Lifeboat Foundation

In what promises to be a revolutionary breakthrough in treatment of various neurological illnesses, Brainstorm is a European project using tiny magnets to restore damaged neurons.

Summary: Researchers successfully mapped the neural activity of the C. elegans worm, correlating it to its behaviors such as movement and feeding.

Using novel technologies and methodologies, they developed a comprehensive atlas that showcases how most of the worm’s neurons encode its various actions.

This study provides an intricate look into how an animal’s nervous system controls behavior. The team’s findings, data, and models are available on the “WormWideWeb.”

Researchers from The University of Queensland applied an algorithm from a video game to study the dynamics of molecules in living brain cells.

Dr. Tristan Wallis and Professor Frederic Meunier from UQ’s Queensland Brain Institute came up with the idea while in lockdown during the COVID-19.

First identified in 2019 in Wuhan, China, COVID-19, or Coronavirus disease 2019, (which was originally called “2019 novel coronavirus” or 2019-nCoV) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It has spread globally, resulting in the 2019–22 coronavirus pandemic.

Brain age was estimated using an algorithm that combined multiple measures of brain structure obtained through MRI scans when the participants were 45 years old. This algorithm quantified the difference between estimated brain age and the participants’ chronological age, referred to as brain age gap estimate.

If the estimated brain age is higher than the chronological age, it suggests that the brain’s structural characteristics are more similar to those of an older individual. Conversely, if the estimated brain age is lower than the chronological age, the brain’s structural characteristics resemble those of a younger individual.

Lay-Yee and his colleagues also adjusted their analyses for various potential confounding factors. These included socio-demographic factors like sex and socio-economic status, as well as family factors (teen-aged mother, single parent, change in residence, maltreatment) and child-behavioral factors (self-control, worry/fearfulness).

Vielight brain photobiomodulation devices combine electrical engineering and neuroscience to improve brain health and performance.

Smartphones and other tech pose special challenges — and opportunities — for young brains.

Scientists have used advanced imaging techniques to identify brain activity and regions linked to cognitive motor dissociation (CMD), or ‘hidden consciousness’. CMD is a state in which a person appears comatose and unresponsive while inwardly showing signs of conscious brain activity.

The findings, reported by a team from Columbia University in the US, may help doctors more easily identify CMD in the future, and better tailor treatments for people who can understand what’s being said to them but can’t respond to it.

CMD happens in around 15–25 percent of people with brain injuries from head trauma, brain hemorrhage, or cardiac arrest. In these patients, something breaks between the instructions coming from the brain and the muscles needed to carry those instructions out.

The largest ever study of the genetics of the brain – encompassing some 36,000 brain scans – has identified more than 4,000 genetic variants linked to brain structure. The results of the study, led by researchers at the University of Cambridge, are published in Nature Genetics today.

A fundamental goal in the field of sensory neuroscience is to understand the complex mechanisms that underlie the neural code responsible for processing natural visual scenes. In neuroscience, a fundamental yet unresolved question is how neural circuits are developed in natural settings by the interaction of multiple cell types. The eyes have evolved to communicate information about natural visual scenes using a wide range of interneurons, which is crucial for transmitting visual information to the brain.

Retina’s functioning is largely based on research into how it reacts to artificial stimuli like flashing lights and noise. These might not accurately represent how the retina interprets actual visual data. The complexity of how these more than 50 different types of interneurons contribute to retinal processing has yet to be fully understood despite the fact that different computations have been detected using such methods. In a recent research paper, a group of researchers has made a significant advancement by showing that a three-layer network model is capable of predicting retinal responses to natural sceneries with amazing precision, almost exceeding the bounds of experimental data. The researchers wanted to understand how the brain processes natural visual scenes, so they focussed on the retina, which is part of the eye that sends signals to the brain.

This model’s interpretability, i.e., the ability to comprehend and examine its internal organization, is one of its key characteristics. There is a strong correlation between the responses of interneurons that were directly included in the model and those that were separately recorded. This suggests that the model captures significant aspects of the retinal interneuron activity. It successfully reproduces a wide range of motion analysis, adaptability, and predictive coding phenomena when they are just trained on natural scenes. On the other hand, models trained on white noise cannot reproduce the same set of events, supporting the idea that examining natural sceneries is necessary to comprehend natural visual processing.