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Generative modeling of brain maps with spatial autocorrelation
NeuroImage ( IF 5.7 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.neuroimage.2020.117038
Joshua B Burt 1 , Markus Helmer 2 , Maxwell Shinn 3 , Alan Anticevic 4 , John D Murray 5
Affiliation  

Studies of large-scale brain organization have revealed interesting relationships between spatial gradients in brain maps across multiple modalities. Evaluating the significance of these findings requires establishing statistical expectations under a null hypothesis of interest. Through generative modeling of synthetic data that instantiate a specific null hypothesis, quantitative benchmarks can be derived for arbitrarily complex statistical measures. Here, we present a generative null model, provided as an open-access software platform, that generates surrogate maps with spatial autocorrelation (SA) matched to SA of a target brain map. SA is a prominent and ubiquitous property of brain maps that violates assumptions of independence in conventional statistical tests. Our method can simulate surrogate brain maps, constrained by empirical data, that preserve the SA of cortical, subcortical, parcellated, and dense brain maps. We characterize how SA impacts p-values in pairwise brain map comparisons. Furthermore, we demonstrate how SA-preserving surrogate maps can be used in gene set enrichment analyses to test hypotheses of interest related to brain map topography. Our findings demonstrate the utility of SA-preserving surrogate maps for hypothesis testing in complex statistical analyses, and underscore the need to disambiguate meaningful relationships from chance associations in studies of large-scale brain organization.

中文翻译:

具有空间自相关的脑图生成模型

对大规模脑组织的研究揭示了跨多种模式的脑图中空间梯度之间的有趣关系。评估这些发现的重要性需要在感兴趣的零假设下建立统计预期。通过实例化特定零假设的合成数据的生成建模,可以为任意复杂的统计测量得出定量基准。在这里,我们提出了一个作为开放访问软件平台提供的生成空模型,该模型生成具有与目标脑图的 SA 匹配的空间自相关 (SA) 的代理图。SA 是脑图的一个突​​出且普遍存在的属性,它违反了传统统计测试中的独立性假设。我们的方法可以模拟受经验数据约束的替代脑图,保留皮质、皮质下、分割和密集脑图的 SA。我们描述了 SA 如何影响成对脑图比较中的 p 值。此外,我们展示了如何在基因集富集分析中使用保留 SA 的代理图来测试与脑图地形相关的感兴趣假设。我们的研究结果证明了保留 SA 的替代图在复杂统计分析中用于假设检验的效用,并强调了在大规模大脑组织研究中消除偶然关联中有意义的关系的必要性。我们演示了如何在基因集富集分析中使用保留 SA 的代理图来测试与脑图地形相关的感兴趣假设。我们的研究结果证明了保留 SA 的替代图在复杂统计分析中用于假设检验的效用,并强调了在大规模大脑组织研究中消除偶然关联中有意义的关系的必要性。我们演示了如何在基因集富集分析中使用保留 SA 的代理图来测试与脑图地形相关的感兴趣假设。我们的研究结果证明了保留 SA 的替代图在复杂统计分析中用于假设检验的效用,并强调了在大规模大脑组织研究中消除偶然关联中有意义的关系的必要性。
更新日期:2020-10-01
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