Graph theory and network topological metrics may be the potential biomarker in Parkinson's disease

This study used Voxel-based morphometry (VBM) and resting-state functional magnetic resonance imaging (rs-fMRI) to investigate changes in brain structure and networks functional connectivity, respectively. We tried to identify the potential biomarkers in Parkinson's disease (PD) progression. We recruited nine idiopathic PD patients and seven healthy control participants (HC group) who were age-matched to undergo T1-weighted images and rs-fMRI on 1.5 T. Brain structure differences were analyzed by VBM. Topological properties of networks functional connectivity were analyzed by graph theory. Thirty-two nodes of 8 networks and 133 nodes of interest then were identified with graph theory approaches. VBM examinations showed significant decreases of brain gray matter regions including the left temporal lobe, left middle temporal, middle temporal gyrus, parietal lobe, postcentral gyrus, left inferior parietal gyrus, medial frontal gyrus and supplement motor area in PD patients compared to the HC group. The 32 ROI of networks topological metrics measurement in PD demonstrated increases of global efficiency, cost, and degree in frontoparietal PPC (R) network, but decreases of local efficiency, clustering coefficient, and average path length in salience ACC, dorsal attention FEF (L), and salience aInsula (R) networks, respectively. All 165 ROI connectomes showed eight connections intensity changes, that decrease in OP r to frontoparietal PPC, putamen r to cereb11, and SFG l to Ver8 in PD. These results suggest that the graph theory and the network topological metrics measurement may be the potential biomarkers in PD to evaluate the disease progress and to monitor the therapeutic results.