Notip: Non-parametric true discovery proportion control for brain imaging.

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 Rosenblatt et al. (2018) 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.

Time-frequency analysis of heart rate variability reveals cardiolocomotor coupling during dynamic cycling exercise in humans.

To test the hypothesis that cycling exercise modulates heart rate variability (HRV), we applied a short-time Fourier transform on the electrocardiogram of subjects performing a maximal graded cycling test. A pedaling frequency component (PFC) in HRV was continuously observed over the time course of the exercise test and extracted from R-R interval series obtained from 15 healthy subjects with a heterogeneous physical fitness, exercising at three different pedaling frequency (n = 5): 70, 80, and 90 rpm. From 30 to 50% of the maximal power output (P(max)), in the 90 rpm group, spectral aliasing caused PFC to overlap with the respiratory sinus arrhythmia (RSA) band, significantly overestimating the PFC amplitude (A(PFC)). In the meantime, A(PFC) did not increase significantly from its minimal values in the 70 rpm ( approximately 1.26 ms) and 80 rpm ( approximately 1.20 ms) groups. Then, from 60 to 100% maximal power output (P(max)), workload increase caused a significant approximately 2.8-, approximately 3.3-, and approximately 3.4-fold increase in A(PFC) in the 70, 80, and 90 rpm groups, respectively, with no significant difference between groups. At peak exercise, A(PFC) accounted for approximately 43, approximately 39, and approximately 49% of the total HRV in the 70, 80, and 90 rpm groups, respectively. Our findings indicate that cycling continuously modulates the cardiac chronotropic response to exercise, inducing a new component in HRV, and that workload increase during intense exercise further accentuates this cardiolocomotor coupling. Moreover, because PFC and RSA overlapped at low workloads, methodological care should be taken in future studies aiming to quantify RSA as an index of parasympathetic activity.