Instantaneous amplitude and shape of postrhinal theta oscillations differentially encode running speed

Ghosh, Megha; Shanahan, Benjamin E.; Furtak, Sharon C.; Mashour, George A.; Burwell, Rebecca D.; Ahmed, Omar J.

doi:10.1101/2020.06.03.130609

Cited by 4 publications

(4 citation statements)

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“…The waveform shape of neural oscillations is often non‐sinusoidal (Cole & Voytek, 2017; Jones, 2016), as seen, for example, in the arc‐shaped sensorimotor mu‐rhythm, visual alpha, which can be triangular, and the sawtooth‐shaped hippocampal theta‐rhythm. These waveform properties of neural oscillations may reflect physiological properties, for example the synchronisation of neural activity (Schaworonkow & Nikulin, 2019), spiking patterns of underlying neurons (Cole & Voytek, 2018), or behavioural correlates such as running speed (Ghosh et al, 2020). Waveform shape can therefore be an important feature of interest, with potential to impose constraints on generative circuit models of oscillations (Sherman et al, 2016) as well as time constants of involved synaptic currents.…”

Section: Neural Oscillations Are Non‐sinusoidalmentioning

confidence: 99%

Methodological considerations for studying neural oscillations

Donoghue

Schaworonkow

Voytek

2021

Eur J of Neuroscience

182

156

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Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, and amenable to computational modeling that investigates neural circuit generating mechanisms and neural population dynamics. Because of this, neural oscillations offer an exciting potential opportunity for linking theory, physiology, and mechanisms of cognition. However, despite their prevalence, there are many concerns-new and old-about how our analysis assumptions are violated by known properties of field potential data. For investigations of neural oscillations to be properly interpreted, and ultimately developed into mechanistic theories, it is necessary to carefully consider the underlying assumptions of the methods we employ. Here, we discuss seven methodological considerations for analyzing neural oscillations. The considerations are to 1) verify the presence of oscillations, as they may be absent; 2) validate oscillation band definitions, to address variable peak frequencies; 3) account for concurrent non-oscillatory aperiodic activity, which might otherwise confound measures; measure and account for 4) temporal variability and 5) waveform shape of neural oscillations, which are often bursty and/or nonsinusoidal, potentially leading to spurious results; 6) separate spatially overlapping rhythms, which may interfere with each other; and 7) consider the required signal-to-noise ratio for obtaining reliable estimates. For each topic, we provide relevant examples, demonstrate potential errors of interpretation, and offer suggestions to address these issues. We primarily focus on univariate measures, such as power and phase estimates, though we discuss how these issues can propagate to multivariate measures. These considerations and recommendations offer a helpful guide for measuring and interpreting neural oscillations.

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Section: Neural Oscillations Are Non‐sinusoidalmentioning

confidence: 99%

Methodological considerations for studying neural oscillations

Donoghue

Schaworonkow

Voytek

2021

Eur J of Neuroscience

182

156

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show abstract

“…The second study examined the instantaneous amplitude and shape of theta oscillations in the POR as they relate to running speed (Ghosh et al, 2020). Theta in the POR was found to mimic theta in the HC in that it is temporally asymmetrical, with the falling phase of theta cycles lasting longer than the subsequent rising phase.…”

Section: Postrhinal Functionsmentioning

confidence: 99%

The anatomy and function of the postrhinal cortex.

Estela-Pro¹,

Burwell²

2022

Behavioral Neuroscience

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The parahippocampal cortex (PHC) in the primate brain is implicated in the medial temporal lobe (MTL) memory network for spatial and episodic memory, but the precise function of this region remains unclear. Importantly, the rodent postrhinal cortex (POR) provides a structural and connectional homolog to the primate PHC. This homology permits the use of the powerful tools available in rodent models to better understand the function of the PHC in the human and nonhuman primate brains. Although many articles have compared and dissociated the function of the rodent POR from other areas in the MTL implicated in learning, memory, and memory-guided behavior, there are no in-depth reviews, particularly covering the last two decades of research. Nor has there been a review of the literature on the potential role of the POR in attention. Here, we review the anatomical and functional connectivity of the POR in rats, examine the evidence for proposed behavioral functions of this region, and suggest a model that accounts for the array of observations. We propose that the rodent POR binds nonspatial information and spatial information to represent the current local physical environment or context, including the geometry of the space and the spatial layout of objects and features in the environment. The POR also automatically monitors the environment for changes and updates representations when changes occur. These representations of context are available to be used by multiple brain regions, including prefrontal, posterior cortical, and hippocampal areas, for context-guided behavior, associative learning, and episodic memory.

show abstract

“…The waveform shape of neural oscillations is often non-sinusoidal Jones, 2016), as seen, for example, in the arc-shaped sensorimotor mu-rhythm, visual alpha, which can be triangular, and the sawtooth-shaped hippocampal theta-rhythm. These waveform properties of neural oscillations may reflect physiological properties, for example the synchronization of neural activity (Schaworonkow & Nikulin, 2019), spiking patterns of underlying neurons (Cole & Voytek, 2018), or behavioral correlates such as running speed (Ghosh et al, 2020). Waveform shape can therefore be an important feature of interest, with potential to impose constraints on generative circuit models of oscillations (Sherman et al, 2016) as well as time constants of involved synaptic currents.…”

Section: Why This Mattersmentioning

confidence: 99%

Methodological Considerations for Studying Neural Oscillations

Donoghue¹,

Schaworonkow²,

Voytek³

2021

Preprint

View full text Add to dashboard Cite

Neural oscillations are ubiquitous across recording methodologies and species, broadly associated with cognitive tasks, and amenable to computational modeling that investigates neural circuit generating mechanisms and mesoscale dynamics. Because of this, neural oscillations may offer an exciting potential opportunity for linking theory, physiology, and mechanisms of cognition. However, despite their prevalence, there are many concerns—new and old—about how our analysis assumptions are violated by known properties of field potential data. For investigations of neural oscillations to be properly interpreted, and ultimately developed into mechanistic theories, it is necessary to carefully consider the underlying assumptions of the methods we employ. Here, we discuss seven methodological considerations for analyzing neural oscillations. The considerations are to 1) verify the presence of oscillations, as they may be absent; 2) validate oscillation band definitions, to address variable peak frequencies; 3) account for concurrent non-oscillatory aperiodic activity, which might otherwise confound measures; measure and account for 4) temporal variability and 5) waveform shape of neural oscillations, which are often bursty and/or nonsinusoidal, potentially leading to spurious results; 6) separate spatially overlapping rhythms, which may interfere with each other; and 7) consider the required signal-to-noise ratio for obtaining reliable estimates. For each topic, we provide relevant examples, demonstrate potential errors of interpretation, and offer suggestions to address these issues. We primarily focus on univariate measures, such as power and phase estimates, though we discuss how these issues can propagate to multivariate measures. These considerations and recommendations offer a helpful guide for measuring and interpreting neural oscillations.

show abstract

Instantaneous amplitude and shape of postrhinal theta oscillations differentially encode running speed

Cited by 4 publications

References 50 publications

Methodological considerations for studying neural oscillations

Methodological considerations for studying neural oscillations

The anatomy and function of the postrhinal cortex.

Methodological Considerations for Studying Neural Oscillations

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