Neurosciences Graduate Program, University of California San Diego, La Jolla, CA, 92092, and Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA, 92037, U.S.A.
Computational Neurobiology Laboratory, Salk Institute for Biological Sciences, La Jolla, CA, 92037, and Division of Biological Sciences, University of California San Diego, La Jolla, CA, 92092, U.S.A.
Neural Comput. 2020 Dec;32(12):2389-2421. doi: 10.1162/neco_a_01323. Epub 2020 Sep 18.
Measuring functional connectivity from fMRI recordings is important in understanding processing in cortical networks. However, because the brain's connection pattern is complex, currently used methods are prone to producing false functional connections. We introduce differential covariance analysis, a new method that uses derivatives of the signal for estimating functional connectivity. We generated neural activities from dynamical causal modeling and a neural network of Hodgkin-Huxley neurons and then converted them to hemodynamic signals using the forward balloon model. The simulated fMRI signals, together with the ground-truth connectivity pattern, were used to benchmark our method with other commonly used methods. Differential covariance achieved better results in complex network simulations. This new method opens an alternative way to estimate functional connectivity.
从 fMRI 记录中测量功能连接对于理解皮质网络中的处理非常重要。然而，由于大脑的连接模式很复杂，目前使用的方法容易产生虚假的功能连接。我们引入了差异协方差分析，这是一种使用信号导数来估计功能连接的新方法。我们使用动态因果建模和 Hodgkin-Huxley 神经元神经网络生成神经活动，然后使用前向气球模型将其转换为血流动力学信号。模拟 fMRI 信号以及真实连接模式被用于与其他常用方法一起对我们的方法进行基准测试。差异协方差在复杂网络模拟中取得了更好的结果。这种新方法为估计功能连接开辟了另一种选择。
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