Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Neural responses to affective speech, including motherese, map onto clinical and social eye tracking profiles in toddlers with ASD

Abstract

Affective speech, including motherese, captures an infant’s attention and enhances social, language and emotional development. Decreased behavioural response to affective speech and reduced caregiver–child interactions are early signs of autism in infants. To understand this, we measured neural responses to mild affect speech, moderate affect speech and motherese using natural sleep functional magnetic resonance imaging and behavioural preference for motherese using eye tracking in typically developing toddlers and those with autism. By combining diverse neural–clinical data using similarity network fusion, we discovered four distinct clusters of toddlers. The autism cluster with the weakest superior temporal responses to affective speech and very poor social and language abilities had reduced behavioural preference for motherese, while the typically developing cluster with the strongest superior temporal response to affective speech showed the opposite effect. We conclude that significantly reduced behavioural preference for motherese in autism is related to impaired development of temporal cortical systems that normally respond to parental affective speech.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Experimental design and data analysis flow chart.
Fig. 2: Reduced language-related brain activation in toddlers with ASD as compared to TD toddlers.
Fig. 3: Scatterplots showing correlations between brain activation to language and social communication abilities in toddlers.
Fig. 4: Gaze-contingent eye tracking measures of preference for motherese and correlations with neural response to motherese in toddlers with ASD and TD toddlers.
Fig. 5: TD and ASD subgroups with distinct fMRI–clinical patterns and correlations with behavioural preference for motherese.

Data availability

The tidy data used in this study are publicly available at https://github.com/Yaqiongxiao/asdmotherese_fmriSNF.

Code availability

Completed R code for implementing all analyses reported in this article is available at https://github.com/Yaqiongxiao/asdmotherese_fmriSNF.

References

  1. Kuhl, P. K. Is speech learning ‘gated’ by the social brain? Dev. Sci. 10, 110–120 (2007).

    PubMed  Article  Google Scholar 

  2. Kuhl, P. K. Brain mechanisms in early language acquisition. Neuron 67, 713–727 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. Saint-Georges, C. et al. Motherese in interaction: at the cross-road of emotion and cognition? (A systematic review). PLoS ONE 8, 1–17 (2013).

    Article  CAS  Google Scholar 

  4. Kuhl, P. K. et al. Cross-language analysis of phonetic units in language addressed to infants. Science 277, 684–686 (1997).

    CAS  PubMed  Article  Google Scholar 

  5. Grieser, D. A. L. & Kuhl, P. K. Maternal speech to infants in a tonal language: support for universal prosodic features in motherese. Dev. Psychol. 24, 14–20 (1988).

    Article  Google Scholar 

  6. Falk, D. Prelinguistic evolution in early hominins: whence motherese? Behav. Brain Sci. 27, 491–541 (2004).

    PubMed  Article  Google Scholar 

  7. Cooper, R. P. & Aslin, R. N. Preference for infant-directed speech in the first month after birth. Child Dev. 61, 1584 (1990).

    CAS  PubMed  Article  Google Scholar 

  8. Fernald, A. Four-month-old infants prefer to listen to motherese. Infant Behav. Dev. 8, 181–195 (1985).

    Article  Google Scholar 

  9. Kuhl, P. K., Coffey-Corina, S., Padden, D. & Dawson, G. Links between social and linguistic processing of speech in preschool children with autism: behavioral and electrophysiological measures. Dev. Sci. 8, F1–F12 (2005).

    PubMed  Article  Google Scholar 

  10. Pegg, J. E., Werker, J. F. & McLeod, P. J. Preference for infant-directed over adult-directed speech: evidence from 7-week-old infants. Infant Behav. Dev. 15, 325–345 (1992).

    Article  Google Scholar 

  11. Saito, Y. et al. Frontal cerebral blood flow change associated with infant-directed speech. Arch. Dis. Child. Fetal Neonatal Ed. 92, F113–F116 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Santesso, D. L., Schmidt, L. A. & Trainor, L. J. Frontal brain electrical activity (EEG) and heart rate in response to affective infant-directed (ID) speech in 9-month-old infants. Brain Cogn. 65, 14–21 (2007).

    PubMed  Article  Google Scholar 

  13. Sulpizio, S. et al. fNIRS reveals enhanced brain activation to female (versus male) infant directed speech (relative to adult directed speech) in young human infants. Infant Behav. Dev. 52, 89–96 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  14. Zangl, R. & Mills, D. L. Increased brain activity to infant-directed speech in 6- and 13-month-old infants. Infancy 11, 31–62 (2007).

    Article  Google Scholar 

  15. Zhang, Y. et al. Neural coding of formant-exaggerated speech in the infant brain. Dev. Sci. 14, 566–581 (2011).

    PubMed  Article  Google Scholar 

  16. Pierce, K. et al. Detecting, studying, and treating autism early: the one-year well-baby check-up approach. J. Pediatr. 159, 458–465.e6 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  17. Pierce, K., Courchesne, E. & Bacon, E. To screen or not to screen universally for autism is not the question: why the task force got it wrong. J. Pediatr. 176, 182–194 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  18. Pierce, K. et al. Evaluation of the diagnostic stability of the early autism spectrum disorder phenotype in the general population starting at 12 months. JAMA Pediatr. 173, 578–587 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  19. Bacon, E. C. et al. Rethinking the idea of late autism spectrum disorder onset. Dev. Psychopathol. 30, 553–569 (2018).

