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Meier A, Kuzdeba S, Jackson L, Daliri A, Tourville JA, Guenther FH, Greenlee JDW. Lateralization and Time-Course of Cortical Phonological Representations during Syllable Production. eNeuro 2023; 10:ENEURO.0474-22.2023. [PMID: 37739786 PMCID: PMC10561542 DOI: 10.1523/eneuro.0474-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 08/15/2023] [Accepted: 08/28/2023] [Indexed: 09/24/2023] Open
Abstract
Spoken language contains information at a broad range of timescales, from phonetic distinctions on the order of milliseconds to semantic contexts which shift over seconds to minutes. It is not well understood how the brain's speech production systems combine features at these timescales into a coherent vocal output. We investigated the spatial and temporal representations in cerebral cortex of three phonological units with different durations: consonants, vowels, and syllables. Electrocorticography (ECoG) recordings were obtained from five participants while speaking single syllables. We developed a novel clustering and Kalman filter-based trend analysis procedure to sort electrodes into temporal response profiles. A linear discriminant classifier was used to determine how strongly each electrode's response encoded phonological features. We found distinct time-courses of encoding phonological units depending on their duration: consonants were represented more during speech preparation, vowels were represented evenly throughout trials, and syllables during production. Locations of strongly speech-encoding electrodes (the top 30% of electrodes) likewise depended on phonological element duration, with consonant-encoding electrodes left-lateralized, vowel-encoding hemispherically balanced, and syllable-encoding right-lateralized. The lateralization of speech-encoding electrodes depended on onset time, with electrodes active before or after speech production favoring left hemisphere and those active during speech favoring the right. Single-electrode speech classification revealed cortical areas with preferential encoding of particular phonemic elements, including consonant encoding in the left precentral and postcentral gyri and syllable encoding in the right middle frontal gyrus. Our findings support neurolinguistic theories of left hemisphere specialization for processing short-timescale linguistic units and right hemisphere processing of longer-duration units.
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Affiliation(s)
- Andrew Meier
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
| | - Scott Kuzdeba
- Graduate Program for Neuroscience, Boston University, Boston, MA 02215
| | - Liam Jackson
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
| | - Ayoub Daliri
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
- College of Health Solutions, Arizona State University, Tempe, AZ 85004
| | - Jason A Tourville
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
| | - Frank H Guenther
- Department of Speech, Language, and Hearing Sciences, Boston University, Boston, MA 02215
- Department of Biomedical Engineering, Boston University, Boston, MA 02215
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02215
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02215
| | - Jeremy D W Greenlee
- Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, IA 52242
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2
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A neuronal prospect theory model in the brain reward circuitry. Nat Commun 2022; 13:5855. [PMID: 36195765 PMCID: PMC9532451 DOI: 10.1038/s41467-022-33579-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 09/22/2022] [Indexed: 11/23/2022] Open
Abstract
Prospect theory, arguably the most prominent theory of choice, is an obvious candidate for neural valuation models. How the activity of individual neurons, a possible computational unit, obeys prospect theory remains unknown. Here, we show, with theoretical accuracy equivalent to that of human neuroimaging studies, that single-neuron activity in four core reward-related cortical and subcortical regions represents the subjective valuation of risky gambles in monkeys. The activity of individual neurons in monkeys passively viewing a lottery reflects the desirability of probabilistic rewards parameterized as a multiplicative combination of utility and probability weighting functions, as in the prospect theory framework. The diverse patterns of valuation signals were not localized but distributed throughout most parts of the reward circuitry. A network model aggregating these signals reconstructed the risk preferences and subjective probability weighting revealed by the animals’ choices. Thus, distributed neural coding explains the computation of subjective valuations under risk. It is unclear how the activity of individual neurons conform to prospect theory. Here, the authors demonstrate that the activity of single neurons in various reward-related regions in the monkey brain can be described as encoding a multiplicative combination of utility and probability weighting, and that this subjective valuation process is achieved via a distributed coding scheme.
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Mankel K, Shrestha U, Tipirneni-Sajja A, Bidelman GM. Functional Plasticity Coupled With Structural Predispositions in Auditory Cortex Shape Successful Music Category Learning. Front Neurosci 2022; 16:897239. [PMID: 35837119 PMCID: PMC9274125 DOI: 10.3389/fnins.2022.897239] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/25/2022] [Indexed: 11/23/2022] Open
Abstract
Categorizing sounds into meaningful groups helps listeners more efficiently process the auditory scene and is a foundational skill for speech perception and language development. Yet, how auditory categories develop in the brain through learning, particularly for non-speech sounds (e.g., music), is not well understood. Here, we asked musically naïve listeners to complete a brief (∼20 min) training session where they learned to identify sounds from a musical interval continuum (minor-major 3rds). We used multichannel EEG to track behaviorally relevant neuroplastic changes in the auditory event-related potentials (ERPs) pre- to post-training. To rule out mere exposure-induced changes, neural effects were evaluated against a control group of 14 non-musicians who did not undergo training. We also compared individual categorization performance with structural volumetrics of bilateral Heschl's gyrus (HG) from MRI to evaluate neuroanatomical substrates of learning. Behavioral performance revealed steeper (i.e., more categorical) identification functions in the posttest that correlated with better training accuracy. At the neural level, improvement in learners' behavioral identification was characterized by smaller P2 amplitudes at posttest, particularly over right hemisphere. Critically, learning-related changes in the ERPs were not observed in control listeners, ruling out mere exposure effects. Learners also showed smaller and thinner HG bilaterally, indicating superior categorization was associated with structural differences in primary auditory brain regions. Collectively, our data suggest successful auditory categorical learning of music sounds is characterized by short-term functional changes (i.e., greater post-training efficiency) in sensory coding processes superimposed on preexisting structural differences in bilateral auditory cortex.
