularly, underscoring the need for effective, culturally relevant strategies. The study’s insights offer a practical framework for policymakers and health professionals working toward healthier communities.

A groundbreaking study using artificial intelligence (AI) has taken a scientific leap in answering an age-old question: why do some people stick with regular exercise, while others quickly fall off the wagon? By analyzing the habits and characteristics of nearly 12,000 individuals, a research team from the University of Mississippi has identified three surprisingly robust predictors of exercise adherence—how much time you spend sitting, your gender, and your education level—shedding new light on what keeps people committed to their fitness goals. This finding could open the door to more individualized health advice and smarter public health campaigns worldwide, including here in Thailand (Neuroscience News, Science Daily).

A groundbreaking new study using artificial intelligence (AI) and machine learning has revealed the most crucial factors that keep people committed to their exercise routines: how much time they spend sitting, their gender, and their education level. Published in the prestigious journal Scientific Reports, this research analyzed health data from nearly 12,000 individuals, offering fresh insights into what helps people meet physical activity guidelines—a finding with important implications for Thailand, where sedentary lifestyles are increasingly common.

A new study using artificial intelligence and machine learning identifies the three strongest predictors of sticking with an exercise routine: sedentary time, gender, and education level. Published in Scientific Reports, the research analyzed health data from nearly 12,000 individuals to understand who meets physical activity guidelines. The findings carry meaningful implications for Thailand, where sedentary lifestyles are rising amid urban life.

Regular exercise is essential for preventing chronic diseases. In Thailand, long hours at desks and routine traffic congestion increase the risk of inactivity-related illnesses such as diabetes and heart disease. Thai health authorities recommend at least 150 minutes of moderate activity or 75 minutes of vigorous activity each week. Yet many Thai people struggle to reach these targets, underscoring the need for effective, culturally informed strategies.

An international team has produced the first detailed three-dimensional map of a mammal’s brain, revealing unprecedented insight into brain structure and function. Focusing on a tiny fragment of a mouse’s visual cortex, the achievement marks a milestone for neuroscience with potential to improve diagnosis and treatment of brain diseases such as Alzheimer’s and autism. For Thai readers, the findings underscore how advances in brain science can influence medicine, education, and future AI applications amid Thailand’s aging society.

In a scientific breakthrough once thought impossible, an international team of researchers has created the first detailed three-dimensional map of a mammal’s brain, offering an unprecedented window into the structure and function of the mind. This ambitious feat, achieved by studying a tiny fragment of a mouse’s visual cortex, marks a pivotal advance in neuroscience and holds profound implications for understanding brain diseases such as Alzheimer’s and autism (CNN/Yahoo! News).

In a groundbreaking advancement, Stanford Medicine researchers have developed a “digital twin” of the mouse brain, leveraging artificial intelligence to simulate the brain’s visual cortex—a region central to processing visual inputs. This development, detailed in a recent study published in Nature, could reshape the way neuroscientists conduct experiments, making brain research significantly more efficient and insightful.

The concept of a digital twin, akin to a highly realistic flight simulator, allows scientists to experiment on a virtual model of the mouse brain. This is a monumental step, as it enables the simulation of neural activities based on extensive datasets gathered from live mice. These animals had their neural responses mapped while watching action-packed films, to mimic their natural visual experiences. Dr. Andreas Tolias, a senior author from Stanford, noted the utility of a precise brain model for conducting experiments that can later be verified in vivo.

A landmark study from Stanford Medicine unveils an AI-driven digital twin of the mouse brain, focused on the visual cortex—the area that processes what we see. Reported in Nature, the work promises to reshape how scientists design experiments by enabling rapid, virtual testing that complements experiments in living animals.

The digital twin functions like a high-fidelity flight simulator for the brain. It runs on large datasets collected from live mice whose neural activity was mapped while they watched action-filled videos. According to senior author Dr. Andreas Tolias, a precise brain model enables experiments that can later be validated in vivo, saving time and resources.

The integration of artificial intelligence (AI) into skin cancer detection is gaining momentum, heralding a potentially transformative shift in medical diagnostics. However, experts emphasize that while AI technologies are advancing, human expertise remains irreplaceable. Ivy Lee, a dermatologist in Los Angeles and chair of the Augmented Intelligence Committee at the American Academy of Dermatology, reflects this sentiment, noting that the industry’s maturity moves beyond previous media hype towards practical applications of AI in healthcare.

AI is accelerating skin cancer detection, offering new ways to improve access to care. Yet experts warn that human clinicians remain essential. A leading dermatologist in Los Angeles and chair of the Augmented Intelligence Committee at the American Academy of Dermatology notes that AI’s maturity is shifting from hype to practical medical use, while doctors oversee care and patient interactions that machines cannot replace.

A landmark Nature study from 2017 showed AI models analyzing more than 129,000 clinical images could outperform several dermatologists in diagnosing skin cancer. Critics, however, remind us that real diagnosis blends image analysis with patient history, physical examination, and interpersonal care. As a senior clinician at a major U.S. cancer center emphasizes, photos alone cannot capture the full clinical picture.

A new AI-enhanced treatment offers fresh hope for people with Parkinson’s disease. Adaptive deep brain stimulation (DBS) tailors therapy to real-time brain activity, reducing symptoms and allowing patients to rely less on medications that often cause side effects. This progress has been celebrated by major outlets, highlighting real patients who benefited from the technology.

Parkinson’s affects millions globally, including thousands in Thailand. Traditional medications can bring nausea, hallucinations, or involuntary movements. The core issue is neurons firing in abnormal, synchronized patterns—an oscillation that disrupts normal brain function.

In a groundbreaking study published in Neuroscience of Consciousness, researchers have unveiled a fascinating psychological dynamic affecting decision-making confidence when individuals believe they’re collaborating with machines. This revelation highlights that the mere perception of working alongside artificial intelligence can undermine human confidence, even when human judgment is accurate. Such insights compel us to reconsider how human-machine interactions might be designed, especially as automation increasingly infiltrates our daily lives and workplaces.

A new study in Neuroscience of Consciousness reveals a surprising finding: simply believing you are working with a machine can lower your confidence in decisions, even when your judgments are correct. The research shows that human–machine interactions shape self-belief in ways that may affect everyday choices at work and in learning environments.

This insight is timely as Thailand expands its tech ecosystem. Thai educators, students, and professionals are increasingly using AI in classrooms, clinics, and offices. Understanding how perceived machine collaboration influences confidence could inform the design of human-centered technologies in education and health services.

A new AI-powered method combined with magnetic resonance elastography (MRE) is changing how experts measure brain aging and assess dementia risk. Researchers from the University of Delaware have developed a technique that maps brain stiffness and volume to estimate brain age with impressive accuracy. By integrating AI with MRE, this approach could support earlier detection of Alzheimer’s and other neurodegenerative conditions.

MRE uses gentle vibrations in tandem with MRI to create stiffness maps of brain tissue. These maps serve as health indicators because brain stiffness shifts with age and during the onset of neurodegenerative processes. Over years, a large dataset of stiffness maps has been built, and researchers apply advanced analytics to identify meaningful patterns. The latest findings suggest that evaluating both brain stiffness and volume yields the most precise brain age estimate, as described in a recent report in Biology Methods and Protocols.