Elisa Valdez - Academia.edu (original) (raw)

Papers by Elisa Valdez

Oscillations are a prevalent feature of brain recordings. They are believed to play key roles in ... more Oscillations are a prevalent feature of brain recordings. They are believed to play key roles in neural communication and computation. Current analysis methods for studying neural oscillations often implicitly assume that the oscillations are sinusoidal. While these approaches have proven fruitful, we show here that there are numerous instances in which neural oscillations are nonsinusoidal. We highlight approaches to characterize nonsinusoidal features and account for them in traditional spectral analysis. Instead of being a nuisance, we discuss how these nonsinusoidal features may provide crucial and so far overlooked physiological information related to neural communication, computation, and cognition. Neural Oscillation Characterization Rhythms in neural activity are observed across various temporal and spatial scales and are often referred to as oscillations (see Glossary) [1]. Traditionally, neural oscillations have been clustered into canonical frequency bands, including delta (1–4 Hz), theta (4–8 Hz), alpha (8– 12 Hz), beta (15–30 Hz), gamma (30–90 Hz), and high gamma (>50 Hz). These bands roughly correspond to frequency ranges commonly observed in human electroencephalography (EEG) studies. Although they have been observed for nearly a century, recent theories suggest that these oscillations play an active role in neural communication [2]. One prominent theory is that oscillations accomplish this function using cross-frequency coupling (CFC), in which multiple neural oscillators in different frequency ranges interact with one another [3]. To characterize this coupling, the phase and amplitude properties of each oscillator are calculated using spectral analysis. A key feature in all spectral analysis methods is that they inherently assume that the fluctuations in brain activity over time can be characterized using a sinusoidal basis. That is, the underlying assumption is that the complexities of oscillatory brain activity are best captured by sinusoidal oscillators. A sinusoid (or sine wave) is a smoothly varying rhythmic signal governed by a mathematical equation. However, as we will discuss below, neural oscillations are commonly nonsinusoidal. Instead of being a nuisance, we argue that these nonsinusoidal features may contain crucial physiological information about the neural systems and dynamics that generate them. We address here the inconsistency between standard neural analysis approaches and the observed nonsinusoidal shapes of oscillatory waveforms. We begin by reviewing a diverse set of examples of nonsinusoidal oscillations across species. Interestingly, studies published before the modern proliferation of advanced computation have focused more on raw, unfiltered data, by necessity. By contrast, recent studies tend to focus on heavily processed data and lack attention to the oscillatory waveform shapes. We discuss methodological approaches for

Oscillations are a prevalent feature of brain recordings. They are believed to play key roles in ... more Oscillations are a prevalent feature of brain recordings. They are believed to play key roles in neural communication and computation. Current analysis methods for studying neural oscillations often implicitly assume that the oscillations are sinusoidal. While these approaches have proven fruitful, we show here that there are numerous instances in which neural oscillations are nonsinusoidal. We highlight approaches to characterize nonsinusoidal features and account for them in traditional spectral analysis. Instead of being a nuisance, we discuss how these nonsinusoidal features may provide crucial and so far overlooked physiological information related to neural communication, computation, and cognition. Neural Oscillation Characterization Rhythms in neural activity are observed across various temporal and spatial scales and are often referred to as oscillations (see Glossary) [1]. Traditionally, neural oscillations have been clustered into canonical frequency bands, including delta (1–4 Hz), theta (4–8 Hz), alpha (8– 12 Hz), beta (15–30 Hz), gamma (30–90 Hz), and high gamma (>50 Hz). These bands roughly correspond to frequency ranges commonly observed in human electroencephalography (EEG) studies. Although they have been observed for nearly a century, recent theories suggest that these oscillations play an active role in neural communication [2]. One prominent theory is that oscillations accomplish this function using cross-frequency coupling (CFC), in which multiple neural oscillators in different frequency ranges interact with one another [3]. To characterize this coupling, the phase and amplitude properties of each oscillator are calculated using spectral analysis. A key feature in all spectral analysis methods is that they inherently assume that the fluctuations in brain activity over time can be characterized using a sinusoidal basis. That is, the underlying assumption is that the complexities of oscillatory brain activity are best captured by sinusoidal oscillators. A sinusoid (or sine wave) is a smoothly varying rhythmic signal governed by a mathematical equation. However, as we will discuss below, neural oscillations are commonly nonsinusoidal. Instead of being a nuisance, we argue that these nonsinusoidal features may contain crucial physiological information about the neural systems and dynamics that generate them. We address here the inconsistency between standard neural analysis approaches and the observed nonsinusoidal shapes of oscillatory waveforms. We begin by reviewing a diverse set of examples of nonsinusoidal oscillations across species. Interestingly, studies published before the modern proliferation of advanced computation have focused more on raw, unfiltered data, by necessity. By contrast, recent studies tend to focus on heavily processed data and lack attention to the oscillatory waveform shapes. We discuss methodological approaches for