Representations of the temporal envelope of sounds in human auditory cortex: Can the results from invasive intracortical “depth” electrode recordings be replicated using non-invasive MEG “virtual electrodes”?

Millman, Rebecca E.; Prendergast, Garreth; Hymers, Mark; Green, Gary

doi:10.1016/j.neuroimage.2012.09.017

Cited by 24 publications

(20 citation statements)

References 49 publications

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“…As such, the relative phase of coupling could constitute a valuable code to partition neural activity for sensory processing (Hyafil et al 2015b;Jensen et al 2012Jensen et al , 2014Lisman and Jensen 2013;Nadasdy 2010;Panzeri et al 2010). Consistent with this, the phase of firing according to low-frequency oscillations has been shown to be a reliable decoder of sensory content Montemurro et al 2008;Ng et al 2013;Panzeri et al 2010), and the relative phase of slow neural oscillations can predict perceptual features and attentional state (Agarwal et al 2014;Bonnefond and Jensen 2012;Kösem et al 2014;van Ede et al 2015). Low-frequency neural oscillations could thus provide temporal metrics for sensory processing, and the entrainment of neural oscillations to external rhythms could support the extraction of timing information without a priori knowledge of external timing (Kösem et al 2014;Scharnowski et al 2013).…”

Section: Origins Of the Difference Between Hfa And Lfo Effectsmentioning

confidence: 87%

“…These effects were systematic within individuals, concentrated in the beta, gamma, and high-gamma frequency bands. Gamma oscillations are markers of neural excitability (Lakatos et al 2005), and HFA has more generally been shown to track the dynamics of speech Hyafil et al 2015a;Kubanek et al 2013;Mesgarani and Chang 2012;Mesgarani et al 2014;Millman et al 2013;Nourski et al 2009;Pasley et al 2012;Zion Golumbic et al 2013). More specifically, the gamma band has been hypothesized to encode speech information at the phonemic level (Poeppel 2003;Poeppel et al 2008).…”

Section: Top-down Control Of Lfo and Hfa During Speech Processingmentioning

confidence: 99%

“…In particular, two main oscillatory regimes are deemed fundamental for the encoding of speech. First, low-frequency neural oscillations in the delta to theta range (2-8 Hz) have been shown to follow natural speech rhythms, enabling the tracking of the temporal structure of acoustic speech features such as syllables and words (Ahissar et al 2001;Ding and Simon 2013;Doelling et al 2014;Gross et al 2013;Poeppel 2007, 2012;Millman et al 2013;Peelle and Davis 2012;Rimmele et al 2015;Zion Golumbic et al 2013). Second, high-frequency neural activities, including the beta (20 -30 Hz) and gamma bands (Ͼ40 Hz), have been hypothesized to encode the finegrained properties of the speech signal such as phonetic features (Ghitza 2011;Giraud and Poeppel 2012;Poeppel 2003;Poeppel et al 2008).…”

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confidence: 99%

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High-frequency neural activity predicts word parsing in ambiguous speech streams

Kösem

Basirat

Azizi

et al. 2016

Journal of Neurophysiology

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Kösem A, Basirat A, Azizi L, van Wassenhove V. High-frequency neural activity predicts word parsing in ambiguous speech streams. J Neurophysiol 116: 2497-2512, 2016. First published September 7, 2016 doi:10.1152/jn.00074.2016.-During speech listening, the brain parses a continuous acoustic stream of information into computational units (e.g., syllables or words) necessary for speech comprehension. Recent neuroscientific hypotheses have proposed that neural oscillations contribute to speech parsing, but whether they do so on the basis of acoustic cues (bottom-up acoustic parsing) or as a function of available linguistic representations (top-down linguistic parsing) is unknown. In this magnetoencephalography study, we contrasted acoustic and linguistic parsing using bistable speech sequences. While listening to the speech sequences, participants were asked to maintain one of the two possible speech percepts through volitional control. We predicted that the tracking of speech dynamics by neural oscillations would not only follow the acoustic properties but also shift in time according to the participant's conscious speech percept. Our results show that the latency of high-frequency activity (specifically, beta and gamma bands) varied as a function of the perceptual report. In contrast, the phase of low-frequency oscillations was not strongly affected by top-down control. Whereas changes in low-frequency neural oscillations were compatible with the encoding of prelexical segmentation cues, high-frequency activity specifically informed on an individual's conscious speech percept.

