Alpha oscillations control cortical gain by modulating excitatory-inhibitory background activity

Peterson, Erik; Voytek, Bradley

doi:10.1101/185074

Cited by 40 publications

(37 citation statements)

References 113 publications

(227 reference statements)

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“…Overall, our results suggest that stimulus-related responses are boosted during a liberal criterion due to increased cortical response gain, which is further supported by recent work linking alpha power suppression to enhanced gain 42 .…”

Section: Discussionsupporting

confidence: 75%

Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation

Kloosterman

Gee

Werkle‐Bergner

et al. 2017

Preprint

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Decision bias is traditionally conceptualized as an internal reference against which sensory evidence is compared. Instead, we show that individuals implement decision bias by shifting the rate of sensory evidence accumulation towards a decision bound. Participants performed a target detection task while we recorded EEG. We experimentally manipulated participants’ decision criterion for reporting targets using different stimulus-response reward contingencies, inducing either a liberal or a conservative bias. Drift diffusion modeling revealed that a liberal strategy biased sensory evidence accumulation towards target-present choices. Moreover, a liberal bias resulted in stronger midfrontal pre-stimulus 2-6 Hz (theta) power and suppression of pre-stimulus 8—12 Hz (alpha) power in posterior cortex. The alpha suppression in turn mediated the output activity of visual cortex, as expressed in 59—100 Hz (gamma) power. These findings show that observers can intentionally control cortical excitability to strategically bias evidence accumulation towards the decision bound that maximizes their reward.

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Section: Discussionsupporting

confidence: 75%

Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation

Kloosterman

Gee

Werkle‐Bergner

et al. 2017

Preprint

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“…In line with these 1217 observations, Peterson & Voytek (2017) recently proposed alpha 'bursts' to increase visual gain 1218 during stimulus onsets and contrasted this role with decreased cortical processing during 1219 sustained alpha rhythms. Our data supports such a distinction between sustained and transient 1220 events, although it should be noted that the present transients resemble single time-domain 1221 deflections that are resolved at alpha frequency ( Figure 10A2) and may therefore not directly 1222 relate to the 'rhythmic bursts' proposed by Peterson & Voytek (2017). Note that the reported 1223 duration of 'burst' events in the literature is still diverse, often exceeding the 3-cycle threshold 1224 used here (Peterson & Voytek, 2017).…”

supporting

confidence: 57%

“…Our data supports such a distinction between sustained and transient 1220 events, although it should be noted that the present transients resemble single time-domain 1221 deflections that are resolved at alpha frequency ( Figure 10A2) and may therefore not directly 1222 relate to the 'rhythmic bursts' proposed by Peterson & Voytek (2017). Note that the reported 1223 duration of 'burst' events in the literature is still diverse, often exceeding the 3-cycle threshold 1224 used here (Peterson & Voytek, 2017). In contrast to eBOSC however, previous work has not 1225 accounted for the impact of wavelet duration.…”

supporting

confidence: 57%

Single-trial characterization of neural rhythms: potential and challenges

Kosciessa

Grandy

Garrett

et al. 2018

Preprint

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10 The average power of rhythmic neural responses as captured by MEG/EEG/LFP recordings is a 11 prevalent index of human brain function. Increasing evidence questions the utility of trial-/group 12 averaged power estimates however, as seemingly sustained activity patterns may be brought about 13 by time-varying transient signals in each single trial. Hence, it is crucial to accurately describe the 14 duration and power of rhythmic and arrhythmic neural responses on the single trial-level. However, 15 it is less clear how well this can be achieved in empirical MEG/EEG/LFP recordings. Here, we 16 extend an existing rhythm detection algorithm (extended Better OSCillation detection: "eBOSC"; 17 cf. Whitten et al., 2011) to systematically investigate boundary conditions for estimating neural 18rhythms at the single-trial level. Using simulations as well as resting and task-based EEG recordings 19 from a micro-longitudinal assessment, we show that alpha rhythms can be successfully captured in 20 single trials with high specificity, but that the quality of single-trial estimates varies greatly between 21 subjects. Despite those signal-to-noise-based limitations, we highlight the utility and potential of 22 rhythm detection with multiple proof-of-concept examples, and discuss implications for single-trial 23 analyses of neural rhythms in electrophysiological recordings. Using an applied example of 24 working memory retention, rhythm detection indicated load-related increases in the duration of 25 frontal theta and posterior alpha rhythms, in addition to a frequency decrease of frontal theta 26 rhythms that was observed exclusively through amplification of rhythmic amplitudes. 27 28Highlights: 29• Traditional narrow-band rhythm metrics conflate the power and duration of rhythmic and arrhythmic 30 periods. We extend a state-of-the-art rhythm detection method (eBOSC) to derive rhythmic episodes in 31 single trials that can disambiguate rhythmic and arrhythmic periods. 32• Simulations indicate that this can be done with high specificity given sufficient rhythmic power, but with 33 strongly impaired sensitivity when rhythmic SNR is low. Empirically, surface EEG recordings exhibit 34 stable inter-individual differences in α-rhythmicity in ranges where simulations suggest a gradual bias, 35 leading to high collinearity between narrow-band and rhythm-specific estimates. 36• Beyond these limitations, we highlight multiple empirical benefits of characterizing rhythmic episodes 37 in single trials, such as (a) a principled separation of rhythmic and arrhythmic content, (b) an 38 amplification of rhythmic amplitudes, and (c) a specific characterization of sustained and transient 39 events. 40• In an exemplary application, rhythm-specific estimates increase sensitivity to working memory load 41 effects, in addition to indicating a frequency modulation of frontal theta rhythms through the 42 amplification of rhythmic power. 43 44

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“…A well-described mechanism of attentional stimulus selection is gain regulation of population responses in sensory regions 2,3 . MEG and EEG studies have shown that gain regulation can be reflected in alpha band dynamics 4 . This has been demonstrated in several studies with respect to anticipatory biasing of visual processing in visuospatial cueing [5][6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

Frontal and parietal alpha oscillations reflect attentional modulation of cross-modal matching

Misselhorn

Friese

Engel

2018

Preprint

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Multisensory perception is shaped by both attentional selection of relevant sensory inputs and exploitation of stimulus-driven factors that promote cross-modal binding. Underlying mechanisms of both top-down and bottom-up modulations have been linked to changes in alpha/gamma dynamics in primary sensory cortices and temporoparietal cortex. Accordingly, it has been proposed that alpha oscillations provide pulsed inhibition for gamma activity and thereby dynamically route cortical information flow. In this study, we employed a recently introduced multisensory paradigm incorporating both bottom-up and top-down aspects of cross-modal attention in an EEG study. The same trimodal stimuli were presented in two distinct attentional conditions, focused on visual-tactile or audio-visual components, for which cross-modal congruence of amplitude changes had to be evaluated. Neither top-down nor bottom-up cross-modal attention modulated alpha or gamma power in primary sensory cortices. Instead, we found alpha band effects in bilateral frontal and right parietal cortex. We propose that frontal alpha oscillations reflect the origin of top-down control regulating perceptual gains and that modulations of parietal alpha oscillations relates to intersensory re-orienting. Taken together, we suggest that the idea of selective cortical routing via alpha oscillations can be extended from sensory cortices to the frontoparietal attention network.

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Alpha oscillations control cortical gain by modulating excitatory-inhibitory background activity

Cited by 40 publications

References 113 publications

Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation

Humans strategically shift decision bias by flexibly adjusting sensory evidence accumulation

Single-trial characterization of neural rhythms: potential and challenges

Frontal and parietal alpha oscillations reflect attentional modulation of cross-modal matching

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