Cummings Jennifer A, Sipes Benjamin, Mathalon Daniel H, Raj Ashish
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, United States.
Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States.
Front Neurosci. 2022 Mar 15;16:810111. doi: 10.3389/fnins.2022.810111. eCollection 2022.
Understanding how complex dynamic activity propagates over a static structural network is an overarching question in the field of neuroscience. Previous work has demonstrated that linear graph-theoretic models perform as well as non-linear neural simulations in predicting functional connectivity with the added benefits of low dimensionality and a closed-form solution which make them far less computationally expensive. Here we show a simple model relating the eigenvalues of the structural connectivity and functional networks using the Gamma function, producing a reliable prediction of functional connectivity with a single model parameter. We also investigate the impact of local activity diffusion and long-range interhemispheric connectivity on the structure-function model and show an improvement in functional connectivity prediction when accounting for such latent variables which are often excluded from traditional diffusion tensor imaging (DTI) methods.
理解复杂动态活动如何在静态结构网络上传播是神经科学领域的一个首要问题。先前的研究表明，线性图论模型在预测功能连接方面与非线性神经模拟表现相当，并且具有低维度和封闭形式解的额外优势，这使得它们的计算成本大大降低。在这里，我们展示了一个使用伽马函数将结构连接性和功能网络的特征值联系起来的简单模型，通过单个模型参数就能可靠地预测功能连接性。我们还研究了局部活动扩散和远程半球间连接性对结构 - 功能模型的影响，并表明在考虑这些通常被传统扩散张量成像（DTI）方法排除的潜在变量时，功能连接性预测得到了改善。
