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      Attenuated processing of vowels in the left temporal cortex predicts speech-in-noise perception deficit in children with autism

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          Abstract

          Background

          Difficulties with speech-in-noise perception in autism spectrum disorders (ASD) may be associated with impaired analysis of speech sounds, such as vowels, which represent the fundamental phoneme constituents of human speech. Vowels elicit early (< 100 ms) sustained processing negativity (SPN) in the auditory cortex that reflects the detection of an acoustic pattern based on the presence of formant structure and/or periodic envelope information ( f0) and its transformation into an auditory “object”.

          Methods

          We used magnetoencephalography (MEG) and individual brain models to investigate whether SPN is altered in children with ASD and whether this deficit is associated with impairment in their ability to perceive speech in the background of noise. MEG was recorded while boys with ASD and typically developing boys passively listened to sounds that differed in the presence/absence of f0 periodicity and formant structure. Word-in-noise perception was assessed in the separate psychoacoustic experiment using stationary and amplitude modulated noise with varying signal-to-noise ratio.

          Results

          SPN was present in both groups with similarly early onset. In children with ASD, SPN associated with processing formant structure was reduced predominantly in the cortical areas lateral to and medial to the primary auditory cortex, starting at ~ 150—200 ms after the stimulus onset. In the left hemisphere, this deficit correlated with impaired ability of children with ASD to recognize words in amplitude-modulated noise, but not in stationary noise.

          Conclusions

          These results suggest that perceptual grouping of vowel formants into phonemes is impaired in children with ASD and that, in the left hemisphere, this deficit contributes to their difficulties with speech perception in fluctuating background noise.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s11689-024-09585-2.

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          Most cited references129

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          Cortical surface-based analysis. I. Segmentation and surface reconstruction.

          Anders Dale, Bruce Fischl, Martin I. Sereno (2000)
          Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.
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            A multi-modal parcellation of human cerebral cortex

            Matthew Glasser, Timothy Coalson, Emma Robinson … (2016)
            Understanding the amazingly complex human cerebral cortex requires a map (or parcellation) of its major subdivisions, known as cortical areas. Making an accurate areal map has been a century-old objective in neuroscience. Using multi-modal magnetic resonance images from the Human Connectome Project (HCP) and an objective semi-automated neuroanatomical approach, we delineated 180 areas per hemisphere bounded by sharp changes in cortical architecture, function, connectivity, and/or topography in a precisely aligned group average of 210 healthy young adults. We characterized 97 new areas and 83 areas previously reported using post-mortem microscopy or other specialized study-specific approaches. To enable automated delineation and identification of these areas in new HCP subjects and in future studies, we trained a machine-learning classifier to recognize the multi-modal ‘fingerprint’ of each cortical area. This classifier detected the presence of 96.6% of the cortical areas in new subjects, replicated the group parcellation, and could correctly locate areas in individuals with atypical parcellations. The freely available parcellation and classifier will enable substantially improved neuroanatomical precision for studies of the structural and functional organization of human cerebral cortex and its variation across individuals and in development, aging, and disease.
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              Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference.

              Stephen M Smith, Thomas E Nichols (2009)
              Many image enhancement and thresholding techniques make use of spatial neighbourhood information to boost belief in extended areas of signal. The most common such approach in neuroimaging is cluster-based thresholding, which is often more sensitive than voxel-wise thresholding. However, a limitation is the need to define the initial cluster-forming threshold. This threshold is arbitrary, and yet its exact choice can have a large impact on the results, particularly at the lower (e.g., t, z < 4) cluster-forming thresholds frequently used. Furthermore, the amount of spatial pre-smoothing is also arbitrary (given that the expected signal extent is very rarely known in advance of the analysis). In the light of such problems, we propose a new method which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems. The method takes a raw statistic image and produces an output image in which the voxel-wise values represent the amount of cluster-like local spatial support. The method is thus referred to as "threshold-free cluster enhancement" (TFCE). We present the TFCE approach and discuss in detail ROC-based optimisation and comparisons with cluster-based and voxel-based thresholding. We find that TFCE gives generally better sensitivity than other methods over a wide range of test signal shapes and SNR values. We also show an example on a real imaging dataset, suggesting that TFCE does indeed provide not just improved sensitivity, but richer and more interpretable output than cluster-based thresholding.
              Article 0 comments Cited 1043 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Elena V. Orekhova: orekhova.elena.v@gmail.com
                Journal
                Journal ID (nlm-ta): J Neurodev Disord
                Journal ID (iso-abbrev): J Neurodev Disord
                Title: Journal of Neurodevelopmental Disorders
                Publisher: BioMed Central (London )
                ISSN (Print): 1866-1947
                ISSN (Electronic): 1866-1955
                Publication date (Electronic): 6 December 2024
                Publication date PMC-release: 6 December 2024
                Publication date Collection: 2024
                Volume: 16
                Electronic Location Identifier: 67
                Affiliations
                [1 ]Center for Neurocognitive Research (MEG Center), Moscow State University of Psychology and Education, ( https://ror.org/04rnxkh71) Moscow, Russian Federation
                [2 ]RSNO “Center for Curative Pedagogics”, Moscow, Russian Federation
                Article
                Publisher ID: 9585
                DOI: 10.1186/s11689-024-09585-2
                PMC ID: 11624601
                PubMed ID: 39643915
                SO-VID: 95b1ec9e-dd69-49a1-861e-fca36e5498d6
                Copyright © © The Author(s) 2024
                License:

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                Date received : 8 August 2024
                Date accepted : 25 November 2024
                Categories
                Subject: Research
                Custom metadata
                issue-copyright-statement © BioMed Central Ltd., part of Springer Nature 2024

                ScienceOpen disciplines: Neurosciences
                Keywords: autism spectrum disorder (asd),magnetoencephalography (meg),speech-in-noise perception,vowels,formant structure,periodicity pitch,sustained processing negativity (spn),children,auditory processing disorder
                Data availability:
                ScienceOpen disciplines: Neurosciences
                Keywords: autism spectrum disorder (asd), magnetoencephalography (meg), speech-in-noise perception, vowels, formant structure, periodicity pitch, sustained processing negativity (spn), children, auditory processing disorder

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