Research On Changes Of Brain Network Induced By Pain Stimulation In Pakinsons’ Deseases

Parkinson’s disease(PD) is a common geriatric neurological disease. It is characterized by movement or non-movement disorder. A large number of studies devoted to the research of movement dysfunction at present. But there also a number of studies found cognitive functional obstacle in Parkinson’s patients, such as the perception of pain. Independent component analysis is a data-driven method. The brain signals can be divided into relatively independent and stable compositions both in space and time through this method, and it can filter out the noise signals at the same time. The network attributes analysis reflects the brain information exchange and integration ability from the perspective of graph theory. We use functional magnetic resonance images induced by thermal stimulation of Parkinson’s patients as the research object in this article. Based on the method of data driven, functional conectivity and brain network construction, we explore Parkinson’s patients? changes of brain network properties induced by pain stimulation.1. From the perspective of interested regions, we choose activated brain regions as seeds, and extract the blood oxygen dependence level(BOLD) signal of those regions. Then we calculate the functional connectivity between these signals. We do analysis of variance on Z values in two level between the two groups of subjects(resting state and 51 degrees hot pain stimulus). We found that functional connnectivity between brain regions(such as basal ganglia and insula) and other brain regions exist significant differences in two groups of subjects under the interaction of two factors, and this differences were significantly associated with clinical pain score.2. From the perspective of local network, we used independent component analysis and we got 47 components which relatively independent and stable both in time and space. We selected seven components as pain related networks according to papers, and calculated the functional connectivity between these networks. Finally, the variance analysis method is adopted to do statistical analysis to the Z values.We mainly found that the functional connection between the basal ganglia and salience in PD is significantly reduced, which further illustrate the changes of the basal ganglia network in PD.3. From the perspective of global brain network, we use the AAL template and calculate the functional connection between these 90 regions to construction of whole brain function network. We analysis and calculate the network properties. Including the degree of centricity, intermediary centricity, clustering coefficient and eigenvector centrality, local efficiency and hub node. We found PD?s changes in global network characteristics, including the degree of centricity reduction, the hub nodes distribution changes, rich-club coefficient reduces, etc.From these three aspects above, this study mainly involved the global and local network, provides PD’s brain network changes, and it also provide a basis in imaging for the diagnosis and treatment of PD.