Imaging Technique May Trace Development of Parkinson’s Disease

UIC Podcast
UIC Podcast
Imaging Technique May Trace Development of Parkinson's Disease
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News Release

 

[Writer] This is research news from U-I-C – the University of Illinois at Chicago. Today David Vaillancourt, assistant professor of kinesiology and nutrition, talks about a non-invasive technique to screen for a biomarker of Parkinson’s disease.

Here’s Professor Vaillancourt:

[Vaillancourt] We’ve been studying Parkinson’s disease in our laboratory for several years, and I want to briefly give you a sense of what goes wrong in Parkinson’s disease. Parkinson’s disease is a disorder that affects movement. It makes movements slow. It causes individuals to shake, which are called tremors, and it also causes the limbs to become rigid. In addition, Parkinson’s disease causes a number of emotional and cognitive disturbances which tend to become more of a problem as the disease progresses.

As far as the number of people who have Parkinson’s disease, in the United States it’s roughly between 1 and 1.5 million. And many people estimate that number is going to increase as the Baby Boomers get older and our society grows older.

Currently, the way Parkinson’s disease is diagnosed is that an individual will typically notice symptoms, or maybe a spouse or a friend will notice symptoms, and that person will be referred to a physician, typically a neurologist. A neurologist will then assess the patient and look for a number of different criteria, basically looking at symptoms that reflect the way in which they move. So they’re looking for slowness of movement and
the tremors. In addition, the neurologist will try to rule out a number of other symptoms which might mimic other diseases to characterize if an individual has Parkinson’s disease or not. So you can see that in diagnosing Parkinson’s disease, it’s primarily subjective.

In the area of biomarkers, individuals want to create and develop new biomarkers that are objective and reliable that can predict or track the progression or onset of Parkinson’s disease.

So in our study, what we did was use a technique called diffusion tensor imaging. This particular technique is used as a standard MRI, or magnetic resonance imaging machine, particularly at 3 tesla, here at the University of Illinois at Chicago.

We based our study on a previous study in a mouse model of Parkinson’s disease, and in that particular mouse model, they showed that using diffusion tensor imaging had measures that correlated with the number of cells – dopaminergic cells – that were lost after injecting a toxin into the mouse model.

Now diffusion tensor imaging itself is a non-invasive technique which essentially looks at the structural characteristics of diffusion of hydrogen within water within different tissue in the brain or in other structures of the spinal cord. It has become much more used widely over the past two decades, primarily to study white matter changes in areas like traumatic brain injury or multiple sclerosis. In our case, we’re studying a structure called the substantia nigra which is mostly grey matter with white matter all around it. So we are basically using diffusion tensor imaging to look at the way in which the structural changes occur within the sustantia nigra, presumably after cell loss occurs.

We studied 14 early-stage patients who had Parkinson’s disease, and 14 healthy age and gender-matched individuals to understand how the substantia nigra changes. We basically used a measure called fractional anisotropy – a measure that reflects the dependence of hydrogen within water diffusion in specific tissues within the brain. So for instance, fractional anisotropy that’s high in a value – which is close to 1 – will have a high degree of directional dependence. For instance, for something like white matter within the brain which are like the cables of the brain, you have a high degree of directional dependence. In contrast, in cerebral spinal fluid, you have a low fractional anisotropy because, for the most part, there is no or minimal directional dependence in cerebral spinal fluid. Now grey matter, which is mainly found in the substantia nigra has a fractional anisotropy that is between the cerebral spinal fluid and the white matter track areas. So what we found was that in early stage patients who have Parkinson’s disease, the fractional anisotropy measure was reduced reliably at a group level compared to healthy individuals. In addition, we found that the fractional anisotropy measure was reduced in every patient that we studied compared to the healthy individuals. The reason this is important is because that’s the way the patients would go and see a physician. A physician is not going to try and sort large groups of patients from health individuals. A physician is going to try and treat and differentiate one person from another, or one person’s values from what is known to be healthy.

In the future, we plan to do a number of things. First, to actually move this forward, we need to study other diseases that mimic the symptoms of Parkinson’s disease to see if we can differentiate what happens in other diseases, compared to individuals diagnosed with Parkinson’s. Some of these diseases might include multiple systems atrophy or even a central tremor. We also plan to work with Professor Jeffrey Kordower at Rush University Medical Center to understand how, in a primate model which mimics symptoms of Parkinson’s disease, exactly what happens in that part of the brain that relates to some of the imaging measures that we collected here. So in the future, we hope to develop a biologically-relevant, non-invasive marker that can be used in the assessment and tracking of Parkinson’s disease. That is the overall goal.

[Writer] David Vaillancourt is assistant professor of kinesiology, bioengineering, neurology and rehabilitation.

For more information about this research, go to www.news.uic.edu, click on “news releases,” and look for the release dated March 24, 2009.

This has been research news from U-I-C – the University of Illinois at Chicago.

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