Power-law dynamics in neuronal and behavioral data introduce spurious correlations.
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.
Department of Neurology, Neurophysics Group, Charité University Medicine, Berlin, Germany.|Bernstein Center for Computational Neuroscience, Berlin, Germany.
Natalie
N
Schaworonkow
Bernstein Center for Computational Neuroscience, Berlin, Germany.|Department of Computer Science, Machine Learning Group, Technical University of Berlin, Germany.
Duncan A J
DA
Blythe
Department of Neurology, Neurophysics Group, Charité University Medicine, Berlin, Germany.
Jewgeni
J
Kegeles
Department of Neurology, Neurophysics Group, Charité University Medicine, Berlin, Germany.|Bernstein Center for Computational Neuroscience, Berlin, Germany.
Gabriel
G
Curio
Department of Neurology, Neurophysics Group, Charité University Medicine, Berlin, Germany.|Bernstein Center for Computational Neuroscience, Berlin, Germany.|Centre for Cognition and Decision Making, National Research University Higher School of Economics, Russian Federation.
Vadim V
VV
Nikulin
D000513:Alpha Rhythm; D016477:Artifacts; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D004576:Electromyography; D005385:Fingers / Q000502:physiology; D006801:Humans; D009043:Motor Activity / Q000502:physiology*; D010812:Physical Stimulation / Q000379:methods; D011930:Reaction Time; D012684:Sensory Thresholds; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors
EEG; LRTC; correlations; oscillations; power-law signals; significance testing