以神經網路模型模擬經顱性磁刺激在腦部運動皮層引起的神經可塑性

A Spiking Neural Network Model of Motor Cortex for Transcranial Magnetic Stimulation-Induced Synaptic Plasticity

顏煜洋(Yu-yang Yen)

指導教授 ： 羅中泉

國立清華大學/生命科學院/系統神經科學研究所/碩士(2012年)

https://doi.org/10.6843/NTHU.2012.00442

摘要

經顱性磁刺激(transcranial magnetic stimulus, TMS)近年在有關腦部神經的醫療及研究上相當的受重視，因為它不必藉由手術或穿刺來對腦部神經元進行刺激，是種非侵入性的方式。TMS在舒緩神經性疾病造成的運動障礙症方面十分的有潛力，但目前只知道TMS藉由改變腦部神經突觸可塑性來達成這個效果；至於TMS如何引起突觸可塑性的改變，詳細的機制仍未清楚。
本研究的目的是建立一個模擬腦部運動皮層的神經網路模型，並以此研究TMS在腦部引起的反應。另外，為了研究神經可塑性的變化，我們同時基於現在的理論發展出新型的神經可塑性模型，用來描述TMS引起神經可塑性的機制。相關研究收錄在陳李睿同學的論文中（以多時間尺度的突觸可塑性模型研究重複經顱性磁刺激所造成的神經可塑性）。
我們所建立的神經網路模型能夠完整的模擬TMS在腦部運動皮層引起的反應，以及隨著TMS刺激強度增加而改變反應波形的特性；在模擬各種腦部連結缺陷的實驗中，也展現出合理且多變的現象。這些結果顯示這個神經網路模型十分有發展潛力，在調整細部的參數之後便可以應用在研究TMS在腦部引起的反應上。更進一步，加入各種條件及額外的模組之後，將神經網路模型用以模擬各種神經性疾病的狀況，這也是我們所期望達成的終極目標。

關鍵字

經顱性磁刺激 ； 神經突觸可塑性

並列摘要

Transcranial magnetic stimulus (TMS) is a non-invasive remedy which had reported the ability in easing the motor disorders caused by neural diseases by inducing modifications in synaptic plasticity. TMS stimulate neurons by a massive but transient magnetic pulse, which creating an electromagnetic induction current within the focus. As a surgery-free method, it has been popular in brain research of medicine and research field. However, the detailed mechanism of TMS-induced plasticity change is still a mystery.
In this study, we built a spiking neural network model of primary motor cortex to simulate the cortical activity and synaptic plasticity responding to TMS stimulations. At the same time, we develop a new hypothesis with single neuron circuit to describe the mechanism of plasticity change induced by TMS. The results are archived in the thesis written by Mr. Li-Rui Chen. (Title: A model of synaptic plasticity at multiple temporal scales for the neuronal plasticity induced by repetitive transcranial magnetic stimulations.)
The cortical network model we built could emulate the full-featured activity aroused by TMS in the motor cortex. In model prediction, it also exhibits a reasonable response and activity. The results in this thesis support that our network model have good potential in the studying of TMS-induced responses, although it needs more parameter tuning. After that, we could apply it further to the study of the response of TMS in different neural diseases model, which is also the ultimate goal we expect to achieve from this first small step.

並列關鍵字

TMS ； TBS ； Transcranial magnetic stimulation ； neural network ； synaptic plasticity ； calcium-dependent plasticity ； computational model

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