Dissecting Transient Burst Events

Zich, Catharina; Quinn, Andrew; Mardell, Lydia C; Ward, Nick; Bestmann, Sven

doi:10.1016/j.tics.2020.07.004

Cited by 36 publications

(43 citation statements)

References 12 publications

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“…Multiple oscillatory features, such as power, waveform shape, burst rate, etc., can covary. These potential multicollinearities need to be explicitly considered and tested by robust analyses that control for multiple potentially confounding features, for example addressing overlapping periodic and aperiodic activity (Donoghue, Haller, et al, 2020;Kosciessa et al, 2020), waveform shape, which may result in spurious power-and/or phase-coupling Schaworonkow & Nikulin, 2019), and examining trial-by-trial dynamics that may be masked or conflated in average measures (Jones, 2016;Stokes & Spaak, 2016;Zich et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…4D), burst occurrence (Fig. 4E), or burst amplitude (4F), each of which can vary within or between analyzed time periods (Quinn et al, 2019;Zich et al, 2020). Understanding the different sources of variability has implications on how these signals should be interpreted, as a change in the length, number or size of bursts each likely reflect different circuit mechanisms and putative relationships to neural function.…”

Section: Why This Mattersmentioning

confidence: 99%

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Methodological Considerations for Studying Neural Oscillations

Donoghue¹,

Schaworonkow²,

Voytek³

2021

Preprint

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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 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.

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Section: Discussionmentioning

confidence: 99%

Section: Why This Mattersmentioning

confidence: 99%

Methodological Considerations for Studying Neural Oscillations

Donoghue¹,

Schaworonkow²,

Voytek³

2021

Preprint

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“…Burst amplitude increases in proportion with burst duration (Tinkhauser et al, 2017), suggesting a single generating mechanism and therefore suitable to be combined. The inclusion of a third dimension (frequency span) allows a compact assessment across the entire set of beta-range frequencies (Zich et al, 2020). The β-rhythm likely inhibits neural processing (Sherman et al, 2016) and the brevity of β-bursts may be critical for intact messaging in the brain (Feingold et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition

et al. 2021

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In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases and of normal aging. It is hypothesised that the right inferior frontal cortex plays a key role by inhibiting the motor cortex via the basal ganglia. The electroencephalographyderived β-rhythm (15-29 Hz) is thought to reflect communication within this network, with increased right frontal β-power often observed prior to successful response inhibition. Recent literature suggests that averaging spectral power obscures the transient, burst-like nature of β-activity. There is evidence that the rate of β-bursts following a Stop signal is higher when a motor response is successfully inhibited. However, other characteristics of β-burst events, and their topographical properties, have not yet been examined. Here, we used a large human (male and female) electroencephalography Stop Signal Task dataset (n=218) to examine averaged normalised β-power, β-burst rate and β-burst 'volume' (which we defined as burst duration x frequency span x amplitude). We first sought to optimise the β-burst detection method. In order to find predictors across the whole scalp, and with high temporal precision, we then used machine learning to (1) classify successful vs. failed stopping and to (2) predict individual Stop Signal Reaction Time. β-Burst volume was significantly more predictive of successful and fast stopping than β-burst rate and normalised β-power. The classification model generalised to an external dataset (n=201). We suggest β-burst volume is a sensitive and reliable measure for investigation of human response inhibition. 3 Significance StatementThe electroencephalography-derived β-rhythm (15-29 Hz) is associated with the ability to inhibit ongoing actions. In this study, we sought to identify the specific characteristics of βactivity that contribute to successful and fast inhibition. In order to search for the most relevant features of β-activityacross the whole scalp and with high temporal precisionwe employed machine learning on two large datasets. Spatial and temporal features of β-burst 'volume' (duration x frequency span x amplitude) predicted response inhibition outcomes in our data significantly better than β-burst rate and normalised β-power. These findings suggest that multidimensional measures of β-bursts, such as burst volume, can add to our understanding of human response inhibition.

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“…4D), burst occurrence (Fig. 4E), or burst amplitude (4F), each of which can vary within or between analyzed time periods (Quinn et al, 2019;Zich et al, 2020…”

Section: Why This Mattersmentioning

confidence: 99%

Methodological considerations for studying neural oscillations

Donoghue

Schaworonkow

Voytek

2021

Eur J of Neuroscience

142

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.

show abstract

Dissecting Transient Burst Events

Cited by 36 publications

References 12 publications

Methodological Considerations for Studying Neural Oscillations

Methodological Considerations for Studying Neural Oscillations

Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition

Methodological considerations for studying neural oscillations

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