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A Framework for Inter-Subject Prediction of Functional Connectivity From Structural Networks

机译：从结构网络进行功能连接的受试者间预测的框架

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摘要

Functional connections between brain regions are supported by structural connectivity. Both functional and structural connectivity are estimated from in vivo magnetic resonance imaging and offer complementary information on brain organization and function. However, imaging only provides noisy measures, and we lack a good neuroscientific understanding of the links between structure and function. Therefore, inter-subject joint modeling of structural and functional connectivity, the key to multimodal biomarkers, is an open challenge. We present a probabilistic framework to learn across subjects a mapping from structural to functional brain connectivity. Expanding on our previous work , our approach is based on a predictive framework with multiple sparse linear regression. We rely on the randomized LASSO to identify relevant anatomo-functional links with some confidence interval. In addition, we describe resting-state functional magnetic resonance imaging in the setting of Gaussian graphical models, on the one hand imposing conditional independences from structural connectivity and on the other hand parameterizing the problem in terms of multivariate autoregressive models. We introduce an intrinsic measure of prediction error for functional connectivity that is independent of the parameterization chosen and provides the means for robust model selection. We demonstrate our methodology with regions within the default mode and the salience network as well as, atlas-based cortical parcellation.
机译：大脑区域之间的功能连接由结构连接性支持。从体内磁共振成像估计功能和结构的连通性，并提供有关大脑组织和功能的补充信息。但是，成像仅提供嘈杂的测量，我们对结构与功能之间的联系缺乏良好的神经科学理解。因此，结构和功能连接性的受试者间联合建模是多式联运生物标志物的关键，是一个开放的挑战。我们提出了一个概率框架，以跨学科学习从结构性大脑连接到功能性大脑连接的映射。在先前工作的基础上，我们的方法是基于具有多个稀疏线性回归的预测框架。我们依靠随机的LASSO来确定具有一定置信区间的相关解剖功能链接。此外，我们在高斯图形模型的设置中描述了静止状态功能磁共振成像，一方面施加了与结构连通性的条件独立性，另一方面利用多元自回归模型对问题进行了参数化。我们介绍了一种针对功能连通性的预测误差的内在度量，该度量独立于所选择的参数设置，并提供了用于可靠模型选择的方法。我们用默认模式和显着网络以及基于图集的皮质细胞分裂中的区域展示了我们的方法。

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