Boukhdhir Amal, Zhang Yu, Mignotte Max, Bellec Pierre
Centre de recherche de l'institut universitaire de gériatrie de Montréal, Montréal, Québec, Canada.
Département de psychologie, Université de Montréal, Montréal, Québec, Canada.
Netw Neurosci. 2021 Feb 1;5(1):28-55. doi: 10.1162/netn_a_00168. eCollection 2021.
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.
数据驱动的脑区划分广泛应用于探索大脑的功能组织，也用于降低功能磁共振成像（fMRI）数据的高维度。尽管文献中提出了众多方法，但即使采集时间很长，个体受试者层面的功能性脑区划分也没有高度的可重复性。一些脑区也比其他脑区更难进行划分，联合异模态皮层是最具挑战性的。经典脑区划分的一个重要局限性在于它们是静态的，也就是说，它们忽略了脑网络的动态重构。在本文中，我们提出了一种新方法来识别脑区划分的动态状态，我们假设这将比静态脑区划分方法提高可重复性。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）时间窗口重新分组为具有高度相似种子脑区的“状态”。我们将午夜扫描俱乐部样本的个体时间序列分成两个独立的2.5小时数据集（测试集和重测集）。我们发现，称为稳定性图的平均状态内脑区划分在测试集和重测集之间具有高度的可重复性（在许多情况下，测试 - 重测空间相关性超过0.9）且具有个体特异性（平均指纹识别准确率超过70%）。与我们的假设一致，异模态皮层（后扣带回和前扣带回）中的种子显示出比单模态（视觉）皮层更丰富的状态库。综上所述，我们的结果表明，静态功能脑区划分错误地平均了脑区划分中定义明确且不同的动态状态。这项工作呼吁重新审视基于静态脑区划分的先前方法，这包括大多数已发表的fMRI数据网络分析。因此，我们的方法可能会影响研究人员对健康和疾病中脑网络之间丰富相互作用的建模方式。
