UNM engineering - School of Engineering - University of New Mexico

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University, Calhoun is uniquely qualified to apply engineering approaches to aninvestigation of how the brain works. The MIND Institute is a perfect match forhis special qualifications. “The MIND is one of the few centers of its kind in thecountry,” says Calhoun. “What’s unique is its focus on mental illness and thefact that it has all of this excellent technology in one place.”The MINDInstituteis one of the fewcenters of its kindin the country.What’s uniqueis its focus onmental illnessand the fact thatit has all ofthis excellenttechnology inone place.Vince CalhounCalhoun is also adept at creating successful collaborations between engineersand psychiatrists, who have distinct approaches to research. “I’ve seen goodideas from engineers go to the point of a paper and then stop because theengineer doesn’t know how to address questions of relevance in psychiatry.And psychiatrists don’t understand how to read an engineer’s paper,” explainsCalhoun. “So, I’ve learned how to work with psychiatrists, but think aboutthings from an engineering perspective.”Detecting DifferencesCalhoun leads a multidisciplinary team investigating how brain imaging canunlock clues to better diagnosis and treatment techniques for schizophrenia,which affects 3.2 million Americans. The team is using brain scans to look forindicators of the disease. They’re also creating powerful new tools to analyzethe data they collect.Using high tech imaging systems, including magnetic resonance imaging (MRI),functional magnetic resonance imaging (fMRI), and electroencephalography(EEG), the team is searching for biomarkers, or measurable changes in thebrain that indicate schizophrenia.Calhoun uses the imaging tools to collect brain scans while subjects performsimple memory or auditory tasks. Each image type yields different informationabout how the brain works. For instance, fMRI provides a dynamic view of bloodflow in the brain, while EEG picks up electrical fields created when neuronsfire in the brain. By comparing images from a control group with those froma group of patients with schizophrenia, the researchers can see differencesin how the brain works. Those differences could point to possible biomarkersfor the disease.Calhoun and his team are also developing better ways to analyze andsynthesize all of the data from hundreds of patients who have had multipleimages made during numerous tests. Calhoun already has an analysis toolboxonline. Now he and his team are developing more robust tools to first analyzeeach data type independently, and then to conduct “data fusion,” analysis ofcombinations of images, such as fMRI and MRI, or EEG, fMRI, and MRI. “Thegoal is to combine the information in a way that gives us a better ability to lookfor differences in the brain,” explains Calhoun. The National Institutes of Healthis funding his data fusion study with a $1.5 million grant. His approach, calledjoint independent component analysis, decomposes the high dimensional datainto a smaller set of independent parts. The different types of data are groupedtogether in the algorithm, and Calhoun then uses an approach based uponinformation theory to identify which parts of this massive decomposition arethe most informative about differences between patients and healthy controls.12 UNM Engineering
Cracking the CodeCalhoun is not alone in his investigation. Terran Lane, assistant professor ofcomputer science, is also on the case. Lane, too, is investigating data fusionand is just starting to develop a sophisticated joint probability model that cancombine the different types of data.Lane is collaborating with Vince Clark, associate professor of psychologyand scientific director of The MIND Institute, to study networks in the brain.“We’re trying to understand how different regions of the brain influence eachother and send messages to each other,” says Lane. “The goal is to untanglethe story and pull out a network.” Lane receives data directly from MINDresearchers and loads it on to computers in the Computer Science Department.Then comes the real challenge: the brain’s complexity and the immenseamount of data involved make teasing out relevant networks very difficult.“It’s as if I gave you a list of how many telephone calls enter and leave everybuilding on the UNM campus without telling you who they came from or wentto. Then, I asked you to tell me who called who,” explains Lane.Lane is cracking the code using powerful machine learning and statisticaltechniques. While his tools are decidedly high-tech, he’s based his processon a probability theorem, developed in the 18th century, called Bayes’ Rule.Using only a small fraction of the available data, software based on Bayesiannetworks can scan the brain images for possible networks, learn about the dataas it goes, and use that information to improve the ongoing analysis process.Lane and senior doctoral student John Burge have created an in-house versionof Bayesian network software that includes anatomical and hierarchicalinformation about the brain’s structure as it evaluates the structural andtemporal data collected by MIND researchers. Lane filters results from thesoftware to improve the statistical confidence, then shares his findings withneuroscientists at The MIND Institute, who provide input and context on theresults. “It’s a feedback loop. We give them results and they give us backnew data and new questions to ask. Hopefully, from this process we’ll gainneuroscience consequences and computational consequences,” says Lane.A joint analysis was performed toidentify a component showing linkedelectrical changes as measured byelectroencephalography (EEG) andblood oxygenation changes asmeasured by functional magneticresonance imaging (fMRI) duringthe presentation of infrequent sounds,called an auditory oddball task. Thepink curve (below) provides informationabout ‘when’ and the orange coloredregions (above) provide informationabout ‘where’ the brain activity isdifferent in patients versus controls.Lane’s research, funded by a three-year grant from the National Institutesof Health, is at its midpoint. The team is currently evaluating some new networksextracted by the software. “In the 20 years that I’ve been studying thehuman brain, I have watched the tools for data acquisition become far moresophisticated, while our methods of analysis have remained very simplistic. Weonly understand about 10 percent of the data we collect; the rest is obscuredby its complexity,” says Clark. “One of my goals for The MIND Institute is tosupport the work of scientists like Vince Calhoun and Terran Lane, who candevelop more sophisticated methods for analyzing brain data. Their algorithmshave already produced a flood of new insights into the kind of data that hasbeen available to us for years, but couldn’t be analyzed using available methods.”MICROVOLTS-1.0-0.500.51.01.5N1N2CONTROLPATIENTCOMPONENTP30 200 400TIME (MS)With clues, collaboration, and cutting-edge technology, UNM’s head detectivesare piecing together answers that will not only unlock the mysteries of themind, but could also improve millions of lives. dUNM Engineering 13
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