ArticleHuman brain mapping2015
Power-law dynamics in neuronal and behavioral data introduce spurious correlations.
Article in Human brain mapping, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Potential mechanisms and functional significance of aperiodic neural activity.Nature human behaviour · 2026Review
- Functional Connectivity Is Dominated by Aperiodic, Rather Than Oscillatory, Coupling.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Review
- Characterising time-on-task effects on oscillatory and aperiodic EEG components and their co-variation with visual task performance.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Decreased long-range temporal correlations in the resting-state functional magnetic resonance imaging blood-oxygen-level-dependent signal reflect motor sequence learning up to 2 weeks following training.Human brain mapping · 2024Article
- Methods for and Use of Functional Magnetic Resonance Imaging in Psychiatry.Advances in neurobiology · 2024Review
- Methodological considerations for studying neural oscillations.The European journal of neuroscience · 2022Article
- Theoretical foundations of studying criticality in the brain.Network neuroscience (Cambridge, Mass.) · 2022Review
- Neural Coding of Cognitive Control: The Representational Similarity Analysis Approach.Trends in cognitive sciences · 2021Review
- Characterizing gaze position signals and synthesizing noise during fixations in eye-tracking data.Behavior research methods · 2020Article
- Temporal Signatures of Criticality in Human Cortical Excitability as Probed by Early Somatosensory Responses.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Measuring shared responses across subjects using intersubject correlation.Social cognitive and affective neuroscience · 2019Article
- Elucidating relations between fMRI, ECoG, and EEG through a common natural stimulus.NeuroImage · 2018Article
- Activity of primate V1 neurons during the gap saccade task.Journal of neurophysiology · 2017Article
- Robust Statistical Detection of Power-Law Cross-Correlation.Scientific reports · 2016Article
- Reliability and signal comparison of OPM-MEG, fMRI & iEEG in a repeated movie viewing paradigm.Imaging neuroscience (Cambridge, Mass.)Article
- From surface to depth: Using deep learning to predict striatal fMRI reward signaling from EEG.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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