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 gray matter segmentation and diffusion MRI tractography to construct connectivity matrices to compare 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. The regions of interest from the FreeSurfer segmentation were combined with the white matter streamline sets resulting from the tractography, to construct connectivity matrices. From these matrices, both global and local efficiencies were calculated, which 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, either in Data-NL or in Data-CA. In Data-NL, however, global and local efficiencies correlated negatively with UPDRS scores at both the whole-brain and the nodal levels [false discovery rate (FDR) 0.05]. At the 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. 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.
Highlights
Neuronal degeneration in the substantia nigra resulting in dopamine deficiency in the basal ganglia is a major characteristic pathophysiological change in Parkinson’s disease (PD)
We aimed to explore structural network differences in PD by combining gray matter (GM) segmentation, diffusion MRI tractography, and complex network analysis in two independent datasets
By applying an fMRI visual optic-flow paradigm, mimicking the perception of forward locomotion, we previously found that interruption of such a gait-supporting stimulus failed to activate thesupplementary motor area in PD, while functional connectivity between this region and the visual motion area V5 was enhanced in patients, a result which is consistent with the increased interference of perceptual stimuli with motor intentions in PD [11]
Summary
Neuronal degeneration in the substantia nigra resulting in dopamine deficiency in the basal ganglia is a major characteristic pathophysiological change in Parkinson’s disease (PD). As the basal ganglia are involved in multiple corticosubcortical networks, PD can be viewed as an extended network disease of the brain [1]. The concept that PD symptoms and signs arise from functional impairment in coherent basal ganglia–cortical networks became generally acknowledged after acceptance of the model of segregated circuits (e.g., motor, oculomotor, and limbic), originating from the cortex via the basal ganglia and the thalamus back to frontal cortical regions [2]. A physiological characteristic of PD-related changes in basal ganglia circuits is a more synchronous firing pattern [4], pointing at reduced segregation of neuronal activities of the basal ganglia loops in PD
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
