King's College London, Institute of Psychiatry, Centre for Neuroimaging Sciences (CNS), London, UK.
Neuroimage. 2011 Mar 15;55(2):688-704. doi: 10.1016/j.neuroimage.2010.11.030. Epub 2010 Nov 21.
Network analysis has become a tool of choice for the study of functional and structural Magnetic Resonance Imaging (MRI) data. Little research, however, has investigated connectivity dynamics in relation to varying cognitive load. In fMRI, correlations among slow (<0.1 Hz) fluctuations of blood oxygen level dependent (BOLD) signal can be used to construct functional connectivity networks. Using an anatomical parcellation scheme, we produced undirected weighted graphs linking 90 regions of the brain representing major cortical gyri and subcortical nuclei, in a population of healthy adults (n=43). Topological changes in these networks were investigated under different conditions of a classical working memory task - the N-back paradigm. A mass-univariate approach was adopted to construct statistical parametric networks (SPNs) that reflect significant modifications in functional connectivity between N-back conditions. Our proposed method allowed the extraction of 'lost' and 'gained' functional networks, providing concise graphical summaries of whole-brain network topological changes. Robust estimates of functional networks are obtained by pooling information about edges and vertices over subjects. Graph thresholding is therefore here supplanted by inference. The analysis proceeds by firstly considering changes in weighted cost (i.e. mean between-region correlation) over the different N-back conditions and secondly comparing small-world topological measures integrated over network cost, thereby controlling for differences in mean correlation between conditions. The results are threefold: (i) functional networks in the four conditions were all found to satisfy the small-world property and cost-integrated global and local efficiency levels were approximately preserved across the different experimental conditions; (ii) weighted cost considerably decreased as working memory load increased; and (iii) subject-specific weighted costs significantly predicted behavioral performances on the N-back task (Wald F=13.39,df(1)=1,df(2)=83,p<0.001), and therefore conferred predictive validity to functional connectivity strength, as measured by weighted cost. The results were found to be highly sensitive to the frequency band used for the computation of the between-region correlations, with the relationship between weighted cost and behavioral performance being most salient at very low frequencies (0.01-0.03 Hz). These findings are discussed in relation to the integration/specialization functional dichotomy. The pruning of functional networks under increasing cognitive load may permit greater modular specialization, thereby enhancing performance.
网络分析已成为研究功能和结构磁共振成像（MRI）数据的首选工具。然而，很少有研究调查与变化的认知负荷相关的连接动力学。在 fMRI 中，可以使用血氧水平依赖（BOLD）信号的缓慢（<0.1 Hz）波动之间的相关性来构建功能连接网络。使用解剖分区方案，我们在健康成年人（n=43）的人群中生成了 90 个大脑区域的无向加权图，这些区域代表主要皮质回和皮质下核。在经典工作记忆任务 - N 回范式下，研究了这些网络的拓扑变化。采用大规模单变量方法构建统计参数网络（SPN），反映 N 回条件下功能连接的显著变化。我们提出的方法允许提取“丢失”和“获得”的功能网络，提供全脑网络拓扑变化的简明图形摘要。通过对受试者的边缘和顶点信息进行汇总，获得了功能网络的稳健估计。因此，在这里通过推断代替了图阈值。该分析首先考虑不同 N 回条件下加权成本（即区域间相关性的平均值）的变化，其次比较网络成本上的小世界拓扑度量，从而控制条件之间平均相关性的差异。结果有三个方面：（i）发现四种条件下的功能网络都满足小世界特性，并且网络成本综合全局和局部效率水平在不同实验条件下大致保持不变；（ii）随着工作记忆负荷的增加，加权成本大大降低；（iii）特定于个体的加权成本显着预测了 N 回任务的行为表现（Wald F=13.39，df(1)=1，df(2)=83，p<0.001），因此赋予了功能连接强度的预测有效性，由加权成本衡量。结果发现对用于计算区域间相关性的频带非常敏感，加权成本与行为表现之间的关系在极低频率（0.01-0.03 Hz）下最为明显。这些发现与整合/专业化的功能二分法有关。在认知负荷增加下，功能网络的修剪可能允许更大的模块专业化，从而提高性能。
