Coupling Neural Network And Monkey Models Of Parkinson’s Disease For Exploring Underlying Mechanism

Parkinson’s disease (PD) is the most common neurodegenerative movement disorder in aged people. To date, there is no effective cure for this disease, which causes strong impairment to patients’daily lives and imposes a substantial burden on their families and society. The current understanding of the pathophysiological basis of PD is often considered to be the irreversible loss of dopaminergic (DA) neurons in the substantia nigra pars compacta (SNc), an important dopamine-producing area located in the midbrain. The first-line treatment of PD is dopamine replacement therapy (including dopamine or dopaminergic receptor agonist supplement). When the effects of first-line treatment wade, the second-line treatment is the deep brain stimulation (DBS) which is re-emerging from the abandoned ablative surgery. However, both treatments are not stable for long-term therapy and some of their complications, for example the motor complications, are intolerable for some patients.Recently, the development of research on PD seems to be in a stage of bottleneck. The underlying reasons of it may be:1) the complexity of the structure and function of the brain surpasses the capacity of traditional research method;2) the animal model of PD (especially non-human primate model, which is closest to human beings) has much room for improvement, and limits the high-quality researches on this model. Therefore, to further improve the theory of the neural anatomy (in terms of neural network) and the non-human primate model could be a way to create new opportunity for PD research in future.In the aspect of neural network, as more and more studies pointed out that many advanced functions, such as motor, memory or emotion, of the brain locate not in a particular area but disperse in the neural network, the brain is more and more widely considered as one of the most complex network system in the universe. Thus it is necessary to establish network-based approach to explore the underlying mechanism.The first part of our study points out that although numerous studies on neural tracing had revealed much connection information, there are few methods to incorporate them into an analyzable neural network, letting alone the following analysis on PD. Therefore, the first work focused on (a) building of brain network without logical overlaps from data in different resolutions, and (b) simulating the changes in an advanced PD brain. To achieve this, this study proposed a series of algorithms, then simulated the SN removal and, lastly, used an interdisciplinary network analysis to understand the changes taking place with PD. To strengthen and sharpen the conclusions made in previous steps, Monte Carlo method was used to evaluate the uncertainty in the brain network, and dimension reduction method was used to summarize and visualize the analysis results. The final results revealed that the areas including striatum, globus pallidus, amygdala, prefrontal lobe, thalamus, hippocampus, visual cortex, insula, etc., showed relatively notable drifts in their own patterns.By using our approach, one can easily establish anatomical neural network and conduct the analysis as needed. However, it is not enough to verify the prediction of computational model in the realistic situation. Therefore, a corresponding animal model should be established. Among all of the common used animals, the non-human primate has obvious advantages, thus is used in the second part of this dissertation.Complete and specific ablation of a single DA pathway is a critical step for verifying the roles of DA pathways in vivo. However, this kind of technique has not been reported in non-human primates. Thus, here a carefully designed infusion route based on MRI stereotactic technique was developed to deliver the highly selective dopaminergic toxin MPP+unilaterally into multiple sites of the substantia nigra pars compacta (SNc) and striatum (Str) in monkeys. After intracerebral infusion, pathological examination revealed a complete ablation of tyrosine hydroxylase positive (TH+) neurons in the SNc and a depletion of TH+projections in the Str, while preserving intact TH+neurons in the ventral tegmental area (VTA) nearby. The monkeys also display stable (>28weeks) rotations and Parkinson’s disease (PD) symptoms, with rest tremor being a notable exception. Taken together, in addition to being an excellent new PD model, this new method provided new information of great importance to understand PD. This new technique is a powerful tool for the complete lesion of any desired DA pathway in order to study its specific functions in the brain.In summary, this dissertation provided methods to establish new neural network model and non-human primate model for PD. These models have a common remarkable feature that a specific lesion (or intervention) can be easily achieved, thus can be used in combination to provide mutually explanation and prediction. In addition of the complex network analysis, this combination would be an improvement in terms of both more precise and more comprehensive model system for the future study.