    PubMed  Article  Google Scholar 

  20. Bruinsma, Y., Koegel, R. L. & Koegel, L. K. Joint attention and children with autism: a review of the literature. Ment. Retard. Dev. Disabil. Res. Rev. 10, 169–175 (2004).

    PubMed  Article  Google Scholar 

  21. Wang, B. et al. Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333–337 (2014).

    CAS  PubMed  Article  Google Scholar 

  22. Pai, S. & Bader, G. D. Patient similarity networks for precision medicine. J. Mol. Biol. 430, 2924–2938 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  23. Lombardo, M. V. et al. Different functional neural substrates for good and poor language outcome in autism. Neuron 86, 267–277 (2015).

    Article  CAS  Google Scholar 

  24. Lombardo, M. V. et al. Large-scale associations between the leukocyte transcriptome and BOLD responses to speech differ in autism early language outcome subtypes. Nat. Neurosci. 21, 1680–1688 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Klin, A. Listening preferences in regard to speech in four children with developmental disabilities. J. Child Psychol. Psychiatry 33, 763–769 (1992).

    CAS  PubMed  Article  Google Scholar 

  26. Klin, A. Young autistic children’s listening preferences in regard to speech: a possible characterization of the symptom of social withdrawal. J. Autism Dev. Disord. 21, 29–42 (1991).

    CAS  PubMed  Article  Google Scholar 

  27. Ferjan Ramírez, N., Lytle, S. R., Fish, M. & Kuhl, P. K. Parent coaching at 6 and 10 months improves language outcomes at 14 months: a randomized controlled trial. Dev. Sci. 22, e12762 (2019).

    PubMed  Article  Google Scholar 

  28. Ferjan Ramírez, N., Lytle, S. R. & Kuhl, P. K. Parent coaching increases conversational turns and advances infant language development. Proc. Natl Acad. Sci. USA 117, 3484–3491 (2020).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  29. Bacon, E. C. et al. Measuring outcome in an early intervention program for toddlers with autism spectrum disorder: use of a curriculum-based assessment. Autism Res. Treat. 2014, 964704 (2014).

    PubMed  PubMed Central  Google Scholar 

  30. Dawson, G. et al. Randomized, controlled trial of an intervention for toddlers with autism: the early start Denver model. Pediatrics 125, e17–e23 (2010).

    PubMed  Article  Google Scholar 

  31. Kasari, C., Freeman, S. & Paparella, T. Joint attention and symbolic play in young children with autism: a randomized controlled intervention study. J. Child Psychol. Psychiatry Allied Discip. 47, 611–620 (2006).

    Article  Google Scholar 

  32. Sandin, S. et al. The heritability of autism spectrum disorder. J. Am. Med. Assoc. 318, 1182–1184 (2017).

    Article  Google Scholar 

  33. Bai, D. et al. Association of genetic and environmental factors with autism in a 5-country cohort. JAMA Psychiatry 76, 1035–1043 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  34. Courchesne, E. et al. The ASD living biology: from cell proliferation to clinical phenotype. Mol. Psychiatry 24, 88–107 (2019).

    PubMed  Article  Google Scholar 

  35. Courchesne, E., Gazestani, V. H. & Lewis, N. E. Prenatal origins of ASD: the when, what, and how of ASD development. Trends Neurosci. 43, 326–342 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Gazestani, V. H. et al. A perturbed gene network containing PI3K–AKT, RAS–ERK and WNT-β-catenin pathways in leukocytes is linked to ASD genetics and symptom severity. Nat. Neurosci. 22, 1624–1634 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Lombardo, M. V. et al. Atypical genomic patterning of the cerebral cortex in autism with poor early language outcome. Sci. Adv. 7, eabh1663 (2021).

    PubMed  PubMed Central  Article  Google Scholar 

  38. Vernetti, A. et al. Simulating interaction: using gaze-contingent eye-tracking to measure the reward value of social signals in toddlers with and without autism. Dev. Cogn. Neurosci. 29, 21–29 (2018).

    PubMed  Article  Google Scholar 

  39. Manning, J. H., Courchesne, E. & Fox, P. T. Intrinsic connectivity network mapping in young children during natural sleep. Neuroimage 83, 288–293 (2013).

    PubMed  Article  Google Scholar 

  40. Buckley, A. W. et al. Rapid eye movement sleep percentage in children with autism compared with children with developmental delay and typical development. Arch. Pediatr. Adolesc. Med. 164, 1032–1037 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  41. Devnani, P. A. & Hegde, A. U. Autism and sleep disorders. J. Pediatr. Neurosci. 10, 304–307 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  42. Goldman, S. E. et al. Defining the sleep phenotype in children with autism. Dev. Neuropsychol. 34, 560–573 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  43. Lehoux, T., Carrier, J. & Godbout, R. NREM sleep EEG slow waves in autistic and typically developing children: morphological characteristics and scalp distribution. J. Sleep. Res. 28, 1–6 (2019).

