Developing brain atlas using deep learning algorithms

#artificialintelligence

A team of researchers from the Brain Research Institute of the University of Zurich and the Swiss Federal Institute of Technology (ETH) have developed a fully automated brain registration method that could be used to segment brain regions of interest in mice. Neuroscientists are always seeking out new methods of exploring the structure and function of different brain regions, which are initially applied on animals but could eventually lead to important discoveries about the organization of the human brain. "My lab aims to reveal how the mammalian brain develops its abilities to process and react to sensory stimuli," Theofanis Karayannis, one of the researchers who carried out the study told Tech Xplore. "Most of the work we do is on the experimental side, utilizing the mouse as a model system and techniques that range from molecular-genetic to functional and anatomical." This study is part of a larger project, which also includes "Exploring Brain-wide Development of Inhibition through Deep Learning," a study in which Karayannis and his colleagues use deep learning algorithms to comprehensively track the so-called inhibitory neurons over time in order to gauge the development of capabilities of the brain at specific points in time.


People with big brains have a different brain structure too

New Scientist

It turns out that in larger human brains, regions involved in bringing together information are hyperexpanded – but we don't know what affect this might have on intelligence yet. Armin Raznahan at the US National Institute of Mental Health in Maryland and his colleagues discovered this by comparing brain images from around 3,000 people. They compared the area of 80,000 points across the cortex – the large part of our brains that is involved in higher functions like thinking. Analysing these, they found that some particular areas expanded more than others in people who had an overall larger brain size. These regions seem to be involved in integrating information from across the brain, he says.


Hungarian research shows how dogs understand what we say AND how we say it

Daily Mail - Science & tech

A groundbreaking study to investigate how dog brains process speech has revealed canines care about both what we say and how we say it. It discovered that dogs, like people, use the left hemisphere to process words, and the right hemisphere brain region to process intonation. It found praise activates dog's reward centre only when both words and intonation match, according to the new study in Science. Trained dogs around the fMRI scanner used in the study: Dogs, like people, use the left hemisphere to process words, and the right hemisphere brain region to process intonation, according to the new study in Science. The brain activation images showed that dogs prefer to use their left hemisphere to process meaningful but not meaningless words.


ExpertoCoder: Capturing Divergent Brain Regions Using Mixture of Regression Experts

arXiv.org Machine Learning

fMRI semantic category understanding using linguistic encoding models attempts to learn a forward mapping that relates stimuli to the corresponding brain activation. Classical encoding models use linear multivariate methods to predict brain activation (all the voxels) given the stimulus. However, these methods mainly assume multiple regions as one vast uniform region or several independent regions, ignoring connections among them. In this paper, we present a mixture of experts model for predicting brain activity patterns. Given a new stimulus, the model predicts the entire brain activation as a weighted linear combination of activation of multiple experts. We argue that each expert captures activity patterns related to a particular region of interest (ROI) in the human brain. Thus, the utility of the proposed model is twofold. It not only accurately predicts the brain activation for a given stimulus, but it also reveals the level of activation of individual brain regions. Results of our experiments highlight the importance of the proposed model for predicting brain activation. This study also helps in understanding which of the brain regions get activated together, given a certain kind of stimulus. Importantly, we suggest that the mixture of regression experts (MoRE) framework successfully combines the two principles of organization of function in the brain, namely that of specialization and integration.


Fluctuating brain networks help you handle complex tasks

Engadget

Researchers already know that the human brain isn't static, but it's now clear just how dynamic the mind can be. A Stanford University team has discovered that the networking between brain regions will fluctuate depending on the complexity of tasks. If you're at rest, your brain's components are relatively isolated. Handle a complicated activity, however, and the level of networking ramps up. The more interconnected your brain is, the better your performance -- in a memory test, those with the most integrated brains were the quickest and most accurate.