Low-frequency cortical oscillations are modulated by temporal prediction and temporal error coding

AbstractMonitoring and updating temporal predictions are critical abilities for adaptive behavior. Here, we investigated whether neural oscillations are related to violation and updating of temporal predictions. Human participants performed an experiment in which they had to generate a target at an expected time point, by pressing a button while taking into account a variable delay between the act and the stimulus occurrence. Our behavioral results showed that participants quickly adapted their temporal predictions in face of an error. Concurrent electrophysiological (EEG) data showed that temporal errors elicited markers that are classically related to error coding. Furthermore, intertrial phase coherence of frontal theta oscillations was modulated by error magnitude, possibly indexing the degree of surprise. Finally, we found that delta phase at stimulus onset was correlated with future behavioral adjustments. Together, our findings suggest that low frequency oscillations play a key role in monitoring and in updating temporal predictions.

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A silent disco: Persistent entrainment of low-frequency neural oscillations underlies beat-based, but not memory-based temporal expectations

10.1101/2020.01.08.899278 ◽

2020 ◽

Author(s):

Fleur L. Bouwer ◽

Johannes J. Fahrenfort ◽

Samantha K. Millard ◽

Heleen A. Slagter

Keyword(s):

Neuronal Activity ◽

Beat Frequency ◽

Low Frequency ◽

Neural Oscillations ◽

Expected Time ◽

Low Frequency Oscillations ◽

Eeg Data ◽

Predictable Pattern ◽

Temporal Intervals ◽

Multivariate Pattern

AbstractTemporal expectations (e.g., predicting “when”) facilitate sensory processing, and are suggested to rely on entrainment of low frequency neural oscillations to regular rhythmic input. However, temporal expectations can be formed not only in response to a regular beat, such as in music (“beat-based” expectations), but also based on a predictable pattern of temporal intervals of different durations (“memory-based” expectations). Here, we examined the neural mechanisms underlying beat-based and memory-based expectations, by assessing EEG activity and behavioral responses during silent periods following rhythmic auditory sequences that allowed for beat-based or memory-based expectations, or had random timing. In Experiment 1 (N = 32), participants rated how well probe tones at various time points fitted the previous rhythm. Beat-based expectations affected fitness ratings for at least two beat-cycles, while the effects of memory-based expectations subsided after the first expected time point in the silence window. In Experiment 2 (N = 27), using EEG, we found a CNV following the final tones of memory-based and random, but not beat-based sequences, suggesting that climbing neuronal activity may specifically reflect memory-based expectations. Moreover, we found enhanced power in the EEG signal at the beat frequency for beat-based sequences both during listening and the silence. For memory-based sequences, we found enhanced power at a frequency inherent to the memory-based pattern only during listening, but not during the silence, suggesting that ongoing entrainment of low frequency oscillations may be specific to beat-based expectations. Finally, using multivariate pattern decoding on the raw EEG data, we could classify above chance from the silence which type of sequence participants had heard before. Together, our results suggest that beat-based and memory-based expectations rely on entrainment and climbing neuronal activity, respectively.

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Low-frequency cortical oscillations are modulated by temporal prediction and temporal error coding

NeuroImage ◽

10.1016/j.neuroimage.2016.11.028 ◽

2017 ◽

Vol 146 ◽

pp. 40-46 ◽

Cited By ~ 10

Author(s):

Louise Catheryne Barne ◽

Peter Maurice Erna Claessens ◽

Marcelo Bussotti Reyes ◽

Marcelo Salvador Caetano ◽

André Mascioli Cravo

Keyword(s):

Low Frequency ◽

Temporal Prediction ◽

Cortical Oscillations ◽

Error Coding

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Low Frequency Oscillations drive EEG's complexity changes during wakefulness and sleep

10.1101/2021.12.16.472983 ◽

2021 ◽

Author(s):

Joaquin Gonzalez ◽

Diego M. Mateos ◽

Matias Cavelli ◽

Alejandra Mondino ◽

Claudia Pascovich ◽

...

Keyword(s):

Slow Wave Sleep ◽

Low Frequency ◽

Neuronal Population ◽

Complexity Measures ◽

Low Frequencies ◽

New Approach ◽

Frequency Bands ◽

Cortical Oscillations ◽

Brain Signals ◽

Low Frequency Oscillations

Recently, the sleep-wake states have been analysed using novel complexity measures, complementing the classical analysis of EEGs by frequency bands. This new approach consistently shows a decrease in EEG's complexity during slow-wave sleep, yet it is unclear how cortical oscillations shape these complexity variations. In this work, we analyse how the frequency content of brain signals affects the complexity estimates in freely moving rats. We find that the low-frequency spectrum - including the Delta, Theta, and Sigma frequency bands - drives the complexity changes during the sleep-wake states. This happens because low-frequency oscillations emerge from neuronal population patterns, as we show by recovering the complexity variations during the sleep-wake cycle from micro, meso, and macroscopic recordings. Moreover, we find that the lower frequencies reveal synchronisation patterns across the neocortex, such as a sensory-motor decoupling that happens during REM sleep. Overall, our works shows that EEG's low frequencies are critical in shaping the sleep-wake states' complexity across cortical scales.

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