Biology subjects

Steingraeber, N.

Publications and source records attributed to Steingraeber, N..

4 recordsLinked to original sources

bioRxiv2022

Predictive coordination of breathing during speaking and listening

It has long been known that human breathing is altered during listening and speaking compared to rest. Theoretical models of human communication suggest two distinct phenomena during speaking and listening: During speaking, inhalation depth is adjusted to the air volume required for the upcoming utterance. During listening, inhalation is temporally aligned to inhalation of the speaker. While evidence for the former is relatively strong, it is virtually absent for the latter. We address both questions using recordings of speech envelope and respiration in 30 participants during 14 minutes of speaking and listening. We extend existing evidence for the first phenomenon by using the speech envelope to show that inhalation depth is positively correlated with the total power of the speech envelope in the following utterance. Pertaining to the second phenomenon, we provide first evidence that inhalation during listening to your own speech is significantly more likely at time points of inhalation during speaking. These findings are compatible with models that postulate alignment of internal forward models of interlocutors with the aim to facilitate communication.

neuroscience↗

bioRxiv2022

Oscillatory brain networks in continuous speaking and listening

Speech production and perception are fundamental processes of human cognition that both rely on an internal forward model that is still poorly understood. Here, we study this forward model by using Magnetoencephalography (MEG) to comprehensively map connectivity of regional brain activity within the brain and to the speech envelope during continuous speaking and listening. Our results reveal a partly shared neural substrate for both processes but also a dissociation in space, delay and frequency. Neural activity in motor and frontal areas is coupled to succeeding speech in delta band (1-3 Hz), whereas coupling in the theta range follows speech in temporal areas during speaking. Neural connectivity results showed a separation of bottom-up and top-down signalling in distinct frequency bands during speaking. Here, we show that frequency-specific connectivity channels for bottom-up and top-down signalling support continuous speaking and listening in a way that is consistent with the predictive coding framework.

neuroscience↗

bioRxiv2021

Comparison of undirected frequency-domain connectivity measures for cerebro-peripheral analysis

Analyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. As there is no consensus regarding best practice, a wide variety of methods has been applied. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Our results show that, overall, wppc and gcmi tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. For group analysis, choosing the appropriate spectral estimation method appeared to be more critical than the connectivity measure. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions.

neuroscience↗

bioRxiv2020

Correcting MEG artifacts caused by overt speech

Recording brain activity during speech production using magnetoencephalography (MEG) can help us to understand the dynamics of speech production. However, these measurements are challenging due to the induced artifacts coming from several sources such as facial muscle activity, lower jaw and head movements. Here, we aimed to characterise speech-related artifacts and subsequently present an approach to remove these artifacts from MEG data. We recorded MEG from 11 healthy participants while they pronounced various syllables in different loudness. Head positions/orientations were extracted during speech production to investigate its role in MEG distortions. Finally, we present an artifact rejection approach using the combination of regression analysis and signal space projection (SSP) in order to correct the induced artifact from MEG data. Our results show that louder speech leads to stronger head movements and stronger MEG distortions. Our proposed artifact rejection approach could successfully remove the speech-related artifact and retrieve the underlying neurophysiological signals. As the presented artifact rejection approach was shown to remove artifacts induced by overt speech in the MEG, it will facilitate research addressing the neural basis of speech production with MEG.

neuroscience↗