Oscillatory and aperiodic neural activity jointly predict language learning

Cross, Zachariah R.; Corcoran, Andrew W.; Schlesewsky, Matthias; Köhler, Manfred

doi:10.1101/2020.03.10.984971

Cited by 20 publications

(36 citation statements)

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“…Aperiodic activity is itself a physiologically informative feature (Gao et al, 2017(Gao et al, , 2020, reflecting processes distinct from neural oscillations (Chaoul & Siegel, 2020). New methods provide new opportunities, for example, the ability to jointly analyze multiple components of the data, such as how oscillations and aperiodic activity jointly contribute to cognitive processing (Cross et al, 2020). New features of interest offer the potential for better understanding underlying physiology and putative computational roles of neural oscillations.…”

Section: Discussionmentioning

confidence: 99%

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%

Methodological Considerations for Studying Neural Oscillations

Donoghue¹,

Schaworonkow²,

Voytek³

2021

Preprint

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“…The aim of the present work was to assess if 1/ƒ aperiodic neural dynamics predict visuomotor performance. Aperiodic resting-state EEG activity is thought to be behaviourally relevant in predicting cognitive capacity (Cross et al, 2020;Ouyang et al, 2020) with higher 1/ƒ intercept reflecting higher neural population spiking (Manning et al, 2009;Miller et al, 2012) and steeper 1/ƒ slope reflecting a greater excitation-inhibition balance (Lendner et al, 2020;Weber et al, 2020). Despite the apparent association between 1/ƒ aperiodic parameters and information processing capacity, to date, there has been no empirical test of these parameters as individual predictors of visuomotor performance.…”

Section: Discussionmentioning

confidence: 99%

“…The 1/ƒ aperiodic parameters (i.e., spectral slope and intercept) appear to be robust markers of neural information processing across a range of domains. Indeed, an emerging body of work has revealed the effectiveness of 1/ƒ in predicting individual capacity for processing speed (Ouyang et al, 2020) and artificial grammar learning (Cross et al, 2020). Specific to the motor domain, there have been earlier applications of 1/ƒ (also referred to as pink noise) scaling to describe self-organizing properties of skilled movement performance (Diniz et al, 2011) including reaction time (Gilden et al, 1995;Clayton & Frey, 1997), movement timing (Wijnants et al, 2009), and oscillation of finger tapping (Rigoli et al, 2014) and music instrument performance (Colley & Dean, 2019).…”

Section: Introductionmentioning

confidence: 99%

Resting-state aperiodic neural dynamics predict individual differences in visuomotor performance and learning

Immink

Cross

Chatburn

et al. 2021

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An emerging body of work has demonstrated that resting-state non-oscillatory, or aperiodic, 1/f neural activity is a functional and behaviorally relevant marker of cognitive function capacity. In the motor domain, previous work has only applied 1/f analyses to description of action coordination and performance. The value of aperiodic resting-state neural dynamics as a marker of individual visuomotor performance capacity remains unknown. Accordingly, the aim of this work was to investigate if individual 1/f intercept and slope parameters of aperiodic resting-state neural activity predict reaction time and perceptual sensitivity in an immersive virtual reality marksmanship task. The marksmanship task required speeded selection of target stimuli and avoidance of non-target stimuli selection. Motor and perceptual demands were incrementally increased across task blocks and participants performed the task across three training sessions spanning one week. When motor demands were high, steeper individual 1/f slope predicted shorter reaction time. This relationship did not change with practice. Increased 1/f intercept and a steeper 1/f slope were associated with higher perceptual sensitivity, measured as d′. However, this association was only observed under the highest levels of perceptual demand and only in the initial exposure to these conditions. Individuals with a lower 1/f intercept and a shallower 1/f slope demonstrated the greatest gains in perceptual sensitivity from task practice. These findings demonstrate that individual differences in motor and perceptual performance can be accounted for with resting-state aperiodic neural dynamics. The 1/f aperiodic parameters are most informative in predicting visuomotor performance under complex and demanding task conditions. In addition to predicting capacity for high visuomotor performance with a novel task, 1/f aperiodic parameters might also be useful in predicting which individuals might derive the most improvements from practice.

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“…Task-related EEG analyses during the baseline and delayed sentence judgement tasks were performed using MNE-Python (Gramfort et al, 2013; for an analysis of the sentence learning task, please see Cross et al 2020c). EEG data were re-referenced offline to the average of both mastoids and filtered with a digital phase-true finite impulse response (FIR) band-pass filter from 0.1 -40 Hz to remove slow signal drifts and high frequency activity.…”

Section: Eeg Recording and Pre-processingmentioning

confidence: 99%

“…While the functional significance of the 1/ƒ exponent is still being investigated, it becomes flatter with age, explaining age-related cognitive decline (Voytek et al, 2015). It also varies across states of consciousness (e.g., Lendner et al, 2020) and predicts individual differences in processing speed (Ouyang et al, 2019) and language learning (Cross et al, 2020c). From this perspective, in order to control for any effect of aperiodic activity on language learning across sleep and wake, the 1/ƒ exponent was included as a covariate in all statistical models.…”

Section: Quantification Of the Broadband Spectral Profilementioning

confidence: 99%

Slow oscillation-spindle coupling predicts sequence-based language learning

Cross

Helfrich

Corcoran

et al. 2020

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word count: 174 Introduction word count: 1,756 Discussion word count: 2,677ABSTRACT Language is one of the most defining human capabilities, involving the coordination of brain networks that generalise the meaning of linguistic units of varying complexity. On a neural level, neocortical slow waves and thalamic spindles during sleep facilitate the reactivation of newly encoded memory traces, manifesting in distinct oscillatory activity during retrieval. However, it is currently unknown if the effect of sleep on memory extends to the generalisation of the mechanisms that subserve sentence comprehension. We address this question by analysing electroencephalogram data recorded from 36 participants during an artificial language learning task and an 8hr nocturnal sleep period. We found that a period of sleep was associated with increased alpha/beta synchronisation and improved behavioural performance. Cross-frequency coupling analyses also revealed that spindle-slow wave coupling predicted the consolidation of varying word order permutations, which was associated with distinct patterns of task-related oscillatory activity during sentence processing. Taken together, this study presents converging behavioural and neurophysiological evidence for a role of sleep in the consolidation of higher order language learning and associated oscillatory neuronal activity.Keywords: Sleep and memory; language learning; modified miniature language; sentence processing; neuronal oscillations SIGNIFICANCE STATEMENTThe endogenous temporal coordination of neural oscillations supports information processing during both wake and sleep states. Here we demonstrate that spindle-slow wave coupling during non-rapid eye movement sleep predicts the consolidation of complex grammatical rules and modulates task-related oscillatory dynamics previously implicated in sentence processing. We show that increases in alpha/beta synchronisation predict enhanced sensitivity to grammatical violations after a period of sleep and strong spindle-slow wave coupling modulates subsequent task-related theta activity to influence behaviour. Our findings reveal a complex interaction between both wake-and sleep-related oscillatory dynamics during the early stages of language learning beyond the single word level.

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Oscillatory and aperiodic neural activity jointly predict language learning

Cited by 20 publications

References 162 publications

Methodological Considerations for Studying Neural Oscillations

Methodological Considerations for Studying Neural Oscillations

Resting-state aperiodic neural dynamics predict individual differences in visuomotor performance and learning

Slow oscillation-spindle coupling predicts sequence-based language learning

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