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

【摘要】：認(rèn)知科學(xué)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(jīng)影像領(lǐng)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(wǎng)絡(luò)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(yīng)連接(effective connectivity)的動(dòng)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(guān)注。兩種模型雖然基于不同的因果概念,但都是應(yīng)用fMRI時(shí)間序列里時(shí)間信息來揭示腦區(qū)之間的有向的信息流動(dòng)�；谶@兩種方法探測效應(yīng)連接的過程中,模型選擇是一個(gè)重要的問題。最優(yōu)模型的選擇,將會(huì)直接影響效應(yīng)連接探測和結(jié)果的分析。 DCM中,不能準(zhǔn)確設(shè)定的生理學(xué)參數(shù)會(huì)對模型選擇結(jié)果和效應(yīng)連接強(qiáng)度產(chǎn)生影響�；贔檢驗(yàn)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(yīng)問題,因此被普遍應(yīng)用在統(tǒng)計(jì)模型選擇領(lǐng)域。 本文旨在通過仿真實(shí)驗(yàn)和真實(shí)數(shù)據(jù)實(shí)驗(yàn),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(yīng)連接強(qiáng)度和整個(gè)效應(yīng)連接網(wǎng)絡(luò)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(gè)模型選擇框架中,通過引入最小描述長度準(zhǔn)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(shí)的BOLD信號在個(gè)體水平和組水平上分別進(jìn)行有效鏈接的探測。結(jié)果證明在一個(gè)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(jì)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(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：O482.532;R741

【參考文獻(xiàn)】

相關(guān)期刊論文 前2條

1 邸新;饒恒毅;;人腦功能連通性研究進(jìn)展[J];生物化學(xué)與生物物理進(jìn)展;2007年01期

2 鄭毅;駱清銘;劉謙;李婷;張中興;龔輝;;適于腦功能活動(dòng)檢測的便攜式近紅外光譜儀的研制[J];中國生物醫(yī)學(xué)工程學(xué)報(bào);2007年06期

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本文編號：1836534