Affichage des articles dont le libellé est nourissons. Afficher tous les articles
Affichage des articles dont le libellé est nourissons. Afficher tous les articles

11 mai 2021

Association entre la puissance de l'électroencéphalographie spectrale et le risque et le diagnostic d'autisme au début du développement

Aperçu: G.M.

Le "trouble du spectre de l'autisme" (TSA) trouve son origine dans le développement atypique des réseaux cérébraux. Les nourrissons qui présentent un risque familial élevé de TSA et qui sont diagnostiqués plus tard avec un TSA présentent une activité atypique dans les mesures oscillatoires d'électroencéphalographie (EEG) multiples. Cependant, les études sur les nourrissons et les frères et sœurs sont souvent limitées par la petite taille des échantillons.
Nous avons utilisé l'International Infant EEG Data Integration Platform, un ensemble de données multi-sites avec 432 participants, dont 222 à haut risque de TSA, auprès desquels des mesures répétées d'EEG ont été collectées entre 3 et 36 mois.
Nous avons appliqué un modèle de courbe de croissance latente pour tester si le statut de risque familial prédit les trajectoires de développement de la puissance spectrale au cours des 3 premières années de la vie, et si ces trajectoires prédisent l'issue des TSA. 

Un changement de puissance spectrale EEG dans toutes les bandes de fréquences s'est produit au cours des 3 premières années de vie. Le risque familial, mais pas un diagnostic ultérieur de TSA, était associé à une puissance réduite à 3 mois et à un changement développemental plus marqué entre 3 et 36 mois dans presque toutes les bandes de puissance absolue.
Le résultat du TSA n'était pas associé à l'interception de puissance absolue ou à la pente. Aucune association n'a été trouvée entre le risque ou le résultat et le pouvoir relatif. 

Cette étude a appliqué une approche analytique non utilisée dans les études prospectives antérieures sur les biomarqueurs des TSA, qui a été modélisée pour refléter la relation temporelle entre la susceptibilité génétique, le développement du cerveau et le diagnostic des TSA.
Les trajectoires de puissance spectrale semblent être prédites par le risque familial; cependant, la puissance spectrale ne permet pas de prédire le résultat du diagnostic au-delà du statut de risque familial. Les divergences entre les résultats actuels et les études précédentes sont discutées. 

RÉSUMÉ: Les nourrissons dont un frère ou une sœur plus âgé reçoit un diagnostic de TSA courent un risque accru de développer eux-mêmes un TSA. Cet article a testé si la puissance spectrale EEG au cours de la première année de vie peut prédire si ces nourrissons ont développé ou non un TSA. 

Association between spectral electroencephalography power and autism risk and diagnosis in early development

Scott Huberty  1 , Virginia Carter Leno  2 , Stefon J R van Noordt  1 , Rachael Bedford  2   3 , Andrew Pickles  2 , James A Desjardins  4 , Sara Jane Webb  5 , BASIS Team; Mayada Elsabbagh  1
Affiliations

Abstract

Autism spectrum disorder (ASD) has its origins in the atypical development of brain networks. Infants who are at high familial risk for, and later diagnosed with ASD, show atypical activity in multiple electroencephalography (EEG) oscillatory measures. However, infant-sibling studies are often constrained by small sample sizes. We used the International Infant EEG Data Integration Platform, a multi-site dataset with 432 participants, including 222 at high-risk for ASD, from whom repeated measurements of EEG were collected between the ages of 3-36 months. We applied a latent growth curve model to test whether familial risk status predicts developmental trajectories of spectral power across the first 3 years of life, and whether these trajectories predict ASD outcome. Change in spectral EEG power in all frequency bands occurred during the first 3 years of life. Familial risk, but not a later diagnosis of ASD, was associated with reduced power at 3 months, and a steeper developmental change between 3 and 36 months in nearly all absolute power bands. ASD outcome was not associated with absolute power intercept or slope. No associations were found between risk or outcome and relative power. This study applied an analytic approach not used in previous prospective biomarker studies of ASD, which was modeled to reflect the temporal relationship between genetic susceptibility, brain development, and ASD diagnosis. Trajectories of spectral power appear to be predicted by familial risk; however, spectral power does not predict diagnostic outcome above and beyond familial risk status. Discrepancies between current results and previous studies are discussed. LAY SUMMARY: Infants with an older sibling who is diagnosed with ASD are at increased risk of developing ASD themselves. This article tested whether EEG spectral power in the first year of life can predict whether these infants did or did not develop ASD.

