Inferring functional connectivity through graphical directed information.
Inferring functional connectivity through graphical directed information.
复制标题
DOI：
10.1088/1741-2552/abecc6
复制
发表时间：
2021-03-30
影响因子：
4
通讯作者：
Aazhang B
中科院分区：
文献类型：
--
作者：
Young J;Neveu CL;Byrne JH;Aazhang B
准确推断功能连接对于理解大脑功能至关重要。由于维度缩放不足，以前的方法区分直接连接和间接连接的能力有限。这种较差的扩展性能减少了可包含在调节中的节点数量。我们的目标是提供一种可更好扩展的技术，从而最大限度地减少间接连接。我们的主要贡献是一个强大的无模型框架，即图形定向信息（GDI），它使成对定向功能连接能够以网络中更多节点的活动为条件，生成更准确的功能连接图，从而减少间接连接。实现这一进步的关键技术是互信息 (MI) 估计的最新进展，它依赖于多层感知器并利用 MI 的 Kullback-Leibler 散度定义的替代表示。我们的第二个主要贡献是将这种技术应用于离散值和连续值时间序列。 GDI 正确推断了任意高斯、非线性和电导网络的电路。此外，GDI 推断出海兔中央模式生成器电路模型的许多连接，同时还减少了许多间接连接。 GDI 是一种通用且无模型的技术，可用于各种尺度和数据类型，以提供准确的直接连接图，并解决神经数据分析中间接连接的关键问题。
Accurate inference of functional connectivity is critical for understanding brain function. Previous methods have limited ability distinguishing between direct and indirect connections because of inadequate scaling with dimensionality. This poor scaling performance reduces the number of nodes that can be included in conditioning. Our goal was to provide a technique that scales better and thereby enables minimization of indirect connections. Our major contribution is a powerful model-free framework, graphical directed information (GDI), that enables pairwise directed functional connections to be conditioned on the activity of substantially more nodes in a network, producing a more accurate graph of functional connectivity that reduces indirect connections. The key technology enabling this advancement is a recent advance in the estimation of mutual information (MI), which relies on multilayer perceptrons and exploiting an alternative representation of the Kullback–Leibler divergence definition of MI. Our second major contribution is the application of this technique to both discretely valued and continuously valued time series. GDI correctly inferred the circuitry of arbitrary Gaussian, nonlinear, and conductance-based networks. Furthermore, GDI inferred many of the connections of a model of a central pattern generator circuit in Aplysia, while also reducing many indirect connections. GDI is a general and model-free technique that can be used on a variety of scales and data types to provide accurate direct connectivity graphs and addresses the critical issue of indirect connections in neural data analysis.
登录
查看更多内容
影响因子：
2.5
作者：
Jiao, Jiantao;Permuter, Haim H.;Weissman, Tsachy
通讯作者：
Weissman, Tsachy
DOI：
10.1007/978-1-59745-520-6_8
发表时间：
2007-01-01
期刊：
Methods in molecular biology (Clifton, N.J.)
影响因子：
--
作者：
Baxter, Douglas A;Byrne, John H
通讯作者：
Byrne, John H
影响因子：
4.3
作者：
Bordier C;Nicolini C;Bifone A
通讯作者：
Bifone A
影响因子：
2.4
作者：
Kraskov, A;Stögbauer, H;Grassberger, P
通讯作者：
Grassberger, P
影响因子：
2.5
作者：
Cai, Zhiting;Neveu, Curtis L.;Aazhang, Behnaam
通讯作者：
Aazhang, Behnaam