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Abstract：
Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain\'s large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show \"core\" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.
摘要：
围绕人脑功能网络组织的许多最新发展都集中在跨个体群体平均的数据上。虽然这种群体层面的方法已经为大脑的大规模分布式系统提供了相当大的启示，他们掩盖了网络组织中的个体差异，最近的工作已经证明是普遍和广泛的。这种个体差异在组分析中产生噪音，它们可以将作为参与者之间不同功能系统一部分的区域平均在一起，限制可解释性。然而,成本和可行性限制可能会限制研究中个体水平映射的可能性。在这里，我们的目标是利用有关个人水平的大脑组织的信息来概率映射常见的功能系统，并确定高受试者间共识的位置，以用于组分析。我们在具有相对较高数据量的多个数据集中概率映射了14个功能网络。所有网络都显示“核心”（高概率）区域，但在它们的高变异性成分的程度上彼此不同。这些模式在具有不同参与者和扫描参数的四个数据集上很好地复制。我们从这些概率图产生了一组高概率感兴趣区域（ROI）；这些和概率图公开可用，以及用于查询与任何给定皮质位置相关联的网络成员资格概率的工具。这些定量估计和公共工具可以允许研究人员将关于受试者间共识的信息应用于他们自己的功能磁共振成像研究。改进对系统及其功能专业化的推论。
