本文選題：功能磁共振成像 + 效應鏈接��； 參考：《浙江大學》2014年碩士論文

【摘要】：認知科學中,從功能整合角度研究大腦不同腦區(qū)間的交互作用具有重要的意義。在神經影像領域,功能磁共振成像(functional Magnetic Resonance Imaging,簡稱fMRI)憑借非侵入性,較高的空間分辨率成為從網絡角度研究大腦功能的有效工具。 在功能整合水平下,目前基于功能磁共振數(shù)據(jù)探測效應連接(effective connectivity)的動態(tài)因果模型(Dynamic Causal Modelling,簡稱DCM)和Granger因果映射(Granger Causality Mapping,簡稱GCM)長期競爭共存,都得到了廣泛的關注。兩種模型雖然基于不同的因果概念,但都是應用fMRI時間序列里時間信息來揭示腦區(qū)之間的有向的信息流動�；谶@兩種方法探測效應連接的過程中,模型選擇是一個重要的問題。最優(yōu)模型的選擇,將會直接影響效應連接探測和結果的分析。 DCM中,不能準確設定的生理學參數(shù)會對模型選擇結果和效應連接強度產生影響�；贔檢驗的Granger因果映射也在模型選擇問題上出現(xiàn)了困難。最小描述長度(Minimum Description Length,簡稱MDL)是一種將奧卡姆剃刀形式化后的一種形式,可以避免模型過適應問題,因此被普遍應用在統(tǒng)計模型選擇領域。 本文旨在通過仿真實驗和真實數(shù)據(jù)實驗,探索在DCM的反演中,靜態(tài)血容積比率(%)對于感興趣區(qū)之間效應連接強度和整個效應連接網絡的影響。在傳統(tǒng)Granger因果映射中,把自回歸模型定階和模型選擇納入到一個模型選擇框架中,通過引入最小描述長度準則選擇最優(yōu)模型。利用仿真數(shù)據(jù)和六只老鼠失神性癲癇發(fā)作時的BOLD信號在個體水平和組水平上分別進行有效鏈接的探測。結果證明在一個框架中,基于MDL的模型選擇過程可以有效的避免人為確定置信區(qū)間帶來的誤差,在多模型的選擇中可以減少F檢測兩兩比較帶來的計算量過大問題,并且對于噪聲不敏感,從而克服了傳統(tǒng)Granger因果映射的缺陷。
[Abstract]:In cognitive science, it is of great significance to study the interaction between different brain regions from the perspective of functional integration. In the field of neuroimaging, functional Magnetic Resonance imaging (fMRI) has become an effective tool for the study of brain function from a network perspective by virtue of its noninvasive and high spatial resolution. At the level of functional integration, the dynamic Causal Modeling (DCM) and the Granger causality Mapping (Granger Causality Mapping), which are based on the functional Magnetic Resonance data (fMRI) detection effect and effective connectivity, have been paid more and more attention. Although the two models are based on different causal concepts, they both use the time information in the fMRI time series to reveal the flow of information between brain regions. Model selection is an important problem in the process of detecting effect connection based on these two methods. The selection of the optimal model will directly affect the detection of the effect connection and the analysis of the results. In DCM, physiological parameters that cannot be accurately set will affect the model selection results and the effect connection strength. Granger causality mapping based on F test also presents difficulties in model selection. Minimum Description length is a formalized form of Occam razor, which can avoid the problem of model overadaptation, so it is widely used in the field of statistical model selection. The purpose of this paper is to explore the effect of static blood volume ratio on the connection strength of the effect between the regions of interest and the whole effect connection network in the inversion of DCM by means of simulation experiments and real data experiments. In the traditional Granger causality mapping, the autoregressive model order determination and model selection are incorporated into a model selection framework, and the optimal model is selected by introducing the minimum description length criterion. Simulated data and BOLD signals of six mice with apocalyptic seizures were detected at individual level and group level respectively. The results show that the model selection process based on MDL can effectively avoid the error caused by artificial determination of confidence interval in a framework, and reduce the computational complexity caused by the comparison of F detection in the selection of multiple models. And it is insensitive to noise, which overcomes the defect of traditional Granger causality mapping.
【學位授予單位】：浙江大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：O482.532;R741

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本文編號：1836534