复杂相关模式的层次结构支持大脑动态,定义了一个强大的功能架构。

Cell systems Pub Date : 2024-08-21 Epub Date: 2024-08-13 DOI:10.1016/j.cels.2024.07.003
Levente Varga, Vasile V Moca, Botond Molnár, Laura Perez-Cervera, Mohamed Kotb Selim, Antonio Díaz-Parra, David Moratal, Balázs Péntek, Wolfgang H Sommer, Raul C Mureșan, Santiago Canals, Maria Ercsey-Ravasz
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引用次数: 0

摘要

功能磁共振成像(fMRI)为认知过程提供了重要的临床潜力。然而,在功能网络研究中,脑区通信的延迟和动态变化往往被忽视。我们证明,考虑到信号之间的时滞,从 fMRI 交叉相关矩阵中提取的网络在关注网络属性的统计分布时显示出显著的可靠性。这揭示了一种稳健的大脑功能连接模式,其特点是由强 0 滞后相关和捕捉不同时间延迟下协调的较弱链接组成的稀疏主干。这种动态而稳定的网络结构在大鼠、狨猴、人类以及脑电图(EEG)数据中都是一致的,表明了大脑动态的潜在普遍性。动态功能网络的二阶特性显示,在组级比较和测试-重复分析中,功能相关性的层次结构非常稳定。通过使用酒精使用障碍的 fMRI 数据进行验证,发现了比以前报道的更广泛的网络属性变化,证明了这种方法在确定疾病生物标记物方面的潜力。
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Brain dynamics supported by a hierarchy of complex correlation patterns defining a robust functional architecture.

Functional magnetic resonance imaging (fMRI) provides insights into cognitive processes with significant clinical potential. However, delays in brain region communication and dynamic variations are often overlooked in functional network studies. We demonstrate that networks extracted from fMRI cross-correlation matrices, considering time lags between signals, show remarkable reliability when focusing on statistical distributions of network properties. This reveals a robust brain functional connectivity pattern, featuring a sparse backbone of strong 0-lag correlations and weaker links capturing coordination at various time delays. This dynamic yet stable network architecture is consistent across rats, marmosets, and humans, as well as in electroencephalogram (EEG) data, indicating potential universality in brain dynamics. Second-order properties of the dynamic functional network reveal a remarkably stable hierarchy of functional correlations in both group-level comparisons and test-retest analyses. Validation using alcohol use disorder fMRI data uncovers broader shifts in network properties than previously reported, demonstrating the potential of this method for identifying disease biomarkers.

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