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Category: neuroscience – Page 51

Mapping dynamical systems: New algorithm infers hypergraph structure from time-series data without prior knowledge

In a network, pairs of individual elements, or nodes, connect to each other; those connections can represent a sprawling system with myriad individual links. A hypergraph goes deeper: It gives researchers a way to model complex, dynamical systems where interactions among three or more individuals—or even among groups of individuals—may play an important part.

Instead of edges that connect pairs of nodes, it is based on hyperedges that connect groups of nodes. Hypergraphs can represent higher-order interactions that represent collective behaviors like swarming in fish, birds, or bees, or processes in the brain.

Scientists usually use a hypergraph to predict dynamic behaviors. But the opposite problem is interesting, too. What if researchers can observe the dynamics but don’t have access to a reliable model? Yuanzhao Zhang, an SFI Complexity Postdoctoral Fellow, has an answer.

A Single Cubic Millimeter of Brain Tissue May Have Just Changed Neuroscience Forever

Scientists have mapped an unprecedentedly large portion of the brain of a mouse. The cubic millimeter worth of brain tissue represents the largest piece of a brain we’ve ever understood to this degree, and the researchers behind this project say that the mouse brain is similar enough to the human brain that they can even extrapolate things about us. A cubic millimeter sounds tiny—to us, it is tiny—but a map of 200,000 brain cells represents just over a quarter of a percent of the mouse brain. In brain science terms, that’s extraordinarily high. A proportionate sample of the human brain would be 240 million cells.

Within the sciences, coding and computer science can sometimes overshadow the physical and life sciences. Rhetoric about artificial intelligence has raced ahead with terms like “human intelligence,” but the human brain is not well enough understood to truly give credence to that idea. Scientists have worked for decades to analyze the brain, and they’re making great progress despite the outsized rhetoric working against them.

Sleep’s dual role: How it consolidates memories while preparing the brain for new learning

Memory formation, storage, and retrieval are fundamental processes that define who we are and how we interact with the world. At the cellular level, these processes rely on specialized neurons called engram cells—neuronal populations that physically encode our experiences and allow us to recall them later. Over the past few decades, scientists have made significant progress in identifying these neuronal ensembles and understanding some aspects of memory allocation.

Although sleep is widely known to be essential for memory processing and consolidation, many of its underlying mechanisms and functions are unclear. Traditional views have largely focused on sleep as a backward-looking process that serves to strengthen past experiences, but could it simultaneously help prepare the brain for new learning?

In a recent effort to tackle this question, a research team from Japan, led by Distinguished Professor Kaoru Inokuchi from the University of Toyama, uncovered a dual role for sleep in memory processing. Their paper, which will be published in Nature Communications on April 28, 2025, explores how the brain simultaneously preserves past memories while preparing for future ones during sleep periods.

Enzymes critical for astrocytic GABA production in Alzheimer’s disease identified!

A research team has identified a previously unknown enzyme, SIRT2, that plays a key role in memory loss associated with Alzheimer’s disease (AD). The study provides critical insights into how astrocytes contribute to cognitive decline by producing excessive amounts of the inhibitory neurotransmitter GABA.

Astrocytes, once thought to only support neurons, are now known to actively influence brain function. In Alzheimer’s disease, astrocytes become reactive, meaning they change their behavior in response to the presence of amyloid-beta (Aβ) plaques, a hallmark of the disease. While astrocytes attempt to clear these plaques, this process triggers a harmful chain reaction. First, they uptake them via autophagy and degrade them by the urea cycle, as discovered in previous research. However, this breakdown results in the overproduction of GABA, which dampens brain activity and leads to memory impairment. Additionally, this pathway generates hydrogen peroxide (H2O2), a toxic byproduct that causes further neuronal death and neurodegeneration.

The research team set out to uncover which enzymes were responsible for excessive GABA production, hoping to find a way to selectively block its harmful effects without interfering with other brain functions. Using molecular analysis, microscopic imaging, and electrophysiology, the researchers identified SIRT2 and ALDH1A1 as critical enzymes involved in GABA overproduction in Alzheimer’s-affected astrocytes.

Computational mechanism underlying switching of motor actions

Author summary Humans exhibit a remarkable ability to regulate their actions in response to changing environmental demands. An essential aspect of action regulation is action inhibition that occurs when stopping unwanted or inappropriate actions. However, everyday life rarely calls for complete inhibition of responses without switching behavior to adapt to new situations. Despite extensive research to understand how the brain switches actions, the computations underlying the switching process and how it relates to the selecting and stopping processes remain elusive. Part of this challenge lies in the fact that these processes are rarely studied together, making it difficult to develop a unified theory that explains the computational aspects of the action regulation mechanism. The current study aims to delineate the computations underlying action regulation functions that involve inhibitory control, explore how these functions interrelate, and how they can be implemented within brain networks, opening new avenues for future neurophysiological investigations.

Hormone supplementation in rhesus monkeys points to potential autism treatment

For years, Florida Tech’s Catherine Talbot, an assistant professor of psychology, has worked to understand the sociality of male rhesus monkeys and how low-social monkeys can serve as a model for humans with autism. Her most recent findings show that replenishing a deficient hormone, vasopressin, helped the monkeys become more social without increasing their aggression—a discovery that could change autism treatment.

Currently, the Centers for Disease Control and Prevention reports that one in 36 children in the United States is affected by autism spectrum disorder (ASD). That’s an increase from one in 44 children reported in 2018. Two FDA-approved treatments currently exist, Talbot said, but they only address associated symptoms, not the root of ASD. The boost in both prevalence and awareness of the disorder prompts the following question: What is the cause?

Some are naturally low-social, meaning they demonstrate poor social cognitive skills, while others are highly social. Their individual variation in sociality is comparable to how human sociality varies, ranging from people we consider social butterflies to those who are not interested in social interactions, similar to some people diagnosed with ASD, Talbot said. Her goal has been to understand how variations in biology and behavior influence social cognition.