A multivariate distance-based analytic framework for connectome-wide association studies.
A multivariate distance-based analytic framework for connectome-wide association studies.
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DOI：
10.1016/j.neuroimage.2014.02.024
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发表时间：
2014-06
期刊：
影响因子：
5.7
通讯作者：
Milham MP
中科院分区：
文献类型：
--
作者：
Shehzad Z;Kelly C;Reiss PT;Cameron Craddock R;Emerson JW;McMahon K;Copland DA;Castellanos FX;Milham MP
在高维大脑连接数据中识别表型关联代表了神经成像连接组学时代的下一个前沿。脑表型关系的探索仍然受到计算密集型的统计方法的限制，依赖于先验假设，或需要严格的多重比较校正。在这里，我们提出了一种计算效率高，数据驱动的技术，用于全连接体关联研究（CWAS），提供了一个全面的体素明智的调查大脑行为的关系，整个连接体的方法确定体素的全脑连接模式显着不同的表型变量。使用静息状态的功能磁共振成像数据，我们证明了我们的分析框架的实用性，通过识别全面的智商显着的连接性-表型关系，并评估其与现有的神经影像学研究结果的重叠，通过公开的自动化荟萃分析（www.neurosynth.org）合成。结果似乎对去除干扰协变量（即，平均连接性、全局信号和运动）和变化的大脑分辨率（即，体素方式的结果与使用800个包裹的结果高度相似）。我们表明，CWAS的研究结果可用于指导后续的种子相关性分析。最后，我们通过检查CWAS的三个额外的数据集，每个数据集包含一个不同的表型变量：神经发育，注意力缺陷/多动障碍诊断状态，和左旋多巴药理学操作的方法的适用性。对于每种表型，我们的CWAS方法确定了不同的连接组范围内的关联概况，这是以前在利用传统单变量方法的单一研究中无法实现的。作为一种计算效率高、可扩展和可扩展的方法，我们的CWAS框架可以加速连接体中大脑行为关系的发现。
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.
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影响因子：
48
作者：
Craddock, R. Cameron;Jbabdi, Saad;Yan, Chao-Gan;Vogelstein, Joshua T.;Castellanos, F. Xavier;Di Martino, Adriana;Kelly, Clare;Heberlein, Keith;Colcombe, Stan;Milham, Michael P.
通讯作者：
Milham, Michael P.
DOI：
10.1146/annurev-clinpsy-040510-143934
发表时间：
2011-01-01
影响因子：
18.4
作者：
Bullmore, Edward T.;Bassett, Danielle S.
通讯作者：
Bassett, Danielle S.
影响因子：
10.6
作者：
Chabernaud, Camille;Mennes, Maarten;Kelly, Clare;Nooner, Kate;Di Martino, Adriana;Castellanos, F. Xavier;Milham, Michael P.
通讯作者：
Milham, Michael P.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
DOI：
10.1073/pnas.0601417103
发表时间：
2006-09-12
影响因子：
11.1
作者：
Damoiseaux, J. S.;Rombouts, S. A. R. B.;Beckmann, C. F.
通讯作者：
Beckmann, C. F.