| Literature DB >> 30474903 |
Salvatore Nigro1, Maria Giovanna Bianco2, Gennarina Arabia3, Maurizio Morelli3, Rita Nisticò4, Fabiana Novellino4, Maria Salsone4, Antonio Augimeri5, Aldo Quattrone3,4,6.
Abstract
Progressive supranuclear palsy (PSP) is a neurodegenerative disorder characterized by white matter (WM) changes in different supra- and infratentorial brain structures. We used track density imaging (TDI) to characterize WM microstructural alterations in patients with PSP-Richardson's Syndrome (PSP-RS). Moreover, we investigated the diagnostic utility of TDI in distinguishing patients with PSP-RS from those with Parkinson's disease and healthy controls (HC). Twenty PSP-RS patients, 21 PD patients, and 23 HC underwent a 3 T MRI diffusion-weighted (DW) imaging. Then, we combined constrained spherical deconvolution and WM probabilistic tractography to reconstruct track density maps by calculating the number of WM streamlines traversing each voxel. Voxel-wise analysis was performed to assess group differences in track density maps. A support vector machine (SVM) approach was also used to evaluate the performance of TDI for discriminating between groups. Relative to PD patients, decreases in track density in PSP-RS patients were found in brainstem, cerebellum, thalamus, corpus callosum, and corticospinal tract. Similar findings were obtained between PSP-RS patients and HC. No differences in TDI were observed between PD and HC. SVM approach based on whole-brain analysis differentiated PD patients from PSP-RS with an area under the curve (AUC) of 0.82. The AUC reached a value of 0.98 considering only the voxels belonging to the superior cerebellar peduncle. This study shows that TDI may represent a useful approach for characterizing WM alterations in PSP-RS patients. Moreover, track density decrease in PSP could be considered a new feature for the differentiation of patients with PSP-RS from those with PD.Entities:
Keywords: progressive supranuclear palsy; superior cerebellar peduncle; support vector machine; track density imaging; white matter
Mesh:
Year: 2018 PMID: 30474903 PMCID: PMC6865691 DOI: 10.1002/hbm.24484
Source DB: PubMed Journal: Hum Brain Mapp ISSN: 1065-9471 Impact factor: 5.038