顫抖症藥物療效差異的小腦機制：以神經活動的時空同步性做探討

Abstract

原發性顫抖（Essential Tremor, ET）是最常見的運動障礙疾病，其主要特徵為動作性顫抖。儘管盛行率高，目前藥物療效普遍有限，且不同顫抖亞型對藥物反應差異顯著，反映其病理機轉可能並不相同。

本研究結合體內電生理記錄與雙光子鈣影像技術，探討兩種顫抖小鼠模型中的神經機制，分別為對藥物反應有效的 harmaline 誘導模型與對藥物具抗性的 Grid2dupE3 模型。在時間編碼層面，兩種模型在產生顫抖頻率的群體編碼機制上高度相似：無論單一神經元的放電頻率為何，多個神經元的平均放電頻率會趨近於各自模型的顫抖頻率。

然而，在空間同步性分析中，我們觀察到小腦蒲肯野細胞（Purkinje cell, PC）在兩模型中的活動模式存在顯著差異：harmaline 小鼠呈現區域性同步，而 Grid2dupE3小鼠則表現出全域性同步。我們將進一步透過計算模型與藥物測試，驗證此同步性差異是否為造成兩者藥物反應差異的關鍵因素。

本研究結果顯示，兩種顫抖模型在小腦神經迴路動態上存在差異，這些差異可能與其對藥物反應的不同有所關聯。我們期許後續的計算模型研究能進一步釐清這類關聯，協助預測治療反應，並為原發性顫抖更有效治療策略的開發提供參考依據。

Essential tremor (ET), the most prevalent movement disorder, is primarily characterized by action tremor. Despite its high prevalence, fewer than 50% of patients respond effectively to current medications, and the development of new treatments has largely stagnated in recent years. Importantly, patients with different tremor subtypes often exhibit variable responses to the same pharmacological therapies. In this study, we employed in vivo electrophysiology and two-photon calcium imaging to investigate the underlying neuronal mechanisms in two mouse models of tremor: the drug-responsive harmaline-induced tremor model and the drug-refractory Grid2dupE3 model. At the level of temporal coding, we found that the population-level mechanism responsible for generating tremor frequency was similar in both models: the activity frequencies of multiple neurons converged toward the tremor frequency, regardless of its precise value. However, spatiotemporal neural dynamics revealed a striking difference in cerebellar Purkinje cell (PC) synchrony. In harmaline mice, PC activity exhibited regional synchrony, whereas in Grid2dupE3 mice, PC activity exhibited widespread global synchrony. To determine whether this difference in spatial synchrony contributes to the models’ differential drug responses, we plan to perform computational modeling and drug testing. We speculate that the divergent drug responses in the two tremor models may be linked to differences in cerebellar network activity. We anticipate that our forthcoming modeling efforts will aid in predicting treatment outcomes and inform the development of more effective therapies for ET.