Keywords: EEG; autism spectrum disorders; infants; siblings.

References

REFERENCES

    1. Bedford, R., Jones, E. J. H., Johnson, M. H., Pickles, A., Charman, T., & Gliga, T. (2016). Sex differences in the association between infant markers and later autistic traits. Molecular Autism, 7, 21. https://doi.org/10.1186/s13229-016-0081-0
    1. Benasich, A. A., Gou, Z., Choudhury, N., & Harris, K. D. (2008). Early cognitive and language skills are linked to resting frontal gamma power across the first 3 years. Behavioural Brain Research, 195(2), 215-222. https://doi.org/10.1016/j.bbr.2008.08.049
    1. Bosl, W., Tierney, A., Tager-Flusberg, H., & Nelson, C. (2011). EEG complexity as a biomarker for autism spectrum disorder risk. BMC Medicine, 9, 18. https://doi.org/10.1186/1741-7015-9-18
    1. Bosl, W. J., Tager-Flusberg, H., & Nelson, C. A. (2018). EEG analytics for early detection of autism spectrum disorder: A data-driven approach. Scientific Reports, 8(1), 6828. https://doi.org/10.1038/s41598-018-24318-x
    1. Bigdely-Shamlo, N., Touryan, J., Ojeda, A., Kothe, C., Mullen, T., & Robbins, K. (2020). Automated EEG mega-analysis I: Spectral and amplitude characteristics across studies. NeuroImage, 207, 116361. https://doi.org/10.1016/j.neuroimage.2019.116361
    1. Botvinik-Nezer, R., Holzmeister, F., Camerer, C. F., Dreber, A., Huber, J., Johannesson, M., Kirchler, M., Iwanir, R., Mumford, J. A., Adcock, R. A., Avesani, P., Baczkowski, B. M., Bajracharya, A., Bakst, L., Ball, S., Barilari, M., Bault, N., Beaton, D., Beitner, J., … Schonberg, T. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature, 582(7810), 84-88. https://doi.org/10.1038/s41586-020-2314-9
    1. Charman, T., Loth, E., Tillmann, J., Crawley, D., Wooldridge, C., Goyard, D., Ahmad, J., Auyeung, B., Ambrosino, S., Banaschewski, T., Baron-Cohen, S., Baumeister, S., Beckmann, C., Bolte, S., Bourgeron, T., Bours, C., Brammer, M., Brandeis, D., Brogna, C., … Buitelaar, J. K. (2017). The EU-AIMS Longitudinal European Autism Project (LEAP): Clinical characterisation. Molecular Autism, 8, 27. https://doi.org/10.1186/s13229-017-0145-9
    1. Cheng, J., Edwards, L. J., Maldonado-Molina, M. M., Komro, K. A., & Muller, K. E. (2010). Real longitudinal data analysis for real people: Building a good enough mixed model. Statistics in Medicine, 29(4), 504-520. https://doi.org/10.1002/sim.3775
    1. Constantino, J. N., Zhang, Y., Frazier, T., Abbacchi, A. M., & Law, P. (2010). Sibling recurrence and the genetic epidemiology of autism. The American Journal of Psychiatry, 167(11), 1349-1356. https://doi.org/10.1176/appi.ajp.2010.09101470
    1. D'Abate, L., Walker, S., Yuen, R. K. C., Tammimies, K., Buchanan, J. A., Davies, R. W., Thiruvahindrapuram, B., Wei, J., Brian, J., Bryson, S. E., Dobkins, K., Howe, J., Landa, R., Leef, J., Messinger, D., Ozonoff, S., Smith, I. M., Stone, W. L., Warren, Z. E., … Scherer, S. W. (2019). Predictive impact of rare genomic copy number variations in siblings of individuals with autism spectrum disorders. Nature Communications, 10(1), 5519. https://doi.org/10.1038/s41467-019-13380-2
    1. Debnath, R., Buzzell, G. A., Morales, S., Bowers, M. E., Leach, S. C., & Fox, N. A. (2020). The Maryland analysis of developmental EEG (MADE) pipeline. Psychophysiology, 57(6), e13580. https://doi.org/10.1111/psyp.13580
    1. Desjardins, J. A., van Noordt, S., Huberty, S., Segalowitz, S. J., & Elsabbagh, M. (2020). EEG integrated platform lossless (EEG-IP-L) pre-processing pipeline for objective signal quality assessment incorporating data annotation and blind source separation. Journal of Neuroscience Methods, 347, 108961. https://doi.org/10.1016/j.jneumeth.2020.108961
    1. Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., Deen, B., Delmonte, S., Dinstein, I., Ertl-Wagner, B., Fair, D. A., Gallagher, L., Kennedy, D. P., Keown, C. L., Keysers, C., … Milham, M. P. (2014). The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659-667. https://doi.org/10.1038/mp.2013.78
    1. Dickinson, A., DiStefano, C., Lin, Y.-Y., Scheffler, A. W., Senturk, D., & Jeste, S. S. (2018). Interhemispheric alpha-band hypoconnectivity in children with autism spectrum disorder. Behavioural Brain Research, 348, 227-234. https://doi.org/10.1016/j.bbr.2018.04.026
    1. Elsabbagh, M. (2020). Linking risk factors and outcomes in autism spectrum disorder: Is there evidence for resilience? BMJ (Clinical Research Ed.), 368, l6880. https://doi.org/10.1136/bmj.l6880
    1. Elsabbagh, M., & Johnson, M. H. (2016). Autism and the social brain: The First-YearPuzzle. Biological Psychiatry, 80(2), 94-99. https://doi.org/10.1016/j.biopsych.2016.02.019
    1. Elsabbagh, M., Mercure, E., Hudry, K., Chandler, S., Pasco, G., Charman, T., Pickles, A., Baron-Cohen, S., Bolton, P., Johnson, M. H., & Team, B. A. S. I. S. (2012). Infant neural sensitivity to dynamic eye gaze is associated with later emerging autism. Current Biology: CB, 22(4), 338-342. https://doi.org/10.1016/j.cub.2011.12.056
    1. Ewen, J. B., Sweeney, J. A., & Potter, W. Z. (2019). Conceptual, regulatory and strategic imperatives in the early days of EEG-based biomarker validation for neurodevelopmental disabilities. Frontiers in Integrative Neuroscience, 13, 45. https://doi.org/10.3389/fnint.2019.00045
    1. Fox, N. A., & Bell, M. A. (1990). Electrophysiological indices of frontal lobe development. Relations to cognitive and affective behavior in human infants over the first year of life. Annals of the New York Academy of Sciences, 608, 677-698 discussion 698-704.
    1. Gabard-Durnam, L. J., Mendez Leal, A. S., Wilkinson, C. L., & Levin, A. R. (2018). The Harvard automated processing pipeline for electroencephalography (HAPPE): Standardized processing software for developmental and high-artifact data. Frontiers in Neuroscience, 12, 97. https://doi.org/10.3389/fnins.2018.00097
    1. Gabard-Durnam, L. J., Wilkinson, C., Kapur, K., Tager-Flusberg, H., Levin, A. R., & Nelson, C. A. (2019). Longitudinal EEG power in the first postnatal year differentiates autism outcomes. Nature Communications, 10(1), 4188. https://doi.org/10.1038/s41467-019-12202-9
    1. Gasser, T., Bächer, P., & Steinberg, H. (1985). Test-retest reliability of spectral parameters of the EEG. Electroencephalography and Clinical Neurophysiology, 60, 312-319.
    1. Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., … Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3(1), 160044. https://doi.org/10.1038/sdata.2016.44
    1. Hazlett, H. C., Gu, H., Munsell, B. C., Kim, S. H., Styner, M., Wolff, J. J., Elison, J. T., Swanson, M. R., Zhu, H., Botteron, K. N., Collins, D. L., Constantino, J. N., Dager, S. R., Estes, A. M., Evans, A. C., Fonov, V. S., Gerig, G., Kostopoulos, P., McKinstry, R. C., … Piven, J. (2017). Early brain development in infants at high risk for autism spectrum disorder. Nature, 542(7641), 348-351. https://doi.org/10.1038/nature21369
