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首页> 外文期刊>Proceedings of the National Academy of Sciences of the United States of America >Predicting Human Resting-state Functional Connectivity From Structural Connectivity
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Predicting Human Resting-state Functional Connectivity From Structural Connectivity

机译：从结构连通性预测人类休息状态功能连通性

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摘要

In the cerebral cortex, the activity levels of neuronal populations are continuously fluctuating. When neuronal activity, as measured using functional MRI (fMRI), is temporally coherent across 2 populations, those populations are said to be functionally connected. Functional connectivity has previously been shown to correlate with structural (anatomical) connectivity patterns at an aggregate level. In the present study we investigate, with the aid of computational modeling, whether systems-level properties of functional networks-including their spatial statistics and their persistence across time-can be accounted for by properties of the underlying anatomical network. We measured resting state functional connectivity (using fMRI) and structural connectivity (using diffusion spectrum imaging tractography) in the same individuals at high resolution. Structural connectivity then provided the couplings for a model of macroscopic cortical dynamics. In both model and data, we observed (ⅰ) that strong functional connections commonly exist between regions with no direct structural connection, rendering the inference of structural connectivity from functional connectivity impractical; (ⅱ) that indirect connections and interregional distance accounted for some of the variance in functional connectivity that was unexplained by direct structural connectivity; and (ⅲ) that resting-state functional connectivity exhibits variability within and across both scanning sessions and model runs. These empirical and modeling results demonstrate that although resting state functional connectivity is variable and is frequently present between regions without direct structural linkage, its strength, persistence, and spatial statistics are nevertheless constrained by the large-scale anatomical structure of the human cerebral cortex.
机译：在大脑皮层中，神经元种群的活动水平不断波动。当使用功能性MRI（fMRI）进行测量的神经元活动在两个族群之间在时间上是连贯的时，这些族群就被认为是功能上相关的。先前已显示功能连接在聚合级别与结构（解剖）连接模式相关。在本研究中，我们将借助计算建模来研究功能网络的系统级属性（包括其空间统计信息及其在整个时间范围内的持久性）是否可以由基础解剖网络的属性来解释。我们以高分辨率测量了同一个人的静息状态功能连接性（使用fMRI）和结构连接性（使用扩散光谱成像术）。然后，结构连通性为宏观皮质动力学模型提供了耦合。在模型和数据中，我们观察到（ⅰ）在没有直接结构连接的区域之间通常存在强大的功能连接，因此从功能连接性推断结构连接性是不切实际的； （ⅱ）间接连接和区域间距离造成了功能连接的某些差异，而直接结构连接无法解释这些差异； （ⅲ）静止状态功能连接性在扫描会话和模型运行之内和之间都表现出可变性。这些经验和建模结果表明，尽管静止状态的功能连通性是可变的，并且经常在没有直接结构链接的区域之间出现，但是其强度，持久性和空间统计仍然受到人类大脑皮层大规模解剖结构的限制。

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    C. J. Honey; O. Sporns; L. Cammoun; X. Gigandet; J. P. Thiran; R. Meuli; P. Hagmann;

  • 作者单位

    Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405;

    Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405;

    Signal Processing Laboratory 5, Ecole Polytechnique Federale de Lausanne, CH-1011 Lausanne, Switzerland;

    Signal Processing Laboratory 5, Ecole Polytechnique Federale de Lausanne, CH-1011 Lausanne, Switzerland;

    Signal Processing Laboratory 5, Ecole Polytechnique Federale de Lausanne, CH-1011 Lausanne, Switzerland;

    Department of Radiology, University Hospital Center and University of Lausanne, CH-1011 Lausanne, Switzerland;

    Signal Processing Laboratory 5, Ecole Polytechnique Federale de Lausanne, CH-1011 Lausanne, Switzerland Department of Radiology, University Hospital Center and University of Lausanne, CH-1011 Lausanne, Switzerland;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    computational model; diffusion mri; neuroanatomy; cerebral cortex; brain networks;

    机译：计算模型;扩散磁共振成像;神经解剖学;脑皮质;脑网络;

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