Table3_The structure of anticorrelated networks in the human brain.XLSX

During the performance of a specific task--or at rest--, the activity of different brain regions shares statistical dependencies that reflect functional connections. While these relationships have been studied intensely for positively correlated networks, considerably less attention has been paid to negatively correlated networks, a. k.a. anticorrelated networks (ACNs). Although the most celebrated of all ACNs is the default mode network (DMN), and has even been extensively studied in health and disease, for systematically all ACNs other than DMN, there is no comprehensive study yet. Here, we have addressed this issue by making use of three neuroimaging data sets: one of N = 192 healthy young adults to fully describe ACN, another of N = 40 subjects to compare ACN between two groups of young and old participants, and another of N = 1,000 subjects from the Human Connectome Project to evaluate the association between ACN and cognitive scores. We first provide a comprehensive description of the anatomical composition of all ACNs, each of which participated in distinct resting-state networks (RSNs). In terms of participation ranking, from highest to the lowest, the major anticorrelated brain areas are the precuneus, the anterior supramarginal gyrus and the central opercular cortex. Next, by evaluating a more detailed structure of ACN, we show it is possible to find significant differences in ACN between specific conditions, in particular, by comparing groups of young and old participants. Our main finding is that of increased anticorrelation for cerebellar interactions in older subjects. Finally, in the voxel-level association study with cognitive scores, we show that ACN has multiple clusters of significance, clusters that are different from those obtained from positive correlated networks, indicating a functional cognitive meaning of ACN. Overall, our results give special relevance to ACN and suggest their use to disentangle unknown alterations in certain conditions, as could occur in early-onset neurodegenerative diseases or in some psychiatric conditions.

在执行特定任务或处于静息状态时，不同脑区的活动会呈现出反映功能连接的统计依赖关系。尽管学界已针对正相关网络开展了大量深入研究，但对负相关网络（又称反相关网络，ACNs）的关注却相对匮乏。尽管所有反相关网络中最受关注的当属默认模式网络（default mode network, DMN），且其在健康与疾病状态下均已被广泛研究，但截至目前，尚无针对除DMN之外所有反相关网络的系统性综合研究。为此，我们借助三项神经影像学数据集开展本研究：其一为样本量N=192的健康青年成年人数据集，用于全面刻画反相关网络的特征；其二为样本量N=40的受试者数据集，用于比较青年与老年两组参与者的反相关网络差异；其三为来自人类连接组项目（Human Connectome Project）的样本量N=1000的受试者数据集，用于评估反相关网络与认知评分之间的关联。我们首先对所有反相关网络的解剖学组成进行了全面描述，这些网络均隶属于不同的静息态网络（resting-state networks, RSNs）。按参与度从高到低排序，主要的反相关脑区依次为楔前叶、缘上回前部以及中央岛盖皮质。随后，通过对反相关网络的精细结构展开分析，我们发现特定状态下的反相关网络存在显著差异；具体而言，对比青年与老年两组参与者时即可观察到这类差异。我们的核心发现为：老年受试者的小脑交互反相关作用显著增强。最后，在针对认知评分的体素（voxel）层面关联分析中，我们发现反相关网络存在多个显著性簇，且这些簇与正相关网络得到的显著性簇存在显著差异，这表明反相关网络具备特定的功能性认知意义。总体而言，本研究结果凸显了反相关网络的重要研究价值，并提示可利用反相关网络解析特定疾病状态下尚未明确的脑功能改变，例如早发性神经退行性疾病或部分精神类疾病中可能出现的脑功能变化。