    1. Islam, M. K., Rastegarnia, A., & Yang, Z. (2016). Methods for artifact detection and removal from scalp EEG: A review. Neurophysiologie Clinique/Clinical Neurophysiology, 46(4), 287-305. https://doi.org/10.1016/j.neucli.2016.07.002
    1. Jamal, W., Das, S., Oprescu, I.-A., Maharatna, K., Apicella, F., & Sicca, F. (2014). Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates. Journal of Neural Engineering, 11(4), 046019. https://doi.org/10.1088/1741-2560/11/4/046019
    1. Jones, E. J. H., Dawson, G., Kelly, J., Estes, A., & Webb, S. J. (2017). Parent-deliveredearly intervention in infants at risk for ASD: Effects on electrophysiological and habituation measures of social attention. Autism Research: Official Journal of the International Society for Autism Research, 10(5), 961-972. https://doi.org/10.1002/aur.1754
    1. Jones, E. J. H., Gliga, T., Bedford, R., Charman, T., & Johnson, M. H. (2014). Developmental pathways to autism: A review of prospective studies of infants at risk. Neuroscience and Biobehavioral Reviews, 39, 1-33. https://doi.org/10.1016/j.neubiorev.2013.12.001
    1. Jones, E. J. H., Venema, K., Earl, R. K., Lowy, R., & Webb, S. J. (2017). Infant social attention: An endophenotype of ASD-related traits? Journal of Child Psychology and Psychiatry, and Allied Disciplines, 58(3), 270-281. https://doi.org/10.1111/jcpp.12650
    1. Jones, E. J. H., Venema, K., Earl, R., Lowy, R., Barnes, K., Estes, A., Dawson, G., & Webb, S. J. (2016). Reduced engagement with social stimuli in 6-month-old infants with later autism spectrum disorder: A longitudinal prospective study of infants at high familial risk. Journal of Neurodevelopmental Disorders, 8, 7. https://doi.org/10.1186/s11689-016-9139-8
    1. Jones, Emily J. H., Kaitlin Venema, Rachel Lowy, Rachel K. Earl, and Sara Jane Webb.“Developmental changes in infant brain activity during naturalistic social experiences.” Developmental Psychobiology 57, no. 7 (November 2015): 842-53. https://doi.org/10.1002/dev.21336.
    1. Leach, S. C., Morales, S., Bowers, M. E., Buzzell, G. A., Debnath, R., Beall, D., & Fox, N. A. (2020). Adjusting ADJUST: Optimizing the ADJUST algorithm for pediatric data using geodesic nets. Psychophysiology, 57(8), e13566. https://doi.org/10.1111/psyp.13566
    1. Levin, A. R., Méndez Leal, A. S., Gabard-Durnam, L. J., & O'Leary, H. M. (2018). BEAPP: The batch electroencephalography automated processing platform. Frontiers in Neuroscience, 12, 513. https://doi.org/10.3389/fnins.2018.00513
    1. Levin, A. R., Varcin, K. J., O'Leary, H. M., Tager-Flusberg, H., & Nelson, C. A. (2017). EEG power at 3 months in infants at high familial risk for autism. Journal of Neurodevelopmental Disorders, 9(1), 34. https://doi.org/10.1186/s11689-017-9214-9
    1. Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C., Pickles, A., & Rutter, M. (2000). The autism diagnostic observation schedule-generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of Autism and Developmental Disorders, 30(3), 205-223.
    1. Lord, C., Rutter, M., DiLavorre, P., Risi, S., Gotham, K., & Bishop, S. (2012). Autism diagnostic observation schedule-2nd edition (ADOS-2). Western Psychological Services.
