Diffusion tensor imaging (DTI) separated patients based on movement disorder diagnosis with a high degree of accuracy, potentially opening the door for early assessment of movement disorders such as Parkinson’s disease (PD), according to a study to be published in the July issue of Movement Disorders.
The study aimed to classify individual subjects with PD, multiple system atrophy, progressive supranuclear palsy and essential tremor. It was the first to combine multiple DTI measures and target regions in the basal ganglia, red nucleus and cerebellum, providing a “road map” to differentiating disorders based on brain region, according to David Vaillancourt, PhD, of the University of Florida, Gainesville, and colleagues.
“A key point is that the pattern of DTI targets and measures that yielded the highest [area under the curve] for differentiating these movement disorders was unique for each classification,” wrote the authors.
Results were based on data from 72 subjects with clinically diagnosed movement disorders, who underwent DTI at 3T MRI.
In group comparisons, DTI was successfully able to contrast:
- Control subjects versus movement disorder;
- Control versus parkinsonism;
- PD versus atypical parkinsonism;
- PD versus multiple system atrophy;
- PD versus progressive supranuclear palsy;
- Multiple system atrophy versus progressive supranuclear palsy; and
- PD versus essential tremor.
All of the above comparisons had a sensitivity and specificity of at least 87 percent, reported Vaillancourt and colleagues.
The authors noted that diagnosing PD is challenging, particularly in the early stages, because symptoms overlap with other movement disorders, though the results of the DTI study showed the technique has more potential for differentiating disorders than other imaging, cerebrospinal fluid or blood markers.
In a press release, Vaillancourt likened DTI to a cholesterol test. “If you have high cholesterol, it raises your chances of developing heart disease in the future,” he said. “There are tests like those that give a probability or likelihood scenario of a particular disease group. We’re going a step further and trying to utilize information to predict the classification of specific tremor and Parkinsonian diseases.”