JIVE integration of imaging and behavioral data
JIVE integration of imaging and behavioral data
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JIVE 整合影像和行为数据
DOI：
10.1016/j.neuroimage.2017.02.072
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发表时间：
2017
期刊：
影响因子：
5.7
通讯作者：
Marron, J.S.
中科院分区：
文献类型：
--
作者：
Yu, Qunqun;Risk, Benjamin B.;Zhang, Kai;Marron, J.S.
神经科学的一个主要目标是了解人类行为背后的神经通路。我们将最近开发的联合和个体变异解释（JIVE）方法介绍给神经科学界，以同时分析来自人类连接组项目的成像和行为数据。最近的计算和理论改进的JIVE方法的动机，我们同时探讨成像和行为数据之间和内部的联合和个体的变化。特别是，我们证明了JIVE是一个有效的和高效的方法整合任务功能磁共振成像和行为变量使用三个例子：一个例子，任务变化是强大的，一个任务变化是弱的和一个参考情况下的行为是不直接相关的图像。提供这些示例以可视化在联合变化中发现的不同水平的信号，包括图像数据中的工作记忆区域以及来自任务中行为变量的准确性和响应时间。联合分析提供了传统的单块分解方法（如奇异值分解）无法提供的见解。此外，联合变异估计JIVE似乎更清楚地确定工作记忆区域比偏最小二乘法（PLS），而典型相关分析（CCA）给出了严重过拟合的结果。JIVE中的个体差异捕获了与行为无关的信号，例如空间上均匀的背景激活和默认模式网络中的激活。这种个体差异所揭示的信息在CCA和PLS等传统方法中没有得到检验。我们建议JIVE可以作为PLS和CCA的替代方案，以改善对两个或多个数据集共同信号的估计，并揭示每个数据集独有的信号的新见解。
A major goal in neuroscience is to understand the neural pathways underlying human behavior. We introduce the recently developed Joint and Individual Variation Explained (JIVE) method to the neuroscience community to simultaneously analyze imaging and behavioral data from the Human Connectome Project. Motivated by recent computational and theoretical improvements in the JIVE approach, we simultaneously explore the joint and individual variation between and within imaging and behavioral data. In particular, we demonstrate that JIVE is an effective and efficient approach for integrating task fMRI and behavioral variables using three examples: one example where task variation is strong, one where task variation is weak and a reference case where the behavior is not directly related to the image. These examples are provided to visualize the different levels of signal found in the joint variation including working memory regions in the image data and accuracy and response time from the in-task behavioral variables. Joint analysis provides insights not available from conventional single block decomposition methods such as Singular Value Decomposition. Additionally, the joint variation estimated by JIVE appears to more clearly identify the working memory regions than Partial Least Squares (PLS), while Canonical Correlation Analysis (CCA) gives grossly overfit results. The individual variation in JIVE captures the behavior unrelated signals such as a background activation that is spatially homogeneous and activation in the default mode network. The information revealed by this individual variation is not examined in traditional methods such as CCA and PLS. We suggest that JIVE can be used as an alternative to PLS and CCA to improve estimation of the signal common to two or more datasets and reveal novel insights into the signal unique to each dataset.
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影响因子：
5.7
作者：
Van Essen, David C.;Smith, Stephen M.;Barch, Deanna M.;Behrens, Timothy E. J.;Yacoub, Essa;Ugurbil, Kamil
通讯作者：
Ugurbil, Kamil
DOI：
10.1214/12-aoas597
发表时间：
2013-03-01
期刊：
The annals of applied statistics
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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影响因子：
2.7
作者：
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通讯作者：
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影响因子：
25
作者：
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通讯作者：
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