1 Department of Radiology, University of Washington , Seattle, Washington.
Brain Connect. 2014 May;4(4):231-41. doi: 10.1089/brain.2013.0205. Epub 2014 Jan 30.
Correlations among low-frequency spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal reflect the connectivity of intrinsic large-scale networks in the brain. These correlations have typically been characterized over the entire timecourse (mean connectivity), but the mean correlations between regions vary dynamically. By focusing on the linear relationship between activity in network nodes within the default mode network (DMN), dorsal attention network (DAN), and fronto-parietal task control network (FPTC) captured by their inter-correlations, we demonstrate that this dynamic pattern of fluctuations reveals a detailed substructure, that this substructure is robust across individuals, and that the expression of specific factors is correlated with age. To do this, we conducted a chained P-technique factor analysis of the correlations in nonoverlapping temporal windows across N=145 normal aging subjects (age 56-89). The expression of factors within the DMN, FPTC, and DAN was highly correlated with age: Decreased intercorrelations within nodes in each factor were correlated with advanced age. Although these findings converge with those from stationary analysis, the ability to quantify age-related changes in the coherence of fluctuating connectivity may yield more insights into age-related cognitive decline.
血氧水平依赖（BOLD）信号低频自发波动的相关性反映了大脑固有大规模网络的连通性。这些相关性通常在整个时间过程中进行描述（平均连通性），但区域之间的平均相关性是动态变化的。通过关注默认模式网络（DMN）、背侧注意网络（DAN）和额顶任务控制网络（FPTC）中网络节点活动之间的线性关系，这些网络节点的活动通过它们的互相关被捕获，我们证明了这种波动的动态模式揭示了一个详细的子结构，这种子结构在个体之间是稳健的，并且特定因素的表达与年龄相关。为此，我们对 N=145 名正常衰老受试者（年龄 56-89 岁）的非重叠时间窗口中的相关性进行了链式 P 技术因子分析。DMN、FPTC 和 DAN 中的因子表达与年龄高度相关：每个因子中节点内的相互关联减少与年龄增长相关。尽管这些发现与静态分析的结果一致，但量化波动连通性相干性随年龄变化的能力可能会为年龄相关性认知衰退提供更多的见解。
