New MRI method can identify early signs of Parkinson's

Using quantitative magnetic resonance imaging, researchers were recently able to visualize early structural changes related to Parkinson’s disease

The researchers achieved this by developing an analysis method targeted specifically towards imaging of the striatum—a part of the brain that is known to deteriorate as Parkinson’s progresses.  

“Mapping structural spatial change (i.e., gradients) in the striatum is essential for understanding the function of the basal ganglia in both health and disease,” lead author Professor Aviv Mezer, of the Edmond and Lily Safra Center for Brain Sciences at the Hebrew University of Jerusalem (HU), and colleagues explained. 

The analysis was conducted on 23 young adults aged 23 to 31 and 18 older adults aged 55 to 75. Researchers completed quantitative magnetic resonance imaging (qMRI) of the brain for each participant, followed by a detailed, multi-step cognitive and image analysis (see here). 

The in-depth imaging analysis revealed abnormal gradients of the putamen in individuals with Parkinson’s disease—specifically, the posterior putamen. This could explain the dopaminergic loss and motor dysfunction of patients with the neurological condition, the authors explained. 

“In generalizing our analysis to clinical MRI, we uncovered an in vivo microstructural correlate of PD, which is associated with both dopamine loss and disease-related motor function impairments. We further demonstrated cortico-striatal relations by showing a structural dependency between striatal position and cortical hierarchy.” 

Including both younger and older participants enabled the researchers to compare aging-related microstructural changes of the striatum. In doing so, they found that they connected normal aging to asymmetry increases in anterior and posterior segments of the caudate. In comparison, PD patients displayed alterations of the posterior segments of the putamen. 

The researchers are planning to use their technique to analyze microstructures in other regions of the brain, while also working on developing a qMRI method that can be incorporated into clinical settings, with an anticipated timeline of three to five years. 

The detailed research and techniques discussed in this article can be viewed in full in Science Advances

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