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Affiliation(s)
- Kelsey Mankel
- School of Communication Sciences and Disorders, University of Memphis, Memphis, TN, United States
- Institute for Intelligent Systems, University of Memphis, Memphis, TN, United States
- Center for Mind and Brain, University of California, Davis, Davis, CA, United States
| | - Utsav Shrestha
- Department of Biomedical Engineering, University of Memphis, Memphis, TN, United States
| | | | - Gavin M. Bidelman
- School of Communication Sciences and Disorders, University of Memphis, Memphis, TN, United States
- Institute for Intelligent Systems, University of Memphis, Memphis, TN, United States
- Department of Speech, Language and Hearing Sciences, Indiana University, Bloomington, IN, United States
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4
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Yankouskaya A, Sui J. Self-prioritization is supported by interactions between large-scale brain networks. Eur J Neurosci 2022; 55:1244-1261. [PMID: 35083806 PMCID: PMC9303922 DOI: 10.1111/ejn.15612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 11/29/2021] [Accepted: 01/23/2022] [Indexed: 11/30/2022]
Abstract
Resting-state functional magnetic resonance imaging (fMRI) has provided solid evidence that the default-mode network (DMN) is implicated in self-referential processing. The functional connectivity of the DMN has also been observed in tasks where self-referential processing leads to self-prioritization (SPE) in perception and decision-making. However, we are less certain about whether (i) SPE solely depends on the interplay within parts of the DMN or is driven by multiple brain networks; and (ii) whether SPE is associated with a unique component of interconnected networks or can be explained by related effects such as emotion prioritization. We addressed these questions by identifying and comparing topological clusters of networks involved in self-and emotion prioritization effects generated in an associative-matching task. Using network-based statistics, we found that SPE controlled by emotion is supported by a unique component of interacting networks, including the medial prefrontal part of the DMN (MPFC), Frontoparietal network (FPN) and insular Salience network (SN). This component emerged as a result of a focal effect confined to few connections, indicating that interaction between DMN, FPC and SN is critical to cognitive operations for the SPE. This result was validated on a separate data set. In contrast, prioritization of happy emotion was associated with a component formed by interactions between the rostral prefrontal part of SN, posterior parietal part of FPN and the MPFC, while sad emotion reveals a cluster of the DMN, Dorsal Attention Network (DAN) and Visual Medial Network (VMN). We discussed theoretical and methodological aspects of these findings within the more general domain of social cognition.
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Affiliation(s)
- A Yankouskaya
- Department of Psychology, Bournemouth University, UK
| | - J Sui
- School of Psychology, University of Aberdeen, UK
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Levy DF, Wilson SM. Categorical Encoding of Vowels in Primary Auditory Cortex. Cereb Cortex 2021; 30:618-627. [PMID: 31241149 DOI: 10.1093/cercor/bhz112] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 04/05/2019] [Accepted: 05/02/2019] [Indexed: 11/14/2022] Open
Abstract
Speech perception involves mapping from a continuous and variable acoustic speech signal to discrete, linguistically meaningful units. However, it is unclear where in the auditory processing stream speech sound representations cease to be veridical (faithfully encoding precise acoustic properties) and become categorical (encoding sounds as linguistic categories). In this study, we used functional magnetic resonance imaging and multivariate pattern analysis to determine whether tonotopic primary auditory cortex (PAC), defined as tonotopic voxels falling within Heschl's gyrus, represents one class of speech sounds-vowels-veridically or categorically. For each of 15 participants, 4 individualized synthetic vowel stimuli were generated such that the vowels were equidistant in acoustic space, yet straddled a categorical boundary (with the first 2 vowels perceived as [i] and the last 2 perceived as [i]). Each participant's 4 vowels were then presented in a block design with an irrelevant but attention-demanding level change detection task. We found that in PAC bilaterally, neural discrimination between pairs of vowels that crossed the categorical boundary was more accurate than neural discrimination between equivalently spaced vowel pairs that fell within a category. These findings suggest that PAC does not represent vowel sounds veridically, but that encoding of vowels is shaped by linguistically relevant phonemic categories.
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Affiliation(s)
- Deborah F Levy
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Stephen M Wilson
- Department of Hearing and Speech Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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6
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Preisig BC, Riecke L, Sjerps MJ, Kösem A, Kop BR, Bramson B, Hagoort P, Hervais-Adelman A. Selective modulation of interhemispheric connectivity by transcranial alternating current stimulation influences binaural integration. Proc Natl Acad Sci U S A 2021; 118:e2015488118. [PMID: 33568530 PMCID: PMC7896308 DOI: 10.1073/pnas.2015488118] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Brain connectivity plays a major role in the encoding, transfer, and integration of sensory information. Interregional synchronization of neural oscillations in the γ-frequency band has been suggested as a key mechanism underlying perceptual integration. In a recent study, we found evidence for this hypothesis showing that the modulation of interhemispheric oscillatory synchrony by means of bihemispheric high-density transcranial alternating current stimulation (HD-TACS) affects binaural integration of dichotic acoustic features. Here, we aimed to establish a direct link between oscillatory synchrony, effective brain connectivity, and binaural integration. We experimentally manipulated oscillatory synchrony (using bihemispheric γ-TACS with different interhemispheric phase lags) and assessed the effect on effective brain connectivity and binaural integration (as measured with functional MRI and a dichotic listening task, respectively). We found that TACS reduced intrahemispheric connectivity within the auditory cortices and antiphase (interhemispheric phase lag 180°) TACS modulated connectivity between the two auditory cortices. Importantly, the changes in intra- and interhemispheric connectivity induced by TACS were correlated with changes in perceptual integration. Our results indicate that γ-band synchronization between the two auditory cortices plays a functional role in binaural integration, supporting the proposed role of interregional oscillatory synchrony in perceptual integration.
