Unexpected story: Use supercomputers to help diagnose depression

A recent study by scientists at the University of Texas showed that using a supercomputer has the ability to help identify and diagnose depression.

Before, depression is the leading cause of neurological disabilities for ages 15 to 55 and affects 15 million Americans a year.

The team studied the signs of depression and mental illness by identifying data on the relationship between brain function and neuronal brain structure through a supercomputer named scientific supercomputer.

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David Schnyer, a neuroscientist at the University of Texas, said: "An important difficulty in doing this research is the ability to visualize," he said in a press release.

Completely different brain network systems, differentiated, have different characteristics between many brain sufferers with depression or dementia. Therefore, forcing us to find a method, tools and techniques for optimal diagnosis.

Schnyer is using a Stampede supercomputer at the Texas Advanced Computer Center, and uses it to create new algorithms to identify similarities, common signs, general expressions of hundreds of data results. Brain MRI of genes, brain function of people with depression, anxiety.

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Schyner and colleagues now use Stampede supercomputers to classify people with depressive disorders with an accuracy of nearly 75%.

In addition, the research team is continuing to explore how to build an input diagnostic model with the results of new data output diagnostics on supercomputers .

To carry out the study, a team of University of Texas scientists used a vector-assisted machine to collect additional data from 52 participants with depression and 45 healthy people.

Participants in this experiment received high-tech MRI amplification and monitored the diffusion of brain cells under a microscope for a certain period of time.

This diffusion is measured in many directions of vectors for each voxel, 3D blocks represent structures or neural activity in the brain. Measurements were then converted into indicators indicating the integrity of white matter and cells in the cortex.

The researchers pointed out that the experiment on these two groups also provided important data in comparison and statistics.

" We will store all of the brain data as well as a subset of predictions for disease classification or any potential pathogen that is detected by the algorithm, " Schyner said . "The new wave, which is our future. There are more and more articles, research and conferences on the application of new research that address difficult problems in neuroscience ."

This research has just been published in the Journal of Psychiatric Research.