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基于血氧水平依赖(BOLD)信号的脑区之间时变定向相互关系的建模与分析

Modelling and analysis of time-variant directed interrelations between brain regions based on BOLD-signals.

作者信息

Hemmelmann D, Ungureanu M, Hesse W, Wüstenberg T, Reichenbach J R, Witte O W, Witte H, Leistritz L

机构信息

Institute of Medical Statistics, Computer Sciences and Documentation, Friedrich Schiller University Jena, Germany.

出版信息

Neuroimage. 2009 Apr 15;45(3):722-37. doi: 10.1016/j.neuroimage.2008.12.065.

Abstract

Time-variant Granger Causality Index (tvGCI) was applied to simulated and measured BOLD signals to investigate the reliability of time-variant analysis approaches for the identification of directed interrelations between brain areas on the basis of fMRI data. Single-shot fMRI data of a single image slice with short repetition times (200 ms, 16000 frames/subject, 64x64 voxels) were acquired from 5 healthy subjects during an externally-driven, self-paced finger-tapping paradigm (57-59 single taps for each subject). BOLD signals were derived from the pre-supplementary motor area (preSMA), the supplementary motor area (SMA), and the primary motor cortex (M1). The simulations were carried out by means of a Dynamic Causal Modelling (DCM) approach. The tvGCI as well as time-variant Partial Directed Coherence (tvPDC) were used to identify the modelled connectivity network (connectivity structure - CS - of the DCM). Different CSs were applied by using dynamic systems (Generalized Dynamic Neural Network - GDNN) and trivariate autoregressive (AR) processes. The influence of the low-pass characteristics of the simulated hemodynamic response (Balloon model) and of the measuring noise was tested. Additionally, our modelling strategy considered "spontaneous" BOLD fluctuations before, during, and after the appearance of the event-related BOLD component. Couplings which were extracted from the simulated signals were statistically evaluated (tvGCI for shuffled data, confidence tubes for tvGCI courses). We demonstrate that connections of our CS models can be correctly identified during the event-related BOLD component and with signal-to-noise-ratios corresponding to those of the measured data. The results based on simulations can be used to examine the reliability of connectivity identification based on BOLD signals by means of time-variant as well as time-invariant connectivity measures and enable a better interpretation of the analysis results using fMRI data. A readiness-BOLD response was only detected in one subject. However, in two subjects a strong time-variant connection (tvGCI) from preSMA to SMA was observed 3 s before the tapping was executed. This connection was accompanied by a weaker rise of the tvGCI from preSMA to M1. These preceding interrelations were confirmed in the other subjects by the dynamics of tvGCI courses. Based on the results of tvGCI analysis, the time-evolution of an individual connectivity network is shown for each subject.

摘要

时变格兰杰因果指数(tvGCI)被应用于模拟和测量的血氧水平依赖(BOLD)信号,以研究基于功能磁共振成像(fMRI)数据识别脑区之间定向相互关系的时变分析方法的可靠性。在外部驱动、自我节奏的手指敲击范式期间(每个受试者57 - 59次单次敲击),从5名健康受试者获取了具有短重复时间(200毫秒,16000帧/受试者,64×64体素)的单个图像切片的单次fMRI数据。BOLD信号来自辅助运动前区(preSMA)、辅助运动区(SMA)和初级运动皮层(M1)。模拟通过动态因果建模(DCM)方法进行。tvGCI以及时变偏定向相干性(tvPDC)被用于识别建模的连接网络(DCM的连接结构 - CS)。通过使用动态系统(广义动态神经网络 - GDNN)和三变量自回归(AR)过程应用不同的CS。测试了模拟血液动力学响应(球囊模型)的低通特性和测量噪声的影响。此外,我们的建模策略考虑了事件相关BOLD成分出现之前、期间和之后的“自发”BOLD波动。从模拟信号中提取的耦合进行了统计评估(对混洗数据的tvGCI,tvGCI曲线的置信区间)。我们证明,在事件相关BOLD成分期间以及在与测量数据对应的信噪比下,可以正确识别我们CS模型的连接。基于模拟的结果可用于通过时变和时不变连接测量来检验基于BOLD信号的连接识别的可靠性,并有助于更好地解释使用fMRI数据的分析结果。仅在一名受试者中检测到准备就绪BOLD响应。然而,在两名受试者中,在执行敲击前3秒观察到从preSMA到SMA的强时变连接(tvGCI)。这种连接伴随着从preSMA到M1的tvGCI较弱的上升。这些先前的相互关系在其他受试者中通过tvGCI曲线的动态变化得到证实。基于tvGCI分析的结果,展示了每个受试者个体连接网络的时间演变。

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