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Affiliation(s)
- Basil C Preisig
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, 6500 HB Nijmegen, The Netherlands;
- Max Planck Institute for Psycholinguistics, 6525 XD Nijmegen, The Netherlands
- Department of Psychology, Neurolinguistics, University of Zurich, 8050 Zurich, Switzerland
| | - Lars Riecke
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6229 GT Maastricht, The Netherlands
| | - Matthias J Sjerps
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, 6500 HB Nijmegen, The Netherlands
- Max Planck Institute for Psycholinguistics, 6525 XD Nijmegen, The Netherlands
| | - Anne Kösem
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, 6500 HB Nijmegen, The Netherlands
- Max Planck Institute for Psycholinguistics, 6525 XD Nijmegen, The Netherlands
- Lyon Neuroscience Research Center, Cognition Computation and Neurophysiology Team, Université Claude Bernard Lyon 1, 69500 Bron, France
| | - Benjamin R Kop
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, 6500 HB Nijmegen, The Netherlands
| | - Bob Bramson
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, 6500 HB Nijmegen, The Netherlands
| | - Peter Hagoort
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, 6500 HB Nijmegen, The Netherlands
- Max Planck Institute for Psycholinguistics, 6525 XD Nijmegen, The Netherlands
| | - Alexis Hervais-Adelman
- Department of Psychology, Neurolinguistics, University of Zurich, 8050 Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and Eidgenössische Technische Hochschule Zurich, 8057 Zurich, Switzerland
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Bidelman GM, Pearson C, Harrison A. Lexical Influences on Categorical Speech Perception Are Driven by a Temporoparietal Circuit. J Cogn Neurosci 2021; 33:840-852. [PMID: 33464162 DOI: 10.1162/jocn_a_01678] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Categorical judgments of otherwise identical phonemes are biased toward hearing words (i.e., "Ganong effect") suggesting lexical context influences perception of even basic speech primitives. Lexical biasing could manifest via late stage postperceptual mechanisms related to decision or, alternatively, top-down linguistic inference that acts on early perceptual coding. Here, we exploited the temporal sensitivity of EEG to resolve the spatiotemporal dynamics of these context-related influences on speech categorization. Listeners rapidly classified sounds from a /gɪ/-/kɪ/ gradient presented in opposing word-nonword contexts (GIFT-kift vs. giss-KISS), designed to bias perception toward lexical items. Phonetic perception shifted toward the direction of words, establishing a robust Ganong effect behaviorally. ERPs revealed a neural analog of lexical biasing emerging within ~200 msec. Source analyses uncovered a distributed neural network supporting the Ganong including middle temporal gyrus, inferior parietal lobe, and middle frontal cortex. Yet, among Ganong-sensitive regions, only left middle temporal gyrus and inferior parietal lobe predicted behavioral susceptibility to lexical influence. Our findings confirm lexical status rapidly constrains sublexical categorical representations for speech within several hundred milliseconds but likely does so outside the purview of canonical auditory-sensory brain areas.
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Affiliation(s)
- Gavin M Bidelman
- University of Memphis, TN.,University of Tennessee Health Sciences Center, Memphis, TN
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8
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Santana AC, Barbosa AV, Yehia HC, Laboissière R. A dimension reduction technique applied to regression on high dimension, low sample size neurophysiological data sets. BMC Neurosci 2021; 22:1. [PMID: 33397293 PMCID: PMC7780417 DOI: 10.1186/s12868-020-00605-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 11/27/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND A common problem in neurophysiological signal processing is the extraction of meaningful information from high dimension, low sample size data (HDLSS). We present RoLDSIS (regression on low-dimension spanned input space), a regression technique based on dimensionality reduction that constrains the solution to the subspace spanned by the available observations. This avoids regularization parameters in the regression procedure, as needed in shrinkage regression methods. RESULTS We applied RoLDSIS to the EEG data collected in a phonemic identification experiment. In the experiment, morphed syllables in the continuum /da/-/ta/ were presented as acoustic stimuli to the participants and the event-related potentials (ERP) were recorded and then represented as a set of features in the time-frequency domain via the discrete wavelet transform. Each set of stimuli was chosen from a preliminary identification task executed by the participant. Physical and psychophysical attributes were associated to each stimulus. RoLDSIS was then used to infer the neurophysiological axes, in the feature space, associated with each attribute. We show that these axes can be reliably estimated and that their separation is correlated with the individual strength of phonemic categorization. The results provided by RoLDSIS are interpretable in the time-frequency domain and may be used to infer the neurophysiological correlates of phonemic categorization. A comparison with commonly used regularized regression techniques was carried out by cross-validation. CONCLUSION The prediction errors obtained by RoLDSIS are comparable to those obtained with Ridge Regression and smaller than those obtained with LASSO and SPLS. However, RoLDSIS achieves this without the need for cross-validation, a procedure that requires the extraction of a large amount of observations from the data and, consequently, a decreased signal-to-noise ratio when averaging trials. We show that, even though RoLDSIS is a simple technique, it is suitable for the processing and interpretation of neurophysiological signals.
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Affiliation(s)
- Adrielle C Santana
- Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos 6627, 31270-901, Belo Horizonte, Brazil. .,Univ. Grenoble Alpes, CNRS, LPNC UMR 5105, 38000, Grenoble, France. .,Department of Control and Automation Engineering, School of Mines, Universidade Federal de Ouro Preto, Campus Morro do Cruzeiro, 35400-000, Ouro Preto, Brazil.
| | - Adriano V Barbosa
- Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos 6627, 31270-901, Belo Horizonte, Brazil.,Department of Electronic Engineering, Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos 6627, 31270-901, Belo Horizonte, Brazil
| | - Hani C Yehia
- Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos 6627, 31270-901, Belo Horizonte, Brazil.,Department of Electronic Engineering, Universidade Federal de Minas Gerais, Av. Pres. Antônio Carlos 6627, 31270-901, Belo Horizonte, Brazil
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9
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Swanborough H, Staib M, Frühholz S. Neurocognitive dynamics of near-threshold voice signal detection and affective voice evaluation. SCIENCE ADVANCES 2020; 6:6/50/eabb3884. [PMID: 33310844 PMCID: PMC7732184 DOI: 10.1126/sciadv.abb3884] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Accepted: 10/29/2020] [Indexed: 05/10/2023]
Abstract
Communication and voice signal detection in noisy environments are universal tasks for many species. The fundamental problem of detecting voice signals in noise (VIN) is underinvestigated especially in its temporal dynamic properties. We investigated VIN as a dynamic signal-to-noise ratio (SNR) problem to determine the neurocognitive dynamics of subthreshold evidence accrual and near-threshold voice signal detection. Experiment 1 showed that dynamic VIN, including a varying SNR and subthreshold sensory evidence accrual, is superior to similar conditions with nondynamic SNRs or with acoustically matched sounds. Furthermore, voice signals with affective meaning have a detection advantage during VIN. Experiment 2 demonstrated that VIN is driven by an effective neural integration in an auditory cortical-limbic network at and beyond the near-threshold detection point, which is preceded by activity in subcortical auditory nuclei. This demonstrates the superior recognition advantage of communication signals in dynamic noise contexts, especially when carrying socio-affective meaning.
