Impact of the resolution of brain parcels on connectome-wide association studies in fMRI
Impact of the resolution of brain parcels on connectome-wide association studies in fMRI
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DOI：
10.1016/j.neuroimage.2015.07.071
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发表时间：
2015-12-01
期刊：
影响因子：
5.7
通讯作者：
Orban, Pierre
中科院分区：
文献类型：
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作者：
Bellec, Pierre;Benhajali, Yassine;Orban, Pierre
功能性磁共振成像的一个最新趋势是测试临床疾病与选定大脑包裹之间的每一种可能的联系。我们研究了从大规模网络到局部区域的功能性大脑包裹的分辨率对连接体的大规模单变量一般线性模型（GLM）的影响。对于独立采取的每个分辨率，Benjamini-Hochberg程序控制在标称水平的真实模拟的错误发现率（FDR）。但是，与分辨率内的FDR相比，所有分辨率下合并的测试的FDR可能会增加。这种膨胀在没有或弱影响的情况下是严重的，但在强影响下则可以忽略不计。因此，我们开发了一个综合测试，以确定所有决议中真正发现的总体存在。虽然不能保证控制不同分辨率的FDR，但综合检验可用于描述性分析分辨率对GLM分析的影响，以补充预定义单一分辨率下的主要分析。在具有显著综合测试（精神分裂症、先天性失明、运动练习）的三个真实的数据集上，在低于50的低分辨率下获得了显著更高的发现率，这与显示在这样的分辨率下灵敏度增加的模拟一致。这种发现率的提高是以定位效果的能力较低为代价的，因为低分辨率的包裹将许多不同的大脑区域合并在一起。然而，对于30个或更多包裹，统计效应图在生物学上是合理的，并且在不同分辨率之间非常一致。这些结果表明，分辨率是具有FDR控制的GLM连接体分析的关键参数，并且具有30至50个包裹的功能性脑包裹可以导致在许多情况下具有良好灵敏度的全连接体效应的准确总结。(C)2015 Elsevier Inc. All rights reserved.
A recent trend in functional magnetic resonance imaging is to test for association of clinical disorders with every possible connection between selected brain parcels. We investigated the impact of the resolution of functional brain parcels, ranging from large-scale networks to local regions, on a mass univariate general linear model (GLM) of connectomes. For each resolution taken independently, the Benjamini-Hochberg procedure controlled the false-discovery rate (FDR) at nominal level on realistic simulations. However, the FDR for tests pooled across all resolutions could be inflated compared to the FDR within resolution. This inflation was severe in the presence of no or weak effects, but became negligible for strong effects. We thus developed an omnibus test to establish the overall presence of true discoveries across all resolutions. Although not a guarantee to control the FDR across resolutions, the omnibus test may be used for descriptive analysis of the impact of resolution on a GLM analysis, in complement to a primary analysis at a predefined single resolution. On three real datasets with significant omnibus test (schizophrenia, congenital blindness, motor practice), markedly higher rate of discovery were obtained at low resolutions, below 50, in line with simulations showing increase in sensitivity at such resolutions. This increase in discovery rate came at the cost of a lower ability to localize effects, as low resolution parcels merged many different brain regions together. However, with 30 or more parcels, the statistical effect maps were biologically plausible and very consistent across resolutions. These results show that resolution is a key parameter for GLM-connectome analysis with FDR control, and that a functional brain parcellation with 30 to 50 parcels may lead to an accurate summary of full connectome effects with good sensitivity in many situations. (C) 2015 Elsevier Inc. All rights reserved.