{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T12:16:00Z","timestamp":1770898560988,"version":"3.50.1"},"reference-count":87,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T00:00:00Z","timestamp":1616630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely during pregnancy and birth. We used a supervised machine learning algorithm with a cross-validation technique to classify NT and ASD babies and performed various statistical tests. With a minimization of the false positive rate, 96% of NT and 41% of ASD babies were identified with a positive predictive value of 77%. We identified the following biomarkers related to ASD: sex, maternal familial history of auto-immune diseases, maternal immunization to CMV, IgG CMV level, timing of fetal rotation on head, femur length in the 3rd trimester, white blood cell count in the 3rd trimester, fetal heart rate during labor, newborn feeding and temperature difference between birth and one day after. Furthermore, statistical models revealed that a subpopulation of 38% of babies at risk of ASD had significantly larger fetal head circumference than age-matched NT ones, suggesting an in utero origin of the reported bigger brains of toddlers with ASD. Our results suggest that pregnancy follow-up measurements might provide an early prognosis of ASD enabling pre-symptomatic behavioral interventions to attenuate efficiently ASD developmental sequels.<\/jats:p>","DOI":"10.1038\/s41598-021-86320-0","type":"journal-article","created":{"date-parts":[[2021,3,25]],"date-time":"2021-03-25T11:06:58Z","timestamp":1616670418000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD"],"prefix":"10.1038","volume":"11","author":[{"given":"Hugues","family":"Caly","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamed","family":"Rabiei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Perrine","family":"Coste-Mazeau","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastien","family":"Hantz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sophie","family":"Alain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jean-Luc","family":"Eyraud","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thierry","family":"Chianea","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catherine","family":"Caly","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Makowski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nouchine","family":"Hadjikhani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric","family":"Lemonnier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yehezkel","family":"Ben-Ari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,25]]},"reference":[{"key":"86320_CR1","unstructured":"Schopler Reichler, R. J. & Renner, B. R., E. Childhood Autism Rating Scale (CARS). Los Angeles West. Psychol. Serv. (1988)."},{"key":"86320_CR2","first-page":"1","volume":"4422","author":"JN Constantino","year":"2015","unstructured":"Constantino, J. N. & Charman, T. Series autism 3 diagnosis of autism spectrum disorder: Reconciling the syndrome, its diverse origins, and variation in expression. Lancet 4422, 1\u201314 (2015).","journal-title":"Lancet"},{"key":"86320_CR3","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1002\/aur.239","volume":"5","author":"M Elsabbagh","year":"2012","unstructured":"Elsabbagh, M. et al. Global prevalence of autism and other pervasive developmental disorders. Autism Res. 5, 160\u2013179 (2012).","journal-title":"Autism Res."},{"key":"86320_CR4","doi-asserted-by":"publisher","first-page":"1766","DOI":"10.1007\/s10803-014-2336-y","volume":"45","author":"S Idring","year":"2015","unstructured":"Idring, S. et al. Changes in prevalence of autism spectrum disorders in 2001\u20132011: Findings from the Stockholm Youth Cohort. J. Autism Dev. Disord. 45, 1766\u20131773 (2015).","journal-title":"J. Autism Dev. Disord."