New sensors monitor brain activity and blood flow deeper in the brain and more accurately

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Magnetic calcium-responsive nanoparticles (dark centers are magnetic cores) respond within seconds to calcium ion changes by clustering (Ca+ ions, right) or expanding (Ca- ions, left), creating a magnetic contrast change that can be detected with MRI, indicating brain activation. (High levels of calcium outside the neurons correlate with low neuron activity; when calcium concentrations drop, it means neurons in that area are firing electrical impulses.) Blue: C2AB “molecular glue” (credit: The researchers)

Calcium-based MRI sensor enables high-sensitivity deep brain imaging

MIT neuroscientists have developed a new magnetic resonance imaging (MRI) sensor that allows them to monitor neural activity deep within the brain by tracking calcium ions.

Calcium ions are directly linked to neuronal firing at high resolution — unlike the changes in blood flow detected by functional MRI (fMRI), which provide only an indirect indication of neural activity. The new sensor is also better than fluorescent molecules used to label calcium in the brain and image it with traditional microscopy, which is limited to small areas of the brain.

A calcium-based MRI sensor could allow researchers to link specific brain functions directly to specific neuron activity, and to determine how distant brain regions communicate with each other during particular tasks. The research is described in a paper in the April 30 issue of Nature Nanotechnology. Source: MIT


New technique for measuring blood flow in the brain uses laser light shined into the head (“sample arm” path) through the skull. The return signal is boosted by a reference light beam and returned to a detector camera chip. (credit: Srinivasan lab, UC Davis)

Measuring deep-tissue blood flow at high speed

Biomedical engineers at the University of California, Davis, have developed a more-effective, lower-cost technique for measuring deep tissue blood flow in the brain with high speed. It could be especially useful for patients with stroke or traumatic brain injury.

The technique, called “interferometric diffusing wave spectroscopy” (iDWS), replaces about 20 photon-counting detectors in diffusing wave spectroscopy (DWS) devices (which cost a few thousand dollars each) with a single low-cost CMOS-based digital-camera chip.

The NIH-funded work is described in an open-access paper published April 26 in the journal Optica. Source: UC Davis

 

 

 

 

 

 

 

 

 

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New sensors monitor brain activity and blood flow deeper in the brain and more accurately

https://ift.tt/2rbDk3C

Magnetic calcium-responsive nanoparticles (dark centers are magnetic cores) respond within seconds to calcium ion changes by clustering (Ca+ ions, right) or expanding (Ca- ions, left), creating a magnetic contrast change that can be detected with MRI, indicating brain activation. (High levels of calcium outside the neurons correlate with low neuron activity; when calcium concentrations drop, it means neurons in that area are firing electrical impulses.) Blue: C2AB “molecular glue” (credit: The researchers)

Calcium-based MRI sensor enables high-sensitivity deep brain imaging

MIT neuroscientists have developed a new magnetic resonance imaging (MRI) sensor that allows them to monitor neural activity deep within the brain by tracking calcium ions.

Calcium ions are directly linked to neuronal firing at high resolution — unlike the changes in blood flow detected by functional MRI (fMRI), which provide only an indirect indication of neural activity. The new sensor is also better than fluorescent molecules used to label calcium in the brain and image it with traditional microscopy, which is limited to small areas of the brain.

A calcium-based MRI sensor could allow researchers to link specific brain functions directly to specific neuron activity, and to determine how distant brain regions communicate with each other during particular tasks. The research is described in a paper in the April 30 issue of Nature Nanotechnology. Source: MIT


New technique for measuring blood flow in the brain uses laser light shined into the head (“sample arm” path) through the skull. The return signal is boosted by a reference light beam and returned to a detector camera chip. (credit: Srinivasan lab, UC Davis)

Measuring deep-tissue blood flow at high speed

Biomedical engineers at the University of California, Davis, have developed a more-effective, lower-cost technique for measuring deep tissue blood flow in the brain with high speed. It could be especially useful for patients with stroke or traumatic brain injury.

The technique, called “interferometric diffusing wave spectroscopy” (iDWS), replaces about 20 photon-counting detectors in diffusing wave spectroscopy (DWS) devices (which cost a few thousand dollars each) with a single low-cost CMOS-based digital-camera chip.

The NIH-funded work is described in an open-access paper published April 26 in the journal Optica. Source: UC Davis

 

 

 

 

 

 

 

 

 

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Aspiring AI Programmer

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I’m currently in school for CS with one of my concentrations being AI, but with summer coming up, I’d like to practice AI programming so that A) I don’t forget what I’ve learned over the past to semesters and B) prepare for future AI classes. What would be some good beginner-level AI projects that could help me practice?

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Keras to extract quantitative information from images? (Physics research software)

I am currently working on an undergraduate research project designing software for a physics lab. The software currently takes microscopic images of particles obtained in the lab, and uses physics based algorithms to automatically extract qualitative data from the images. This data includes the particle diameter, and it’s refractive index. The downside of the software is that it is very slow, and computation heavy due to the physics algorithms in place. So I had an idea. I am under the impression that a CNN, via Keras, should be able to identify features of interest (localization) and analyze them (regression) in one shot, or at least in one clean cascade of similar components. I haven’t worked much with ML, and am wondering if someone more experience/knowledge might know if this sounds possible. If so, any leads on how to proceed, or any pointers relevant research papers, etc. would be appreciated.

The following stackexchange discussion explains how to get a number (such as particle diameter) from a network created with Keras: (I’ve been referencing this) https://stats.stackexchange.com/questions/243578/how-to-get-continuous-output-with-convolutional-network-keras

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How to start research in Artificial Intelligence?

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Hi,

I am undergrad student and I really like to start to learn about artificial intelligence and publish paper, what do you think is the starting point to learn about different topics in Artificial Intelligence?

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