Time-resolved parameterization of aperiodic and periodic brain activity

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    Evaluation Summary:

    The paper addresses the highly timely questions of how to quantify aperiodic and periodic neural activity. This was done by extending previous work by embracing time-resolved parametrization of both simulated, noninvasive EEG and intracranial data. The new approach is termed Spectral Parametrization Resolved in Time (SPRiNT) and the paper shows that the slope of aperiodic activity is linked with both behaviour and age. The method thus demonstrates the importance of evaluating the state-dependence of aperiodic activity and dynamic properties of oscillatory components in a time-resolved manner.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share their name with the authors.)

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Abstract

Macroscopic neural dynamics comprise both aperiodic and periodic signal components. Recent advances in parameterizing neural power spectra offer practical tools for evaluating these features separately. Although neural signals vary dynamically and express non-stationarity in relation to ongoing behaviour and perception, current methods yield static spectral decompositions. Here, we introduce Spectral Parameterization Resolved in Time (SPRiNT) as a novel method for decomposing complex neural dynamics into periodic and aperiodic spectral elements in a time-resolved manner. First, we demonstrate, with naturalistic synthetic data, SPRiNT’s capacity to reliably recover time-varying spectral features. We emphasize SPRiNT’s specific strengths compared to other time-frequency parameterization approaches based on wavelets. Second, we use SPRiNT to illustrate how aperiodic spectral features fluctuate across time in empirical resting-state EEG data (n=178) and relate the observed changes in aperiodic parameters over time to participants’ demographics and behaviour. Lastly, we use SPRiNT to demonstrate how aperiodic dynamics relate to movement behaviour in intracranial recordings in rodents. We foresee SPRiNT responding to growing neuroscientific interests in the parameterization of time-varying neural power spectra and advancing the quantitation of complex neural dynamics at the natural time scales of behaviour.

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  1. Author Response*

    Reviewer 2 (Public Review):

    1. The periodic components of the simulated power did not overlap as is often seen in empirical data, they were confined to 1-40 Hz (e.g. no gamma activity was simulated), and the simulations did not include a knee in the aperiodic component. This means that it Is unclear whether SPRiNT would work as well in more complex or excessively noisy datasets. The non-sinusoidal waveform shape of the periodic component in the rodent data reiterates this concern.

    We are grateful that the Reviewer raised these important considerations about the practical value of SPRiNT in more complex data scenarios.

    We wish to clarify that in the simulations reported, although two simultaneous periodic components would not share the same centre frequency, a substantial number of realizations of the simulations made …

  2. Evaluation Summary:

    The paper addresses the highly timely questions of how to quantify aperiodic and periodic neural activity. This was done by extending previous work by embracing time-resolved parametrization of both simulated, noninvasive EEG and intracranial data. The new approach is termed Spectral Parametrization Resolved in Time (SPRiNT) and the paper shows that the slope of aperiodic activity is linked with both behaviour and age. The method thus demonstrates the importance of evaluating the state-dependence of aperiodic activity and dynamic properties of oscillatory components in a time-resolved manner.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #2 and Reviewer #3 agreed to share …

  3. Reviewer #1 (Public Review):

    Until recently EEG/MEG research has been primarily focused on the analysis of neural oscillations and evoked responses. A noise component i.e. 1/f or aperiodic part of the spectrum was mostly considered as a nuisance. Recently, however, this aperiodic part was recognized as an important and neurophysiologically meaningful form of neural activity reflecting a balance between excitation and inhibition. Yet estimation of such aperiodic component has been performed for relatively long recordings. Recognizing that brain states are dynamic and that both periodic and aperiodic components can change quickly in time, the authors introduced an approach (SPRiNT) allowing estimation of these components in a time-resolved manner, which provides parameters for consecutive time segments. This study includes a large number …

  4. Reviewer #2 (Public Review):

    This paper describes a new tool for decomposing neural data into periodic and aperiodic spectral components. Traditionally the aperiodic (1/f) component has been viewed as noise that is static over time, but recently it has become clear that it is physiologically meaningful and variable. Ignoring its parameters and temporal variation thereof can lead to misinterpretation of the periodic components. The authors build on a recent parameterisation approach (Donoghue et al., 2020) to make it temporally resolved, and more robust to transient periodic components.

    Using this new method, the authors show that SPRiNT outperforms the original static method based on realistic simulations, and further show that its performance in detecting periodic components is generally high, though it struggles to detect …

  5. Reviewer #3 (Public Review):

    The authors developed a novel method (Spectral Parametrization Resolved in Time, SPRiNT) intended for conducting time-resolved parametrization of both periodic and aperiodic neural activity. The method builds largely on the specparam/fooof-toolbox (fitting oscillations & one over f) and extends it by implementing a short-time Fourier transform (STFT) based approach for estimating time-resolved periodograms which are followed by the parametrization of neural activity via specparam and elimination of outlier spectral peaks. SPRiNT is then tested using simulated data against an alternative wavelet-based approach for conducting time-resolved parametrization of aperiodic and periodic activity as well applied to both resting-state human EEG data and intracranial data from rodents to evaluate the value of …