Prospective longitudinal study of plasticity control and motor complications in Parkinson's disease

  • Huang, Ying-Zu (PI)
  • Chen, Rou Shayn (CoPI)
  • Chen, Jia Jin (CoPI)
  • Chuang, Wen-Li  (CoPI)
  • Lu, Chin-Song (CoPI)
  • Wang, Jiun-Jie (CoPI)
  • Weng, Yi-Hsin (CoPI)

Project: National Health Research InstitutesNational Health Research Institutes Grants Research

Project Details

Abstract

Dystonia is a neurological movement disorder characterized by abnormal postures of the affected body part. TMS, as a non-invasive method, involves the majority of physiological studies in dystonia. (Edwards, Huang, Mir, Rothwell, & Bhatia, 2006) The aim of this project was to build a massive neuronal network of cerebral cortex on a computer, with a realistic neuron and synapse model, so that we could further analysis the TMS-induced pathological/physiological aftereffects of dystonia on this simulating environment. Furthermore, this general cortical network model could be applied to not only the study of dystonia but also the studies of many other neuropathic diseases. During the first period of this project, we built a massive neuronal network simulation environment, which was capable of parallel computing, to simulate the primary motor cortex in the human’s brain. At the same time, we proposed a mathematical model to describe theta-burst TMS (TBS) aftereffects with cell-level details. The next period, on this network model we successfully simulated the neuron activity responses on specific cortical layers after a single TMS pulse. Meanwhile, we further expended our TBS aftereffect hypothesis to a bio-functional synaptic plasticity model based on the kinase interactions in post-synapse end. A rough sketch of basal ganglia circuit was built to qualify the function of the basal ganglia, too. In the third period, we introduced the concept of short-term depression (STD) into our synaptic plasticity model. With the help of the hypothesis, we were able to correctly reproduce several aftereffects induced by a variety of TMS paradigms, such as low and high frequency traditional rTMS, iTBS, cTBS…etc. Besides, we optimized the massive neuronal network on size and performance, making it able to simulate more detail and for a longer simulation time. The tweaked network model was then applied to study TMS-induced activities on networks with connection deficiencies and preliminarily demonstrated its potential on the research of several different neuronal disorders. In this period, we focused on several issues: (1) systematical analysis on the neural activity induced by TMS paradigms. (2) Calibration on the synaptic plasticity model to have stable responses in different simulation protocols. (3) Implementation of the synaptic plasticity model into the massive network. (1) Systematical analysis on the neural activity induced by TMS paradigms In the third year, we proposed and verified a synaptic plasticity model with the idea of an integration of post-synaptic plasticity and pre-synaptic STD, which has potential to be the regulatory factor to post-synaptic long-term plasticity. We were able to reproduce the plasticity change after rTMS at low and high regular frequency, iTBS, imTBS and cTBS (Figure 1, Appendix) in a single synapse with this model. To further understand the mechanism of TMS-induced plasticity, we targeted TBS and systematically analyzed two key factors affecting aftereffects greatly: a) continuous burst number (CBN) that defines the number of continuous bursts in a non-paused TBS train and b) interrupted period (IP) that defines the length of the pause between two TBS stimulus trains. All TBS paradigms could be described with these two factors, for example (CBN, IP)iTBS = 10, 50、(CBN, IP)cTBS = 200, 1…etc. We composed the simulated aftereffects of hypothetical TBS patterns with these two factors, red on the color bar represents augmented plasticity while blue represents depressed plasticity (Figure 2, Appendix). We use 160 milliseconds (ms), the shortest IP and also the IP of cTBS paradigm, as the unit in the figure. In this assay we found two major trends: 1) longer the IP is, stronger the potentiation is. When IP is less than 300 ms, it is impossible to potentiate the synaptic strength. 2) A smaller CBN tends to arouse augmented synaptic plasticity; additionally, the potentiation starts to decrease after CBN exceeds 20. This phenomenon consists with our hypothesis – the pre-synaptic STD is a major factor to control the direction of the aftereffects induced by rTMS paradigms. When IP is too short, or CBN is too larger, the transmitter vesicles in pre-synaptic axon end-plate cannot be replenished to compensate its release. As a result, the released amount of transmitter upon a spike is reduced in repetitive stimulus and decrease the calcium influx into post-synaptic neuron through NMDA channel, which produce a depressed aftereffect. On contrast, with a longer IP or smaller CBN, the effect of pre-synaptic STD is not enough to modify the post-synaptic calcium current, and thus raise a default, augmented aftereffect. The major limitation of this part of simulation is that the recovery from the end of a burst or a train is ignored because the rates of recovery have been so far unclear. The neglect of recovery makes the prediction on extreme values, including very large and very small CBN and IP, unreliable. (2) Calibration on the synaptic plasticity model to have stable responses in different simulation protocols. Besides the aftereffects induced by rTMS and TBS paradigms, we hope this model could be applied on, as many as possible, various protocols. Among them, quadra-pulse TMS (QPS) was the first