Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples
Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples
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DOI：
10.1016/j.neuroimage.2012.06.026
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发表时间：
2012-10-15
期刊：
影响因子：
5.7
通讯作者：
Molenaar, Peter C. M.
中科院分区：
文献类型：
--
作者：
Gates, Kathleen M.;Molenaar, Peter C. M.
在最好的情况下，连接图可以为研究人员提供巨大的洞察力，让他们了解人类大脑的不同空间区域是如何在大脑处理过程中协调活动的。Smith和他的同事最近(2011年)对估计连接图的方法进行的一项调查表明，那些试图确定ROI之间影响方向的方法很少提供可靠的结果。另一个日益受到关注的问题是连通性地图的异构性。大多数组级方法要求数据来自同类样本，如果个体的连接图在样本中不同(很可能是这种情况)，则当前方法可能会产生误导性的结果。因此，由于不能准确地向研究人员提供信息，个人或群体层面的有效连通性所产生的地图的效用就会降低。本文介绍了一种新的功能磁共振成像研究人员的估计技术--组迭代多模型估计(GIMME)，它表明使用跨个体的信息有助于恢复Smith和他的同事(2011)使用的感兴趣区之间存在的联系以及影响的方向。使用异质的内部数据，我们证明了Gimme通过获得可靠的组和个人结构，提供了相对于当前方法的独特改进，即使数据在组成组的个人之间是高度异质的。Gimme的另一个好处是，它使用来自静止状态、块或事件相关设计的数据，同样可以很好地获得可靠的连接图估计。Gimme为研究人员提供了一个强大、灵活的工具，用于在团体和个人层面识别有向连接图。(C)2012 Elsevier Inc.保留所有权利。
At its best, connectivity mapping can offer researchers great insight into how spatially disparate regions of the human brain coordinate activity during brain processing. A recent investigation conducted by Smith and colleagues (2011) on methods for estimating connectivity maps suggested that those which attempt to ascertain the direction of influence among ROIs rarely provide reliable results. Another problem gaining increasing attention is heterogeneity in connectivity maps. Most group-level methods require that the data come from homogeneous samples, and misleading findings may arise from current methods if the connectivity maps for individuals vary across the sample (which is likely the case). The utility of maps resulting from effective connectivity on the individual or group levels is thus diminished because they do not accurately inform researchers. The present paper introduces a novel estimation technique for fMRI researchers, Group Iterative Multiple Model Estimation (GIMME), which demonstrates that using information across individuals assists in the recovery of the existence of connections among ROIs used by Smith and colleagues (2011) and the direction of the influence. Using heterogeneous in-house data, we demonstrate that GIMME offers a unique improvement over current approaches by arriving at reliable group and individual structures even when the data are highly heterogeneous across individuals comprising the group. An added benefit of GIMME is that it obtains reliable connectivity map estimates equally well using the data from resting state, block, or event-related designs. GIMME provides researchers with a powerful, flexible tool for identifying directed connectivity maps at the group and individual levels. (C) 2012 Elsevier Inc. All rights reserved.