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Human Brain - Astrophysics Data System

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Last Updated: 11 September 2022

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Ready function to evaluate the dose distribution of the deep space radiation in human brains

A key determinant of astronaut safety on long-duration missions to outer space toward the Moon or Mars is the health risks of space radiation. "Brain Response Functions" are estimated in different areas of the brain based on a realistic model of the head/brain system in Geant4 here. A BRF is a probabilistic representation of all potential interactions that a particle with a specified form and energy may encounter when approaching the human head and depositing energy in specific areas of the brain. We fold our BRFs with SEP spectra and discover the pivotal energy at which the SEP flux alone can be used to determine the human head's radiation deposit.

Source link: https://ui.adsabs.harvard.edu/abs/2022cosp...44.2657K/abstract


Whole tissue and single cell mechanics are correlated in human brain tumors

However, the relationship between single cell rheology measured ex-vivo and the living tumor is uncertain. We combined single-cell rheology of cells isolated from primary tumors with in vivo bulk tumor rheology in patients with brain tumors. MRE was performed in a 3-Tesla clinical MRI scanner and magnitude modulus |G*|, loss angle u03c6, storage modulus Gu2032, and loss modulus Gu2032 were created, and MRE was performed in a 3-Tesla clinical MRI scanner and magnitude modulus G*|, loss angle Gu2032u2032 were calculated, and loss modulus Gu2032 u2032u2032u2032 In a time period that correlates with MRE, we used a Kelvin-Voigt model to determine two parameters related to cellular stiffness and cellular viscosity from OS measurements. These results show that single-cell stiffness in brain tumors influences tissue viscosity. The finding that individual cell viscosity measurements were not linked suggested shows that collective mechanical interactions of several cancer cells, which are dependent on cell stiffness, may influence the bulk tissue's mechanical dissipation behavior.

Source link: https://ui.adsabs.harvard.edu/abs/2021SMat...1710744S/abstract


Net2Brain: A Toolbox to compare artificial vision models with human brain responses

We've developed Net2Brain, a graphical and command-line user interface package for comparing the representational spaces of artificial deep neural networks and human brain recordings. Although most toolboxes support only single operations or a small subset of image classification schemes, Net2Brain allows the extraction of activations of more than 600 DNNs capable of performing a variety of vision-related tasks across both image and video datasets, since some toolboxes are limited to single functionalities or exclusive to a small subset of supervised image classification schemes.

Source link: https://ui.adsabs.harvard.edu/abs/2022arXiv220809677B/abstract


Multimodal foundation models are better simulators of the human brain

With the support of non-invasive brain imaging techniques such as functional magnetic resonance imaging, we come to an end. To this end, we propose to investigate the usefulness of multimodal learning schemes with the support of non-invasive brain imaging techniques such as functional magnetic resonance imaging. We first present a newly developed multimodal foundation model pre-trained on 15 million image-text pairs, which has demonstrated excellent multimodal understanding and generalization skills in a variety of cognitive downstream tasks. We find a number of brain regions where multimodally-trained encoders exhibit better neural encoding results in particular. Also, we find that multimodal foundation models are more effective tools for neuroscientists to investigate the human brain's multimodal signal processing pathways. Both AI-for-brain and brain-for-AI research can be promoted by multimodal foundation models as the most effective computational simulators to support both AI-for-brain and brain-for-AI research. Our results also show the benefits of multimodal foundation models as excellent computational simulators to support both AI-for-brain and brain-for-AI study.

Source link: https://ui.adsabs.harvard.edu/abs/2022arXiv220808263L/abstract

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions