AUTHOR=Kok Jelmer G. , Leemans Alexander , Teune Laura K. , Leenders Klaus L. , McKeown Martin J. , Appel-Cresswell Silke , Kremer Hubertus P. H. , de Jong Bauke M. 

TITLE=Structural Network Analysis Using Diffusion MRI Tractography in Parkinson's Disease and Correlations With Motor Impairment

JOURNAL=Frontiers in Neurology

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2020.00841

DOI=10.3389/fneur.2020.00841

ISSN=1664-2295

ABSTRACT=Functional impairment of spatially distributed brain regions in Parkinson’s disease (PD) suggests changes in integrative and segregative network characteristics, for which novel analysis methods are available. To assess underlying structural network differences between PD patients and controls, we employed MRI T1 grey matter segmentation and diffusion MRI tractography to construct connectivity matrices for comparing patients and controls with data originating from two different centers.
In the Dutch dataset (Data-NL), 14 PD patients and 15 healthy controls were analyzed, while 19 patients and 18 controls were included in the Canadian dataset (Data-CA). All subjects underwent T1 and diffusion weighted MRI. Patients were assessed with Part 3 of the Unified Parkinson's Disease Rating Scale (UPDRS). T1 images were segmented using FreeSurfer, while tractography was performed using ExploreDTI. Regions of interest based on parcellation of all gray matter in the cerebral hemispheres and white matter streamline sets enabled construction of connectivity matrices, from which both global and local efficiencies were calculated. These measures were compared between the PD and control groups and related to the UPDRS motor scores. 
The connectivity matrices showed consistent patterns among the four groups, without significant differences between PD patients and control subjects, neither in Data-NL, nor in Data-CA. In Data-NL, however, global and local efficiencies correlated negatively with UPDRS scores both at the whole brain and the nodal level (FDR 0.05). At nodal level, particularly the posterior parietal cortex showed a negative correlation between UPDRS and local efficiency, while global efficiency correlated negatively with the UPDRS in the sensorimotor cortex.
Similarity of connection matrices among groups points at a general robustness of the employed methods. The spatial patterns of negative correlations between UPDRS and parameters for network efficiency seen in Data-NL suggest subtle structural differences in PD that were below sensitivity thresholds in Data-CA. These correlations are in line with previously described functional differences. The methodological approaches to detect such differences are discussed.