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Affiliation(s)
- Huw Swanborough
- Cognitive and Affective Neuroscience Unit, Department of Psychology, University of Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Matthias Staib
- Cognitive and Affective Neuroscience Unit, Department of Psychology, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Sascha Frühholz
- Cognitive and Affective Neuroscience Unit, Department of Psychology, University of Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland
- Department of Psychology, University of Oslo, Oslo, Norway
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10
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Keitel A, Gross J, Kayser C. Shared and modality-specific brain regions that mediate auditory and visual word comprehension. eLife 2020; 9:e56972. [PMID: 32831168 PMCID: PMC7470824 DOI: 10.7554/elife.56972] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 08/18/2020] [Indexed: 12/22/2022] Open
Abstract
Visual speech carried by lip movements is an integral part of communication. Yet, it remains unclear in how far visual and acoustic speech comprehension are mediated by the same brain regions. Using multivariate classification of full-brain MEG data, we first probed where the brain represents acoustically and visually conveyed word identities. We then tested where these sensory-driven representations are predictive of participants' trial-wise comprehension. The comprehension-relevant representations of auditory and visual speech converged only in anterior angular and inferior frontal regions and were spatially dissociated from those representations that best reflected the sensory-driven word identity. These results provide a neural explanation for the behavioural dissociation of acoustic and visual speech comprehension and suggest that cerebral representations encoding word identities may be more modality-specific than often upheld.
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Affiliation(s)
- Anne Keitel
- Psychology, University of DundeeDundeeUnited Kingdom
- Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
| | - Joachim Gross
- Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
- Institute for Biomagnetism and Biosignalanalysis, University of MünsterMünsterGermany
| | - Christoph Kayser
- Department for Cognitive Neuroscience, Faculty of Biology, Bielefeld UniversityBielefeldGermany
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11
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Combining predictive coding and neural oscillations enables online syllable recognition in natural speech. Nat Commun 2020; 11:3117. [PMID: 32561726 PMCID: PMC7305192 DOI: 10.1038/s41467-020-16956-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 06/04/2020] [Indexed: 11/08/2022] Open
Abstract
On-line comprehension of natural speech requires segmenting the acoustic stream into discrete linguistic elements. This process is argued to rely on theta-gamma oscillation coupling, which can parse syllables and encode them in decipherable neural activity. Speech comprehension also strongly depends on contextual cues that help predicting speech structure and content. To explore the effects of theta-gamma coupling on bottom-up/top-down dynamics during on-line syllable identification, we designed a computational model (Precoss—predictive coding and oscillations for speech) that can recognise syllable sequences in continuous speech. The model uses predictions from internal spectro-temporal representations of syllables and theta oscillations to signal syllable onsets and duration. Syllable recognition is best when theta-gamma coupling is used to temporally align spectro-temporal predictions with the acoustic input. This neurocomputational modelling work demonstrates that the notions of predictive coding and neural oscillations can be brought together to account for on-line dynamic sensory processing. The authors present a model to parse and recognise syllables on-line in natural speech sentences that combine predictive coding and neural oscillations. They use simulations from different versions of the model to establish the importance of both theta-gamma coupling and the reset of accumulated evidence in continuous speech processing.
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12
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Baroni F, Morillon B, Trébuchon A, Liégeois-Chauvel C, Olasagasti I, Giraud AL. Converging intracortical signatures of two separated processing timescales in human early auditory cortex. Neuroimage 2020; 218:116882. [PMID: 32439539 DOI: 10.1016/j.neuroimage.2020.116882] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 03/30/2020] [Accepted: 04/23/2020] [Indexed: 11/15/2022] Open
Abstract
Neural oscillations in auditory cortex are argued to support parsing and representing speech constituents at their corresponding temporal scales. Yet, how incoming sensory information interacts with ongoing spontaneous brain activity, what features of the neuronal microcircuitry underlie spontaneous and stimulus-evoked spectral fingerprints, and what these fingerprints entail for stimulus encoding, remain largely open questions. We used a combination of human invasive electrophysiology, computational modeling and decoding techniques to assess the information encoding properties of brain activity and to relate them to a plausible underlying neuronal microarchitecture. We analyzed intracortical auditory EEG activity from 10 patients while they were listening to short sentences. Pre-stimulus neural activity in early auditory cortical regions often exhibited power spectra with a shoulder in the delta range and a small bump in the beta range. Speech decreased power in the beta range, and increased power in the delta-theta and gamma ranges. Using multivariate machine learning techniques, we assessed the spectral profile of information content for two aspects of speech processing: detection and discrimination. We obtained better phase than power information decoding, and a bimodal spectral profile of information content with better decoding at low (delta-theta) and high (gamma) frequencies than at intermediate (beta) frequencies. These experimental data were reproduced by a simple rate model made of two subnetworks with different timescales, each composed of coupled excitatory and inhibitory units, and connected via a negative feedback loop. Modeling and experimental results were similar in terms of pre-stimulus spectral profile (except for the iEEG beta bump), spectral modulations with speech, and spectral profile of information content. Altogether, we provide converging evidence from both univariate spectral analysis and decoding approaches for a dual timescale processing infrastructure in human auditory cortex, and show that it is consistent with the dynamics of a simple rate model.
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Affiliation(s)
- Fabiano Baroni
- Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland; School of Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Benjamin Morillon
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Neurosciences des Systémes (INS), Marseille, France
| | - Agnès Trébuchon
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Neurosciences des Systémes (INS), Marseille, France; Clinical Neurophysiology and Epileptology Department, Timone Hospital, Assistance Publique Hôpitaux de Marseille, Marseille, France
| | - Catherine Liégeois-Chauvel
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Neurosciences des Systémes (INS), Marseille, France; Department of Neurological Surgery, University of Pittsburgh, PA, 15213, USA
| | - Itsaso Olasagasti
- Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland
| | - Anne-Lise Giraud
- Department of Fundamental Neuroscience, University of Geneva, Geneva, Switzerland
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13
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Xu K, Wu DH, Duann JR. Dynamic brain connectivity attuned to the complexity of relative clause sentences revealed by a single-trial analysis. Neuroimage 2020; 217:116920. [PMID: 32422404 DOI: 10.1016/j.neuroimage.2020.116920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 04/28/2020] [Accepted: 05/02/2020] [Indexed: 10/24/2022] Open
Abstract
To explore the issue of how the human brain processes sentences with different levels of complexity, we sought to compare the neural substrates underlying the processing of Chinese subject-extracted relative clause (SRC) and object-extracted relative clause (ORC) sentences in a trial-by-trial fashion. Previous neuroimaging studies have demonstrated that the involvement of the left inferior frontal gyrus (LIFG) and the left superior temporal gyrus (LSTG) is critical for the processing of relative clause (RC) sentences. In this study, we employed independent component analysis (ICA) to decompose brain activity into a set of independent components. Then, the independent component maps were spatially normalized using a surface-based approach in order to further spatially correlate and match the equivalent components from individual participants. The selected equivalent components indicated that the LIFG and the LSTG were consistently engaged in sentence processing among the participants. Subsequently, we observed alterations in the functional coupling between the LIFG and the LSTG in response to SRCs and ORCs using a Granger causality analysis. Specifically, comprehending Chinese ORCs with a canonical word order only involved a unidirectional connection from the LIFG to the LSTG for the integration of lexical-syntactic information. On the other hand, comprehending Chinese SRCs required bi-directional connectivity between the LIFG and the LSTG to fulfill increased integration demands in reconstructing the argument hierarchy due to a non-canonical word order. Furthermore, through a single-trial analysis, the strength of the connectivity from the LIFG to the LSTG was found to be significantly correlated with the complexity of the SRC sentences as quantified by eye-tracking measures. These findings indicated that the effective connectivity from the LIFG to the LSTG played an important role in the comprehension of complex sentences and that enhanced strength of this connectivity might reflect increased integration demands and restructuring attempts during sentence processing. Taken together, the results of the present study reveal that interregional interaction in the brain network for sentence processing can be dynamically engaged in response to different levels of complexity and also shed some light on the interpretation of neuroimaging and behavioral evidence when accounting for the nature of sentence complexity during reading.
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Affiliation(s)
- Kunyu Xu
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, 32001, Taiwan; Institute of Modern Languages and Linguistics, Fudan University, Shanghai, 200433, China
| | - Denise H Wu
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, 32001, Taiwan
| | - Jeng-Ren Duann
- Institute of Cognitive Neuroscience, National Central University, Taoyuan, 32001, Taiwan; Institute for Neural Computation, University of California San Diego, La Jolla, CA, 92093, USA; Institute of Education, National Chiao Tung University, Hsinchu, 30010, Taiwan.
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14
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Yan Y, Qian T, Xu X, Han H, Ling Z, Zhou W, Liu H, Hong B. Human cortical networking by probabilistic and frequency-specific coupling. Neuroimage 2020; 207:116363. [PMID: 31740339 DOI: 10.1016/j.neuroimage.2019.116363] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2019] [Revised: 11/03/2019] [Accepted: 11/13/2019] [Indexed: 11/26/2022] Open
Abstract
Large-scale cortical networking patterns have been established based on the correlation of slow fluctuations of resting fMRI signals. However, the electrophysiological mechanism of cortical networking remained to be elucidated. With large-scale human ECoG recording, we developed a novel approach for functional network parcellation on the basis of probabilistic co-activation of cortical sites in spatio-temporal microstates. The parcellated networks were verified by electrical cortical stimulation (ECS) and somatosensory evoked potentials recording, which showed significantly higher accuracy than the traditional long-term correlation method. This provides direct electrophysiological evidence supporting the dynamic nature of cortical networking. Further analysis revealed that the brain-wide connectivity is likely established on the coupling of ECoG power envelop over a common carrier frequency ranging from alpha to low-beta (8-32Hz). Surprisingly, the cortical networking pattern over this specific frequency was found to be consistent across various tasks, which resembles the resting networks. The high similarity between the above functional network parcellation and the fMRI resting network atlas in individuals also suggested the slow power-envelope coupling of band-limited neural oscillations as the electrophysiological basis of spontaneous BOLD signals. Collectively, our findings on direct human recording revealed a probabilistic and frequency specific coupling mechanism for large-scale cortical networking shared by task and resting brain.
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Affiliation(s)
- Yuxiang Yan
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Tianyi Qian
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Xin Xu
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, 100853, China
| | - Hao Han
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Zhipei Ling
- Department of Neurosurgery, Chinese PLA General Hospital, Beijing, 100853, China
| | - Wenjin Zhou
- Epilepsy Center, Yuquan Hospital, Tsinghua University, Beijing, 100040, China
| | - Hesheng Liu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, 02129, USA.
| | - Bo Hong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, 100084, China.
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15
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Al-Fahad R, Yeasin M, Bidelman GM. Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions. J Neural Eng 2020; 17:016045. [PMID: 31822643 PMCID: PMC7004853 DOI: 10.1088/1741-2552/ab6040] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Categorical perception (CP) is an inherent property of speech perception. The response time (RT) of listeners' perceptual speech identification is highly sensitive to individual differences. While the neural correlates of CP have been well studied in terms of the regional contributions of the brain to behavior, functional connectivity patterns that signify individual differences in listeners' speed (RT) for speech categorization is less clear. In this study, we introduce a novel approach to address these questions. APPROACH We applied several computational approaches to the EEG, including graph mining, machine learning (i.e., support vector machine), and stability selection to investigate the unique brain states (functional neural connectivity) that predict the speed of listeners' behavioral decisions. MAIN RESULTS We infer that (i) the listeners' perceptual speed is directly related to dynamic variations in their brain connectomics, (ii) global network assortativity and efficiency distinguished fast, medium, and slow RTs, (iii) the functional network underlying speeded decisions increases in negative assortativity (i.e., became disassortative) for slower RTs, (iv) slower categorical speech decisions cause excessive use of neural resources and more aberrant information flow within the CP circuitry, (v) slower responders tended to utilize functional brain networks excessively (or inappropriately) whereas fast responders (with lower global efficiency) utilized the same neural pathways but with more restricted organization. SIGNIFICANCE Findings show that neural classifiers (SVM) coupled with stability selection correctly classify behavioral RTs from functional connectivity alone with over 92% accuracy (AUC = 0.9). Our results corroborate previous studies by supporting the engagement of similar temporal (STG), parietal, motor, and prefrontal regions in CP using an entirely data-driven approach.
