Duggento A, Passamonti L, Guerrisi M, Valenza G, Barbieri R, Toschi N
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:4367-4370. doi: 10.1109/EMBC.2017.8037823.
While a large body of research has focused on the study of within-brain physiological networks (i.e. brain connectivity) as well as their disease-related aberration, few investigators have focused on estimating the directionality of these brain-brain interaction which, given the complexity of brain networks, should be properly conditioned in order to avoid the high number of false positives commonly encountered when using bivariate approaches to brain connectivity estimation. Additionally, the constituents of a number of brain subnetworks, and in particular of the central autonomic network (CAN), are still not completely determined. In this study we present and validate a global conditioning approach to reconstructing directed networks using complex synthetic networks of nonlinear oscillators. We then employ our framework, along with a probabilistic model for heartbeat generation, to characterize the directed functional connectome of the human brain and to establish which parts of this connectome effect the directed central modulation of peripheral autonomic cardiovascular control. We demonstrate the effectiveness of our conditioning approach and unveil a top-down directed influence of the default mode network on the salience network, which in turn is seen to be the strongest modulator of directed autonomic cardiovascular control.
虽然大量研究集中在脑内生理网络（即脑连接性）及其与疾病相关的异常上，但很少有研究者专注于估计这些脑-脑相互作用的方向性。鉴于脑网络的复杂性，在使用双变量方法进行脑连接性估计时，为避免常见的大量假阳性结果，应适当考虑这些方向性。此外，一些脑子网，特别是中枢自主网络（CAN）的组成部分仍未完全确定。在本研究中，我们提出并验证了一种全局条件方法，该方法使用非线性振荡器的复杂合成网络来重建有向网络。然后，我们运用我们的框架，结合心跳生成的概率模型，来表征人类大脑的有向功能连接组，并确定该连接组的哪些部分影响外周自主心血管控制的有向中枢调节。我们证明了我们的条件方法的有效性，并揭示了默认模式网络对突显网络的自上而下的有向影响，而突显网络反过来又被视为有向自主心血管控制的最强调节者。
