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A multivariate distance-based analytic framework for connectome-wide association studies

机译:基于多元距离的连接 - 宽协会研究的分析框架

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The identification of phenotypic associations in high-dimensional brain connectivity data represents the next frontier in the neuroimaging connectomics era. Exploration of brain-phenotype relationships remains limited by statistical approaches that are computationally intensive, depend on a priori hypotheses, or require stringent correction for multiple comparisons. Here, we propose a computationally efficient, data-driven technique for connectome-wide association studies (CWAS) that provides a comprehensive voxel-wise survey of brain-behavior relationships across the connectome; the approach identifies voxels whose whole-brain connectivity patterns vary significantly with a phenotypic variable. Using resting state fMRI data, we demonstrate the utility of our analytic framework by identifying significant connectivity-phenotype relationships for full-scale IQand assessing their overlap with existent neuroimaging findings, as synthesized by openly available automated meta-analysis (www.neurosynth.org). The results appeared to be robust to the removal of nuisance covariates (i.e., mean connectivity, global signal, and motion) and varying brain resolution (i.e., voxelwise results are highly similar to results using 800 parcellations). We show that CWAS findings can be used to guide subsequent seed-based correlation analyses. Finally, we demonstrate the applicability of the approach by examining CWAS for three additional datasets, each encompassing a distinct phenotypic variable: neurotypical development, Attention-Deficit/Hyperactivity Disorder diagnostic status, and L-DOPA pharmacological manipulation. For each phenotype, our approach to CWAS identified distinct connectome-wide association profiles, not previously attainable in a single study utilizing traditional univariate approaches. As a computationally efficient, extensible, and scalable method, our CWAS framework can accelerate the discovery of brain-behavior relationships in the connectome.
机译:高维脑连接数据中的表型关联的识别代表了神经影像元件时代的下一个前沿。脑表型关系的探索仍然受到计算密集的统计方法的限制,取决于先验假设,或者需要对多种比较进行严格校正。在这里,我们提出了一种用于连接 - 宽协会研究(CWAS)的计算有效,数据驱动技术,提供了在连接的脑行为关系中提供了全面的体素;该方法识别葡萄素,其全脑连接模式与表型变量显着变化。使用休息状态FMRI数据,我们通过识别全尺寸IQAND的显着连通性表型关系来展示我们的分析框架的实用性,该表型如何评估其与存在的神经影像调查结果重叠,如公开可用的自动化元分析(www.neurosynth.org)合成的。结果似乎对去除滋扰协变量(即,平均连通性,全局信号和运动)和变化的脑分辨率(即,VoxelWise的结果与使用800个局部的结果高度相似)。我们表明,CWAS发现可用于指导随后的基于种子的相关分析。最后,我们通过检查三个额外数据集的CWA来证明该方法的适用性,每个数据集包括不同的表型变量:神经型发育,注意力缺陷/多动障碍诊断状态和L-DOPA药理学操作。对于每种表型,我们对CWAS的方法确定了不同的连接内容,并且在使用传统单变量方法的单一研究中之前不能达到。作为一种计算上高效,可扩展和可扩展的方法,我们的CWAS框架可以加速在Connectome中发现脑行为关系的发现。

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