A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity.
A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity.
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DOI：
10.3389/fncom.2015.00022
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发表时间：
2015
影响因子：
3.2
通讯作者：
Mayer AR
中科院分区：
文献类型：
--
作者：
Xue W;Bowman FD;Pileggi AV;Mayer AR
神经成像技术的最新创新为研究人员提供了机会，通过检查解剖电路以及大脑区域之间的功能关系来研究人类大脑的连通性。现有的连通性统计方法通常检查大脑区域之间的静息状态或任务相关的功能连通性（FC）或单独检查结构联系。作为确定大脑网络的一种手段，我们提出了一个统一的贝叶斯框架，用于分析FC利用相关的结构连接的知识，这扩展了Patel等人的方法。只考虑功能数据。我们介绍了一个FC措施，依赖于功能磁共振成像（fMRI）数据确定的区域大脑活动之间的功能一致性的评估。我们的结构连接性（SC）信息来自扩散张量成像（DTI）数据，该数据用于量化大脑区域之间SC的概率。我们制定了一个先验分布的FC，取决于大脑区域之间的SC的概率，这种依赖性坚持我们的功能磁共振成像和DTI数据所揭示的结构功能的联系。我们通过定义一个优势度量来进一步描述功能连接的大脑区域的功能层次结构，该优势度量比较了区域之间活动升高的边缘概率。此外，我们描述了网络的拓扑性质，这是由连通区域对，通过执行图论分析。我们演示了使用我们的贝叶斯模型，使用功能磁共振成像和DTI数据的听觉处理的研究。我们进一步说明了我们的方法的优势，通过比较，只纳入功能信息的方法。
Recent innovations in neuroimaging technology have provided opportunities for researchers to investigate connectivity in the human brain by examining the anatomical circuitry as well as functional relationships between brain regions. Existing statistical approaches for connectivity generally examine resting-state or task-related functional connectivity (FC) between brain regions or separately examine structural linkages. As a means to determine brain networks, we present a unified Bayesian framework for analyzing FC utilizing the knowledge of associated structural connections, which extends an approach by Patel et al. that considers only functional data. We introduce an FC measure that rests upon assessments of functional coherence between regional brain activity identified from functional magnetic resonance imaging (fMRI) data. Our structural connectivity (SC) information is drawn from diffusion tensor imaging (DTI) data, which is used to quantify probabilities of SC between brain regions. We formulate a prior distribution for FC that depends upon the probability of SC between brain regions, with this dependence adhering to structural-functional links revealed by our fMRI and DTI data. We further characterize the functional hierarchy of functionally connected brain regions by defining an ascendancy measure that compares the marginal probabilities of elevated activity between regions. In addition, we describe topological properties of the network, which is composed of connected region pairs, by performing graph theoretic analyses. We demonstrate the use of our Bayesian model using fMRI and DTI data from a study of auditory processing. We further illustrate the advantages of our method by comparisons to methods that only incorporate functional information.
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影响因子：
4.8
作者：
Mayer, Andrew R.;Franco, Alexandre R.;Harrington, Deborah L.
通讯作者：
Harrington, Deborah L.
影响因子：
4.8
作者：
Hampson, M;Peterson, BS;Gore, JC
通讯作者：
Gore, JC
影响因子：
1.2
作者：
Kollias, S.
通讯作者：
Kollias, S.
影响因子：
4.8
作者：
Patel, RS;Bowman, FD;Rilling, JK
通讯作者：
Rilling, JK
DOI：
10.1073/pnas.0811168106
发表时间：
2009-02-10
影响因子：
11.1
作者：
Honey, C. J.;Sporns, O.;Hagmann, P.
通讯作者：
Hagmann, P.