Queensland Brain Institute Uses Dell Supercomputer for Alzheimer Research

Dell announced one of the first computational projects for The University of Queensland’s HPC system may enable a non-invasive disease-modifying strategy for Alzheimer’s Disease.

The HPC system, built by Dell for the university’s Research Computing Centre, is a GPU-accelerated supercomputer. GPUs are well-suited to processing massive amounts of computational tasks in parallel, including intensive tasks such as data visualisation and machine learning. It is also used for modelling possible treatments for some of the most debilitating illnesses, such as Alzheimer’s Disease.

The Queensland Brain Institute, the university’s neuroscience research institute, is using the Wiener system to model the behaviour of ultrasound using an analysis technique called Finite Element Method. The modelling calculates what happens to each element of the brain when an ultrasound is passed through the skull.

It is hoped that ultrasound can be used to temporarily allow direct delivery of therapeutic drugs to the brain, something not currently possible due to the presence of a blood-brain barrier and activate cells that can digest the plaques that are a hallmark of Alzheimer’s disease. The promising results will now be confirmed in a sheep study, an animal with similar skull properties as humans, and may be instrumental in developing treatments that stop or reserve degeneration, rather than just relieving symptoms.

“Australia prides itself on its research achievements, especially in medicine,“ said Chris Kelly, vice president, Infrastructure Solutions Group, Dell T, Asia Pacific and Japan. “With this supercomputer, the University of Queensland can harness machine learning to drive innovation, across a broad range of use cases, that previously wasn’t possible. We’re honoured to play our part in the resulting discoveries that can change lives for the better.“

This research is just one of the Weiner’s many workloads. Demand to implement the supercomputer in projects across the university has led to the expansion of the system’s power and size, allowing for a broader range of applications, including climate modelling, psychological testing and learning, and disease identification. In other projects, The University of Queensland’s School of Information Technology and Electrical Engineering is working on developing new digital pathology techniques for faster blood samples results, while another machine learning algorithm will be able to diagnose the presence of skin cancer from histology slides with the accuracy of a trained pathologist.