Multiple oscillatory rhythms determine the temporal organization of perception

Ronconi, Luca; Oosterhof, Nikolaas N.; Bonmassar, Claudia; Melcher, David

doi:10.1073/pnas.1714522114

Cited by 85 publications

(109 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In particular, phase yielded strongly significant (p<0.001) decoding results in 9/10 subjects for the hard discrimination, while power did so only in 4/10 subjects. Primacy of phase information over power information, especially for distinguishing between activity patterns corresponding to different sensory stimuli, is in accordance with several prior reports in auditory, visual and tactile modalities (Luo and Poeppel, 2007;Howard and Poeppel, 2010;Ng et al, 2013;Gross et al, 2013;Schyns et al, 2011;Ronconi et al, 2017;Wang et al, 2018;Baumgarten et al, 2015), in motor signals (Hammer et al, 2013), as well as in more complex paradigms involving working memory and decision making (Rizzuto et al, 2003;Lopour et al, 2013). In our dataset and for the binary decoding analyses we considered, the combination of power and phase did not convey additional information beyond the one conveyed by the most informative between power and phase ( Fig.…”

Section: Stimulus Encoding Occurs Preferentially At Delta-theta and Gsupporting

confidence: 91%

“…The relationships between spectral features of neural activity and its stimulus encoding properties is a topic of great interest in sensory neuroscience (e.g., (Belitski et al, 2008;Belitski et al, 2010;Tsuchiya et al, 2008;Schyns et al, 2011;Gross et al, 2013;Ronconi et al, 2017)). The development of a neuronal network model that exhibits remarkable correspondence to iEEG data in terms of both spectral properties and stimulus encoding enabled us to formulate an approach for relating these two aspects of neural activity, which have hitherto been mostly investigated separately.…”

Section: Model-derived Relationships Between Spectral Features and Stmentioning

confidence: 99%

See 1 more Smart Citation

Converging intracortical signatures of two separated processing timescales in human early auditory cortex

Baroni

Morillon

Trébuchon

et al. 2019

Preprint

View full text Add to dashboard Cite

Neural oscillations in auditory cortex are argued to support parsing and representing speech constituents at their corresponding temporal scales. Yet, how incoming sensory information interacts with ongoing spontaneous brain activity, what features of the neuronal microcircuitry underlie spontaneous and stimulus-evoked spectral fingerprints, and what these fingerprints entail for stimulus encoding, remain largely open questions. We used a combination of human invasive electrophysiology, computational modeling and decoding techniques to assess the information encoding properties of brain activity and to relate them to a plausible underlying neuronal microarchitecture. We analyzed intracortical auditory EEG activity from 10 patients while they were listening to short sentences. Pre-stimulus neural activity in early auditory cortical regions often exhibited power spectra with a shoulder in the delta range and a small bump in the beta range. Speech decreased power in the beta range, and increased power in the delta-theta and gamma ranges. Using multivariate machine learning techniques, we assessed the spectral profile of information content for two aspects of speech processing: detection and discrimination. We obtained better phase than power information decoding, and a bimodal spectral profile of information content with better decoding at low (delta-theta) and high (gamma) frequencies than at intermediate (beta) frequencies. These experimental data were reproduced by a simple rate model made of two subnetworks with different timescales, each composed of coupled excitatory and inhibitory units, and connected via a negative feedback loop. Modeling and experimental results were similar in terms of pre-stimulus spectral profile (except for the iEEG beta bump), spectral modulations with speech, and spectral profile of information content. Altogether, we provide converging evidence from both univariate spectral analysis and decoding approaches for a dual timescale processing infrastructure in human auditory cortex, and show that it is consistent with the dynamics of a simple rate model. Author summaryLike most animal vocalizations, speech results from a pseudo-rhythmic process that reflects the convergence of motor and auditory neural substrates and the natural resonance properties of the vocal apparatus towards efficient communication. Here, we leverage the excellent temporal and spatial resolution of intracranial EEG to demonstrate that neural activity in human early auditory cortical areas during speech perception exhibits a dual-scale spectral profile of power changes, with speech increasing power in low (delta-theta) and high (gamma -high-gamma) frequency ranges, while decreasing power in intermediate (alpha-beta) frequencies. Single-trial multivariate decoding also resulted in a bimodal spectral profile of information content, with better decoding at low and high frequencies than at intermediate ones.From both spectral and informational perspectives, these patterns are consistent with the activity of a relatively s...

