Schlesinger Kimberly J, Turner Benjamin O, Lopez Brian A, Miller Michael B, Carlson Jean M
Department of Physics, University of California, Santa Barbara, CA, USA.
Department of Psychological & Brain Sciences, University of California, Santa Barbara, CA, USA.
Neuroimage. 2017 Feb 1;146:741-762. doi: 10.1016/j.neuroimage.2016.09.001. Epub 2016 Sep 3.
As humans age, cognition and behavior change significantly, along with associated brain function and organization. Aging has been shown to decrease variability in functional magnetic resonance imaging (fMRI) signals, and to affect the modular organization of human brain function. In this work, we use complex network analysis to investigate the dynamic community structure of large-scale brain function, asking how evolving communities interact with known brain systems, and how the dynamics of communities and brain systems are affected by age. We analyze dynamic networks derived from fMRI scans of 104 human subjects performing a word memory task, and determine the time-evolving modular structure of these networks by maximizing the multislice modularity, thereby identifying distinct communities, or sets of brain regions with strong intra-set functional coherence. To understand how community structure changes over time, we examine the number of communities as well as the flexibility, or the likelihood that brain regions will switch between communities. We find a significant positive correlation between age and both these measures: younger subjects tend to have less fragmented and more coherent communities, and their brain regions tend to change communities less often during the memory task. We characterize the relationship of community structure to known brain systems by the recruitment coefficient, or the probability of a brain region being grouped in the same community as other regions in the same system. We find that regions associated with cingulo-opercular, somatosensory, ventral attention, and subcortical circuits have a significantly higher recruitment coefficient in younger subjects. This indicates that the within-system functional coherence of these specific systems during the memory task declines with age. Such a correspondence does not exist for other systems (e.g. visual and default mode), whose recruitment coefficients remain relatively uniform across ages. These results confirm that the dynamics of functional community structure vary with age, and demonstrate methods for investigating how aging differentially impacts the functional organization of different brain systems.
随着人类年龄的增长，认知和行为会发生显著变化，同时大脑功能和组织结构也会相应改变。研究表明，衰老会降低功能磁共振成像（fMRI）信号的变异性，并影响人类大脑功能的模块化组织。在这项研究中，我们运用复杂网络分析方法来探究大规模脑功能的动态群落结构，研究不断演变的群落如何与已知脑系统相互作用，以及群落和脑系统的动态变化如何受到年龄的影响。我们分析了104名执行单词记忆任务的人类受试者的fMRI扫描数据所衍生的动态网络，并通过最大化多层模块度来确定这些网络随时间演变的模块化结构，从而识别出不同的群落，即具有强组内功能一致性的脑区集合。为了理解群落结构如何随时间变化，我们考察了群落数量以及灵活性，即脑区在不同群落之间切换的可能性。我们发现年龄与这两个指标之间均存在显著的正相关：年轻受试者的群落往往碎片化程度较低且连贯性更强，并且在记忆任务期间他们的脑区在不同群落之间切换的频率较低。我们通过招募系数来表征群落结构与已知脑系统之间的关系，招募系数即一个脑区与同一系统中其他区域归为同一群落的概率。我们发现，与扣带 - 脑岛、体感、腹侧注意和皮层下回路相关的区域在年轻受试者中的招募系数显著更高。这表明在记忆任务期间，这些特定系统的系统内功能一致性会随着年龄增长而下降。而其他系统（如视觉和默认模式）则不存在这种对应关系，它们的招募系数在不同年龄段相对保持一致。这些结果证实了功能群落结构的动态变化随年龄而异，并展示了研究衰老如何对不同脑系统的功能组织产生不同影响的方法。