},{"key":"86320_CR5","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.rasd.2010.06.004","volume":"5","author":"JL Matson","year":"2011","unstructured":"Matson, J. L. & Kozlowski, A. M. The increasing prevalence of autism spectrum disorders. Res. Autism Spectr. Disord. 5, 418\u2013425 (2011).","journal-title":"Res. Autism Spectr. Disord."},{"key":"86320_CR6","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1016\/S2215-0366(19)30181-6","volume":"6","author":"JY Kim","year":"2019","unstructured":"Kim, J. Y. et al. Environmental risk factors and biomarkers for autism spectrum disorder: an umbrella review of the evidence. Lancet Psychiatry 6, 590\u2013600 (2019).","journal-title":"Lancet Psychiatry"},{"key":"86320_CR7","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1038\/nrn3978","volume":"16","author":"ML Estes","year":"2015","unstructured":"Estes, M. L. & McAllister, A. K. Immune mediators in the brain and peripheral tissues in autism spectrum disorder. Nat. Rev. Neurosci. 16, 469\u2013486 (2015).","journal-title":"Nat. Rev. Neurosci."},{"key":"86320_CR8","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1542\/peds.2008-2445","volume":"124","author":"H\u00d3 Atlad\u00f3ttir","year":"2009","unstructured":"Atlad\u00f3ttir, H. \u00d3. et al. Association of family history of autoimmune diseases and autism spectrum disorders. Pediatrics 124, 687\u2013694 (2009).","journal-title":"Pediatrics"},{"key":"86320_CR9","doi-asserted-by":"publisher","first-page":"e1447","DOI":"10.1542\/peds.2012-1107","volume":"130","author":"H\u00d3 Atlad\u00f3ttir","year":"2012","unstructured":"Atlad\u00f3ttir, H. \u00d3., Henriksen, T. B., Schendel, D. E. & Parner, E. T. Autism after infection, febrile episodes, and antibiotic use during pregnancy: An exploratory study. Pediatrics 130, e1447\u2013e1454 (2012).","journal-title":"Pediatrics"},{"key":"86320_CR10","doi-asserted-by":"publisher","first-page":"1696","DOI":"10.1001\/jama.2013.2270","volume":"309","author":"J Christensen","year":"2013","unstructured":"Christensen, J. et al. Prenatal valproate exposure and risk of autism spectrum disorders and childhood autism. JAMA 309, 1696\u20131703 (2013).","journal-title":"JAMA"},{"key":"86320_CR11","doi-asserted-by":"publisher","first-page":"987","DOI":"10.3389\/fpsyt.2019.00987","volume":"10","author":"N Principi","year":"2019","unstructured":"Principi, N. & Esposito, S. Vitamin D deficiency during pregnancy and autism spectrum disorders development. Front. Psychiatry 10, 987 (2019).","journal-title":"Front. Psychiatry"},{"key":"86320_CR12","doi-asserted-by":"publisher","first-page":"944","DOI":"10.1289\/ehp.1104553","volume":"120","author":"JF Shelton","year":"2012","unstructured":"Shelton, J. F., Hertz-Picciotto, I. & Pessah, I. N. Tipping the balance of autism risk: Potential mechanisms linking pesticides and autism. Environ. Health Perspect. 120, 944\u2013951 (2012).","journal-title":"Environ. Health Perspect."},{"key":"86320_CR13","doi-asserted-by":"publisher","first-page":"117004","DOI":"10.1289\/EHP535","volume":"126","author":"I Hertz-Picciotto","year":"2018","unstructured":"Hertz-Picciotto, I. et al. A prospective study of environmental exposures and early biomarkers in autism spectrum disorder: Design, protocols, and preliminary data from the MARBLES study. Environ. Health Perspect. 126, 117004 (2018).","journal-title":"Environ. Health Perspect."},{"key":"86320_CR14","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1056\/NEJMoa1307491","volume":"370","author":"R Stoner","year":"2014","unstructured":"Stoner, R. et al. Patches of disorganization in the neocortex of children with autism. N. Engl. J. Med. 370, 1209\u20131219 (2014).","journal-title":"N. Engl. J. Med."},{"key":"86320_CR15","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1001\/jama.290.3.337","volume":"290","author":"E Courchesne","year":"2003","unstructured":"Courchesne, E., Carper, R. & Akshoomoff, N. Evidence of brain overgrowth in the first year of life in autism. J. Am. Med. Assoc. 