Science Success Story
XSEDE-Allocated Supercomputers Help Accelerate Alzheimer's Research
Comet, Stampede2 supercomputers assist study of nearly 50,000 brain scans
By Kimberly Mann Bruch, SDSC Communications
|On the left, the transentorhinal cortex is shown as a pair of triangulated surfaces; the curved white lines represent cortical columns, which are used to accurately estimate thickness. On the right side, regions are shown where differences in atrophy patterns are observed between those with mild cognitive impairment (MCI) and those without MCI. Credit: Daniel Tward, UCLA.|
Since 2009, Daniel Tward and his collaborators at University of California Los Angeles and Johns Hopkins University have analyzed more than 47,000 images of human brains via MRI Cloud—a gateway created to collect and share quantitative information from human brain images, including subtle changes in shape and cortical thickness. The latter was the topic of a recently published study in the journal Neuroimage: Clinical by Tward and his team.
Entitled Cortical Thickness Atrophy in the Transentorhinal Cortex in Mild Cognitive Impairment, the study detailed new findings related to this particular area of the brain's thinning during the early stages of Alzheimer's disease and how it impacts mild cognitive impairment.
"Until now, we haven't been able to measure these changes in living people," said Tward, assistant professor of computational medicine and neurology at the University of California Los Angeles. "By using supercomputers like Comet at the San Diego Supercomputer Center at UC San Diego and Stampede2 at Texas Advanced Supercomputing Center, we were able to study a large cohort of patient images over time."
Specifically, Tward said he and his team used allocations from the National Science Foundation (NSF) Extreme Science and Engineering Discovery Environment (XSEDE) to access supercomputers that allowed for observation and quantification of thinning in the transentorhinal cortex, in a pattern that agrees with autopsy results. Located in the temporal lobe of the brain, the transentorhinal cortex has been believed to be the first area impacted by Alzheimer's disease; however, until now, this was only able to be shown in autopsy results.
Having access and support with these large-scale systems, rather than buying and maintaining our own, is a huge advantage in terms of both time and money. -- Daniel Tward, Assistant Professor of Computational Medicine and Neurology at the University of California Los Angeles.
He said that being able to confirm that this thinning of the transentorhinal cortex is caused by Alzheimer's could help clinicians provide patients with an earlier diagnosis, which is currently not diagnosed until autopsy. Additionally, the newfound discovery could result in shorter and less expensive clinical trials, which again allows for faster discovery of potential treatment for those suffering from Alzheimer's disease.
What Was the Role of Supercomputers?
Tward and his colleagues used XSEDE allocations on Comet and Stampede2 in conjunction with MRI Cloud, to analyze hundreds of large imaging volumes of human brains—with a focus on the transentorhinal cortex.
"Reducing computation time from months to days allowed this complex neuroimaging project to be feasible," said Tward. "XSEDE provided us with a platform to exceed our expectations as we conducted a study with significant results for both academic researchers and clinicians working on Alzheimer's disease diagnoses and treatment."
This work relied on allocations from XSEDE, which is supported by the NSF (ACI-1548562). The research was supported by the National Institutes of Health (P41-EB015909, R01-AG048349, RO1-DC016784, and R01-EB020062).
At a Glance:
- XSEDE allocations were used to help UCLA researchers learn more about an area of the brain believed to be the first area impacted by Alzheimer's disease.
- SDSC's Comet and TACC's Stampede2 supercomputers provided a platform that allowed for significant results that can aid in future treatment plans.
- With the help of supercomputers, the researchers analyzed almost 50,000 brain images!