Identifying the default mode network structure using dynamic causal modeling on resting-state functional magnetic resonance imaging.
Identifying the default mode network structure using dynamic causal modeling on resting-state functional magnetic resonance imaging.
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DOI：
10.1016/j.neuroimage.2013.07.071
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发表时间：
2014-02-01
期刊：
影响因子：
5.7
通讯作者：
Biswal BB
中科院分区：
文献类型：
--
作者：
Di X;Biswal BB
默认模式网络是大脑结构的一部分，当人处于休息状态时，它显示出较高的神经活动和能量消耗。默认模式网络中的关键区域通过白质纤维追踪以及静息态功能磁共振成像信号的同步性显示出高度的相互连接。然而，默认模式网络内的因果信息流仍然知之甚少。当前的研究利用静息态功能磁共振成像数据集上的动态因果模型来确定默认模式网络背后的网络结构。在0.01 - 0.08Hz的低频段，通过傅里叶级数对大脑内源性波动进行了明确建模，并将这些傅里叶级数设定为动态因果模型（DCM）的驱动输入。模型比较过程倾向于一种模型，即内侧前额叶皮质（MPFC）向后扣带回皮质（PCC）发送信息，双侧下顶叶小叶向PCC和MPFC发送信息。进一步的分析提供了证据，表明右半球的内源性连接可能比左半球更高。这些数据为默认模式网络（DMN）中每个节点的功能提供了见解，也验证了动态因果模型在静息态功能磁共振成像数据上的应用。
The default mode network is part of the brain structure that shows higher neural activity and energy consumption when one is at rest. The key regions in the default mode network are highly interconnected as conveyed by both the white matter fiber tracing and the synchrony of resting-state functional magnetic resonance imaging signals. However, the causal information flow within the default mode network is still poorly understood. The current study used the dynamic causal modeling on resting-state fMRI dataset to identify the network structure underlying the default mode network. The endogenous brain fluctuations were explicitly modeled by Fourier series at the low frequency band of 0.01–0.08 Hz, and those Fourier series were set as driving inputs of the DCM models. Model comparison procedures favored a model that the MPFC sends information to the PCC and the bilateral inferior parietal lobule sends information to both the PCC and MPFC. Further analyses provide evidence that the endogenous connectivity might be higher in the right hemisphere than in the left hemisphere. These data provided insight on the functions of each node in the DMN, and also validate the usage of DCM on resting-state fMRI data.
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DOI：
10.1016/j.physd.2009.08.002
发表时间：
2009-11-01
期刊：
Physica D. Nonlinear phenomena
影响因子：
--
作者：
Daunizeau J;Friston KJ;Kiebel SJ
通讯作者：
Kiebel SJ
影响因子：
3.2
作者：
Laird AR;Fox PM;Eickhoff SB;Turner JA;Ray KL;McKay DR;Glahn DC;Beckmann CF;Smith SM;Fox PT
通讯作者：
Fox PT
影响因子：
2.5
作者：
Andrews-Hanna, Jessica R.;Reidler, Jay S.;Buckner, Randy L.
通讯作者：
Buckner, Randy L.
影响因子：
3.1
作者：
Caspers, Svenja;Eickhoff, Simon B.;Amunts, Katrin
通讯作者：
Amunts, Katrin
DOI：
10.1523/jneurosci.4004-09.2009
发表时间：
2009-11-18
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Laird AR;Eickhoff SB;Li K;Robin DA;Glahn DC;Fox PT
通讯作者：
Fox PT