Kim Junghi, Pan Wei
Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.
Neuroimage Clin. 2015 Oct 22;9:625-39. doi: 10.1016/j.nicl.2015.10.004. eCollection 2015.
Resting-state functional magnetic resonance imaging (rs-fMRI) and other technologies have been offering evidence and insights showing that altered brain functional networks are associated with neurological illnesses such as Alzheimer's disease. Exploring brain networks of clinical populations compared to those of controls would be a key inquiry to reveal underlying neurological processes related to such illnesses. For such a purpose, group-level inference is a necessary first step in order to establish whether there are any genuinely disrupted brain subnetworks. Such an analysis is also challenging due to the high dimensionality of the parameters in a network model and high noise levels in neuroimaging data. We are still in the early stage of method development as highlighted by Varoquaux and Craddock (2013) that "there is currently no unique solution, but a spectrum of related methods and analytical strategies" to learn and compare brain connectivity. In practice the important issue of how to choose several critical parameters in estimating a network, such as what association measure to use and what is the sparsity of the estimated network, has not been carefully addressed, largely because the answers are unknown yet. For example, even though the choice of tuning parameters in model estimation has been extensively discussed in the literature, as to be shown here, an optimal choice of a parameter for network estimation may not be optimal in the current context of hypothesis testing. Arbitrarily choosing or mis-specifying such parameters may lead to extremely low-powered tests. Here we develop highly adaptive tests to detect group differences in brain connectivity while accounting for unknown optimal choices of some tuning parameters. The proposed tests combine statistical evidence against a null hypothesis from multiple sources across a range of plausible tuning parameter values reflecting uncertainty with the unknown truth. These highly adaptive tests are not only easy to use, but also high-powered robustly across various scenarios. The usage and advantages of these novel tests are demonstrated on an Alzheimer's disease dataset and simulated data.
静息态功能磁共振成像（rs-fMRI）及其他技术一直在提供证据和见解，表明大脑功能网络的改变与诸如阿尔茨海默病等神经疾病相关。将临床人群的大脑网络与对照组的进行比较，将是揭示与此类疾病相关的潜在神经过程的关键探究。出于这一目的，为了确定是否存在真正受到破坏的脑子网，组水平推断是必要的第一步。由于网络模型中参数的高维度以及神经成像数据中的高噪声水平，这种分析也具有挑战性。正如瓦罗夸克斯和克拉多克（2013年）所强调的，我们仍处于方法开发的早期阶段，即“目前没有唯一的解决方案，而是一系列相关的方法和分析策略”来学习和比较大脑连接性。在实践中，如何在估计网络时选择几个关键参数这一重要问题，例如使用何种关联度量以及估计网络的稀疏度是多少，尚未得到仔细解决，很大程度上是因为答案仍然未知。例如，尽管在文献中已经广泛讨论了模型估计中调整参数的选择，但如下文所示，在当前假设检验的背景下，用于网络估计的参数的最优选择可能并非最优。随意选择或错误指定这些参数可能导致检验效能极低。在此，我们开发了高度自适应的检验方法，以检测大脑连接性的组间差异，同时考虑到一些调整参数的未知最优选择。所提出的检验方法将来自多个来源、跨越一系列反映不确定性的合理调整参数值的针对原假设的统计证据与未知真值相结合。这些高度自适应的检验方法不仅易于使用，而且在各种情况下都具有强大的稳健性。这些新颖检验方法的用法和优势在一个阿尔茨海默病数据集和模拟数据上得到了展示。
