Human thought is highly flexible, achieved by evolving patterns of brain activity across groups of cells. Neuroscience aims to understand cognition in the brain by analysing these intricate patterns. We argue that this goal is impeded by the time format of our data—clock time. The brain is a system with its own dynamics and regime of time, with no intrinsic concern for the human-invented second. Here, we present the Brain Time Toolbox, a software library that retunes electrophysiology data in line with oscillations that orchestrate neural patterns of cognition. These oscillations continually slow down, speed up and undergo abrupt changes, introducing a disharmony between the brain’s internal regime and clock time. The toolbox overcomes this disharmony by warping the data to the dynamics of coordinating oscillations, setting oscillatory cycles as the data’s new time axis. This enables the study of neural patterns as they unfold in the brain, aiding neuroscientific enquiry into dynamic cognition. In support of this, we demonstrate that the toolbox can reveal results that are absent in a default clock time format.
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Code for the brain time analysis of the rodent and simulated data is included in the Brain Time Toolbox at https://github.com/sandervanbree/braintime. Custom code for analysis of the human data is available from the corresponding author upon request.
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We thank E. Newman, F. Meconi, G.-Y. Bae and S. J. Luck for sharing their data and B. Griffiths for his insightful comments on the Brain Time Toolbox. This research was funded by the European Research Council (grant no. 715714 for M.W. and 647954 for S.H.), the Economic and Social Research Council (ES/R010072/2 for S.H.) and the Autónoma University of Madrid (FPI-UAM 2017 for M.M.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
The authors declare no competing interests.
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van Bree, S., Melcón, M., Kolibius, L.D. et al. The brain time toolbox, a software library to retune electrophysiology data to brain dynamics. Nat Hum Behav (2022). https://doi.org/10.1038/s41562-022-01386-8