期刊
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
卷 9, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fncom.2015.00022
关键词
functional connectivity; structural connectivity; Bayesian analysis; small-world network; MCMC; fMRI; DTI; neuroimaging
资金
- NIMH NIH HHS [R01 MH101512] Funding Source: Medline
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. (2006a) 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据