By mapping all the possible variations in a single gene, researchers have uncovered a previously hidden neurodevelopmental condition.
ReNU syndrome is a rare, inherited neurodevelopmental disorder identified in 2024 that affects brain function, development, and motor skills and is predicted to affect tens of thousands of individuals worldwide.
One possible explanation is that resilient brains are better at repairing themselves during Alzheimer’s. “Perhaps they can add new brain cells to a network that is degenerating”, the author says.
This idea is linked to a process called adult neurogenesis, which refers to the birth of new brain cells (neurons) in the adult brain. It has been well-established in other animals, but its existence in humans has been debated for years.
To study this, the team used human brain tissue from the Netherlands Brain Bank, which collects and stores donated brain samples for research. They included brains from control donors with no brain pathology, Alzheimer’s patients, and individuals with Alzheimer’s pathology who remained resilient to developing dementia.
The team focused on a small part of the brain’s memory center, likely one of the few areas where these new brain cells could form. “These cells are extremely rare, so we had to develop new ways to find them,” the author says. “We really zoomed in on the exact spot where we expected them to be.”
The team found what they were looking for: so-called “immature” neurons. These cells resemble young, not fully developed neurons. “Even at an average age of over 80, we still found these immature neurons in all groups,” the author says.
But the biggest surprise came next. While the team had expected to find much more of these cells in the resilient group than in the Alzheimer’s patients, the difference was not as big as expected.
Surprisingly, the team found that the key difference lies in how the immature neurons behave. “In resilient individuals, these cells seem to activate programs that help them survive and cope with damage,” the author says. “We also see lower signals related to inflammation and cell death.”
Most contemporary artificial intelligence (AI) systems learn to complete tasks via machine learning and deep learning. Machine learning is a computational approach that allows models to uncover patterns in data that are useful for making predictions. Deep learning, on the other hand, is a subset of machine learning that entails the use of multi-layered neural networks, which can autonomously extract features and learn complex patterns from unstructured data, sometimes with little or no human supervision.
Many AI systems trained with these approaches also produce confidence scores for their predictions. These scores are essentially estimates of how probable it is for a specific prediction to be accurate. Past studies suggest that in many cases, AI systems are overconfident and assign high confidence scores to wrong answers, or even present inaccurate information as a fact. This limits their reliability, particularly in high-stakes applications where wrong predictions can have serious consequences.
Researchers at the Korea Advanced Institute of Science and Technology recently introduced a new brain-inspired training approach that could yield more realistic AI confidence estimates. Their proposed strategy, introduced in a paper published in Nature Machine Intelligence, entails briefly training artificial neural networks on random noise (i.e., data with no meaningful patterns) and arbitrary outputs, so that they can learn to produce more realistic confidence estimates before learning specific tasks.
Theoretical physicists in the US have discovered a “speed limit” on the time taken for quantum information to spread through larger systems. Publishing their results in Physical Review Letters, Amit Vikram and colleagues at the University of Maryland have proved for the first time that this minimum time is closely linked with a system’s entropy and temperature, perhaps paving the way for a deeper understanding of quantum information across a wide range of physical settings.
In 1974, Stephen Hawking proposed for the first time that black holes aren’t entirely black. As well as emitting thermal radiation (now known as “Hawking radiation”), they also exhibit thermodynamic properties including temperature and an entropy proportional to their surface area.
Since entropy is a measure of the information carried by a system, this means a black hole’s surface effectively stores a finite number of “qubits”: the quantum equivalent of classical bits, each capable of storing quantum information as a superposition of two states simultaneously. In this way, the black hole’s temperature as described by Hawking governs how these qubits interact and evolve over time.
When a writer comes up with a striking metaphor, when an engineer solves a tricky problem by combining seemingly unrelated tools, or when a child invents the rules of a new game, what happens in the brain? In cognitive neuroscience, creativity is defined as the ability to produce ideas that are both original and relevant within a given context.
For several years, one hypothesis has gained traction in this field of research: Creativity involves two major brain networks. On the one hand, the default mode network (DMN), associated with the spontaneous generation of ideas and free associations. On the other hand, the executive control network (ECN) comes into play when we deliberately control our thinking in order to achieve a goal.
“Creativity is, in a sense, the result of dynamic cooperation between these two networks,” explains Emmanuelle Volle, neurologist and co-leader of the FrontLab team at the Paris Brain Institute. “We believe that creative ideas do not emerge from nothing, but result from the synthesis and reorganization of existing knowledge stored in semantic memory.”
A new study calls for caution in using the well-known senolytic treatment of dasatinib and quercetin (D+Q), showing that it causes damage in certain regions of the brain, similar to what is observed in multiple sclerosis [1].
Stem cell senescence prevents brain repair
Multiple sclerosis (MS) is a brain disorder in which the patient’s own immune system attacks oligodendrocytes: cells in the nervous system that provide a myelin coating for neurons, which is essential for their function and survival. MS is much more common in older patients, who are also more likely to have progressive disease and a worse response to treatment.
Dimension Zero takes a closer look at sci-fi civilizations through the lens of the Kardashev scale. We explore crucial facts about how different fictional societies would rank, from Type Zero to Type One and beyond. This examination provides a science fiction perspective on future energy and expansion into space. #startrek #starwars #stargate #celestials #dimensionzero …
Researchers have found that a “pencil beam” laser allows brain imaging 25 times faster than current methods. This could help scientists quickly test whether new drugs for diseases like Alzheimer’s or ALS are reaching their targets in the brain.
After a surprising discovery that overcomes a longstanding problem in fiber optics, MIT researchers demonstrated a biomedical imaging technique that is faster and more precise than other methods, which could help scientists and clinicians study new brain therapies.
A study by University of Wollongong (UOW) physicist Dr. Enbang Li has demonstrated that gravity can subtly influence the behavior of light, a breakthrough that could underpin future technologies for monitoring groundwater, tracking glacier melt, locating mineral deposits and detecting underground changes linked to volcanic activity and carbon storage.
The study, published in Scientific Reports, shows early experimental evidence that photons—particles of light—interact with Earth’s gravitational field in measurable ways, laying the groundwork for a new generation of ultra-sensitive gravity sensors.
Dr. Li said the work could lead to more precise and compact next-generation sensing technologies for environmental monitoring, navigation and underground mapping.