Department of Psychology, Yale University, New Haven, CT, USA; Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA; Center for the Developing Brain, Child Mind Institute, New York, NY, USA.
Phyllis Green and Randolph Cōwen Institute for Pediatric Neuroscience, Department of Child and Adolescent Psychiatry, New York University, New York, NY, USA.
Neuroimage. 2014 Jun;93 Pt 1(0 1):74-94. doi: 10.1016/j.neuroimage.2014.02.024. Epub 2014 Feb 28.
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 IQ and 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.
在高维脑连接数据中识别表型关联代表了神经影像学连接组学时代的下一个前沿。对脑-表型关系的探索仍然受到统计方法的限制，这些方法计算密集、依赖先验假设或需要对多重比较进行严格校正。在这里，我们提出了一种计算效率高、数据驱动的连接组学全脑关联研究 (CWAS) 技术，该技术对连接组中大脑-行为关系进行了全面的体素级调查; 该方法确定了整个大脑连接模式与表型变量显著变化的体素。我们使用静息态 fMRI 数据，通过识别全尺度智商的显著连通性-表型关系并评估其与现有的神经影像学发现的重叠，证明了我们分析框架的有效性，这些发现是通过公开可用的自动元分析 (www.neurosynth.org) 综合得到的。结果似乎对去除干扰协变量 (即平均连通性、全局信号和运动) 和变化的大脑分辨率 (即体素结果与使用 800 个分割的结果高度相似) 具有鲁棒性。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 CWAS 来展示该方法的适用性，每个数据集都包含一个独特的表型变量：神经典型发育、注意缺陷/多动障碍诊断状态和 L-DOPA 药物干预。对于每种表型，我们的 CWAS 方法识别出了独特的全脑关联图谱，这是以前使用传统单变量方法在单个研究中无法实现的。作为一种计算效率高、可扩展和可扩展的方法，我们的 CWAS 框架可以加速连接组中大脑-行为关系的发现。