    Article  Google Scholar 

  44. Redcay, E. & Courchesne, E. Deviant functional magnetic resonance imaging patterns of brain activity to speech in 2–3-year-old children with autism spectrum disorder. Biol. Psychiatry 64, 589–598 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  45. Eyler, L. T., Pierce, K., Courchesne, E., Cheng, A. & Barnes, C. C. A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism. Brain 135, 949–960 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  46. Pierce, K. et al. Get SET early to identify and treatment refer autism spectrum disorder at 1 year and discover factors that influence early diagnosis. J. Pediatr. 236, 179–188 (2021).

    PubMed  Article  Google Scholar 

  47. Lord, C., Elsabbagh, M., Baird, G. & Veenstra-Vanderweele, J. Autism spectrum disorder. Lancet 392, 508–520 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  48. Mullen, E. M. Mullen Scales of Early Learning (American Guidance Service, 1995).

  49. Sparrow, S., Cicchetti, D. & Balla, D. Vineland-II Scales of Adaptive Behavior: Survey Form Manual (American Guidance Service, 2005).

  50. Dehaene-Lambertz, G., Dehaene, S. & Hertz-Pannier, L. Functional neuroimaging of speech perception in infants. Science 298, 2013–2015 (2002).

    CAS  PubMed  Article  Google Scholar 

  51. Redcay, E., Kennedy, D. P. & Courchesne, E. fMRI during natural sleep as a method to study brain function during early childhood. Neuroimage 38, 696–707 (2007).

    PubMed  Article  Google Scholar 

  52. Kundu, P., Inati, S. J., Evans, J. W., Luh, W. M. & Bandettini, P. A. Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. Neuroimage 60, 1759–1770 (2012).

    PubMed  Article  Google Scholar 

  53. Kundu, P. et al. Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proc. Natl Acad. Sci. USA 110, 16187–16192 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173 (1996).

    CAS  PubMed  Article  Google Scholar 

  55. Shi, F. et al. Infant brain atlases from neonates to 1- and 2-year-olds. PLoS ONE 6, e18746 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  56. Kundu, P. et al. Multi-echo fMRI: a review of applications in fMRI denoising and analysis of BOLD signals. Neuroimage 154, 59–80 (2017).

    PubMed  Article  Google Scholar 

  57. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).

    Article  PubMed  Google Scholar 

  58. Chen, G., Adleman, N. E., Saad, Z. S., Leibenluft, E. & Cox, R. W. Applications of multivariate modeling to neuroimaging group analysis: a comprehensive alternative to univariate general linear model. Neuroimage 99, 571–588 (2014).

    PubMed  Article  Google Scholar 

  59. Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).

    PubMed  Article  Google Scholar 

  60. Jenkinson, M. & Smith, S. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5, 143–156 (2001).

    CAS  PubMed  Article  Google Scholar 

  61. Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008, P10008 (2008).

    Article  Google Scholar 

  62. Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

We thank the parents and children in San Diego who participated in our research, without whom this would not be possible. We are also fortunate to work with wonderful paediatricians and family practice physicians spanning a range of medical groups including UCSD, Sharp Rees-Stealy, Scripps, Rady-Children’s Primary Care Medical Group, Chula Vista Pediatrics, Graybill Medical Group, Grossmont Pediatrics, Linda Vista Health Care Center, Mills Pediatrics, North County Health Services, San Diego Family Care and Sea Breeze Pediatrics. We are grateful for their support. This work was supported by NIDCD grant 1R01DC016385 awarded to E.C. and K.P.; NIMH grants R01MH118879 and R01MH104446 awarded to K.P.; and 755816 European Research Council awarded to M.V.L. and E.C.. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

E.C., K.P., L.T.E. and L.K. conceived the idea and designed the study. L.K., D.G., T.H.W. and K.V. recruited the participants. L.K., D.G., T.H.W., Y.X., L.T.E. and E.C. collected the data. Y.X. conceived and performed all analyses. E.C., M.V.L. and N.E.L. aided in data analyses. E.C., K.P. and M.V.L. obtained grant funding. Y.X. and E.C. wrote the manuscript. All authors contributed to editing the manuscript.

Corresponding authors

Correspondence to Yaqiong Xiao, Karen Pierce or Eric Courchesne.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Human Behaviour thanks Laura Edwards and Giorgia Silani for their contribution to the peer review of this work. Peer reviewer reports are available.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Xiao, Y., Wen, T.H., Kupis, L. et al. Neural responses to affective speech, including motherese, map onto clinical and social eye tracking profiles in toddlers with ASD. Nat Hum Behav 6, 443–454 (2022). https://doi.org/10.1038/s41562-021-01237-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41562-021-01237-y

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing