Acoustic and semantic auditory processing of continuous speech: a time response function analysis for MEG data

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Acesso é pago ou somente para assinantes

Resumo

Speech perception is a complex process involving multilevel neurophysiological processing of various speech components. In the presented work, we used temporal response function (TRF), a novel method of processing the magnetoencephalogram (MEG) recorded while listening to continuous speech to analyse the neural response to auditory and semantic components of speech during its perception in vivo. The temporal response to dynamic changes in the sound envelope demonstrated an early neurophysiological response: from 20 ms with an amplitude peak at 100 ms, and the response to the perception of word onset had a peak latency at 120 ms. The semantic component of speech showed a later temporal response: from 200 ms with a peak latency of 300 ms bilaterally in temporal magnetometers. Thus, TRF showed a later response to semantic changes in speech than to changes in acoustic features in the MEG study.

Texto integral

Acesso é fechado

Sobre autores

A. Ovakimian

Institute of Higher Nervous Activity and Neurophysiology of the RAS

Autor responsável pela correspondência
Email: alena@ovakimian.ru
Rússia, Moscow

G. Soghoyan

Institute of Higher Nervous Activity and Neurophysiology of the RAS; Skolkovo Institute of Science and Technology

Email: alena@ovakimian.ru
Rússia, Moscow; Moscow

O. Martynova

Institute of Higher Nervous Activity and Neurophysiology of the RAS

Email: alena@ovakimian.ru
Rússia, Moscow

O. Sysoeva

Institute of Higher Nervous Activity and Neurophysiology of the RAS; Sirius University of Science and Technology

