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功能连接组学中的规范途径。

Normative pathways in the functional connectome.

机构信息

Department of Psychiatry, University of Cambridge, Cambridge, UK.

Department of Psychiatry, University of Cambridge, Cambridge, UK; China-UK Centre for Cognition and Ageing Research, Faculty of Psychology, Southwest University, Chongqing, China.

出版信息

Neuroimage. 2019 Jan 1;184:317-334. doi: 10.1016/j.neuroimage.2018.09.028. Epub 2018 Sep 14.

Abstract

Functional connectivity is frequently derived from fMRI data to reduce a complex image of the brain to a graph, or "functional connectome". Often shortest-path algorithms are used to characterize and compare functional connectomes. Previous work on the identification and measurement of semi-metric (shortest circuitous) pathways in the functional connectome has discovered cross-sectional differences in major depressive disorder (MDD), autism spectrum disorder (ASD), and Alzheimer's disease. However, while measurements of shortest path length have been analyzed in functional connectomes, less work has been done to investigate the composition of the pathways themselves, or whether the edges composing pathways differ between individuals. Developments in this area would help us understand how pathways might be organized in mental disorders, and if a consistent pattern can be found. Furthermore, studies in structural brain connectivity and other real-world graphs suggest that shortest pathways may not be as important in functional connectivity studies as previously assumed. In light of this, we present a novel measurement of the consistency of pathways across functional connectomes, and an algorithm for improvement by selecting the most frequently occurring "normative pathways" from the k shortest paths, instead of just the shortest path. We also look at this algorithm's effect on various graph measurements, using randomized matrix simulations to support the efficacy of this method and demonstrate our algorithm on the resting-state fMRI (rs-fMRI) of a group of 34 adolescent control participants. Additionally, a comparison of normative pathways is made with a group of 82 age-matched participants, diagnosed with MDD, and in doing so we find the normative pathways that are most disrupted. Our results, which are carried out with estimates of connectivity derived from correlation, partial correlation, and normalized mutual information connectomes, suggest disruption to the default mode, affective, and ventral attention networks. Normative pathways, especially with partial correlation, make greater use of critical anatomical pathways through the striatum, cingulum, and the cerebellum. In summary, MDD is characterized by a disruption of normative pathways of the ventral attention network, increases in alternative pathways in the frontoparietal network in MDD, and a mixture of both in the default mode network. Additionally, within- and between-groups findings depend on the estimate of connectivity.

摘要

功能连接通常是从 fMRI 数据中提取的,以将大脑的复杂图像简化为一个图,即“功能连接组”。通常使用最短路径算法来描述和比较功能连接组。以前在识别和测量功能连接组中的半度量(最短迂回)路径方面的工作发现,在重度抑郁症(MDD)、自闭症谱系障碍(ASD)和阿尔茨海默病中存在横断面差异。然而,尽管已经在功能连接组中分析了最短路径长度的测量值,但对路径本身的组成或个体之间组成路径的边缘是否不同的研究较少。该领域的发展将帮助我们了解路径在精神障碍中的组织方式,以及是否可以找到一致的模式。此外,结构脑连接和其他真实世界图的研究表明,最短路径在功能连接研究中可能不如以前假设的那么重要。有鉴于此,我们提出了一种新的功能连接组中路径一致性的测量方法,以及一种通过从 k 条最短路径中选择最常出现的“规范路径”来改进的算法,而不是仅仅选择最短路径。我们还使用随机矩阵模拟来支持这种方法的有效性,并在一组 34 名青少年对照参与者的静息状态 fMRI(rs-fMRI)上演示我们的算法,研究了该算法对各种图测量的影响。此外,还将规范路径与一组 82 名年龄匹配的参与者进行了比较,这些参与者被诊断患有 MDD,并通过这种方式发现了最受干扰的规范路径。我们的结果是基于相关性、偏相关性和归一化互信息连接组中连接的估计值得出的,结果表明默认模式、情感和腹侧注意网络受到干扰。规范路径,尤其是偏相关性,更广泛地利用了通过纹状体、扣带和小脑的关键解剖路径。总之,MDD 的特征是腹侧注意网络的规范路径中断,MDD 中额顶叶网络的替代路径增加,以及默认模式网络的两者混合。此外,组内和组间的发现取决于连接的估计值。

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