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Affiliation(s)
- Rakib Al-Fahad
- Department of Electrical and Computer Engineering, University of Memphis, Memphis, 38152 TN, USA
| | - Mohammed Yeasin
- Department of Electrical and Computer Engineering, University of Memphis, Memphis, 38152 TN, USA
- Institute for Intelligent Systems, University of Memphis, Memphis, TN, USA
| | - Gavin M. Bidelman
- Institute for Intelligent Systems, University of Memphis, Memphis, TN, USA
- School of Communication Sciences & Disorders, University of Memphis, Memphis, TN, USA
- University of Tennessee Health Sciences Center, Department of Anatomy and Neurobiology, Memphis, TN, USA
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16
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Preisig BC, Sjerps MJ, Hervais-Adelman A, Kösem A, Hagoort P, Riecke L. Bilateral Gamma/Delta Transcranial Alternating Current Stimulation Affects Interhemispheric Speech Sound Integration. J Cogn Neurosci 2019; 32:1242-1250. [PMID: 31682569 DOI: 10.1162/jocn_a_01498] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Perceiving speech requires the integration of different speech cues, that is, formants. When the speech signal is split so that different cues are presented to the right and left ear (dichotic listening), comprehension requires the integration of binaural information. Based on prior electrophysiological evidence, we hypothesized that the integration of dichotically presented speech cues is enabled by interhemispheric phase synchronization between primary and secondary auditory cortex in the gamma frequency band. We tested this hypothesis by applying transcranial alternating current stimulation (TACS) bilaterally above the superior temporal lobe to induce or disrupt interhemispheric gamma-phase coupling. In contrast to initial predictions, we found that gamma TACS applied in-phase above the two hemispheres (interhemispheric lag 0°) perturbs interhemispheric integration of speech cues, possibly because the applied stimulation perturbs an inherent phase lag between the left and right auditory cortex. We also observed this disruptive effect when applying antiphasic delta TACS (interhemispheric lag 180°). We conclude that interhemispheric phase coupling plays a functional role in interhemispheric speech integration. The direction of this effect may depend on the stimulation frequency.
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Affiliation(s)
- Basil C Preisig
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.,Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.,University of Zurich
| | - Matthias J Sjerps
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.,Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | | | - Anne Kösem
- Lyon Neuroscience Research Center (CRNL), Lyon, France
| | - Peter Hagoort
- Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.,Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
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17
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Gehrig J, Michalareas G, Forster MT, Lei J, Hok P, Laufs H, Senft C, Seifert V, Schoffelen JM, Hanslmayr S, Kell CA. Low-Frequency Oscillations Code Speech during Verbal Working Memory. J Neurosci 2019; 39:6498-6512. [PMID: 31196933 PMCID: PMC6697399 DOI: 10.1523/jneurosci.0018-19.2019] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 05/09/2019] [Accepted: 05/10/2019] [Indexed: 11/21/2022] Open
Abstract
The way the human brain represents speech in memory is still unknown. An obvious characteristic of speech is its evolvement over time. During speech processing, neural oscillations are modulated by the temporal properties of the acoustic speech signal, but also acquired knowledge on the temporal structure of language influences speech perception-related brain activity. This suggests that speech could be represented in the temporal domain, a form of representation that the brain also uses to encode autobiographic memories. Empirical evidence for such a memory code is lacking. We investigated the nature of speech memory representations using direct cortical recordings in the left perisylvian cortex during delayed sentence reproduction in female and male patients undergoing awake tumor surgery. Our results reveal that the brain endogenously represents speech in the temporal domain. Temporal pattern similarity analyses revealed that the phase of frontotemporal low-frequency oscillations, primarily in the beta range, represents sentence identity in working memory. The positive relationship between beta power during working memory and task performance suggests that working memory representations benefit from increased phase separation.SIGNIFICANCE STATEMENT Memory is an endogenous source of information based on experience. While neural oscillations encode autobiographic memories in the temporal domain, little is known on their contribution to memory representations of human speech. Our electrocortical recordings in participants who maintain sentences in memory identify the phase of left frontotemporal beta oscillations as the most prominent information carrier of sentence identity. These observations provide evidence for a theoretical model on speech memory representations and explain why interfering with beta oscillations in the left inferior frontal cortex diminishes verbal working memory capacity. The lack of sentence identity coding at the syllabic rate suggests that sentences are represented in memory in a more abstract form compared with speech coding during speech perception and production.
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Affiliation(s)
- Johannes Gehrig
- Department of Neurology, Goethe University, 60528 Frankfurt, Germany
| | | | | | - Juan Lei
- Department of Neurology, Goethe University, 60528 Frankfurt, Germany
- Institute for Cell Biology and Neuroscience, Goethe University, 60438 Frankfurt, Germany
| | - Pavel Hok
- Department of Neurology, Goethe University, 60528 Frankfurt, Germany
- Department of Neurology, Palacky University and University Hospital Olomouc, 77147 Olomouc, Czech Republic
| | - Helmut Laufs
- Department of Neurology, Goethe University, 60528 Frankfurt, Germany
- Department of Neurology, Christian-Albrechts-University, 24105 Kiel, Germany
| | - Christian Senft
- Department of Neurosurgery, Goethe University, 60528 Frankfurt, Germany
| | - Volker Seifert
- Department of Neurosurgery, Goethe University, 60528 Frankfurt, Germany
| | - Jan-Mathijs Schoffelen
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, 6525 HR Nijmegen, The Netherlands, and
| | - Simon Hanslmayr
- School of Psychology at University of Birmingham, B15 2TT Birmingham, United Kingdom
| | - Christian A Kell
- Department of Neurology, Goethe University, 60528 Frankfurt, Germany,
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18
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Bidelman GM, Walker B. Plasticity in auditory categorization is supported by differential engagement of the auditory-linguistic network. Neuroimage 2019; 201:116022. [PMID: 31310863 DOI: 10.1016/j.neuroimage.2019.116022] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Revised: 06/30/2019] [Accepted: 07/12/2019] [Indexed: 12/21/2022] Open
Abstract
To construct our perceptual world, the brain categorizes variable sensory cues into behaviorally-relevant groupings. Categorical representations are apparent within a distributed fronto-temporo-parietal brain network but how this neural circuitry is shaped by experience remains undefined. Here, we asked whether speech and music categories might be formed within different auditory-linguistic brain regions depending on listeners' auditory expertise. We recorded EEG in highly skilled (musicians) vs. less experienced (nonmusicians) perceivers as they rapidly categorized speech and musical sounds. Musicians showed perceptual enhancements across domains, yet source EEG data revealed a double dissociation in the neurobiological mechanisms supporting categorization between groups. Whereas musicians coded categories in primary auditory cortex (PAC), nonmusicians recruited non-auditory regions (e.g., inferior frontal gyrus, IFG) to generate category-level information. Functional connectivity confirmed nonmusicians' increased left IFG involvement reflects stronger routing of signal from PAC directed to IFG, presumably because sensory coding is insufficient to construct categories in less experienced listeners. Our findings establish auditory experience modulates specific engagement and inter-regional communication in the auditory-linguistic network supporting categorical perception. Whereas early canonical PAC representations are sufficient to generate categories in highly trained ears, less experienced perceivers broadcast information downstream to higher-order linguistic brain areas (IFG) to construct abstract sound labels.
