Speech Rhythms and Multiplexed Oscillatory Sensory Coding in the Human Brain

Groß, Joachim; Hoogenboom, Nienke; Thut, Gregor; Schyns, Philippe G.; Panzeri, Stefano; Belin, Pascal; Garrod, Simon

doi:10.1371/journal.pbio.1001752

Cited by 587 publications

(932 citation statements)

References 75 publications

Supporting

Mentioning

841

Contrasting

Unclassified

Order By: Relevance

“…To evaluate the performance of the continuous–continuous GCMI estimator (Section 3.1), we consider MEG data collected from a single subject within a continuous design with an auditory speech stimulus [Gross et al, 2013; Park et al, 2015]. For simplicity we focus here on a sensor‐level time‐domain analysis.…”

Section: Resultsmentioning

confidence: 99%

“…For simplicity we focus here on a sensor‐level time‐domain analysis. Since previous work has shown speech entrainment mostly at lower frequencies, we extracted the wideband amplitude envelope of the speech stimulus [Chandrasekaran et al, 2009; Gross et al, 2013] and then low‐pass filtered with a 12 Hz cutoff (third order noncausal Butterworth). The MEG signal was obtained from a 248‐magnetometer whole‐head MEG system (MAGNES 3600 WH, 4D Neuroimaging).…”

Section: Resultsmentioning

confidence: 99%

“…Binned MI measured have already been applied to these sorts of problems [Belitski et al, 2010; Gross et al, 2013; Kayser et al, 2009; Schyns et al, 2011; Szymanski et al, 2011], and a number of other techniques have also been developed [Kempter et al, 2012; Lachaux et al, 1999; Voytek et al, 2013]. In such analyses, it is unclear how best to model or bin the data, because phase, the angle of the complex spectral signal, is a circular variable which “wraps around” and has no clear ranking or extremal values [Berens, 2009; Lee, 2010].…”

Section: A Novel Methods For MI Estimation Using a Gaussian Copulamentioning

confidence: 99%

“…MI has been used to compare different neural response codes [Ince et al, 2013; Kayser et al, 2009; Reich et al, 2001], characterize different neurons [Sharpee, 2014] as well as quantify the effect of correlations between neurons [Ince et al, 2009, 2010; Moreno‐Bote et al, 2014] and the importance of spike timing [Kayser et al, 2010; Nemenman et al, 2008; Panzeri et al, 2001]. Recent studies have begun to explore its application to neuroimaging [Afshin‐Pour et al, 2011; Caballero‐Gaudes et al, 2013; Gross et al, 2013; Guggenmos et al, 2015; Ostwald and Bagshaw, 2011; Panzeri et al, 2008; Salvador et al, 2007; Saproo and Serences, 2010; Schyns et al, 2011; Serences et al, 2009]. …”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Ince

Giordano

Kayser

et al. 2016

Human Brain Mapping

Self Cite

267

298

View full text Add to dashboard Cite

We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open‐source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541–1573, 2017. © 2016 Wiley Periodicals, Inc.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: A Novel Methods For MI Estimation Using a Gaussian Copulamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Ince

Giordano

Kayser

et al. 2016

Human Brain Mapping

Self Cite

267

298

View full text Add to dashboard Cite

show abstract

“…To interpret these responses in relation to empirical results, we assume that outcomes are sampled every 250 ms. Although this is a little fast for overt exploratory movements in a maze, it corresponds to the intervals between saccadic eye movements in visual exploration (Srihasam, Bullock, & Grossberg, 2009) and the rate at which syllables are articulated in normal speech (Gross et al, 2013). Furthermore, it corresponds to the timescale of neuronal dynamics in the hippocampus (e.g., the duty cycle of theta activity).…”

Section: Thementioning

confidence: 99%

Active Inference: A Process Theory

et al. 2017

View full text Add to dashboard Cite

This is the published version of the paper.This version of the publication may differ from the final published version. behavior prescribed by these dynamics has a degree of face validity, providing a formal explanation for reward seeking, context learning, and epistemic foraging. Technically, the fact that a gradient descent appears to be a valid description of neuronal activity means that variational free energy is a Lyapunov function for neuronal dynamics, which therefore conform to Hamilton's principle of least action. Permanent repository link

show abstract

Perceptual Rhythms

VanRullen

2018

Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience

View full text Add to dashboard Cite

It is increasingly evident that brain function involves oscillatory activity in various frequency bands. This realization encouraged psychologists to consider that perception and cognition may operate periodically, as a succession of cycles mirroring the underlying oscillatory cycles. This idea, related to the age‐old notion of discrete perception, has resurfaced in recent years, fueled by advances in neuroscientific techniques. Contrary to earlier views of discrete perception as a unitary sampling rhythm affecting all perceptual and cognitive functions, contemporary evidence points not to one but several rhythms of perception that may depend on the sensory modality, the task, the stimulus, the brain region(s) involved, or the state of the subject. In the visual domain for example, a sensory alpha rhythm (∼10 Hz) may coexist with at least one more rhythm performing attentional sampling around 7 Hz. How these multiple periodic functions coordinate with each other and how internal sampling rhythms coordinate with overt sampling behavior are key questions for the future.

show abstract

Speech Rhythms and Multiplexed Oscillatory Sensory Coding in the Human Brain

Abstract: A neuroimaging study reveals how coupled brain oscillations at different frequencies align with quasi-rhythmic features of continuous speech such as prosody, syllables, and phonemes.

Cited by 587 publications

References 75 publications

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula

Active Inference: A Process Theory

Perceptual Rhythms

Contact Info

Product

Resources

About