    1. McDermott, J. E., Wang, J., Mitchell, H., Webb-Robertson, B.-J., Hafen, R., Ramey, J., & Rodland, K. D. (2013). Challenges in biomarker discovery: Combining expert insights with statistical analysis of complex omics data. Expert Opinion on Medical Diagnostics, 7(1), 37-51. https://doi.org/10.1517/17530059.2012.718329
    1. McDonald, N. M., Senturk, D., Scheffler, A., Brian, J. A., Carver, L. J., Charman, T., Chawarska, K., Curtin, S., Hertz-Piccioto, I., Jones, E. J. H., Klin, A., Landa, R., Messinger, D. S., Ozonoff, S., Stone, W. L., Tager-Flusberg, H., Webb, S. J., Young, G., Zwaigenbaum, L., & Jeste, S. S. (2019). Developmental trajectories of infants with multiplex family risk for autism: A baby siblings research consortium study. JAMA Neurology, 77(1), 73-81. https://doi.org/10.1001/jamaneurol.2019.3341
    1. Messinger, D. S., Young, G. S., Webb, S. J., Ozonoff, S., Bryson, S. E., Carter, A., Carver, L., Charman, T., Chawarska, K., Curtin, S., Dobkins, K., Hertz-Picciotto, I., Hutman, T., Iverson, J. M., Landa, R., Nelson, C. A., Stone, W. L., Tager-Flusberg, H., & Zwaigenbaum, L. (2015). Early sex differences are not autism-specific: A Baby Siblings Research Consortium (BSRC) study. Molecular Autism, 6(1). http://dx.doi.org/10.1186/s13229-015-0027-y.
    1. Mundy, P., Card, J., & Fox, N. (2000). EEG correlates of the development of infant joint attention skills. Developmental Psychobiology, 36(4), 325-338.
    1. Noreika, V., Georgieva, S., Wass, S., & Leong, V. (2020). 14 challenges and their solutions for conducting social neuroscience and longitudinal EEG research with infants. Infant Behavior and Development, 58, 101393. https://doi.org/10.1016/j.infbeh.2019.101393
    1. O'Reilly, C., Lewis, J. D., & Elsabbagh, M. (2017). Is functional brain connectivity atypical in autism? A systematic review of EEG and MEG studies. PLoS One, 12(5), e0175870. https://doi.org/10.1371/journal.pone.0175870
    1. Orekhova, E. V., Elsabbagh, M., Jones, E. J., Dawson, G., Charman, T., & Johnson, M. H. (2014). EEG hyper-connectivity in high-risk infants is associated with later autism. Journal of Neurodevelopmental Disorders, 6(1), 40. https://doi.org/10.1186/1866-1955-6-40
    1. Orekhova, E. V., Stroganova, T. A., Posikera, I. N., & Elam, M. (2006). EEG theta rhythm in infants and preschool children. Clinical Neurophysiology, 117(5), 1047-1062. https://doi.org/10.1016/j.clinph.2005.12.027
    1. Parikshak, N. N., Luo, R., Zhang, A., Won, H., Lowe, J. K., Chandran, V., Horvath, S., & Geschwind, D. H. (2013). Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell, 155(5), 1008-1021. https://doi.org/10.1016/j.cell.2013.10.031
    1. Payakachat, N., Tilford, J. M., & Ungar, W. J. (2016). National database for autism research (NDAR): Big data opportunities for health services research and health technology assessment. PharmacoEconomics, 34(2), 127-138. https://doi.org/10.1007/s40273-015-0331-6
    1. Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., & Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6(1), 103. https://doi.org/10.1038/s41597-019-0104-8
    1. Righi, G., Tierney, A. L., Tager-Flusberg, H., & Nelson, C. A. (2014). Functional connectivity in the first year of life in infants at risk for autism spectrum disorder: An EEG study. PLoS One, 9(8), e105176. https://doi.org/10.1371/journal.pone.0105176
    1. Salinsky, M. C., Oken, B. S., & Morehead, L. (1991). Test-retest reliability in EEG frequency analysis. Electroencephalography and Clinical Neurophysiology, 79(5), 382-392. https://doi.org/10.1016/0013-4694(91)90203-g