290, 337\u2013344 (2003).","journal-title":"J. Am. Med. Assoc."},{"key":"86320_CR16","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1002\/aur.1755","volume":"10","author":"DG Amaral","year":"2017","unstructured":"Amaral, D. G. et al. In pursuit of neurophenotypes: The consequences of having autism and a big brain. Autism Res. 10, 711\u2013722 (2017).","journal-title":"Autism Res."},{"key":"86320_CR17","doi-asserted-by":"publisher","first-page":"1635","DOI":"10.1002\/aur.2036","volume":"11","author":"F Bonnet-Brilhault","year":"2018","unstructured":"Bonnet-Brilhault, F. et al. Autism is a prenatal disorder: Evidence from late gestation brain overgrowth. Autism Res. 11, 1635\u20131642 (2018).","journal-title":"Autism Res."},{"key":"86320_CR18","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1136\/adc.52.2.105","volume":"52","author":"M Fujimura","year":"1977","unstructured":"Fujimura, M. & Seryu, J. I. Velocity of head growth during the perinatal period. Arch. Dis. Child. 52, 105\u2013112 (1977).","journal-title":"Arch. Dis. Child."},{"key":"86320_CR19","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1002\/aur.1921","volume":"11","author":"LME Blanken","year":"2018","unstructured":"Blanken, L. M. E. et al. A prospective study of fetal head growth, autistic traits and autism spectrum disorder. Autism Res. 11, 602\u2013612 (2018).","journal-title":"Autism Res."},{"key":"86320_CR20","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1002\/aur.1518","volume":"9","author":"LM Unwin","year":"2016","unstructured":"Unwin, L. M. et al. A prospective ultrasound study of prenatal growth in infant siblings of children with autism. Autism Res. 9, 210\u2013216 (2016).","journal-title":"Autism Res."},{"key":"86320_CR21","doi-asserted-by":"publisher","first-page":"1048","DOI":"10.1016\/j.biopsych.2007.03.020","volume":"62","author":"K Hobbs","year":"2007","unstructured":"Hobbs, K. et al. A retrospective fetal ultrasound study of brain size in autism. Biol. Psychiatry 62, 1048\u20131055 (2007).","journal-title":"Biol. Psychiatry"},{"key":"86320_CR22","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1001\/archpsyc.61.6.618","volume":"61","author":"EJ Glasson","year":"2004","unstructured":"Glasson, E. J. et al. Perinatal factors and the development of autism: A population study. Arch. Gen. Psychiatry 61, 618\u2013627 (2004).","journal-title":"Arch. Gen. Psychiatry"},{"key":"86320_CR23","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000006696","author":"C Wang","year":"2017","unstructured":"Wang, C., Geng, H., Liu, W. & Zhang, G. Prenatal, perinatal, and postnatal factors associated with autism: A meta-analysis. Medicine (United States) https:\/\/doi.org\/10.1097\/MD.0000000000006696 (2017).","journal-title":"Medicine (United States)"},{"key":"86320_CR24","doi-asserted-by":"publisher","first-page":"3358","DOI":"10.1523\/JNEUROSCI.3642-17.2018","volume":"38","author":"Y Li","year":"2018","unstructured":"Li, Y. et al. Maternal and early postnatal immune activation produce dissociable effects on neurotransmission in mPFC\u2013amygdala circuits. J. Neurosci. 38, 3358\u20133372 (2018).","journal-title":"J. Neurosci."},{"key":"86320_CR25","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1126\/science.aad0314","volume":"351","author":"GB Choi","year":"2016","unstructured":"Choi, G. B. et al. The maternal interleukin-17a pathway in mice promotes autism-like phenotypes in offspring. Science (80-. ) 351, 933\u2013939 (2016).","journal-title":"Science (80-. )"},{"key":"86320_CR26","doi-asserted-by":"publisher","first-page":"3982","DOI":"10.1093\/cercor\/bhy279","volume":"29","author":"A Fernandez","year":"2019","unstructured":"Fernandez, A. et al. The GABA developmental shift is abolished by maternal immune activation already at birth. Cereb. Cortex 29, 3982\u20133992 (2019).","journal-title":"Cereb. Cortex"},{"key":"86320_CR27","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1016\/j.biopsych.2013.06.025","volume":"75","author":"MD Bauman","year":"2014","unstructured":"Bauman, M. D. et al. Activation of the maternal immune system during pregnancy alters behavioral development of rhesus monkey offspring. Biol. Psychiatry 75, 332\u2013341 (2014).","journal-title":"Biol. Psychiatry"},{"key":"86320_CR28","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/j.bbi.2012.01.011","volume":"26","author":"NV Malkova","year":"2012","unstructured":"Malkova, N. V., Yu, C. Z., Hsiao, E. Y., Moore, M. J. & Patterson, P. H. Maternal immune activation yields offspring displaying mouse versions of the three core symptoms of autism. Brain. Behav. Immun. 26, 607\u2013616 (2012).","journal-title":"Brain. Behav. Immun."},{"key":"86320_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41398-019-0608-1","volume":"9","author":"H Zhao","year":"2019","unstructured":"Zhao, H. et al. Maternal valproic acid exposure leads to neurogenesis defects and autism-like behaviors in non-human primates. Transl. Psychiatry 9, 1\u201313 (2019).","journal-title":"Transl. Psychiatry"},{"key":"86320_CR30","doi-asserted-by":"publisher","first-page":"eaav0394","DOI":"10.1126\/sciadv.aav0394","volume":"5","author":"R Cloarec","year":"2019","unstructured":"Cloarec, R. et al. Pyramidal neuron growth and increased hippocampal volume during labor and birth in autism. Sci. Adv. 5, eaav0394 (2019).","journal-title":"Sci. Adv."},{"key":"86320_CR31","doi-asserted-by":"publisher","DOI":"10.1126\/science.1247190","author":"R Tyzio","year":"2014","unstructured":"Tyzio, R. et al. Oxytocin-mediated GABA inhibition during delivery attenuates autism pathogenesis in rodent offspring. Science (80-. ) https:\/\/doi.org\/10.1126\/science.1247190 (2014).","journal-title":"Science (80-. )"},{"key":"86320_CR32","doi-asserted-by":"publisher","first-page":"1300","DOI":"10.1111\/j.1469-7610.2010.02308.x","volume":"51","author":"KS Wallace","year":"2010","unstructured":"Wallace, K. S. & Rogers, S. J. Intervening in infancy: Implications for autism spectrum disorders. J. Child Psychol. Psychiatry Allied Discip. 51, 1300\u20131320 (2010).","journal-title":"J. Child Psychol. Psychiatry Allied Discip."},{"key":"86320_CR33","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1177\/070674370304800802","volume":"48","author":"SE Bryson","year":"2003","unstructured":"Bryson, S. E., Rogers, S. J. & Fombonne, E. Autism spectrum disorders: Early detection, intervention, education, and psychopharmacological management. Can. J. Psychiatry 48, 506\u2013516 (2003).","journal-title":"Can. J. Psychiatry"},{"key":"86320_CR34","doi-asserted-by":"publisher","first-page":"348","DOI":"10.1038\/nature21369","volume":"542","author":"HC Hazlett","year":"2017","unstructured":"Hazlett, H. C. et al. Early brain development in infants at high risk for autism spectrum disorder. Nature 542, 348\u2013351 (2017).","journal-title":"Nature"},{"key":"86320_CR35","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s11689-020-9308-7","volume":"12","author":"JB Girault","year":"2020","unstructured":"Girault, J. B. et al. Quantitative trait variation in ASD probands and toddler sibling outcomes at 24 months. J. Neurodev. Disord. 12, 5 (2020).","journal-title":"J. Neurodev. Disord."},{"key":"86320_CR36","doi-asserted-by":"publisher","first-page":"eqqg2882","DOI":"10.1126\/scitranslmed.aag2882","volume":"9","author":"RW Emerson","year":"2017","unstructured":"Emerson, R. W. et al. Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. Sci. Transl. Med. 9, eqqg2882 (2017).","journal-title":"Sci. Transl. Med."},{"key":"86320_CR37","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1002\/aur.81","volume":"2","author":"SJ Rogers","year":"2009","unstructured":"Rogers, S. J. What are infant siblings teaching us about autism in infancy?. Autism Res. 2, 125\u2013137 (2009).","journal-title":"Autism Res."