Our results are highly promising, especially since currently there are no approved treatments that address the underlying cause of Dravet syndrome. Since this gene regulation product targets the actual root cause of Dravet syndrome, we observed improvements in other developmental and cognitive symptoms, in addition to seizure control. This is unprecedented.
Decades of research show that people who have more years of education, more cognitively demanding jobs or more mentally stimulating hobbies all tend to have a reduced risk of cognitive impairment as they get older. Experts think this is partly thanks to cognitive reserve: Basically, the more brain power you've built up over the years, the more you can stand to lose before you experience impairment.
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).
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.
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.
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.
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.
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.
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.