Cluster-level inference procedures are widely used for brain mapping. These methods compare the size of clusters obtained by thresholding brain maps to an upper bound under the global null hypothesis, computed using Random Field Theory or permutations. However, the guarantees obtained by this type of inference - i.e. at least one voxel is truly activated in the cluster - are not informative with regards to the strength of the signal therein. There is thus a need for methods to assess the amount of signal within clusters; yet such methods have to take into account that clusters are defined based on the data, which creates circularity in the inference scheme. This has motivated the use of post hoc estimates that allow statistically valid estimation of the proportion of activated voxels in clusters. In the context of fMRI data, the All-Resolutions Inference framework introduced in [25] provides post hoc estimates of the proportion of activated voxels. However, this method relies on parametric threshold families, which results in conservative inference. In this paper, we leverage randomization methods to adapt to data characteristics and obtain tighter false discovery control. We obtain Notip, for Non-parametric True Discovery Proportion control: a powerful, non-parametric method that yields statistically valid guarantees on the proportion of activated voxels in data-derived clusters. Numerical experiments demonstrate substantial gains in number of detections compared with state-of-the-art methods on 36 fMRI datasets. The conditions under which the proposed method brings benefits are also discussed.
翻译:集束级推断程序被广泛用于大脑绘图。这些方法将通过门槛大脑地图获得的组群规模与全球无效假设的上限加以比较,使用随机字段理论或变位法计算。然而,这种类型的推断获得的保证----即至少一个 voxel 真正在集中激活----对于其中信号的强度并不具有知情性;因此,需要采用方法评估群群体内的信号数量;但这类方法必须考虑到根据数据界定的组群,从而在推断方案中产生循环性。这促使使用后期估计,以便能够对组群中活性狐狸的比例进行统计上有效的估计。在FMRI数据方面,在[25] 中引入的所有分辨率推断框架提供了对其中信号的强度的事后估计。然而,这一方法依赖于分级阈值的临界值,从而得出保守的推断值。在本文中,我们利用随机化方法来适应数据特性并获得更精确的误发现控制。我们获得的后期估算估计数估计数,在比例分析方法下,我们还获取了可靠的数据排序下分析方法。