HPC Helps Researchers Follow Progression of Huntington Disease

Hans Johnson and Brain Images

University of Iowa neuroscientists are using high-performance computing (HPC) to study changes in the brains of Huntington disease (HD) patients before they begin to experience symptoms.


The research could lead to earlier interventions for people diagnosed with the hereditary disorder that causes widespread brain tissue atrophy, interfering with mobility, memory, speech, and mood.

The UI is the flagship institution of a National Institutes of Health-supported study called PREDICT-HD, which aims to identify the earliest detectable changes in thinking skills, emotions, and brain structure as a person begins the transition from health to being diagnosed with Huntington disease.

“We are trying to find subtle brain changes in affected individuals 25 years before they have problems in hopes of intervening early on,” says Hans Johnson, Ph.D., an assistant professor of psychiatry. “HPC allows us to identify tools that sensitively measure those changes. With that information, the ultimate goal is to find a treatment that halts the disease and prevents the negative symptoms associated with it.”

Huntington disease affects one of every 10,000 Americans, and children of an HD afflicted parent have a 50/50 chance of inheriting it, according to the Huntington’s Disease Society of America.

The effects of Huntington disease usually become debilitating in middle age, and the average lifespan after onset is 10 to 20 years. Science has yet to find a cure or treatment that can slow the deadly progression, though medication provides relief from some of the symptoms for certain individuals.

PREDICT-HD, which is led by UI Professor of Psychiatry, Neurology, Psychology and Neurosciences Jane Paulsen, Ph.D., involves approximately 1,500 research subjects from 32 sites around the world, ranging in age from 18 to 80. Researchers are analyzing brain scans from the patients over a 10-year period. They apply algorithms to the scans in order to extract measurements that quantify progression of the disease.

The measurements include changes in the brain volume, tissue composition, structural size, anatomical regions, and cortical depth. Researchers look at how changes in different regions of the brain correlate with psychiatric, behavioral, and cognitive measures.

“It helps us develop the story, to better understand how the disease is progressing and which manifestations of the disease are caused by those brain changes,” Johnson says.

Johnson and his colleagues use the university’s Helium compute cluster, administered by Information Technology Services, to test algorithms published by other neuroimaging researchers.

Their goal is to see which algorithms, or methods, are most effective. Testing each method takes more than 42 hours of computation per imaging scan session to process. There are 4,400 data sets to test with each method, and many parameters to modify.

“Testing the algorithms on a single computer would take two or three years of data processing,” Johnson says. “The Helium cluster allows us to do that in one day.”

Running thousands of data sets quickly means more exploration – which is good news not only for Huntington patients, but also for people with Alzheimer’s disease and other brain-damaging diseases.

“We can run minor modifications in our pipeline and investigate how that affects the results,” Johnson says. “Each day we can test a hypothesis. Does changing a single parameter in the algorithm improve the result, ruin it, or have no effect? We find out quickly, and if it fails, we can make an adjustment and move on to quickly get better results. In just one day, we can test thousands of data sets.”

This sub-cortical shape analysis was developed by researchers at the University of Utah to illustrate the progression of Huntington disease over a three-year period. The simulations were created with MRI data that is being analyzed with the University of Iowa’s high-performance computing cluster to understand how the disease advances even before patients begin to experience negative symptoms.