Trial‐by‐trial co‐variation of pre‐stimulus<scp>EEG</scp>alpha power and visuospatial bias reflects a mixture of stochastic and deterministic effects

Benwell, Christopher S. Y.; Keitel, Christian; Harvey, Monika; Groß, Joachim; Thut, Gregor

doi:10.1111/ejn.13688

Cited by 59 publications

(82 citation statements)

References 123 publications

(239 reference statements)

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“…To test this prediction, we analyzed how prestimulus power is related to the ERD magnitude, and in turn to the amplitude of the late ERP component. We found that trials with strong prestimulus power were related to strong ERD magnitude, consistent with previous studies (Min et al, 2007;Becker et al,2008;Tenke et al, 2015;Benwell et al, 2017a). Due to circularity in these measures (i.e., ERD is computed with prestimulus power), the statistical estimates of this relationship are inflated.…”

Section: Baseline-shift Mechanismsupporting

confidence: 90%

“…The idea that the ERD contributes to the generation of the slow ERP component, implies that the larger the ERD, the stronger the slow ERP component. Accordingly, we predicted that states of strong prestimulus power would yield a strong ERD (Min et al, 2007;Becker et al, 2008;Tenke et al, 2015;Benwell et al, 2017a), resulting in an enhancement of the slow ERP component during the late time window.…”

Section: Introductionmentioning

confidence: 99%

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Multiple mechanisms link prestimulus neural oscillations to sensory responses

Iemi

Busch

Laudini

et al. 2018

Preprint

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We thank Francesco Di Russo and Volodymyr B Bogdanov for help with designing the stimuli, Johanna Rehder for help with the literature search, and Charles Schroeder and Omri Raccah for comments on the manuscript. AbstractSpontaneous fluctuations of neural activity may explain why sensory responses vary across repeated presentations of the same physical stimulus. To test this hypothesis, we recorded electroencephalography in humans during stimulation with identical visual stimuli and analyzed how prestimulus neural oscillations modulate different stages of sensory processing reflected by distinct components of the event-related potential (ERP). We found that strong prestimulus alpha-and beta-band power resulted in a suppression of early ERP components (C1 and N150) and in an amplification of late components (after 0.4 s), even after controlling for fluctuations in 1/f aperiodic signal and sleepiness. Whereas functional inhibition of sensory processing underlies the reduction of early ERP responses, we found that the modulation of non-zero-mean oscillations (baseline shift) accounted for the amplification of late responses. Distinguishing between these two mechanisms is crucial for understanding how internal brain states modulate the processing of incoming sensory information.

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Section: Baseline-shift Mechanismsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Multiple mechanisms link prestimulus neural oscillations to sensory responses

Iemi

Busch

Laudini

et al. 2018

Preprint

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“…While this may appear to be small, and is an order of magnitude smaller than the typical alpha band width, some studies have identified brain-behaviour relationships with absolute effect sizes well within the range of the non-stationarities we observe here, hence suggesting that changes of this magnitude are of perceptual and cognitive relevance (see Samaha and Postle (2015) and Wutz et al (2018) for reports of perceptually relevant frequency differences of ~0.02-0.04 Hz). In particular, non-stationarities should be considered for the interpretation of M/EEG-behaviour relationships when not only present in the M/EEG signal but also displayed in the employed behavioural measures, as can be the case for psychometric function threshold and slope, detection rate and reaction time (Benwell et al, 2013;Benwell et al, 2018;Doll et al, 2015;Frund et al, 2011). In this situation, a correlation between the two measures of interest may be detected simply because both measures systematically change over time (i.e.…”

Section: Methodological and Applied Considerationsmentioning

confidence: 99%

“…Accordingly, it is plausible that changes in vigilance and fatigue over the course of the experimental session may be related to the shift in alpha power and also frequency observed here. Drifts in oscillatory power within experimental sessions have also been linked to non-stationarities in psychophysical measures (Benwell et al, 2013;Benwell et al, 2018;Bompas et al, 2015;Mathewson et al, 2009) and cognitive processes necessary for the maintenance of task performance (Stoll et al, 2016). In terms of alpha-frequency, previous studies have shown long-term changes over the life-span related to aging and pathology (Aurlien et al, 2004;Klimesch, 1999;Mierau et al, 2017) as well as short-term changes in the sub-second to second range.…”

Section: Understanding Neural Network Activity At the Macro-scalementioning

confidence: 99%

Frequency and power of human alpha oscillations drift systematically with time-on-task

