The Constrained Network-Based Statistic: A New Level of Inference for Neuroimaging.
The Constrained Network-Based Statistic: A New Level of Inference for Neuroimaging.
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DOI：
10.1007/978-3-030-59728-3_45
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发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
Scheinost D
中科院分区：
文献类型：
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作者：
Noble S;Scheinost D
以解剖大脑网络组织为目标的神经成像研究在重大举措下有望蓬勃发展，但越来越多的证据表明，需要更准确的推理程序来促进发现。推理通常在集群级使用基于网络的统计数据（NBS）执行，该统计数据通过利用本地邻域中已知的依赖性来提高能力。然而，现有的NBS方法忽略了大规模大脑网络中另一种重要的依赖共享成员形式。在这里，我们提出了一种新的推断水平，即在预定义的大规模网络中汇集信息：基于约束网络的统计（cNBS）。我们通过从最大的公开可用的fMRI数据库：人类连接组项目中重新采样任务数据，评估了cNBS与现有标准NBS和无阈值NBS的敏感性和特异性。cNBS对中等以下的效应量最为敏感，占基础真值效应的大部分。相比之下，无阈值NBS对较高的效应量最为敏感。地面真值图显示了大规模网络中的分组效应，支持cNBS的相关性。所有方法都按预期控制了FWER。综上所述，cNBS是促进更有效推理的一个有希望的新水平，是神经科学中更可重复性发现的关键一步。
Neuroimaging research aimed at dissecting the network organization of the brain is poised to flourish under major initiatives, but converging evidence suggests more accurate inferential procedures are needed to promote discovery. Inference is typically performed at the cluster level with a network-based statistic (NBS) that boosts power by leveraging known dependence within the local neighborhood. However, existing NBS methods overlook another important form of dependence—shared membership in large-scale brain networks. Here, we propose a new level of inference that pools information within predefined large-scale networks: the Constrained Network-Based Statistic (cNBS). We evaluated sensitivity and specificity of cNBS against existing standard NBS and threshold-free NBS by resampling task data from the largest openly available fMRI database: the Human Connectome Project. cNBS was most sensitive to effect sizes below medium, which accounts for the majority of ground truth effects. In contrast, threshold-free NBS was most sensitive to higher effect sizes. Ground truth maps showed grouping of effects within large-scale networks, supporting the relevance of cNBS. All methods controlled FWER as intended. In summary, cNBS is a promising new level of inference for promoting more valid inference, a critical step towards more reproducible discovery in neuroscience.