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Affiliation(s)
- Gavin M Bidelman
- Institute for Intelligent Systems, University of Memphis, Memphis, TN, USA; School of Communication Sciences & Disorders, University of Memphis, Memphis, TN, USA; University of Tennessee Health Sciences Center, Department of Anatomy and Neurobiology, Memphis, TN, USA.
| | - Breya Walker
- Institute for Intelligent Systems, University of Memphis, Memphis, TN, USA; Department of Psychology, University of Memphis, Memphis, TN, USA; Department of Mathematical Sciences, University of Memphis, Memphis, TN, USA
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19
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Bigenwald A, Chambon V. Criminal Responsibility and Neuroscience: No Revolution Yet. Front Psychol 2019; 10:1406. [PMID: 31316418 PMCID: PMC6610327 DOI: 10.3389/fpsyg.2019.01406] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 05/31/2019] [Indexed: 11/25/2022] Open
Abstract
Since the 1990's, neurolaw is on the rise. At the heart of heated debates lies the recurrent theme of a neuro-revolution of criminal responsibility. However, caution should be observed: the alleged foundations of criminal responsibility (amongst which free will) are often inaccurate and the relative imperviousness of its real foundations to scientific facts often underestimated. Neuroscientific findings may impact on social institutions, but only insofar as they also engage in a political justification of the changes being called for, convince populations, and take into consideration the ensuing consequences. Moreover, the many limits of neuroscientific tools call for increased vigilance when, if ever, using neuroscientific evidence in a courtroom. In this article, we aim at setting the basis for future sound debates on the contribution of neuroscience to criminal law, and in particular to the assessment of criminal responsibility. As such, we provide analytical tools to grasp the political and normative nature of criminal responsibility and review the current or projected use of neuroscience in the law, all the while bearing in mind the highly publicized question: can neuroscience revolutionize criminal responsibility? Answering this question implicitly requires answering a second question: should neuroscience revolutionize the institution of criminal responsibility? Answering both, in turn, requires drawing the line between science and normativity, revolution and dialogue, fantasies and legitimate hopes.
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Affiliation(s)
- Ariane Bigenwald
- Département de Philosophie, Université Paris I Panthéon Sorbonne, Paris, France
- Institut Jean Nicod (ENS – EHESS – CNRS), Département d’Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
| | - Valerian Chambon
- Institut Jean Nicod (ENS – EHESS – CNRS), Département d’Etudes Cognitives, Ecole Normale Supérieure, PSL University, Paris, France
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20
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Daube C, Ince RAA, Gross J. Simple Acoustic Features Can Explain Phoneme-Based Predictions of Cortical Responses to Speech. Curr Biol 2019; 29:1924-1937.e9. [PMID: 31130454 PMCID: PMC6584359 DOI: 10.1016/j.cub.2019.04.067] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 03/25/2019] [Accepted: 04/25/2019] [Indexed: 01/06/2023]
Abstract
When we listen to speech, we have to make sense of a waveform of sound pressure. Hierarchical models of speech perception assume that, to extract semantic meaning, the signal is transformed into unknown, intermediate neuronal representations. Traditionally, studies of such intermediate representations are guided by linguistically defined concepts, such as phonemes. Here, we argue that in order to arrive at an unbiased understanding of the neuronal responses to speech, we should focus instead on representations obtained directly from the stimulus. We illustrate our view with a data-driven, information theoretic analysis of a dataset of 24 young, healthy humans who listened to a 1 h narrative while their magnetoencephalogram (MEG) was recorded. We find that two recent results, the improved performance of an encoding model in which annotated linguistic and acoustic features were combined and the decoding of phoneme subgroups from phoneme-locked responses, can be explained by an encoding model that is based entirely on acoustic features. These acoustic features capitalize on acoustic edges and outperform Gabor-filtered spectrograms, which can explicitly describe the spectrotemporal characteristics of individual phonemes. By replicating our results in publicly available electroencephalography (EEG) data, we conclude that models of brain responses based on linguistic features can serve as excellent benchmarks. However, we believe that in order to further our understanding of human cortical responses to speech, we should also explore low-level and parsimonious explanations for apparent high-level phenomena.
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Affiliation(s)
- Christoph Daube
- Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, UK.
| | - Robin A A Ince
- Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, UK
| | - Joachim Gross
- Institute of Neuroscience and Psychology, University of Glasgow, 62 Hillhead Street, Glasgow G12 8QB, UK; Institute for Biomagnetism and Biosignalanalysis, University of Münster, Malmedyweg 15, 48149 Münster, Germany
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21
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Kroczek LO, Gunter TC, Rysop AU, Friederici AD, Hartwigsen G. Contributions of left frontal and temporal cortex to sentence comprehension: Evidence from simultaneous TMS-EEG. Cortex 2019; 115:86-98. [DOI: 10.1016/j.cortex.2019.01.010] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Revised: 08/03/2018] [Accepted: 01/15/2019] [Indexed: 02/03/2023]
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22
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Preisig BC, Sjerps MJ. Hemispheric specializations affect interhemispheric speech sound integration during duplex perception. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2019; 145:EL190. [PMID: 31067965 DOI: 10.1121/1.5092829] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Accepted: 02/13/2019] [Indexed: 06/09/2023]
Abstract
The present study investigated whether speech-related spectral information benefits from initially predominant right or left hemisphere processing. Normal hearing individuals categorized speech sounds composed of an ambiguous base (perceptually intermediate between /ga/ and /da/), presented to one ear, and a disambiguating low or high F3 chirp presented to the other ear. Shorter response times were found when the chirp was presented to the left ear than to the right ear (inducing initially right-hemisphere chirp processing), but no between-ear differences in strength of overall integration. The results are in line with the assumptions of a right hemispheric dominance for spectral processing.
