運用動態因果模型為基礎之方法去研究誘發和引發之動態神經網路;Investigating the Evoked and Induced Neuronal Network Dynamics Using a DCM-Based Generative Model

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題名:	運用動態因果模型為基礎之方法去研究誘發和引發之動態神經網路;Investigating the Evoked and Induced Neuronal Network Dynamics Using a DCM-Based Generative Model
作者:	陳純娟
貢獻者:	國立中央大學生物醫學工程研究所
關鍵詞:	醫學工程
日期:	2012-12-01
上傳時間:	2014-03-17 11:50:08 (UTC+8)
出版者:	行政院國家科學委員會
摘要:	研究期間：10108~10207;Neurons exhibit a broad range of synchronized oscillations. Brain rhythms are of interest in neuroscience studies because of the idea that event-related oscillations might facilitate integration both within and between functionally segregated brain areas and are the key to probe the neuronal network functions in both normal and disease states. Operationally speaking, there are two main components in brain oscillations: evoked and induced. Evoked and induced power differ in their phase relationship to the stimulus and their functional roles: induced responses has been described as mediating ‘top-down’ nonlinear modulation through backward connections; as opposed to the bottom-up driving process that may be more manifest evoked components. Furthermore, the conventional way of separating evoked and induced responses posits an assumption that they are linearly related. However, as suggested by a recent study using simulated data, the evoked and induced responses may share certain common generating framework. Nevertheless, the detection of induced responses relies upon comparisons between carefully matched experimental and control conditions and the results may be misleading if there are uncontrolled factors that contribute to conditions. In this proposal, we propose a generalized model based on Dynamic Causal Modelling which can account for the shared generating mechanisms and allow the direct study of the relationship between the evoked and induced neural activity in terms of their underlying processing .Computer simulation will be used to provide the face validity of this method. Importantly, we test whether induced responses in the motor control circuits are mediated by ‘top-down’ or backward connections and its frequency-dependent network mechanisms using EEG data during a well-leaned grip task. This provides a good balance of methodology development and experimental applications. We believe that the outcome of this proposal will help to shed some light on the cognitive studies by resolving the confounding problem as well as the motor control researches by providing some starting reference. On the basis of this study result, in a future study, we will be looking for the key factors that facilitate the motor training efficiency.
關聯:	財團法人國家實驗研究院科技政策研究與資訊中心
顯示於類別:	[生物醫學工程研究所 ] 研究計畫

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