Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.
This summer, high school students from the Salt Lake area are coming to the University of Utah to participate in hands-on research in image analysis of the human brain. In conjunction with graduate students and faculty in the School of Computing and the Scientific Computing and Imaging Institute, the students are learning how computer science can help neuroscience researchers understand the brain and disorders that affect it, such as Alzheimer's disease and Autism.

Advances in medical imaging devices, such as magnetic resonance imaging (MRI), have led to our ability to acquire detailed information about the living human brain, including its anatomical structure, function, and connectivity. However, making sense of this complex data is a difficult task, especially in large imaging studies that may include hundreds or even thousands of participants. This is where computer science can play an important role. Image analysis algorithms can automatically quantify properties of the brain, such as the size of brain structures, or the functional activity in different brain regions. This provides neuroscience researchers with insights into how the brain functions and what abnormalities are present in diseased brains.

The students in the program are using state-of-the-art image analysis software tools to analyze MRIs from real-world brain imaging studies. For example, the students are using ITK SNAP ( to build a 3D model of the hippocampus from brain images of patients with Alzheimer's disease. The students are then using statistical analysis to see how the volume of this structure, important in memory function, decreases by using publicly-available MRI data from the Open Access Series of Imaging Studies ( In another project, the students are exploring the functional connectivity between different regions of the brain, using functional MRI (or fMRI). The data comes from the Autism Brain Imaging Data Exchange (ABIDE). Here students are using machine learning tools to explore differences between functional connectivity in subjects with typically developing brains and subjects with autism spectrum disorder.

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Alexander Kennedy, and Max Xiong work together to analyze an MRI of the brain, using software to build a model of the hippocampus. Screenshot of the hippocampus model and MRI in ITK SNAP.

Student Feedback

"This program taught me some basic computer programming skills. It also taught me that the answer to a problem may not be the straightforward one, and that you need creativity and great skill in the art of guess and check to solve some of the problems we were presented with. This program also gave me a feel of what being a programmer is like and how programming and research are now intertwined."

"I had an extraordinary experience at the SCI Institute for the Brain Image Analysis program. Prior to participating, I honestly had no significant interest in coding or computing on a level past what was taught in my high school Computer Programming 1 class. After first-hand experience in brain analysis, though, I was given a glimpse into the endless gamut of applications computing allowed for (in contrast to the "Hello World"-esque projects that had been the extent of my experience in the past). I will definitely be going on to learn more in computing with renewed enthusiasm and inspiration from my experience at the SCI Institute."

"In addition to that, I was introduced to an entire new field I never actually knew existed and had an excellent time exploring and gaining knowledge with the incredibly helpful staff at the SCI Institute and my peers who completed the internship with me. Thank you to Tom, Michelle, Prasanna, Corinne, and the SCI Institute for such a great experience!"

"The internship gave me great insight into computer science. I also really enjoyed working with other people and trying to figure out some topics I had no experience with."

"I've had experience in the past with programming, but I've never had the opportunity to use programming with regard to scientific computing. This internship opened another path to me, to see what it is like to use computing so that it could be useful in science beyond just making software tools. I felt that the internship was at a reasonable level for high school students, and was definitely attainable - it pushed me to try harder, particularly at the end of the month, to put together a better working project and understand the subject matter better. Thank you for this opportunity!"