Sustained neural rhythms reveal endogenous oscillations supporting speech perception

Bree, Sander van; Sohoglu, Ediz; Davis, Matthew H.; Zoefel, Benedikt

doi:10.1101/2020.06.26.170761

Cited by 13 publications

(16 citation statements)

References 90 publications

(168 reference statements)

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“…This is consistent with the proposed role for beta oscillations of the motor system for sensory predictions [44], in addition to its likely primary function in timed movement preparation [65,66] and further supported by the rebound peak latency scaling with the period in a rhythmic motor task [65]. In terms of a correlation with behaviour in the sensory domain, as suggested for rhythmic motor learning by [60], or a possible specific role in the processing of speech [67,68], further work is needed.…”

Section: Functional Interpretationssupporting

confidence: 73%

Fourier SPoC: A customised machine-learning analysis pipeline for auditory beat-based entrainment in the MEG

Brandl

Haumann²,

Radloff³

et al. 2021

Preprint

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We propose here (the informed use) of a customised, data-driven machine-learning pipeline to analyse magnetoencephalography (MEG) in a theoretical source space, with respect to the processing of a regular beat. This hypothesis- and data-driven analysis pipeline allows us to extract the maximally relevant components in MEG source-space, with respect to the oscillatory power in the frequency band of interest and, most importantly, the beat-related modulation of that power. Our pipeline combines Spatio-Spectral Decomposition as a first step to seek activity in the frequency band of interest (SSD, [1]) with a Source Power Co-modulation analysis (SPoC; [2]), which extracts those components that maximally entrain their activity with the given target function, that is here with the periodicity of the beat in the frequency domain (hence, f-SPoC). MEG data (102 magnetometers) from 28 participants passively listening to a 5-min long regular tone sequence with a 400 ms beat period (the “target function” for SPoC) were segmented into epochs of two beat periods each to guarantee a sufficiently long time window. As a comparison pipeline to SSD and f-SpoC, we carried out a state-of-the-art cluster-based permutation analysis (CBPA, [3]). The time-frequency analysis (TFA) of the extracted activity showed clear regular patterns of periodically occurring peaks and troughs across the alpha and beta band (8-20 Hz) in the f-SPoC but not in the CBPA results, and both the depth and the specificity of modulation to the beat frequency yielded a significant advantage. Future applications of this pipeline will address target the relevance to behaviour and inform analogous analyses in the EEG, in order to finally work toward addressing dysfunctions in beat-based timing and their consequences.Author summaryWhen listening to a regular beat, oscillations in the brain have been shown to synchronise with the frequency of that given beat. This phenomenon is called entrainment and has in previous brain-imaging studies been shown in the form of one peak and trough per beat cycle in a range of frequency bands within 15-25 Hz (beta band). Using machine-learning techniques, we designed an analysis pipeline based on Source-Power Co-Modulation (SPoC) that enables us to extract spatial components in MEG recordings that show these synchronisation effects very clearly especially across 8-20 Hz. This approach requires no anatomical knowledge of the individual or even the average brain, it is purely data driven and can be applied in a hypothesis-driven fashion with respect to the “function” that we expect the brain to entrain with and the frequency band within which we expect to see this entrainment. We here apply our customised pipeline using “f-SPoC” to MEG recordings from 28 participants passively listening to a 5-min long tone sequence with a regular 2.5 Hz beat. In comparison to a cluster-based permutation analysis (CBPA) which finds sensors that show statistically significant power modulations across participants, our individually extracted f-SPoC components find a much stronger and clearer pattern of peaks and troughs within one beat cycle. In future work, this pipeline can be implemented to tackle more complex “target functions” like speech and music, and might pave the way toward rhythm-based rehabilitation strategies.

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Section: Functional Interpretationssupporting

confidence: 73%

Fourier SPoC: A customised machine-learning analysis pipeline for auditory beat-based entrainment in the MEG

Brandl

Haumann²,

Radloff³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…A first tentative explanation for the present results is that speech-brain tracking would not always reflect linguistic processing in speech. Speech-brain tracking could reflect a domaingeneral temporal attention mechanism (Schroeder & Lakatos, 2009) that would influence speech parsing and hence comprehension (Kösem et al, 2018;2020;van Bree et al, 2021). An alternative explanation is that speech-brain tracking would correctly mark the processing of phrasal and sentential information in speech, and that linguistic processing of unattended speech would be limited to phonological, sub-lexical and /or lexical semantic level.…”

Section: Discussionmentioning

confidence: 99%

Distracting Linguistic Information Impairs Neural Tracking of Attended Speech

Dai

McQueen

Terporten

et al. 2018

Preprint

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Listening to speech is difficult in noisy environments, and is even harder when the interfering noise consists of intelligible speech as compared to non-intelligible sounds. This suggests that the ignored speech is not fully ignored, and that competing linguistic information interferes with the neural processing of target speech. We tested this hypothesis using magnetoencephalography (MEG) while participants listened to target clear speech in the presence of distracting noisevocoded signals. Crucially, the noise vocoded distractors were initially unintelligible but were perceived as intelligible speech after a small training session. We compared participants' performance in the speech-in-noise task before and after training, and neural entrainment to both target and distracting speech. The comprehension of the target clear speech was reduced in the presence of intelligible distractors as compared to when they were unintelligible. The neural entrainment to target speech in the delta range (1-4 Hz) reduced in strength in the presence of an intelligible distractor. In contrast, neural entrainment to distracting signals was not significantly modulated by intelligibility. These results support and extend previous findings, showing, first, that the masking effects of distracting speech originate from the degradation of the linguistic representation of target speech, and second, that delta entrainment reflects linguistic processing of speech.

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“…This phase shift could result in higher intertrial phase coherence several cycles after the stimulus ceases. Indeed, some studies have found shifts in phase on this basis [ 28 – 30 ]. However, as Fig 1C shows, poststimulus, the oscillator reverts to its intrinsic (spontaneous) frequency.…”

Section: What An Oscillator Is Notmentioning

confidence: 99%

Neural oscillations are a start toward understanding brain activity rather than the end

Doelling

Assaneo

2021

PLoS Biol

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Does rhythmic neural activity merely echo the rhythmic features of the environment, or does it reflect a fundamental computational mechanism of the brain? This debate has generated a series of clever experimental studies attempting to find an answer. Here, we argue that the field has been obstructed by predictions of oscillators that are based more on intuition rather than biophysical models compatible with the observed phenomena. What follows is a series of cautionary examples that serve as reminders to ground our hypotheses in well-developed theories of oscillatory behavior put forth by theoretical study of dynamical systems. Ultimately, our hope is that this exercise will push the field to concern itself less with the vague question of “oscillation or not” and more with specific biophysical models that can be readily tested.

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Sustained neural rhythms reveal endogenous oscillations supporting speech perception

Cited by 13 publications

References 90 publications

Fourier SPoC: A customised machine-learning analysis pipeline for auditory beat-based entrainment in the MEG

Fourier SPoC: A customised machine-learning analysis pipeline for auditory beat-based entrainment in the MEG

Distracting Linguistic Information Impairs Neural Tracking of Attended Speech

Neural oscillations are a start toward understanding brain activity rather than the end

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