Objective: The impact of dopaminergic treatment on motor and cognitive networks in Parkinson's Disease (PD) is not fully understood. In our study we focused on brain connectivity change derived from low frequency fluctuations of the blood oxygenation level-dependent signal. We used eigenvector centrality (EC) mapping which automatically detects all brain areas serving as strong communication hubs.
Participants and methods: Twenty-nine PD patients (aged 64.7 ± (SD) 8.0 years, PD duration 11.0 ± 3.6 years) were assessed with the Unified Parkinson's disease rating scale motor score (UPDRS-III) and the Montreal Cognitive Assessment (MoCA). Patients were instructed to watch a cross while lying motionless in the supine position for 10 min during 3 T-fMRI acquisition in off and on medication states. The EC analysis was conducted with Lipsia software (Leipzig, Germany). The 2nd level analysis of general connectivity was based on voxel-wise correlations of the EC maps with the UPDRS-III and MoCA, respectively (P < 0.05 corrected).
Results: The UPDRS-III score positively correlated with the EC values in the premotor, primary sensorimotor and associative parietal cortices bilaterally regardless of medication state (r = 0.83, P < 0.001). In addition, the EC value correlated positively with the MoCA score in the right prefrontal cortex (r = 0.66, P < 0.001) only in the off medication state.
Conclusions: Our data driven approach enabled an automatic separation of resting state networks of PD patients into motor and cognitive domains. While the motor network showed increased global connectivity with the worsening of motor symptoms, the lower global connectivity of the frontal cognitive network potentially limited cognitive performance. Supported by IGA-NT12282-5-2011; PRVOUK-P26/LF1/4.
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© 2015 Published by Elsevier Inc.
