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Clock time: a foreign measure to brain dynamics

View ORCID ProfileSander van Bree, María Melcón, Luca Kolibius, Casper Kérren, Maria Wimber, Simon Hanslmayr
doi: https://doi.org/10.1101/2021.06.09.447763
Sander van Bree
1Centre for Cognitive Neuroimaging, Institute for Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
2Centre for Human Brain Health, School for Psychology, Birmingham, United Kingdom
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  • ORCID record for Sander van Bree
  • For correspondence: sandervanbree@gmail.com
María Melcón
3Department of Biological and Health Psychology, Autónoma University of Madrid, Spain
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Luca Kolibius
1Centre for Cognitive Neuroimaging, Institute for Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
2Centre for Human Brain Health, School for Psychology, Birmingham, United Kingdom
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Casper Kérren
4Center for Adaptive Rationality, Max Planck Institute for Human Development, Berlin, Germany
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Maria Wimber
1Centre for Cognitive Neuroimaging, Institute for Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
2Centre for Human Brain Health, School for Psychology, Birmingham, United Kingdom
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Simon Hanslmayr
1Centre for Cognitive Neuroimaging, Institute for Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
2Centre for Human Brain Health, School for Psychology, Birmingham, United Kingdom
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Abstract

Human thought is highly flexible and dynamic, achieved by evolving patterns of brain activity across groups of cells. Neuroscience aims to understand cognition in the brain by analysing these intricate patterns. Here, 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. A more appropriate time format is cycles of brain oscillations, which coordinate neural firing and are widely implicated in cognition. These brain dynamics do not obey clock time – they start out of tune with clock time and drift apart even further as oscillations unpredictably slow down, speed up, and undergo abrupt changes. Since oscillations clock cognition, their dynamics should critically inform our analysis. We describe brain time warping as a new method to transform data in accordance with brain dynamics, which sets the time axis to cycles of clocking oscillations (a native unit) rather than milliseconds (a foreign unit). We also introduce the Brain Time Toolbox, a software library that implements brain time warping for electrophysiology data and tests whether it reveals information patterns in line with how the brain uses them.

Competing Interest Statement

The authors have declared no competing interest.

  • Glossary

    Brain oscillations
    Rhythmic fluctuations of brain activity generated by populations of cells
    Brain time
    Time as sequences of cycles of a coordinating brain oscillation
    Brain time warping
    Algorithm that employs dynamic time warping to transform electrophysiology data in accordance with brain time dynamics
    Brain time toolbox
    Software library that implements brain time warping and tests its effects
    Clock time
    Time as sequences of seconds
    Dynamic time warping (DTW)
    Algorithm that can measure the similarity between signals and minimize their difference
    Frequency
    Number of cycles per time window (typically a second)
    Linear Discriminant Analysis (LDA)
    Machine learning method that maximizes the separability between two classes of data by applying linear transformations to it
    Local Field Potential (LFP)
    The electric potential recorded from extracellular space around cells
    Neural signature
    Brain activity that systematically correlates with, in this context, a cognitive process
    Non-stationarity
    A signal is non-stationary when it undergoes spectral changes over time. We focus on frequency drift, variable starting phases, and phase jumps.
    Periodicity
    Fluctuating patterns of a neural signature
    Phase
    Metric to indicate the specific point in the cycle of an oscillation. Two oscillations are in phase when (for example) their peaks align.
    Temporal Generalization Matrix (TGM)
    Representation of how a classifier trained to separate classes of data on one timepoint performs on other timepoints. When a classifier generalizes, it indicates the neural signature remains stable.
    Warping path
    Representation of how two signals need to be resampled to minimize their difference
    Warping source
    Data structure containing potential coordinating brain oscillations used for brain time warping
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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    Posted June 10, 2021.
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    Clock time: a foreign measure to brain dynamics
    Sander van Bree, María Melcón, Luca Kolibius, Casper Kérren, Maria Wimber, Simon Hanslmayr
    bioRxiv 2021.06.09.447763; doi: https://doi.org/10.1101/2021.06.09.447763
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    Clock time: a foreign measure to brain dynamics
    Sander van Bree, María Melcón, Luca Kolibius, Casper Kérren, Maria Wimber, Simon Hanslmayr
    bioRxiv 2021.06.09.447763; doi: https://doi.org/10.1101/2021.06.09.447763

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