Machine learning algorithm allows a brain-computer interface to readjust itself continually in the background to ensure the system is always calibrated and ready to use.
A new machine learning model uses data from the COVID-19 pandemic in conjunction with a neural network and can determine the efficacy of social distancing measures and better predict viral spread. With current quarantine measures in place, the AI model predicts a plateau in coronavirus infections between April 15-20.
Dietary triglycerides directly alter signaling in the reward circuit to regulate behavior. The findings reveal a potential mechanism by which triglyceride-rich diets may lead to adaptions in dopamine signaling that underlie reward deficit and compulsive behaviors.
A newborn's brain is more adult-like than previously assumed. Neuroimaging revealed much of the visual cortex scaffolding is in place, along with patterns of brain activity at 27 days of age, although it is not quite as strong as seen in adult brains.
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Combining neuroimaging data with machine learning technology, researchers discover children with well-connected brain hubs have either very specific cognitive problems, such as poor listening skills, or no cognitive difficulties at all. Children with poorly connected hubs have widespread and severe cognitive difficulties.
Researchers have developed a new method of measuring axons using MRI neuroimaging.
Study shows how brain networks unique to each child can predict cognition. Using a combination of neuroimaging data and machine learning, researchers reveal functional neuroanatomy can vary greatly among children and is refined during development.
Functional connectivity in the human brain changes in two distinct ways during adolescence.
Individual variations define a specific structural fingerprint with a direct impact on the functional organization of individual brains. The findings stress the importance of using individual models to understand brain function.