show abstract

Section: Stimulus Encoding Occurs Preferentially At Delta-theta and Gsupporting

confidence: 91%

Section: Model-derived Relationships Between Spectral Features and Stmentioning

confidence: 99%

Converging intracortical signatures of two separated processing timescales in human early auditory cortex

Baroni

Morillon

Trébuchon

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…1A). For instance, in both visual and somatosensory systems, the detection of a near-threshold stimulus and the ability to distinguish two rapidly presented stimuli correlate with EEG/MEG phase (Ai and Ro, 2014;Baumgarten et al, 2015;Busch et al, 2009;Milton and Pleydell-Pearce, 2016;Ronconi et al, 2017). In these studies, stimuli are often presented at an a-priori unknown, random neural phase in each trial; this leads to the possibility of testing how performance depends on the phase of spontaneous (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…the null distribution). This distribution can, for instance, be constructed by repeatedly shuffling performance labels (e.g., "hits" vs "misses") and re-running the original analysis on the permuted datasets (Busch et al, 2009;Dugué et al, 2011;Hanslmayr et al, 2013;Ng et al, 2012;Ronconi et al, 2017;Strauß et al, 2015;ten Oever and Sack, 2015;Wutz et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

How to test for phasic modulation of neural and behavioural responses

Zoefel

Davis

Valente

et al. 2019

Preprint

View full text Add to dashboard Cite

 The phase of neural oscillations modulates neural responses and behaviour  We test the ability of different methods to detect such a phase effect  We test how this ability changes with nature of effect and experimental parameters  Results represent groundwork for future investigations of oscillatory phase effects AbstractThe question whether perception or other processes depend on the phase of neural oscillations is a research topic that is rapidly gaining popularity. Importantly however, it is unknown which methods are optimally suited to evaluate the hypothesized phase effect. Using a simulation approach, we here test the ability of different methods to detect such an effect and reject an absent one. We reveal optimal methods for different parameters that characterise the phase effect or depend on the experimental design to test for it. We demonstrate that permutation-based methods perform better than parametric ones for all of the simulated parameters. The identity of the optimal parametric method varies with several parameters, in particular with the number of phase bins chosen for the analysis and the width of the phase effect. In contrast, the identity of the best permutation-based method is unaffected by changes in the simulated parameters. In sum, our study lays a foundation for optimized experimental designs and analyses in future studies investigating the role of neural phase for behaviour and neural function. We supply MATLAB code that can be used to identify the ideal method (among all of the methods tested) for a specific scientific hypothesis and set of experimental parameters.

show abstract

“…It has been proposed that perception is discrete and cyclic in a manner of perceptual cycles [15,16,41,54,55]. Accumulating recent evidence showed that perceptual performance depends on the frequency of the critical rhythm at around the onset time of stimuli [46,47,56]. A higher frequency of the brain oscillations should be equivalent to a faster frame rate of discrete perception, and vice versa.…”

Section: Discussionmentioning

confidence: 99%

Perceptual inference employs intrinsic alpha frequency to resolve perceptual ambiguity

Shen

Han²,

Chen

et al. 2018

Preprint

View full text Add to dashboard Cite

The brain uses its intrinsic dynamics to actively predict observed sensory inputs, especially under perceptual ambiguity. However, it remains unclear how this inference process is neurally implemented in biasing perception of ambiguous inputs towards the predicted percepts. Using electroencephalography and intracranial recordings, we first show that the alpha-band frequency defines a unified time window for perceptual grouping across both space and time: information segments, either spatially or temporally segregated, will be integrated if they fall within the same alpha cycle. Moreover, predictions employ this prior knowledge on intrinsic alpha frequency to shift perceptual inference towards the most possibly observed percepts. Multivariate decoding analysis showed that perceptual inference, based on variance in prestimulus alpha frequency (PAF), biases post-stimulus neural representations by inducing preactivation of the predicted percepts. fMRI results additionally showed that prestimulus activity and intrinsic organization status in the frontoparietal attentional network predict perceptual outcomes, probably by modulating occipitoparietal PAFs.

show abstract

Multiple oscillatory rhythms determine the temporal organization of perception

Cited by 85 publications

References 65 publications

Converging intracortical signatures of two separated processing timescales in human early auditory cortex

Converging intracortical signatures of two separated processing timescales in human early auditory cortex

How to test for phasic modulation of neural and behavioural responses

Perceptual inference employs intrinsic alpha frequency to resolve perceptual ambiguity

Contact Info

Product

Resources

About