},{"key":"86320_CR38","doi-asserted-by":"publisher","first-page":"4188","DOI":"10.1038\/s41467-019-12202-9","volume":"10","author":"LJ Gabard-Durnam","year":"2019","unstructured":"Gabard-Durnam, L. J. et al. Longitudinal EEG power in the first postnatal year differentiates autism outcomes. Nat. Commun. 10, 4188 (2019).","journal-title":"Nat. Commun."},{"key":"86320_CR39","doi-asserted-by":"publisher","first-page":"1454","DOI":"10.1038\/nn.4353","volume":"19","author":"A Krishnan","year":"2016","unstructured":"Krishnan, A. et al. Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nat. Neurosci. 19, 1454\u20131462 (2016).","journal-title":"Nat. Neurosci."},{"key":"86320_CR40","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1038\/ng.3039","volume":"46","author":"T Gaugler","year":"2014","unstructured":"Gaugler, T. et al. Most genetic risk for autism resides with common variation. Nat. Genet. 46, 881\u2013885 (2014).","journal-title":"Nat. Genet."},{"key":"86320_CR41","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.tins.2006.05.010","volume":"29","author":"AM Persico","year":"2006","unstructured":"Persico, A. M. & Bourgeron, T. Searching for ways out of the autism maze: Genetic, epigenetic and environmental clues. Trends Neurosci. 29, 349\u2013358 (2006).","journal-title":"Trends Neurosci."},{"key":"86320_CR42","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1038\/s41380-018-0056-y","volume":"24","author":"E Courchesne","year":"2019","unstructured":"Courchesne, E. et al. The ASD living biology: From cell proliferation to clinical phenotype. Mol. Psychiatry 24, 88\u2013107 (2019).","journal-title":"Mol. Psychiatry"},{"key":"86320_CR43","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1186\/2040-2392-3-9","volume":"3","author":"L Klei","year":"2012","unstructured":"Klei, L. et al. Common genetic variants, acting additively, are a major source of risk for autism. Mol. Autism 3, 9 (2012).","journal-title":"Mol. Autism"},{"key":"86320_CR44","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1016\/S0140-6736(12)62129-1","volume":"381","author":"Cross-Disorder Group of the Psychiatric Genomics Consortium","year":"2013","unstructured":"Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: A genome-wide analysis. Lancet 381, 1371\u20131379 (2013).","journal-title":"Lancet"},{"key":"86320_CR45","doi-asserted-by":"publisher","first-page":"216","DOI":"10.1038\/nature13908","volume":"515","author":"I Iossifov","year":"2014","unstructured":"Iossifov, I. et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature 515, 216\u2013221 (2014).","journal-title":"Nature"},{"key":"86320_CR46","doi-asserted-by":"publisher","first-page":"e0208141","DOI":"10.1371\/journal.pone.0208141","volume":"14","author":"MA Konerman","year":"2019","unstructured":"Konerman, M. A. et al. Machine learning models to predict disease progression among veterans with hepatitis C virus. PLoS ONE 14, e0208141 (2019).","journal-title":"PLoS ONE"},{"key":"86320_CR47","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1186\/s12933-019-0879-0","volume":"18","author":"QA Hathaway","year":"2019","unstructured":"Hathaway, Q. A. et al. Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics. Cardiovasc. Diabetol. 18, 78 (2019).","journal-title":"Cardiovasc. Diabetol."},{"key":"86320_CR48","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-48263-5","volume":"9","author":"M Makino","year":"2019","unstructured":"Makino, M. et al. Artificial intelligence predicts the progression of diabetic kidney disease using big data machine learning. Sci. Rep. 9, 1\u20139 (2019).","journal-title":"Sci. Rep."},{"key":"86320_CR49","doi-asserted-by":"publisher","first-page":"954","DOI":"10.1038\/s41591-019-0447-x","volume":"25","author":"D Ardila","year":"2019","unstructured":"Ardila, D. et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25, 954\u2013961 (2019).","journal-title":"Nat. Med."