Literature DB >> 28588065

The Hierarchical Cortical Organization of Human Speech Processing.

Wendy A de Heer1, Alexander G Huth1, Thomas L Griffiths1, Jack L Gallant1, Frédéric E Theunissen2.   

Abstract

Speech comprehension requires that the brain extract semantic meaning from the spectral features represented at the cochlea. To investigate this process, we performed an fMRI experiment in which five men and two women passively listened to several hours of natural narrative speech. We then used voxelwise modeling to predict BOLD responses based on three different feature spaces that represent the spectral, articulatory, and semantic properties of speech. The amount of variance explained by each feature space was then assessed using a separate validation dataset. Because some responses might be explained equally well by more than one feature space, we used a variance partitioning analysis to determine the fraction of the variance that was uniquely explained by each feature space. Consistent with previous studies, we found that speech comprehension involves hierarchical representations starting in primary auditory areas and moving laterally on the temporal lobe: spectral features are found in the core of A1, mixtures of spectral and articulatory in STG, mixtures of articulatory and semantic in STS, and semantic in STS and beyond. Our data also show that both hemispheres are equally and actively involved in speech perception and interpretation. Further, responses as early in the auditory hierarchy as in STS are more correlated with semantic than spectral representations. These results illustrate the importance of using natural speech in neurolinguistic research. Our methodology also provides an efficient way to simultaneously test multiple specific hypotheses about the representations of speech without using block designs and segmented or synthetic speech.SIGNIFICANCE STATEMENT To investigate the processing steps performed by the human brain to transform natural speech sound into meaningful language, we used models based on a hierarchical set of speech features to predict BOLD responses of individual voxels recorded in an fMRI experiment while subjects listened to natural speech. Both cerebral hemispheres were actively involved in speech processing in large and equal amounts. Also, the transformation from spectral features to semantic elements occurs early in the cortical speech-processing stream. Our experimental and analytical approaches are important alternatives and complements to standard approaches that use segmented speech and block designs, which report more laterality in speech processing and associated semantic processing to higher levels of cortex than reported here.
Copyright © 2017 the authors 0270-6474/17/376539-19$15.00/0.

Entities:  

Keywords:  fMRI; natural stimuli; regression; speech

Mesh:

Year:  2017        PMID: 28588065      PMCID: PMC5511884          DOI: 10.1523/JNEUROSCI.3267-16.2017

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  65 in total

1.  Event-related fMRI of the auditory cortex.

Authors:  P Belin; R J Zatorre; R Hoge; A C Evans; B Pike
Journal:  Neuroimage       Date:  1999-10       Impact factor: 6.556

2.  Phoneme and word recognition in the auditory ventral stream.

Authors:  Iain DeWitt; Josef P Rauschecker
Journal:  Proc Natl Acad Sci U S A       Date:  2012-02-01       Impact factor: 11.205

3.  Low-Frequency Cortical Entrainment to Speech Reflects Phoneme-Level Processing.

Authors:  Giovanni M Di Liberto; James A O'Sullivan; Edmund C Lalor
Journal:  Curr Biol       Date:  2015-09-24       Impact factor: 10.834

4.  Right-hemisphere auditory cortex is dominant for coding syllable patterns in speech.

Authors:  Daniel A Abrams; Trent Nicol; Steven Zecker; Nina Kraus
Journal:  J Neurosci       Date:  2008-04-09       Impact factor: 6.167

5.  Predicting neuronal responses during natural vision.

Authors:  Stephen V David; Jack L Gallant
Journal:  Network       Date:  2005 Jun-Sep       Impact factor: 1.273

6.  Functional specialization in rhesus monkey auditory cortex.

Authors:  B Tian; D Reser; A Durham; A Kustov; J P Rauschecker
Journal:  Science       Date:  2001-04-13       Impact factor: 47.728

7.  Feature analysis of natural sounds in the songbird auditory forebrain.

Authors:  K Sen; F E Theunissen; A J Doupe
Journal:  J Neurophysiol       Date:  2001-09       Impact factor: 2.714