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Section: Origins Of the Difference Between Hfa And Lfo Effectsmentioning

confidence: 87%

Section: Top-down Control Of Lfo and Hfa During Speech Processingmentioning

confidence: 99%

mentioning

confidence: 99%

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High-frequency neural activity predicts word parsing in ambiguous speech streams

Kösem

Basirat

Azizi

et al. 2016

Journal of Neurophysiology

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“…Peaks of cross-correlograms between the stimulus envelope and the cortical response (AEP waveform and high gamma ERBP) were found between lags of 0 and 150 ms. Following the approach of Millman et al (2013), stimulus-response cross-correlation in the time domain was evaluated using a bootstrap procedure to generate surrogate data, followed by an approximate permutation test that provided the estimated P values and critical thresholds (Nichols and Holmes 2002). A surrogate data set was generated from the original single-trial ECoG waveforms by selecting, at random, half of the trials and inverting their waveforms.…”

Section: Discussionmentioning

confidence: 99%

Direct Recordings from the Auditory Cortex in a Cochlear Implant User

Nourski

Etler

Brugge

et al. 2013

JARO

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Electrical stimulation of the auditory nerve with a cochlear implant (CI) is the method of choice for treatment of severe-to-profound hearing loss. Understanding how the human auditory cortex responds to CI stimulation is important for advances in stimulation paradigms and rehabilitation strategies. In this study, auditory cortical responses to CI stimulation were recorded intracranially in a neurosurgical patient to examine directly the functional organization of the auditory cortex and compare the findings with those obtained in normal-hearing subjects. The subject was a bilateral CI user with a 20-year history of deafness and refractory epilepsy. As part of the epilepsy treatment, a subdural grid electrode was implanted over the left temporal lobe. Pure tones, click trains, sinusoidal amplitude-modulated noise, and speech were presented via the auxiliary input of the right CI speech processor. Additional experiments were conducted with bilateral CI stimulation. Auditory event-related changes in cortical activity, characterized by the averaged evoked potential and eventrelated band power, were localized to posterolateral superior temporal gyrus. Responses were stable across recording sessions and were abolished under general anesthesia. Response latency decreased and magnitude increased with increasing stimulus level. More apical intracochlear stimulation yielded the largest responses. Cortical evoked potentials were phaselocked to the temporal modulations of periodic stimuli and speech utterances. Bilateral electrical stimulation resulted in minimal artifact contamination. This study demonstrates the feasibility of intracranial electrophysiological recordings of responses to CI stimulation in a human subject, shows that cortical response properties may be similar to those obtained in normal-hearing individuals, and provides a basis for future comparisons with extracranial recordings.

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“…Significant phase-locking to the sound envelope is also observed for unintelligible, time-inverted, or noise-vocoded speech as well as non-speech sounds (Lalor et al 2009; Howard and Poeppel 2012; Hämäläinen et al 2012; Peelle et al 2012; Wang et al 2012; Millman et al 2013; Steinschneider et al 2013; Ding et al 2014). Nonetheless, speech tracking is more robust for natural compared to noise-vocoded speech, in which the fine structure information is removed but the low-frequency temporal fluctuations contained in the speech envelope are preserved (Luo and Poeppel 2007; Howard and Poeppel 2010; Peelle et al 2012; Wild, Davis, et al 2012; Ding et al 2014).…”

Section: 1 the Speech-tracking Responsementioning

confidence: 93%

The effects of selective attention and speech acoustics on neural speech-tracking in a multi-talker scene

Rimmele

Golumbic

Schröger

et al. 2015

Cortex

150

132

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Attending to one speaker in multi-speaker situations is challenging. One neural mechanism proposed to underlie the ability to attend to a particular speaker is phase-locking of low-frequency activity in auditory cortex to speech’s temporal envelope (“speech-tracking”), which is more precise for attended speech. However, it is not known what brings about this attentional effect, and specifically if it reflects enhanced processing of the fine structure of attended speech. To investigate this question we compared attentional effects on speech-tracking of natural vs. vocoded speech which preserves the temporal envelope but removes the fine-structure of speech. Pairs of natural and vocoded speech stimuli were presented concurrently and participants attended to one stimulus and performed a detection task while ignoring the other stimulus. We recorded magnetoencephalography (MEG) and compared attentional effects on the speech-tracking response in auditory cortex. Speech-tracking of natural, but not vocoded, speech was enhanced by attention, whereas neural tracking of ignored speech was similar for natural and vocoded speech. These findings suggest that the more precise speech tracking of attended natural speech is related to processing its fine structure, possibly reflecting the application of higher-order linguistic processes. In contrast, when speech is unattended its fine structure is not processed to the same degree and thus elicits less precise speech tracking more similar to vocoded speech.

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Representations of the temporal envelope of sounds in human auditory cortex: Can the results from invasive intracortical “depth” electrode recordings be replicated using non-invasive MEG “virtual electrodes”?

Cited by 24 publications

References 49 publications

High-frequency neural activity predicts word parsing in ambiguous speech streams

High-frequency neural activity predicts word parsing in ambiguous speech streams

Direct Recordings from the Auditory Cortex in a Cochlear Implant User

The effects of selective attention and speech acoustics on neural speech-tracking in a multi-talker scene

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