Heterogeneous fractionation profiles of meta-analytic coactivation networks.
Heterogeneous fractionation profiles of meta-analytic coactivation networks.
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DOI：
10.1016/j.neuroimage.2016.12.037
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发表时间：
2017-04-01
期刊：
影响因子：
5.7
通讯作者：
Sutherland MT
中科院分区：
文献类型：
--
作者：
Laird AR;Riedel MC;Okoe M;Jianu R;Ray KL;Eickhoff SB;Smith SM;Fox PT;Sutherland MT
计算认知神经成像方法可以用来表征人类大脑中分布式、功能专业化网络的分层组织。为此，我们从超过10,000个基于任务的实验中，对基于坐标的激活位置的BrainMap数据库进行了大规模挖掘。通过联合应用独立成分分析（ICA）和元分析连通性建模（MACM），在广泛的模型阶数范围内（即d = 20至300）确定了元分析协同激活网络。然后，我们迭代地计算了连续模型顺序的两两相关系数，以比较空间网络拓扑，最终得出了描述“父母”功能大脑系统如何分解为组成“孩子”子网络的分异剖面。在不同的规范网络中，分异特征显著不同：一些规范网络在整个模型阶数范围内表现出复杂而广泛的分异，而另一些规范网络则随着模型阶数的增加而表现出很少甚至没有分解。分层聚类应用于评估这种异质性，产生三组不同的网络分馏概况：高、中等和低分馏。基于脑图的功能解码结果共激活网络揭示了一个多领域的关联，而不管分馏的复杂性。这些结果不是强调认知-运动-知觉梯度，而是表明脑叶间连通性在功能性大脑组织中的重要性。我们的结论是，高分馏网络是复杂的，由许多组成子网络组成，反映了远距离的、脑叶间的连通性，特别是在额顶叶区域。相比之下，低分异网络可能反映了持久和稳定的网络，这些网络在内部更连贯，并表现出较少的脑叶间通信。
Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d = 20 to 300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how “parent” functional brain systems decompose into constituent “child” sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication.