Department of Radiology and Biomedical Imaging, University of California San Francisco, 513 Parnassus Ave, San Francisco, 94143, California, USA.
Neuroimage. 2023 May 15;272:119975. doi: 10.1016/j.neuroimage.2023.119975. Epub 2023 Mar 3.
Understanding the connection between the brain's structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject's functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability.
理解大脑结构连接和功能连接之间的关系是计算神经科学中非常感兴趣的问题。虽然一些研究表明，整个大脑的功能连接是由基础结构塑造的，但解剖结构如何限制大脑动力学的规则仍然是一个悬而未决的问题。在这项工作中，我们引入了一个计算框架，该框架可以识别功能连接组和结构连接组的特征模式的联合子空间。我们发现，这些特征模式中的一小部分就足以从结构连接组中重建功能连接，从而作为低维基函数集。然后，我们开发了一种算法，可以从结构特征谱中估计这个联合空间中的功能特征谱。通过同时估计联合特征模式和功能特征谱，我们可以从结构连接组中重建给定个体的功能连接。我们进行了详细的实验，并证明了从结构连接组使用联合空间特征模式估计功能连接的提出算法与现有基准方法相比具有竞争力，并且具有更好的可解释性。
