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Cycle-by-cycle analysis of neural oscillations

View ORCID ProfileScott R Cole, View ORCID ProfileBradley Voytek
doi: https://doi.org/10.1101/302000
Scott R Cole
University of California, San Diego
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  • For correspondence: scott.cole0@gmail.com
Bradley Voytek
University of California, San Diego
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Abstract

Neural oscillations are widely studied using methods based on the Fourier transform, which models data as sums of sinusoids. For decades these Fourier-based approaches have successfully uncovered links between oscillations and cognition or disease. However, because of the fundamental sinusoidal basis, these methods might not fully capture neural oscillatory dynamics, because neural data are both nonsinusoidal and non-stationary. Here, we present a new analysis framework, complementary to Fourier analysis, that quantifies cycle-by-cycle time-domain features. For each cycle, the amplitude, period, and waveform symmetry are measured, the latter of which is missed using conventional approaches. Additionally, oscillatory bursts are algorithmically identified, allowing us to investigate the variability of oscillatory features within and between bursts. This approach is validated on simulated noisy signals with oscillatory bursts and outperforms conventional metrics. Further, these methods are applied to real data, including hippocampal theta, motor cortical beta, and visual cortical alpha, and can differentiate behavioral conditions.

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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-NC 4.0 International license.
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Posted April 16, 2018.
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Cycle-by-cycle analysis of neural oscillations
Scott R Cole, Bradley Voytek
bioRxiv 302000; doi: https://doi.org/10.1101/302000
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Cycle-by-cycle analysis of neural oscillations
Scott R Cole, Bradley Voytek
bioRxiv 302000; doi: https://doi.org/10.1101/302000

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