Functional brain network modularity captures inter- and intra-individual variation in working memory capacity.
Functional brain network modularity captures inter- and intra-individual variation in working memory capacity.
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DOI：
10.1371/journal.pone.0030468
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Fair DA
中科院分区：
文献类型：
--
作者：
Stevens AA;Tappon SC;Garg A;Fair DA
认知能力，如工作记忆，因人而异;然而，个体在日常认知表现上也各不相同。认知变异性的一个潜在来源可能是神经系统功能组织的波动。这些功能网络的组织优化程度可能与个体的有效认知功能有关。在这里，我们具体研究如何通过静息状态下的功能连接MRI和图论测量的大规模网络的组织的变化跟踪工作记忆容量的变化。22名参与者进行了工作记忆容量测试，然后进行了静息状态fMRI。17名受试者在三周后重复了该方案。我们应用图论技术来测量34个大脑感兴趣区域（ROI）的网络组织。网络模块性，衡量跨子网络的整合和隔离水平，和小世界性，衡量全球网络连接效率，都预测个体差异的记忆容量;然而，只有模块性预测个体内的变化在两个会话。部分相关控制的工作记忆的组成部分，是稳定的跨会话显示，模块性几乎完全与工作记忆的变异性在每个会话。对特定子网络和单个回路的分析无法一致地解释工作记忆容量的变化。结果表明，内在的功能组织的先验定义的认知控制网络在休息时测量提供了大量的信息，实际的认知性能。网络模块性与个体工作记忆容量的可变性之间的关联表明，该网络在模块内的高连接性和模块之间的稀疏连接的组织可能反映了跨大脑区域的有效信号传递，可能是通过信号的调制或噪声传播的抑制。
Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity. Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability. The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise.
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影响因子：
5.3
作者：
Achard, S;Salvador, R;Bullmore, ET
通讯作者：
Bullmore, ET
影响因子：
5.7
作者：
Barabasi, Albert-Lashlo
通讯作者：
Barabasi, Albert-Lashlo
影响因子：
4.8
作者：
Hampson, M;Peterson, BS;Gore, JC
通讯作者：
Gore, JC
DOI：
10.1073/pnas.0601602103
发表时间：
2006-06-06
影响因子：
11.1
作者：
Newman, M. E. J.
通讯作者：
Newman, M. E. J.
影响因子：
25
作者：
McNab, Fiona;Klingberg, Torkel
通讯作者：
Klingberg, Torkel