Parallel streams define the temporal dynamics of speech processing across human auditory cortex

Hamilton, Liberty S.; Edwards, Erik; Chang, Edward F.

doi:10.1101/097485

Cited by 4 publications

(3 citation statements)

References 78 publications

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“…In particular, a large positive peak is observed at approximately 10 ms followed by a large negative peak at 25-50 ms. A second large negative peak is observed around 125 ms after the "deviant" sound, and a third follows at approximately 200 ms. The temporal dynamic of the mode suggests distinct onset and terminating responses following deviant sound onset that matches well with the dynamics observed in core or primary auditory cortices in other studies [20,38,44,55]. The reader should also notice that all signs, amplitudes, and time relative to the signal onset of the deviant match well with those depicted in Fig.…”

Section: Temporal Coordinatessupporting

confidence: 87%

Data-driven Koopman operator approach for computational neuroscience

Marrouch

Sławińska

Giannakis

et al. 2019

Ann Math Artif Intell

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This article presents a novel, nonlinear, data-driven signal processing method, which can help neuroscience researchers visualize and understand complex dynamical patterns in both time and space. Specifically, we present applications of a Koopman operator approach for eigendecomposition of electrophysiological signals into orthogonal, coherent components and examine their associated spatiotemporal dynamics. This approach thus provides enhanced capabilities over conventional computational neuroscience tools restricted to analyzing signals in either the time or space domains. This is achieved via machine learning and kernel methods for data-driven approximation of skew-product dynamical systems. The approximations successfully converge to theoretical values in the limit of long embedding windows. First, we describe the method, then using electrocorticographic (ECoG) data from a mismatch negativity experiment, we extract time-separable frequencies without bandpass filtering or prior selection of wavelet features. Finally, we discuss in detail two of the extracted components, Beta (∼ 13 Hz) and high Gamma (∼ 50 Hz) frequencies, and explore the spatiotemporal dynamics of high-and low-frequency components.

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Section: Temporal Coordinatessupporting

confidence: 87%

Data-driven Koopman operator approach for computational neuroscience

Marrouch

Sławińska

Giannakis

et al. 2019

Ann Math Artif Intell

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show abstract

“…However, the relatively simple movements examined here exhibit temporal autocorrelation, which makes it difficult to dissociate feedforward activity from feedback. Examining speech tasks with faster, less stereotyped movements (e.g., naturally produced words or sentences) would make it possible to disentangle feedforward and feedback signals, and is an interesting and important future direction (Chang et al, 2013;Greenlee et al, 2013;Kingyon et al, 2015;Behroozmand et al, 2016;W. Li et al, 2016;Cao et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

Human Sensorimotor Cortex Control of Directly Measured Vocal Tract Movements during Vowel Production

et al. 2018

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During speech production, we make vocal tract movements with remarkable precision and speed. Our understanding of how the human brain achieves such proficient control is limited, in part due to the challenge of simultaneously acquiring high-resolution neural recordings and detailed vocal tract measurements. To overcome this challenge, we combined ultrasound and video monitoring of the supralaryngeal articulators (lips, jaw, and tongue) with electrocorticographic recordings from the cortical surface of 4 subjects (3 female, 1 male) to investigate how neural activity in the ventral sensory-motor cortex (vSMC) relates to measured articulator movement kinematics (position, speed, velocity, acceleration) during the production of English vowels. We found that high-gamma activity at many individual vSMC electrodes strongly encoded the kinematics of one or more articulators, but less so for vowel formants and vowel identity. Neural population decoding methods further revealed the structure of kinematic features that distinguish vowels. Encoding of articulator kinematics was sparsely distributed across time and primarily occurred during the time of vowel onset and offset. In contrast, encoding was low during the steady-state portion of the vowel, despite sustained neural activity at some electrodes. Significant representations were found for all kinematic parameters, but speed was the most robust. These findings enabled by direct vocal tract monitoring demonstrate novel insights into the representation of articulatory kinematic parameters encoded in the vSMC during speech production. Speaking requires precise control and coordination of the vocal tract articulators (lips, jaw, and tongue). Despite the impressive proficiency with which humans move these articulators during speech production, our understanding of how the brain achieves such control is rudimentary, in part because the movements themselves are difficult to observe. By simultaneously measuring speech movements and the neural activity that gives rise to them, we demonstrate how neural activity in sensorimotor cortex produces complex, coordinated movements of the vocal tract.

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“…We take advantage of tools provided in the nipy software package ( https://github.com/nipy/nipy/ ), dural surface reconstruction from ielu (LaPlante et al, 2016 ), 3D plotting in mayavi ( http://mayavi.sourceforge.net/ ; Ramachandran, 2001 ), and extend on functions available in the MATLAB-based CTMR package (Hermes et al, 2010 ). This protocol has been used to localize and label electrodes in our previously published work (Dichter et al, 2016 ; Hamilton et al, 2016 ; Leonard et al, 2016 ; Moses et al, 2016 ; Muller et al, 2016b ; Tang et al, 2017 ). In an effort to promote open and affordable access to these tools, all requirements to run the pipeline (aside from physical hardware) are freely available for download at no cost to the user.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated Anatomical Labeling and Inter-subject Warping of High-Density Intracranial Recording Electrodes in Electrocorticography

Hamilton

Chang

Lee

et al. 2017

Front. Neuroinform.

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103

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In this article, we introduce , our open source python package for preprocessing of imaging data for use in intracranial electrocorticography (ECoG) and intracranial stereo-EEG analyses. The process of electrode localization, labeling, and warping for use in ECoG currently varies widely across laboratories, and it is usually performed with custom, lab-specific code. This python package aims to provide a standardized interface for these procedures, as well as code to plot and display results on 3D cortical surface meshes. It gives the user an easy interface to create anatomically labeled electrodes that can also be warped to an atlas brain, starting with only a preoperative T1 MRI scan and a postoperative CT scan. We describe the full capabilities of our imaging pipeline and present a step-by-step protocol for users.

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Parallel streams define the temporal dynamics of speech processing across human auditory cortex

Cited by 4 publications

References 78 publications

Data-driven Koopman operator approach for computational neuroscience

Data-driven Koopman operator approach for computational neuroscience

Human Sensorimotor Cortex Control of Directly Measured Vocal Tract Movements during Vowel Production

Semi-automated Anatomical Labeling and Inter-subject Warping of High-Density Intracranial Recording Electrodes in Electrocorticography

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