Unraveling reproducible dynamic states of individual brain functional parcellation.
Unraveling reproducible dynamic states of individual brain functional parcellation.
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DOI：
10.1162/netn_a_00168
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Bellec P
中科院分区：
文献类型：
--
作者：
Boukhdhir A;Zhang Y;Mignotte M;Bellec P
数据驱动的分组被广泛用于探索大脑的功能组织，也用于降低fMRI数据的高维数。尽管文献中提出了一系列的方法，但在个体受试者的水平上，即使是长时间的获取，功能性脑包裹也不能高度重现。一些大脑区域也比其他区域更难分割，其中关联异模态皮层是最具挑战性的。经典分组的一个重要限制是它们是静态的，也就是说，它们忽略了大脑网络的动态重新配置。在本文中，我们提出了一种新的方法来识别动态的包封状态，我们假设这将提高可重复性比静态包封方法。对于大脑中的一系列种子体素，我们应用聚类分析将短（3分钟）时间窗口重新组合为具有高度相似种子包的“状态”。我们将午夜扫描俱乐部样本的单独时间序列分成两个独立的2.5小时组（测试和重新测试）。我们发现，被称为稳定性图的州内平均分组在测试和重新测试之间具有高度可重复性（在许多情况下超过0.9测试-重新测试空间相关性）和主题特异性（指纹准确性平均超过70%）。与我们的假设一致，异模皮质（后扣带和前扣带）中的种子比单模皮质（视觉）显示出更丰富的状态库。综上所述，我们的结果表明，静态功能包裹是不正确的平均定义良好的和不同的动态状态的大脑包裹。这项工作要求重新审视以前的基于静态分组的方法，其中包括大多数已发表的fMRI数据的网络分析。因此，我们的方法可能会影响研究人员如何模拟健康和疾病中大脑网络之间丰富的相互作用。在过去的二十年里，功能性脑分割一直是一个非常活跃的研究课题，但迄今为止还没有证据表明在个体水平上可重复的结果（参见Gordon， Laumann， Gilmore等人的图2），使用40分钟或更长时间的数据，Dice系数稳定在0.7左右。在本文中，我们展示了使用短（3分钟）的时间窗可以观察到高度可重复性的脑包。在一个大脑区域中可以观察到不同的可复制的包块模式或状态，而这些模式彼此之间只有很少的重叠。我们使用午夜脑扫描数据集仔细量化了这些个体的动态状态，每个受试者都有5小时的功能性MRI。我们的结果表明，静态功能分割是不正确地平均定义良好的和不同的动态状态。这给任何基于静态地图集的工作带来了重要的警告，这是目前所谓的fMRI数据网络分析的主要方法。
Data-driven parcellations are widely used for exploring the functional organization of the brain, and also for reducing the high dimensionality of fMRI data. Despite the flurry of methods proposed in the literature, functional brain parcellations are not highly reproducible at the level of individual subjects, even with very long acquisitions. Some brain areas are also more difficult to parcellate than others, with association heteromodal cortices being the most challenging. An important limitation of classical parcellations is that they are static, that is, they neglect dynamic reconfigurations of brain networks. In this paper, we proposed a new method to identify dynamic states of parcellations, which we hypothesized would improve reproducibility over static parcellation approaches. For a series of seed voxels in the brain, we applied a cluster analysis to regroup short (3 min) time windows into “states” with highly similar seed parcels. We split individual time series of the Midnight scan club sample into two independent sets of 2.5 hr (test and retest). We found that average within-state parcellations, called stability maps, were highly reproducible (over 0.9 test-retest spatial correlation in many instances) and subject specific (fingerprinting accuracy over 70% on average) between test and retest. Consistent with our hypothesis, seeds in heteromodal cortices (posterior and anterior cingulate) showed a richer repertoire of states than unimodal (visual) cortex. Taken together, our results indicate that static functional parcellations are incorrectly averaging well-defined and distinct dynamic states of brain parcellations. This work calls to revisit previous methods based on static parcellations, which includes the majority of published network analyses of fMRI data. Our method may, thus, impact how researchers model the rich interactions between brain networks in health and disease. Functional brain parcellation has been a very active topic of investigation for the past two decades, yet there is no evidence to date of reproducible results at the individual level—see for example Figure 2 in Gordon, Laumann, Gilmore, et al., with a Dice coefficient plateauing around 0.7 using 40 min or more of data. In this paper, we show that highly reproducible brain parcels can be observed using short (3 min) time windows. Different modes—or states—of reproducible parcellations can be observed in a single brain region, and these modes have only little overlap with each other. We carefully quantified these individual dynamic states of parcellation using the Midnight Brain Scan dataset, featuring 5 hr of functional MRI per subject. Our results indicate that static functional parcellation are incorrectly averaging well-defined and distinct dynamic states. This brings important caution for any work based on static atlases, which is the dominant approach currently in so-called network analysis of fMRI data.
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影响因子：
5.7
作者：
Betzel RF;Bertolero MA;Gordon EM;Gratton C;Dosenbach NUF;Bassett DS
通讯作者：
Bassett DS
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
4.8
作者：
Donnelly-Kehoe, Patricio;Saenger, Victor M.;Deco, Gustavo
通讯作者：
Deco, Gustavo
影响因子：
3.5
作者：
Bellec P;Lavoie-Courchesne S;Dickinson P;Lerch JP;Zijdenbos AP;Evans AC
通讯作者：
Evans AC