Diversity of meso-scale architecture in human and non-human connectomes
Diversity of meso-scale architecture in human and non-human connectomes
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DOI：
10.1038/s41467-017-02681-z
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发表时间：
2018-01-24
影响因子：
16.6
通讯作者：
Bassett, Danielle S.
中科院分区：
文献类型：
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作者：
Betzel, Richard F.;Medaglia, John D.;Bassett, Danielle S.
大脑功能反映在连接体的群落结构中。主流观点认为，社区是分类的，相互隔离，支持专门的信息处理。然而，这一观点排除了非分类社区的可能性，其复杂的社区间互动可能会产生更丰富的功能剧目。我们使用加权随机区块模型来揭示果蝇、小鼠、大鼠、猕猴和人类连接体的中尺度结构。我们发现，大多数群落是分类的，尽管其他群落形成了核心-外围和错配结构，与标准的群落检测技术相比，这更好地概括了人和鼠连接中功能连接和基因共表达的观察模式。我们定义了量化大脑区域参与的社区多样性的指标，表明这一指标在人类的控制和皮质下系统中达到顶峰，个体之间的差异与认知表现相关。我们的报告描绘了一幅更多样化的连接体社区的肖像，并展示了它们的认知相关性。
Brain function is reflected in connectome community structure. The dominant view is that communities are assortative and segregated from one another, supporting specialized information processing. However, this view precludes the possibility of non-assortative communities whose complex inter-community interactions could engender a richer functional repertoire. We use weighted stochastic blockmodels to uncover the meso-scale architecture of Drosophila, mouse, rat, macaque, and human connectomes. We find that most communities are assortative, though others form core-periphery and disassortative structures, which better recapitulate observed patterns of functional connectivity and gene co-expression in human and mouse connectomes compared to standard community detection techniques. We define measures for quantifying the diversity of communities in which brain regions participate, showing that this measure is peaked in control and subcortical systems in humans, and that inter-individual differences are correlated with cognitive performance. Our report paints a more diverse portrait of connectome communities and demonstrates their cognitive relevance.