Benwell

London

Tagliabue

et al. 2018

Preprint

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Oscillatory neural activity is a fundamental characteristic of the mammalian brain spanning multiple levels of spatial and temporal scales. Current theories of neural oscillations and analysis techniques employed to investigate their functional significance are based on an often implicit assumption: In the absence of experimental manipulation, the spectral content of any given EEG-or MEG-recorded neural oscillator remains approximately stationary over the course of a typical experimental session (~1 hour). Here, we show that this assumption is inaccurate for ongoing neural oscillations in one of the most prominent frequency bands of the EEG/MEG signal, the alpha band (8:13 Hz). We found that alpha peak frequency systematically decreased over time, while alpha power increased. Intriguingly, these systematic trends occurred independently of each other suggesting the existence of two nonstationary endogenous processes inherent in macroscopic alpha band activity. Our findings provide novel insight into the time-varying intrinsic properties of large-scale neural networks.They have important implications for the analysis and interpretation of studies that aim at identifying functionally relevant oscillatory networks or at driving them through external stimulation. Significance statementMounting evidence suggests that neural oscillations underlie cognitive processes in the brain.Hence, mapping endogenous characteristics of oscillatory networks represents an important venture for modern neuroscience. Here, we reveal two endogenous non-stationary processes occurring in the alpha band -a prominent rhythm of the brain. Alpha peak frequency systematically decreased and alpha power systematically increased over ~1 hour of . CC-BY 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/263103 doi: bioRxiv preprint first posted online Feb. 12, 2018; 3 psychophysical task performance, even during trial baselines. These non-stationarities do not appear to be two manifestations of a single process but rather two independent effects. These findings inform signal processing and neuro-stimulation techniques and advance our understanding of the physiology of functionally relevant neural oscillations.

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“…91 Participants performed a pitch discrimination task, comparing tone pairs embed-92 ded in noise, as illustrated in Figure 1A. They were instructed to indicate after each 93 tone pair whether the second tone was lower or higher than the first. 94 A black fixation cross was displayed on gray background throughout the whole 95 block.…”

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confidence: 99%

Implicit temporal predictability biases slow oscillatory phase in auditory cortex and enhances pitch discrimination sensitivity

Herbst

Obleser

2018

Preprint

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Can human listeners use implicit temporal contingencies in auditory input to form temporal predictions, and if so, how are these predictions represented endogenously? To assess this question, we manipulated foreperiods in an auditory pitch discrimination task: unbeknownst to participants, the pitch of the standard tone could either be deterministically predictive of the temporal onset of the target tone, or convey no predictive information. Predictive and non-predictive conditions were presented interleaved in one stream, and separated by variable inter-stimulus intervals such that there was no dominant stimulus rhythm throughout. Even though participants were unaware of the implicit temporal contingencies, pitch discrimination sensitivity (the slope of the psychometric function) increased when the onset of the target tone was predictable in time (N = 49, 28 female, 21 male). Concurrently recorded EEG data (N = 24) revealed that standard tones that conveyed temporal predictions evoked a more negative N1 component than non-predictive standards. We observed no significant differences in oscillatory power or phase coherence between conditions during the foreperiod. Importantly, the phase angle of delta oscillations (1-3 Hz) in auditory areas in the post-standard and pre-target time window predicted behavioral pitch discrimination sensitivity. This suggests that temporal predictions can be initiated by an optimized delta phase reset and are encoded in delta oscillatory phase during the foreperiod interval. In sum, we show that auditory perception benefits from implicit temporal contingencies, and provide evidence for a role of slow neural phase in the endogenous representation of temporal predictions, in absence of exogenously driven entrainment to rhythmic input. Acknowledgments: This research was supported by a DFG grant (HE 7520/1-1) to SKH.Auditory environments come with an inherent temporal structure, which human listeners can use to predict the timing of future inputs. Yet, how these regularities in sensory inputs are transformed into temporal predictions is not known. Here, we implicitly induced temporal predictability in the absence of a rhythmic input structure, to avoid exogenously driven entrainment of neural oscillations. Our results show that even implicit and non-rhythmic temporal predictions are extracted and used by human listeners, underlining the role of timing for auditory processing. Furthermore, our EEG results point towards an instrumental role of delta oscillations in initiating temporal predictions, possibly by an optimized phase reset in response to a temporally predictive cue.

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Trial‐by‐trial co‐variation of pre‐stimulusEEGalpha power and visuospatial bias reflects a mixture of stochastic and deterministic effects

Cited by 59 publications

References 123 publications

Multiple mechanisms link prestimulus neural oscillations to sensory responses

Multiple mechanisms link prestimulus neural oscillations to sensory responses

Frequency and power of human alpha oscillations drift systematically with time-on-task

Implicit temporal predictability biases slow oscillatory phase in auditory cortex and enhances pitch discrimination sensitivity

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