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Affiliation(s)
- Basil C Preisig
- Donders Institute for Brain, Cognition and Behavior, Radboud University, P.O. Box 9101, Nijmegen, 6500 HB, The ,
| | - Matthias J Sjerps
- Donders Institute for Brain, Cognition and Behavior, Radboud University, P.O. Box 9101, Nijmegen, 6500 HB, The ,
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23
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Chen Y, Cichy RM, Stannat W, Haynes JD. Scale-specific analysis of fMRI data on the irregular cortical surface. Neuroimage 2018; 181:370-381. [PMID: 30033391 DOI: 10.1016/j.neuroimage.2018.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2017] [Revised: 05/21/2018] [Accepted: 07/02/2018] [Indexed: 11/25/2022] Open
Abstract
To fully characterize the activity patterns on the cerebral cortex as measured with fMRI, the spatial scale of the patterns must be ascertained. Here we address this problem by constructing steerable bandpass filters on the discrete, irregular cortical mesh, using an improved Gaussian smoothing in combination with differential operators of directional derivatives. We demonstrate the utility of the algorithm in two ways. First, using modelling we show that our algorithm yields superior results in numerical precision and spatial uniformity of filter kernels compared to the most widely adopted approach for cortical smoothing. As the effective scales of information differ from the nominal filter sizes applied to extract them, we evaluated the effective scales empirically for different filters to make subsequent comparisons well calibrated. Second, we applied the algorithm to an fMRI dataset to assess the scale and pattern form of cortical encoding of information about visual objects in the ventral visual pathway. We found that filtering by our method improved the detection of discriminant information about experimental conditions over previous methods, that the level of categorization (subordinate versus superordinate) of objects was differentially related to the spatial scale of fMRI patterns, and that the spatial scale at which information was encoded increased along the ventral visual pathway. In sum, our results indicate that the proposed algorithm is particularly suited to assess and detect scale-specific information encoding in cortex, and promises further insight into the topography of cortical encoding in the human brain.
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Affiliation(s)
- Yi Chen
- Bernstein Center for Computational Neuroscience, Berlin Center of Advanced Neuroimaging & Clinic of Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Humboldt-Universität zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany; Institute of Cognitive Neurology and Dementia Research, University Hospital Magdeburg, Magdeburg, Germany.
| | - Radoslaw Martin Cichy
- Bernstein Center for Computational Neuroscience, Berlin Center of Advanced Neuroimaging & Clinic of Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Humboldt-Universität zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany; Department of Education and Psychology, Free University Berlin, Berlin, Germany
| | - Wilhelm Stannat
- Institute for Mathematics, Technical University Berlin, Berlin, Germany
| | - John-Dylan Haynes
- Bernstein Center for Computational Neuroscience, Berlin Center of Advanced Neuroimaging & Clinic of Neurology, Charité-Universitätsmedizin Berlin, Corporate Member of Humboldt-Universität zu Berlin, Freie Universität Berlin, Berlin Institute of Health, Berlin, Germany
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24
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Grootswagers T, Cichy RM, Carlson TA. Finding decodable information that can be read out in behaviour. Neuroimage 2018; 179:252-262. [DOI: 10.1016/j.neuroimage.2018.06.022] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2018] [Revised: 06/05/2018] [Accepted: 06/06/2018] [Indexed: 11/26/2022] Open
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25
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Keitel A, Gross J, Kayser C. Perceptually relevant speech tracking in auditory and motor cortex reflects distinct linguistic features. PLoS Biol 2018. [PMID: 29529019 PMCID: PMC5864086 DOI: 10.1371/journal.pbio.2004473] [Citation(s) in RCA: 131] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
During online speech processing, our brain tracks the acoustic fluctuations in speech at different timescales. Previous research has focused on generic timescales (for example, delta or theta bands) that are assumed to map onto linguistic features such as prosody or syllables. However, given the high intersubject variability in speaking patterns, such a generic association between the timescales of brain activity and speech properties can be ambiguous. Here, we analyse speech tracking in source-localised magnetoencephalographic data by directly focusing on timescales extracted from statistical regularities in our speech material. This revealed widespread significant tracking at the timescales of phrases (0.6–1.3 Hz), words (1.8–3 Hz), syllables (2.8–4.8 Hz), and phonemes (8–12.4 Hz). Importantly, when examining its perceptual relevance, we found stronger tracking for correctly comprehended trials in the left premotor (PM) cortex at the phrasal scale as well as in left middle temporal cortex at the word scale. Control analyses using generic bands confirmed that these effects were specific to the speech regularities in our stimuli. Furthermore, we found that the phase at the phrasal timescale coupled to power at beta frequency (13–30 Hz) in motor areas. This cross-frequency coupling presumably reflects top-down temporal prediction in ongoing speech perception. Together, our results reveal specific functional and perceptually relevant roles of distinct tracking and cross-frequency processes along the auditory–motor pathway. How we comprehend speech—and how the brain encodes information from a continuous speech stream—is of interest for neuroscience, linguistics, and research on language disorders. Previous work that examined dynamic brain activity has addressed the issue of comprehension only indirectly, by contrasting intelligible speech with unintelligible speech or baseline activity. Recent work, however, suggests that brain areas can show similar stimulus-driven activity but differently contribute to perception or comprehension. To directly address the perceptual relevance of dynamic brain activity for speech encoding, we used a straightforward, single-trial comprehension measure. Furthermore, previous work has been vague regarding the analysed timescales. We therefore base our analysis directly on the timescales of phrases, words, syllables, and phonemes of our speech stimuli. By incorporating these two conceptual innovations, we demonstrate that different areas of the brain track acoustic information at the time-scales of words and phrases. Moreover, our results suggest that the motor cortex uses a cross-frequency coupling mechanism to predict the timing of phrases in ongoing speech. Our findings suggest spatially and temporally distinct brain mechanisms that directly shape our comprehension.
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Affiliation(s)
- Anne Keitel
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
- * E-mail:
| | - Joachim Gross
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Christoph Kayser
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, United Kingdom
- Cognitive Neuroscience, Bielefeld University, Bielefeld, Germany
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