Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches.
Characterizing individual differences in functional connectivity using dual-regression and seed-based approaches.
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DOI：
10.1016/j.neuroimage.2014.03.042
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发表时间：
2014-07-15
期刊：
影响因子：
5.7
通讯作者：
Huettel SA
中科院分区：
文献类型：
--
作者：
Smith DV;Utevsky AV;Bland AR;Clement N;Clithero JA;Harsch AE;McKell Carter R;Huettel SA
神经科学的一个中心挑战在于将个体间的变异性与特定大脑区域的功能特性联系起来。然而，不同大脑区域之间的连接模式存在相当大的变异性，可能会产生可靠的群体差异。使用性别差异作为激励的例子，我们检查了两个独立的休息状态数据集，总共包含188名人类参与者。利用概率空间独立分量分析(ICA)将两个数据集分解成静息状态网络(RSN)。我们使用双回归分析估计了与这些网络的体素功能连通性，该分析描述了每个网络的参与者水平的时空动态，同时控制(通过多元回归)其他网络和可变性来源的影响。我们发现，男性和女性与多个RSN表现出不同的连接模式，包括视觉和听觉网络以及右侧额顶网络。这些结果在两个数据集中重复，不能用头部运动、数据质量、脑体积、皮质醇水平或睾酮水平的差异来解释。重要的是，我们还证明了双回归功能连接性在检测个体间变异性方面比传统的基于种子的功能连接性方法更好。我们的发现描述了男性和女性之间强健但经常被忽视的神经差异，指出了在神经科学研究中对个体差异进行性别控制的必要性。此外，我们的结果强调了使用基于网络的模型来研究功能连接的可变性的重要性。
A central challenge for neuroscience lies in relating inter-individual variability to the functional properties of specific brain regions. Yet, considerable variability exists in the connectivity patterns between different brain areas, potentially producing reliable group differences. Using sex differences as a motivating example, we examined two separate resting-state datasets comprising a total of 188 human participants. Both datasets were decomposed into resting-state networks (RSNs) using a probabilistic spatial independent components analysis (ICA). We estimated voxelwise functional connectivity with these networks using a dual-regression analysis, which characterizes the participant-level spatiotemporal dynamics of each network while controlling for (via multiple regression) the influence of other networks and sources of variability. We found that males and females exhibit distinct patterns of connectivity with multiple RSNs, including both visual and auditory networks and the right frontal-parietal network. These results replicated across both datasets and were not explained by differences in head motion, data quality, brain volume, cortisol levels, or testosterone levels. Importantly, we also demonstrate that dual-regression functional connectivity is better at detecting inter-individual variability than traditional seed-based functional connectivity approaches. Our findings characterize robust—yet frequently ignored—neural differences between males and females, pointing to the necessity of controlling for sex in neuroscience studies of individual differences. Moreover, our results highlight the importance of employing network-based models to study variability in functional connectivity.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
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DOI：
10.1073/pnas.162356599
发表时间：
2002-08-06
影响因子：
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