Email: alena@ovakimian.ru
Rússia, Moscow; Sochi

Bibliografia

  1. Forseth K.J., Hickok G., Rollo P.S., Tandon N. Language prediction mechanisms in human auditory cortex // Nat. Commun. 2020. V. 11. № 1. P. 5240.
  2. Foti D., Roberts F. The neural dynamics of speech perception: Dissociable networks for processing linguistic content and monitoring speaker turn-taking // Brain Lang. 2016. V. 157. P. 63.
  3. Evans S., Davis M.H. Hierarchical organization of auditory and motor representations in speech perception: Evidence from searchlight similarity analysis // Cereb. Cortex. 2015. V. 25. № 12. P. 4772.
  4. Okada K., Rong F., Venezia J. et al. Hierarchical organization of human auditory cortex: Evidence from acoustic invariance in the response to intelligible speech // Cereb. Cortex. 2010. V. 20. № 10. P. 2486.
  5. Petrushevskiy A.G., Mayorova L.A. Speech and Non-Speech Sound Categorization in Auditory Cortex: fMRI Correlates // Human Physiology. 2019. V. 45. № 6. P. 5.
  6. Henry M.J., Obleser J. Frequency modulation entrains slow neural oscillations and optimizes human listening behavior // Proc. Natl. Acad. Sci. 2012. V. 109. № 49. P. 20095.
  7. Kleeva D.F., Rebreikina A.B., Soghoyan G.A. et al. Generalization of sustained neurophysiological effects of short‐term auditory 13‐Hz stimulation to neighbouring frequency representation in humans // Eur. J. Neurosci. 2022. V. 55. № 1. P. 175.
  8. Tomé D., Barbosa F., Nowak K., Marques-Teixeira J. The development of the N1 and N2 components in auditory oddball paradigms: A systematic review with narrative analysis and suggested normative values // J. Neural Transm. 2015. V. 122. № 3. P. 375.
  9. Voola M., Nguyen A.T., Marinovic W. et al. Oddeven oddball task: Evaluating event-related potentials during word discrimination compared to speech-token and tone discrimination // Front. Neurosci. 2022. V. 16. P. 983498.
  10. Larionova E.V., Martynova O.V. Frequency effects on spelling error recognition: An ERP study // Front. Psychol. 2022. V. 13. P. 834852.
  11. Liaukovich K., Ukraintseva Y., Martynova O. Implicit auditory perception of local and global irregularities in passive listening condition // Neuropsychologia. 2022. V. 165. P. 108129.
  12. Rebreikina A.B., Larionova E.V., Martynova O.V. Event-related potentials during literacy acquisition // J. Mod. Foreign Psychol. 2020. V. 9. № 2. P. 21.
  13. Rebreikina A.B., Kleeva D.F., Soghoyan G.A., Sysoeva O.V. [Effect of auditory ltp-like stimulation on the processing of sounds] // Sensornye Sistemy [Sensory Systems]. 2021. V. 35. № 2. P. 144.
  14. Kraus N., McGee T., Carrell T.D., Sharma A. Neurophysiologic bases of speech discrimination // Ear Hear. 1995. V. 16. № 1. P. 19.
  15. Sysoeva O.V., Molholm S., Djukic A. et al. Atypical processing of tones and phonemes in Rett Syndrome as biomarkers of disease progression // Transl. Psychiatry. 2020. V. 10. № 1. P. 188.
  16. Näätänen R. The perception of speech sounds by the human brain as reflected by the mismatch negativity (MMN) and its magnetic equivalent (MMNm) // Psychophysiology. 2001. V. 38. № 1. P. 1.
  17. Chen T.C., Hsieh M.H., Lin Y.T. et al. Mismatch negativity to different deviant changes in autism spectrum disorders: A meta-analysis // Clin. Neurophysiol. 2020. V. 131. № 3. P. 766.
  18. Rogachev A.O, Sysoeva O.V. The Temporal Response Function — a New Method for Investigating Neurophysiological Mechanisms of Speech Perception under Ecologically Valid Conditions // J. Mod. Foreign Psychol. 2024. V. 13. № 1. P. 92.
  19. Lalor E.C., Pearlmutter B.A., Reilly R.B. et al. The VESPA: A method for the rapid estimation of a visual evoked potential // Neuroimage. 2006. V. 32. № 4. P. 1549.
  20. Lalor E.C., Power A.J., Reilly R.B., Foxe J.J. Resolving precise temporal processing properties of the auditory system using continuous stimuli // J. Neurophysiol. 2009. V. 102. № 1. P. 349.
  21. Brodbeck C., Simon J. Z. Continuous speech processing // Curr. Opin. Physiol. 2020. V. 18. P. 25.
  22. Brodbeck C., Das P., Gillis M. et al. Eelbrain, a Python toolkit for time-continuous analysis with temporal response functions // Elife. 2023. V. 12. P. e85012.
  23. Brodbeck C., Hong L.E., Simon J.Z. Rapid trans-formation from auditory to linguistic representations of continuous speech // Curr. Biol. 2018. V. 28. № 24. P. 3976.
  24. Donhauser P.W., Baillet S. Two distinct neural timescales for predictive speech processing // Neuron. 2020. V. 105. № 2. P. 385.
  25. Mayorova L.A., Martynova O.V., Balaban P.M. et al. [Mismatch Negativity and its Hemodynamic Equivalent (Based on fMRI in Research of Speech Perception in Healthy and in Speech Disorders] // Usp. Fiz. Nauk. 2014. V. 45. № 1. P. 27.
  26. Butorina A.V. Shestakova A.N., Nikolaeva A.Y. et al. Magneto encephalography (MEG): Perspectives of speech areas functional mapping in human subjects // J. Mod. Foreign Psychol. 2012. V. 1. № 1. P. 103.
  27. Khalighinejad B., da Silva G.C., Mesgarani N. Dynamic encoding of acoustic features in neural responses to continuous speech // J. Neurosci. 2017. V. 37. № 8. P. 2176.
  28. MacGregor L.J., Pulvermüller F., van Casteren M., Shtyrov Y. Ultra-rapid access to words in the brain // Nat. Commun. 2012. V. 3. № 1. P. 711.
  29. Lowe M.X., Mohsenzadeh Y., Lahner B. et al. Cochlea to categories: The spatiotemporal dynamics of semantic auditory representations // Cogn. Neuropsychol. 2021. V. 38. № 7–8. P. 468.
  30. Verkhlyutov V.M., Burlakov. E.O., Gurtovoy K.G., Vvedensky V.L. [Recognition of oral speech according to MEG data by covariance filters] // Zh. Vyssh. Nerv. Deyat. Im. I.P. Pavlova. 2023. V. 73. № 6. P. 800.
  31. Van Petten C., Coulson S., Rubin S. et al. Time course of word identification and semantic integration in spoken language // J. Exp. Psychol. Learn. Mem. Cogn. 1999. V. 25. № 2. P. 394.
  32. Orepic P., Truccolo W., Halgren E. et al. Neural manifolds carry reactivation of phonetic representations during semantic processing // bioRxiv. [Electronic resourse]. 2024. doi: 10.1101/2023.10.30.564638
  33. Broderick M.P., Anderson A.J., Lalor E C. Semantic context enhances the early auditory encoding of natural speech // J. Neurosci. 2019. V. 39. № 38. P. 7564.
  34. Getz L.M., Toscano J.C. Electrophysiological evidence for top-down lexical influences on early speech perception // Psychol. Sci. 2019. V. 30. № 6. P. 830.
  35. Vaitulevich S.F., Petropavlovskaya E.A., Shestopalova L.B., Nikitin N.I. Functional hemispheric asymmetry of the human brain in audition // Human Physiology. 2019. V. 45. № 2. P. 103.
  36. Dmitrieva E.S., Gelman V.Y., Zayceva K.A., Orlov A.M. [Measuring the Association Between Emotional Prosody Perception and Emotional Intelligence Components] // Psychol. J. High. Sch. Econ. 2012. V. 9. № 1. P. 126.
  37. Lebedeva N.N., Karimova E.D., Kazimitova E.A. [Speech signal analysis in human functional status studies] // Biomed. Radioelectron. 2015. № 2. P. 3.
  38. Verwoert M., Amigó-Vega J., Gao Y. et al. Whole-brain dynamics of articulatory, acoustic and semantic speech representations // bioRxiv. [Electronic resourse]. 2024. doi: 10.1101/2024.08.15.608082
  39. Menenti L., Petersson K.M., Hagoort P. From reference to sense: how the brain encodes meaning for speaking // Front. Psychol. 2012. V. 2. P. 384.
  40. Luthra S. The role of the right hemisphere in processing phonetic variability between talkers // Neurobiol. Lang. 2021. V. 2. № 1. P. 138.

