Relating behavioral and neuroimaging measures is essential to understanding human brain function. Often, this is achieved by computing a correlation between behavioral measures, e.g., reaction times, and neurophysiological recordings, e.g., prestimulus EEG alpha-power, on a single-trial-basis. This approach treats individual trials as independent measurements and ignores the fact that data are acquired in a temporal order. It has already been shown that behavioral measures as well as neurophysiological recordings display power-law dynamics, which implies that trials are not in fact independent. Critically, computing the correlation coefficient between two measures exhibiting long-range temporal dependencies may introduce spurious correlations, thus leading to erroneous conclusions about the relationship between brain activity and behavioral measures. Here, we address data-analytic pitfalls which may arise when long-range temporal dependencies in neural as well as behavioral measures are ignored. We quantify the influence of temporal dependencies of neural and behavioral measures on the observed correlations through simulations. Results are further supported in analysis of real EEG data recorded in a simple reaction time task, where the aim is to predict the latency of responses on the basis of prestimulus alpha oscillations. We show that it is possible to "predict" reaction times from one subject on the basis of EEG activity recorded in another subject simply owing to the fact that both measures display power-law dynamics. The same is true when correlating EEG activity obtained from different subjects. A surrogate-data procedure is described which correctly tests for the presence of correlation while controlling for the effect of power-law dynamics.
In the last decade, many studies have used automated processes to analyze magnetic resonance imaging (MRI) data such as cortical thickness, which is one indicator of neuronal health. Due to the convenience of image processing software (e.g., FreeSurfer), standard practice is to rely on automated results without performing visual inspection of intermediate processing. In this work, structural MRIs of 40 healthy controls who were scanned twice were used to determine the test–retest reliability of FreeSurfer-derived cortical measures in four groups of subjects—those 25 that passed visual inspection (approved), those 15 that failed visual inspection (disapproved), a combined group, and a subset of 10 subjects (Travel) whose test and retest scans occurred at different sites. Test–retest correlation (TRC), intraclass correlation coefficient (ICC), and percent difference (PD) were used to measure the reliability in the Destrieux and Desikan–Killiany (DK) atlases. In the approved subjects, reliability of cortical thickness/surface area/volume (DK atlas only) were: TRC (0.82/0.88/0.88), ICC (0.81/0.87/0.88), PD (0.86/1.19/1.39), which represent a significant improvement over these measures when disapproved subjects are included. Travel subjects’ results show that cortical thickness reliability is more sensitive to site differences than the cortical surface area and volume. To determine the effect of visual inspection on sample size required for studies of MRI-derived cortical thickness, the number of subjects required to show group differences was calculated. Significant differences observed across imaging sites, between visually approved/disapproved subjects, and across regions with different sizes suggest that these measures should be used with caution.