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A waveform-independent measure of recurrent neural activity
Rhythmic neural activity, so-called oscillations, plays a key role in neural information transmission, processing, and storage. Neural oscillations in distinct frequency bands are central to physiological brain function, and alterations thereof have been associated with several neurological and p...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Philipps-Universität Marburg
2022
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| Subjects: | |
| Online Access: | PDF Full Text |
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| Summary: | Rhythmic neural activity, so-called oscillations, plays a key role in neural information
transmission, processing, and storage. Neural oscillations in distinct frequency bands
are central to physiological brain function, and alterations thereof have been associated
with several neurological and psychiatric disorders. The most common methods to
analyze neural oscillations, e.g., short-time Fourier transform or wavelet analysis,
assume that measured neural activity is composed of a series of symmetric prototypical
waveforms, e.g., sinusoids. However, usually, the models generating the signal,
including waveform shapes of experimentally measured neural activity are unknown.
Decomposing asymmetric waveforms of nonlinear origin using these classic methods
may result in spurious harmonics visible in the estimated frequency spectra. Here, we
introduce a new method for capturing rhythmic brain activity based on recurrences
of similar states in phase-space. This method allows for a time-resolved estimation
of amplitude fluctuations of recurrent activity irrespective of or specific to waveform
shapes. The algorithm is derived from the well-established field of recurrence analysis,
which, in comparison to Fourier-based analysis, is still very uncommon in neuroscience.
In this paper, we show its advantages and limitations in comparison to short-time Fourier
transform and wavelet convolution using periodic signals of different waveform shapes.
Furthermore, we demonstrate its application using experimental data, i.e., intracranial
and noninvasive electrophysiological recordings from the human motor cortex of one
epilepsy patient and one healthy adult, respectively. |
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| Item Description: | Gefördert durch den Open-Access-Publikationsfonds der UB Marburg. |
| Physical Description: | 17 Pages |
| DOI: | 10.3389/fninf.2022.800116 |