A joint subspace mapping between structural and functional brain connectomes.
A joint subspace mapping between structural and functional brain connectomes.
复制标题
结构性和功能性大脑连接体之间的联合子空间映射。
DOI：
10.1016/j.neuroimage.2023.119975
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发表时间：
2023
期刊：
影响因子：
5.7
通讯作者：
Nagarajan,SrikantanS
中科院分区：
文献类型：
--
作者：
Ghosh,Sanjay;Raj,Ashish;Nagarajan,SrikantanS
理解大脑的结构连接和功能连接之间的联系是计算神经科学的巨大兴趣。尽管一些研究表明，整个大脑的功能连接是由潜在的结构形成的，但解剖学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，用于识别功能和结构连接体的特征模态的联合子空间。我们发现少量的特征模态足以从结构连接体中重建功能连接，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出该关节空间中的功能特征谱。通过同时估计关节特征模态和功能特征谱，我们可以从结构连接体中重建给定受试者的功能连接。我们进行了详细的实验并证明，与现有的基准方法相比，使用联合空间特征模态从结构连接组估计功能连通性的算法具有更好的可解释性，具有竞争力。
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.