论文标题
使用功能连通性多元模式分析（FC-MVPA）的大脑范围连接组推理（FC-MVPA）
Brain-wide connectome inferences using functional connectivity MultiVariate Pattern Analyses (fc-MVPA)
论文作者
论文摘要
当前的功能磁共振成像技术能够解决数十亿个表征人类连接组的单个功能连接。试图通过减少的观测值和有限数量的受试者对许多措施进行有效推断的经典统计推理程序，除了最大的效果大小以外，任何受试者都可能严重降低了能力。该手稿讨论了FC-MVPA（功能连通性多元模式分析），这是多元模式分析技术在大脑范围连接组推断的背景下的新应用。提出了FC-MVPA背后的理论，并通过公开可用的静止状态数据集的示例来说明其几个关键概念，其中包括评估整个功能连接组中性别差异的示例分析。最后，使用蒙特卡洛模拟来证明该方法的有效性和灵敏度。除了提供强大的全脑推断外，FC-MVPA还提供了对跨受试者功能连通性异质性的有意义的表征。
Current functional Magnetic Resonance Imaging technology is able to resolve billions of individual functional connections characterizing the human connectome. Classical statistical inferential procedures attempting to make valid inferences across this many measures from a reduced set of observations and from a limited number of subjects can be severely underpowered for any but the largest effect sizes. This manuscript discusses fc-MVPA (functional connectivity Multivariate Pattern Analysis), a novel application of multivariate pattern analysis techniques in the context of brain-wide connectome inferences. The theory behind fc-MVPA is presented, and several of its key concepts are illustrated through examples from a publicly available resting state dataset, including an example analysis evaluating gender differences across the entire functional connectome. Last, Monte Carlo simulations are used to demonstrated this method's validity and sensitivity. In addition to offering powerful whole-brain inferences, fc-MVPA also provides a meaningful characterization of the heterogeneity in functional connectivity across subjects.