    1. Robbins, K. A., Touryan, J., Mullen, T., Kothe, C., & Bigdely-Shamlo, N. (2020). How sensitive are EEG results to preprocessing methods: A benchmarking study. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 28(5), 1081-1090. https://doi.org/10.1109/TNSRE.2020.2980223
    1. Szatmari, P., Chawarska, K., Dawson, G., Georgiades, S., Landa, R., Lord, C., Messinger, D. S., Thurm, A., & Halladay, A. (2016). Prospective longitudinal studies of infant siblings of children with autism: Lessons learned and future directions. Journal of the American Academy of Child and Adolescent Psychiatry, 55(3), 179-187. https://doi.org/10.1016/j.jaac.2015.12.014
    1. Tierney, A. L., Gabard-Durnam, L., Vogel-Farley, V., Tager-Flusberg, H., & Nelson, C. A. (2012). Developmental trajectories of resting EEG power: An endophenotype of autism spectrum disorder. PLoS One, 7(6), e39127. https://doi.org/10.1371/journal.pone.0039127
    1. Traut, N., Beggiato, A., Bourgeron, T., Delorme, R., Rondi-Reig, L., Paradis, A. L., & Toro, R. (2018). Cerebellar volume in autism: Literature meta-analysis and analysis of the autism brain imaging data exchange cohort. Biological Psychiatry, 83(7), 579-588. https://doi.org/10.1016/j.biopsych.2017.09.029
    1. van Noordt, S., Desjardin, J., Huberty, S., Abbou-Abbas, L., Webb, S., Levin, A., Segalowitz, S., Evans, A., The BASIS Team, & Elsabbagh, M. (2020). EEG-IP: An international infant EEG data integration platform for the study of risk and resilience in autism and related conditions. Molecular Medicine, 26(1), 40. https://doi.org/10.1186/s10020-020-00149-3.
    1. Wang, J., Barstein, J., Ethridge, L. E., Mosconi, M. W., Takarae, Y., & Sweeney, J. A. (2013). Resting state EEG abnormalities in autism spectrum disorders. Journal of Neurodevelopmental Disorders, 5(1), 24. https://doi.org/10.1186/1866-1955-5-24
    1. Webb, S. J., Bernier, R., Henderson, H. A., Johnson, M. H., Jones, E. J. H., Lerner, M. D., McPartland, J. C., Nelson, C. A., Rojas, D. C., Townsend, J., & Westerfield, M. (2015). Guidelines and best practices for electrophysiological data collection, analysis and reporting in autism. Journal of Autism and Developmental Disorders, 45(2), 425-443. https://doi.org/10.1007/s10803-013-1916-6
    1. Wilkinson, C. L., Gabard-Durnam, L. J., Kapur, K., Tager-Flusberg, H., Levin, A. R., & Nelson, C. A. (2019). Use of longitudinal EEG measures in estimating language development in infants with and without familial risk for autism spectrum disorder. Neurobiology of Language, 1, 33-53.
    1. Wolff, J. J., Gu, H., Gerig, G., Elison, J. T., Styner, M., Gouttard, S., Botteron, K. N., Dager, S. R., Dawson, G., Estes, A. M., Evans, A. C., Hazlett, H. C., Kostopoulos, P., McKinstry, R. C., Paterson, S. J., Schultz, R. T., Zwaigenbaum, L., & Piven, J. (2012). Differences in white matter fiber tract development present from 6 to 24 months in infants with autism. The American Journal of Psychiatry, 169(6), 589-600. https://doi.org/10.1176/appi.ajp.2011.11091447
    1. Wolff, J. J., Jacob, S., & Elison, J. T. (2018). The journey to autism: Insights from neuroimaging studies of infants and toddlers. Development and Psychopathology, 30(2), 479-495. https://doi.org/10.1017/S0954579417000980t
Publié par à Aucun commentaire:
Libellés : , , ,
Inscription à : Articles (Atom)