To Explore The Relationship Between PD Brain Network Topological Parameters And Autonomic Nervous Dysfunction

Background and Objective:Autonomic nervous dysfunction is one of the common non-motor features of Parkinson’s disease（PD）.In general,the incidence of PD is higher as the disease progresses.However,symptoms of autonomic nervous dysfunction can appear in PD patients before motor symptoms or early in the course of PD.Pathological changes in PD patients have been shown to have the deposition ofα-synuclein in the peripheral nerve and central autonomic nervous system in the early stage of the disease,suggesting that pathological changes have been observed before the onset of the corresponding symptoms.Autonomic nervous dysfunction not only affects the quality of life of PD patients,but also affects the overall severity of exercise,and is a predictor of the rapid decline of motor ability in early PD.With the development of neuroimaging and brain network analysis methods,the information of the brain can be obtained through Magnetic Resonance Imaging（MRI）,a non-invasive examination tool,and the data can be preprocessed and then used to examine the structural and functional changes of the brain through brain network analysis based on"graph theory".At present,this method has been widely used in the alteration of brain network organization under a variety of conditions,including Alzheimer’s Disease（AD）,schizophrenia,PD and indifference and other diseases.Existing evidence that changes in brain function network may be ahead of the cerebral cortex structure change,in order to improve the patients with PD and autonomic nerve dysfunction the understanding of the pathophysiological mechanisms,this study by using the graph theory methods to analyze subjects brain network topological properties,PD patients and normal control group brain network topological properties difference,and the differences and relations of autonomic nerve dysfunction,reveal PD and brain network structure and function of autonomic nervous dysfunction of change,to understand the pathophysiology of PD and autonomic nerve dysfunction provide some clues.Materials and Methods:A total of 42 outpatients or inpatients with PD from Department of Neur ology of the Second Affiliated Hospital of Nanchang University and 17 norm al controls matched with gender and age were selected.All subjects complete d function of 3.0 T MRI brain scans,at the same time by a professional trai ned neurologists in all subjects completed MDS-UPDRS,Autonomic Nerve Scale（SCOPA-AUT）and Hamilton Depression Scale（HAMD）,the Nonmot or Symptoms of Parkinson’s Disease rating scale（NMSS）,Pittsburgh sleep qu ality index（PSQI）,Hamilton anxiety scale（HAMA）,Mini-mental State Exami nation（MMSE）.According to H-Y middle-late stage PD patients can be di vided into early,reapply SPM12（https://www.fil.ion.ucl.ac.uk/SPM/software/SP M12/）software package and GRETNA（www.nitrc.org/projects/gretna/）software package for data processing analysis,construction of Parkinson’s disease（PD）group,the middle-late early Parkinson’s disease group and healthy controls（HC）group of brain networks,calculate all subjects brain network topology p arameters,Including local efficiency(E_loc),clustering coefficient（C_p）and the global efficiency(E_glob),shortest path length（L_p）,lambda,sigma,gamma,brai n network topology analysis to compare different stage PD group parameters,to determine whether PD occurs in the development of existing network of th e brain changes,again by identifying PD group and the control group differen ce in parameter of a brain network topology,and use the Pearson correlation coefficient analysis of the above differences and correlation of PD autonomic nerve dysfunction.Results:Within 0.05<S_thr<0.5,there was no statistical difference in global efficiency(E_lob)and local efficiency(E_loc)between PD group and control group,but the shortest path length（L_p）was higher in PD group than in control group.However,compared with the control group,λdecreased in PD group（P<0.05）.Moreover,there was no significant difference inσbetween PD group and normal control group,and the small-world attribute existed.According to H-Y stage,there was no significant difference in brain network topological parameters of patients with early,middle and advanced PD.After finding out the brain network topological parameters that were different between the PD group and the normal control group,the correlation between all the different parameters and SCOPA-AUT score was studied.We found that the clustering coefficient（C_p）of PD patients was significantly negatively correlated with SCOPA-AUT.Conclusion:1.Compared with the normal control group,the shortest path length（L_p）,clustering coefficient（C_p）and small-world attributeλwere all changed in PD group.2.There was no significant difference in brain network topological parameters among PD patients with different stages;3.The clustering coefficient（C_p）of PD patients is significantly negatively correlated with SCOPA-AUT,which may be a marker of PD with autonomic nervous dysfunction.