#deep-brain-stimulation-dbs

#deep-brain-stimulation-dbs

The condition, called recessive RNU2-2-related neurodevelopmental disorder, is associated with seizures and severe developmental delay in children less than a year-old, in areas such as speech and walking.

NEO, the implant developed by Neuracle Medical Technology, translates the thoughts of a person with paralysis into movements of an assistive robotic hand, allowing users to perform basic tasks.

Dopamine is one of the most extensively studied neurotransmitters, chemicals that convey signals from cell to cell. It's the one with the highest profile outside neuroscience: often known as the 'pleasure chemical', it's depicted as the hit of reward that people get from recreational drugs or scrolling through social media. That's a gross simplification of what dopamine does; on that, researchers agree.

Summer passed Valerie Zeko by when she was 27, as she vegged out on the couch watching TV instead of seeing friends or exploring the overcast beach near her house. She later learned that period was her first episode of depression. I felt like the fog was in my head as well as outside, said Zeko, now 57, describing the mood disorder that would squelch her happiness, motivation and self-esteem for 28 years until she finally found effective treatment.

Artificial intelligence (AI) machine learning is making a difference in assistive technology to help restore movement for the paralyzed. A new study in the American Institute of Physics journal APL Bioengineering shows how AI has the potential to restore lower-limb functions in those with severe spinal cord injuries (SCIs) by identifying patterns in brain signals captured noninvasively via electroencephalography (EEG).

Before treatment began, participants underwent neuroimaging. Instead of relying on a single modality, the researchers fused structural connectivity (how regions are physically wired) with functional connectivity (how regions co-activate at rest). The goal was not to throw every possible feature at a black box, but to learn a constrained pattern-what the authors call structure-function "covariation"-that carries the most predictive signal for outcome. In other words, the model tries to find the smallest set of connections that meaningfully forecasts symptom change.

When I first read that, I was skeptical. But after trying it myself and digging deeper into the studies, the mechanisms started making sense. When we actively look for things to appreciate, we're essentially rewiring our brain's default mode. Instead of scanning for threats and problems (which our brains love to do), we're training it to notice the good stuff. It's like changing the channel from a disaster documentary to something that doesn't spike your cortisol.

According to University of Michigan neuroscientists, not only can their AI vision language model diagnose neurological disorders from MRI scans with high performance accuracy, but it also has foundation model capabilities, making it a flexible, general-purpose solution that can be tailored for a wide variety of medical imaging. "These results demonstrate that Prima has foundation model properties, and reported performance will continue to improve with additional health system training data and larger compute budgets," wrote the study's authors in the preprint.

The human brain is complex. Artificial intelligence (AI) machine learning and medical imaging data are accelerating breakthroughs in brain health, especially in medical diagnostics. A peer-reviewed study published today in Nature Neuroscience unveils an AI foundation model called BrainIAC (Brain Imaging Adaptive Core) that is capable of predicting brain age, dementia, time-to-stroke, and brain cancer from brain magnetic resonance imaging (MRI).

It might come as a surprise to learn that the brain responds to training in much the same way as our muscles, even though most of us never think about it that way. Clear thinking, focus, creativity, and good judgment are built through challenge, when the brain is asked to stretch beyond routine rather than run on autopilot. That slight mental discomfort is often the sign that the brain is actually being trained, a lot like that good workout burn in your muscles.

There was a group of neurons that predicted the wrong answer, yet they kept getting stronger as the model learned. So we went back to the original macaque data, and the same signal was there, hiding in plain sight. It wasn't a quirk of the model - the monkeys' brains were doing it too. Even as their performance improved, both the real and simulated brains maintained a reserve of neurons that continued to predict the incorrect answer.

When a person suffers a stroke, physicians must restore blood flow to the brain as quickly as possible to save their life. But, ironically, that life-saving rush of blood can also trigger a second wave of damage - killing brain cells, fueling inflammation and increasing the odds of long-term disability. Now, in a study published in the journal Neurotherapeutics, Northwestern University scientists have developed an injectable regenerative nanomaterial that helps protect the brain during this vulnerable window.

The team, which is being led by Jülich neurophysics professor Markus Diesmann, will leverage the Joint Undertaking Pioneer for Innovative and Transformative Exascale Research (JUPITER) supercomputer for their simulation. JUPITER is currently the fourth most powerful supercomputer in the world according to the TOP500 list, and features thousands of graphical processing units. The team demonstrated last month that a " spiking neural network " could be scaled up and run on JUPITER, effectively matching the cerebral cortex's 20 billion neurons and 100 trillion connections.

When Dr. Homoud Aldahash started the three-hour process of removing a tumor about the size of a walnut from a patient's brain, it was an experience unlike any other in his 25 years as a neurosurgeon. It wasn't Aldahash's gloved hands slicing 68-year-old Mohammed Almutrafi's right frontal lobe, but surgical instruments attached to a set of robotic arms, which Aldahash controlled from a console where he sat three meters away.

They then used emerging mathematical methods to isolate signals originating from nine brain regions previously implicated in mediating consciousness and examined connections between pairs of these regions. Among them were the parietal cortex, which is at the top of the brain about halfway between the forehead and the back of the skull; the occipital cortex, at the back of the head; and several small, deeper structures, such as one called the thalamus.

Betley and his colleagues were curious about what happens in the brain as people get stronger through exercise. They decided to focus on the ventromedial hypothalamus, a brain region that regulates appetite and blood sugar. The team then zeroed in on a group of neurons in that region that produce a protein called steroidogenic factor 1 (SF1), which is known to play a part in regulating metabolism. A previous study found that the deletion of the gene that codes for SF1 impairs endurance in mice.