RT Journal Article
SR Electronic
T1 Nonsense correlations in neuroscience
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.11.29.402719
DO 10.1101/2020.11.29.402719
A1 Kenneth D. Harris
YR 2020
UL http://biorxiv.org/content/early/2020/11/30/2020.11.29.402719.1.abstract
AB Many neurophysiological signals exhibit slow continuous trends over time. Because standard correlation analyses assume that all samples are independent, they can yield apparently significant “nonsense correlations” even for signals that are completely unrelated. Here we compare the performance of several methods for assessing correlations between timeseries, using simulated slowly drifting signals with and without genuine correlations. The best performance was obtained from a “pseudosession method”, which relies on one of the signals being randomly generated by the experimenter, or a “session perturbation” method which requires multiple recordings under the same conditions. If neither of these is applicable, we find that a “linear shift” method can work well, but only when one of the signals is stationary. Methods based on cross-validation, circular shifting, phase randomization, or detrending gave up to 100% false positive rates in our simulations. We conclude that analysis of neural timeseries is best performed when stationarity and randomization is built into the experimental design.Competing Interest StatementThe authors have declared no competing interest.