8.  Human Superior Temporal Gyrus Organization of Spectrotemporal Modulation Tuning Derived from Speech Stimuli.

Authors:  Patrick W Hullett; Liberty S Hamilton; Nima Mesgarani; Christoph E Schreiner; Edward F Chang
Journal:  J Neurosci       Date:  2016-02-10       Impact factor: 6.167

9.  Encoding of natural sounds at multiple spectral and temporal resolutions in the human auditory cortex.

Authors:  Roberta Santoro; Michelle Moerel; Federico De Martino; Rainer Goebel; Kamil Ugurbil; Essa Yacoub; Elia Formisano
Journal:  PLoS Comput Biol       Date:  2014-01-02       Impact factor: 4.475

10.  A neurosemantic theory of concrete noun representation based on the underlying brain codes.

Authors:  Marcel Adam Just; Vladimir L Cherkassky; Sandesh Aryal; Tom M Mitchell
Journal:  PLoS One       Date:  2010-01-13       Impact factor: 3.240

View more
  58 in total

1.  Neural responses to natural and model-matched stimuli reveal distinct computations in primary and nonprimary auditory cortex.

Authors:  Sam V Norman-Haignere; Josh H McDermott
Journal:  PLoS Biol       Date:  2018-12-03       Impact factor: 8.029

2.  Encoding of natural timbre dimensions in human auditory cortex.

Authors:  Emily J Allen; Michelle Moerel; Agustín Lage-Castellanos; Federico De Martino; Elia Formisano; Andrew J Oxenham
Journal:  Neuroimage       Date:  2017-11-04       Impact factor: 6.556

3.  The Representation of Semantic Information Across Human Cerebral Cortex During Listening Versus Reading Is Invariant to Stimulus Modality.

Authors:  Fatma Deniz; Anwar O Nunez-Elizalde; Alexander G Huth; Jack L Gallant
Journal:  J Neurosci       Date:  2019-08-19       Impact factor: 6.167

4.  fMRI reveals language-specific predictive coding during naturalistic sentence comprehension.

Authors:  Cory Shain; Idan Asher Blank; Marten van Schijndel; William Schuler; Evelina Fedorenko
Journal:  Neuropsychologia       Date:  2019-12-24       Impact factor: 3.139

5.  Hierarchical Encoding of Attended Auditory Objects in Multi-talker Speech Perception.

Authors:  James O'Sullivan; Jose Herrero; Elliot Smith; Catherine Schevon; Guy M McKhann; Sameer A Sheth; Ashesh D Mehta; Nima Mesgarani
Journal:  Neuron       Date:  2019-10-21       Impact factor: 17.173

6.  An Integrated Neural Decoder of Linguistic and Experiential Meaning.

Authors:  Andrew James Anderson; Jeffrey R Binder; Leonardo Fernandino; Colin J Humphries; Lisa L Conant; Rajeev D S Raizada; Feng Lin; Edmund C Lalor
Journal:  J Neurosci       Date:  2019-09-30       Impact factor: 6.167

7.  Two Distinct Neural Timescales for Predictive Speech Processing.

Authors:  Peter W Donhauser; Sylvain Baillet
Journal:  Neuron       Date:  2019-12-02       Impact factor: 17.173

8.  Are We Ready for Real-world Neuroscience?

Authors:  Pawel J Matusz; Suzanne Dikker; Alexander G Huth; Catherine Perrodin
Journal:  J Cogn Neurosci       Date:  2018-06-19       Impact factor: 3.225

9.  Parietal Cortex Is Required for the Integration of Acoustic Evidence.

Authors:  Justin D Yao; Justin Gimoto; Christine M Constantinople; Dan H Sanes
Journal:  Curr Biol       Date:  2020-07-02       Impact factor: 10.834

10.  Dynamic Time-Locking Mechanism in the Cortical Representation of Spoken Words.

Authors:  A Nora; A Faisal; J Seol; H Renvall; E Formisano; R Salmelin
Journal:  eNeuro       Date:  2020-08-31
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.