During the last several years, the focus of research on resting-state functional magnetic resonance imaging (fMRI) has shifted from the analysis of functional connectivity averaged over the duration of scanning sessions to the analysis of changes of functional connectivity within sessions. Although several studies have reported the presence of dynamic functional connectivity (dFC), statistical assessment of the results is not always carried out in a sound way and, in some studies, is even omitted. In this study, we explain why appropriate statistical tests are needed to detect dFC, we describe how they can be carried out and how to assess the performance of dFC measures, and we illustrate the methodology using spontaneous blood-oxygen level-dependent (BOLD) fMRI recordings of macaque monkeys under general anesthesia and in human subjects under resting-state conditions. We mainly focus on sliding-window correlations since these are most widely used in assessing dFC, but also consider a recently proposed non-linear measure. The simulations and methodology, however, are general and can be applied to any measure. The results are twofold. First, through simulations, we show that in typical resting-state sessions of 10 min, it is almost impossible to detect dFC using sliding-window correlations. This prediction is validated by both the macaque and the human data: in none of the individual recording sessions was evidence for dFC found. Second, detection power can be considerably increased by session- or subject-averaging of the measures. In doing so, we found that most of the functional connections are in fact dynamic. With this study, we hope to raise awareness of the statistical pitfalls in the assessment of dFC and how they can be avoided by using appropriate statistical methods.

Despite recent advancements in MR imaging, non-invasive mapping of myelin in the brain still remains an open issue. Here we attempted to provide a potential solution. Specifically, we developed a processing workflow based on T1-w and T2-w MR data to generate an optimized myelin enhanced contrast image. The workflow allows whole brain mapping using the T1-w/T2-w technique, which was originally introduced as a non-invasive method for assessing cortical myelin content. The hallmark of our approach is a retrospective calibration algorithm, applied to bias-corrected T1-w and T2-w images, that relies on image intensities outside the brain. This permits standardizing the intensity histogram of the ratio image, thereby allowing for across-subject statistical analyses. Quantitative comparisons of image histograms within and across different datasets confirmed the effectiveness of our normalization procedure. Not only did the calibrated T1-w/T2-w images exhibit a comparable intensity range, but also the shape of the intensity histograms was largely corresponding. We also assessed the reliability and specificity of the ratio image compared to other MR-based techniques, such as magnetization transfer ratio (MTR), fractional anisotropy (FA), and fluid-attenuated inversion recovery (FLAIR). With respect to these other techniques, T1-w/T2-w had consistently high values, as well as low inter-subject variability, in brain structures where myelin is most abundant. Overall, our results suggested that the T1-w/T2-w technique may be a valid tool supporting the non-invasive mapping of myelin in the brain. Therefore, it might find important applications in the study of brain development, aging and disease.

The aim of this study was to examine the concurrent validity of 2 portable systems for vertical jump (VJ) assessment under field conditions. The VJ flight times assessed using an optical mat (Optojump) and an accelerometer-based (Myotest) system were compared with that of a force platform. The flight times recorded during a countermovement jump (CMJ) were collected from 20 rugby players (n = 86 jumps) concurrently using the 3 tracking systems. Significant bias between the Force platform and either the Optojump (bias = 0.006 ± 0.007; 95% confidence interval [CI] 0.004-0.007 seconds) and Myotest (bias = -0.031 ± 0.021; 95% CI 0.035 to -0.026s; p < 0.0001) occurred. A nearly perfect correlation was found between force platform and Optojump (r = 0.99; 95% CI 0.098-0.99; p < 0.0001). Force platform and Myotest (r = 0.89; 95% CI 0.084-0.93; p < 0.0001) flight times showed very large association. Difference between Optojump and Myotest systems was significant (-0.036 ± 0.021 seconds; 95% CI -0.041 to -0.032; p < 0.0001), which results in Myotest mean flight time being approximately 7.2% longer than the Optojump flight time. The association between Optojump and Myotest was nearly perfect (r = 0.91, 95% CI 0.86-0.94; p < 0.0001). This study showed that the Optojump and Myotest systems possess convergent validity and can be successfully used under field conditions to assess VJ while performing a CMJ. However, caution should be exercised when interpreting data obtained from different portable systems for field measurement.