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML
Baixar
2. Fig. 1. Topogram of the predictive ability of the obtained time response function. r is the correlation coefficient of the calculated and original signal.

Baixar (414KB)
Metadados
3. Fig. 2. Time response function (TRF) to the sound envelope. A is the overall topogram for the averaged time response. B is the graph of the time response to the dynamics of the sound envelope for each lead with the topogram of the peak latency on the right (100 ms). B is the spatio-temporal representation of the TRF for all leads for peak latencies. The color corresponds to the localization of the sensors on the topograms below.

Baixar (1MB)
Metadados
4. Fig. 3. Results of the permutation one-sample t-test of the time series of the evoked response to the sound envelope of the listened stimulus. Significant segments of the time series and the topogram for the response with a latency of 100 ms (on the right) are shown at the top in black. The significant spatiotemporal segments are shown below: the abscissa axis shows the response time, the ordinate axis shows the magnetometer numbers.

Baixar (1MB)
Metadados
5. Fig. 4. The time response function (TRF) to the word onset vector. See Fig. 2 for notations A and B. B is the graph of the time response to the dynamics of the sound envelope for each lead with the peak latency topogram on the right (120 ms).

Baixar (1MB)
Metadados
6. Fig. 5. Results of the permutation one-sample t-test of the time series of the evoked response to the word onset vector of the listened stimulus. The significant segments of the time series and the topogram for the response with a latency of 120 ms (on the right) are shown above in black. For other explanations, see Fig. 3.

Baixar (1MB)
Metadados
7. Fig. 6. The time response function (TRF) to the semantic difference vector for meaningful words. See Fig. 2 for notations A and B. 2. B – graph of the time response to the dynamics of the sound envelope for each lead with the peak latency topogram on the right (310 ms).

Baixar (2MB)
Metadados
8. Fig. 7. Results of the permutation one-sample t-test of the time series of the evoked response to the semantic difference vector for the content words of the listened stimulus. Significant segments of the time series and the topogram for the response with a latency of 310 ms (on the right) are shown in black at the top. Other explanations see Fig. 3.

Baixar (1MB)
Metadados
9. Fig. 8. The time response function (TRF) to the semantic difference vector for auxiliary words. Designations A and B see Fig. 2. B – graph of the time response to the dynamics of the sound envelope for each lead with the peak latency topogram on the right (360 ms).

Baixar (2MB)
Metadados

Declaração de direitos autorais © Russian Academy of Sciences, 2025