candidate to test. QPS is a protocol gives four TMS pulses every 5 seconds, with an adjustable time interval between pulses which majorly controls the direction of aftereffects. The figure shows simulated results of two major QPS paradigms (with inter-pulse intervals 5 ms and 50 ms) representing typical potentiation and depression aftereffects. The results (Figure 3, Appendix) obtained from our model was consistent with those of physiological experiments reported by Hamada. (Hamada et al., 2008) So far, plasticity model we built had been developed in a single synapse. To incorporate the synaptic model into the massive neuronal network we built and fulfill the purpose we aimed, adjustment in many parameters was required. We stared from adding more noise signals into the single synpase circuit to emulate the noisy background inputs in the real brain. The background inputs to post-synaptic neuron were given as a form of excitatory Poisson spike train, from 0 Hz to 90 Hz with a 10Hz step. This excitatory input raise the membrane potential of post-synaptic neuron and affected the calcium influx through voltage-gated NMDA channel. The synaptic plasticity thus changed upon different level of background inputs as consequences. We simulated the aftereffects induced by three different TBS paradigms under different strength of background inputs (Figure 4, Appendix). Though the aftereffects vary from conditions and paradigms, the characteristics was consistent with previous report. (iTBS tend to raise augmented plasticity, imTBS does no significant modification and cTBS majorly arouse depressed plasticity) (Huang, Edwards, Rounis, Bhatia, & Rothwell, 2005) Our previous simulation background input was set to 40Hz, happened to be the minimum requirement to obtain correct aftereffects in all three paradigms. Below this input strength, all paradigms produce depressed aftereffects, while the aftereffects move toward potentiation when input strength increases, including cTBS which have strong inhibitory characteristic. This phenomenon might give a clue to the wide variety of aftereffect recorded in real physiology assays. The flaw of this approach is that there are inhibitory neurons in real neuronal networks, so the background inputs should not be only excitatory signals. To obtain a more realistic response from the synaptic plasticity model, we must further adjust it under the environment similar to the neuronal network. (3) Implementation of the synaptic plasticity model into the massive network. In this year’s project, we had connected the massive neuronal network model of cortical circuit with basal ganglia circuit. This work was performed to the final stage in which the connectivity and activity of each brain areas were tweaked in details to fit physiology records. While this stage still need more efforts to be done, we established a partial network environment, with connective features extracted from full-scaled neuronal network, to initiatively test TBS mechanism at network level. Compared with previous single neuron environment, this model provides more realistic ways to simulate TMS stimuli. Taking L23 pyramidal cell as an example, it has near 60 pre-synaptic axons innovate in with 2:1 ratio between excitatory and inhibitory synapses. (Figure 5, Appendix) The TMS strength could also be simulated by modifying the percentage of randomly activated pre-synaptic axons. The TMS-activated rate of excitatory and inhibitory synapses was not yet revealed, so we temporarily assumed both types of synapse has the same probability to be activated. The synaptic plasticity model we developed could robustly exhibit the reported aftereffects of iTBS and cTBS (Figure 6, Appendix). This qualified the flexibility of our plasticity model to be functionally consistent under different reasonable environments. Moreover, the ratio between activated excitatory/inhibitory synapses, which affects the results greatly in this model, might be another important factor to cause the subject difference in TBS physiology assays. The adaptation of massive neuronal network is quite a complex and time-consuming work. Moreover, there is no enough physiological details available. After deploying basal ganglia circuit and synaptic plasticity module into our massive network model, the network became unstable and in need of plenty of time for the adjustment. Even though, we still acquired some preliminary data, which match values obtained from physiological studies, from the extended model. These analyses showed the potential to help us understanding the physiological mechanism of TMS-induced aftereffects and developing more efficient and reliabile protocols or devices. In addition, in comparison with massive neuronal network, the partial network environment may be more suitable to simulate delicate intracellular dynamics such as the interaction between enzyme, kinases, ion channel and other biological factors. It could be another approach that deserves further studies to discover the baseline mechanism of neurological disorders. Hopefully, in the following months, we could complete the adjustment in the massive network model. With the help of the model, we expect to analyze TMS activities more systematically and find out the mechanism of how TMS affects and eases neurological symptoms. Furthermore, we may design a novel protocol of rTMS accordingly to fulfill the clinical and research needs.

Project IDs

Project ID:PG10301-0151
External Project ID:NHRI-EX103-10343NI
StatusFinished
Effective start/end date01/01/1431/12/14

Keywords

  • immunosuppressive phenomenon
  • tumor microenvironment

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