},{"key":"86320_CR50","doi-asserted-by":"publisher","first-page":"973","DOI":"10.1038\/s41588-019-0420-0","volume":"51","author":"J Zhou","year":"2019","unstructured":"Zhou, J. et al. Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nat. Genet. 51, 973\u2013980 (2019).","journal-title":"Nat. Genet."},{"key":"86320_CR51","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.ejogrb.2015.05.011","volume":"191","author":"A Tournemire","year":"2015","unstructured":"Tournemire, A. et al. Prenasal thickness to nasal bone length ratio: Effectiveness as a second or third trimester marker for Down syndrome. Eur. J. Obstet. Gynecol. Reprod. Biol. 191, 28\u201332 (2015).","journal-title":"Eur. J. Obstet. Gynecol. Reprod. Biol."},{"key":"86320_CR52","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1016\/S0306-9877(02)00400-0","volume":"60","author":"JT Manning","year":"2003","unstructured":"Manning, J. T., Callow, M. & Bundred, P. E. Finger and toe ratios in humans and mice: Implications for the aetiology of diseases influenced by HOX genes. Med. Hypotheses 60, 340\u2013343 (2003).","journal-title":"Med. Hypotheses"},{"key":"86320_CR53","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1152\/physrev.00043.2017","volume":"98","author":"CA Ducsay","year":"2018","unstructured":"Ducsay, C. A. et al. Gestational hypoxia and developmental plasticity. Physiol. Rev. 98, 1241\u20131334 (2018).","journal-title":"Physiol. Rev."},{"key":"86320_CR54","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1111\/j.1471-0528.1997.tb10934.x","volume":"104","author":"I Ingemarsson","year":"1997","unstructured":"Ingemarsson, I., Herbst, A. & Thorngren-Jerneck, K. Long term outcome after umbilical artery acidaemia at term birth: Influence of gender and duration of fetal heart rate abnormalities. BJOG An Int. J. Obstet. Gynaecol. 104, 1123\u20131127 (1997).","journal-title":"BJOG An Int. J. Obstet. Gynaecol."},{"key":"86320_CR55","doi-asserted-by":"publisher","first-page":"1160","DOI":"10.3109\/14767051003677996","volume":"23","author":"SR Sekuli\u0107","year":"2010","unstructured":"Sekuli\u0107, S. R., Mikov, A. & Petrovi, D. S. Probability of breech presentation and its significance. J. Matern. Neonatal Med. 23, 1160\u20131164 (2010).","journal-title":"J. Matern. Neonatal Med."},{"key":"86320_CR56","first-page":"105","volume":"103","author":"EC Miller","year":"1981","unstructured":"Miller, E. C. & Kouam, L. Frequency of breech presentation during pregnancy and on full term (author\u2019s transl). Zentralbl. Gynakol. 103, 105\u2013109 (1981).","journal-title":"Zentralbl. Gynakol."},{"key":"86320_CR57","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1111\/j.1651-2227.2000.tb03355.x","volume":"89","author":"G Marchini","year":"2000","unstructured":"Marchini, G., Berggren, V., Djilali-Merzoug, R. & Hansson, L.-O. The birth process initiates an acute phase reaction in the fetus-newborn infant. Acta Paediatr. 89, 1082\u20131086 (2000).","journal-title":"Acta Paediatr."},{"key":"86320_CR58","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1038\/nrneurol.2014.187","volume":"10","author":"I Knuesel","year":"2014","unstructured":"Knuesel, I. et al. Maternal immune activation and abnormal brain development across CNS disorders. Nat. Rev. Neurol. 10, 643\u2013660 (2014).","journal-title":"Nat. Rev. Neurol."},{"key":"86320_CR59","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1038\/nature23910","volume":"549","author":"S Kim","year":"2017","unstructured":"Kim, S. et al. Maternal gut bacteria promote neurodevelopmental abnormalities in mouse offspring. Nature 549, 528\u2013532 (2017).","journal-title":"Nature"},{"key":"86320_CR60","doi-asserted-by":"publisher","first-page":"12776","DOI":"10.1073\/pnas.1202556109","volume":"109","author":"EY Hsiao","year":"2012","unstructured":"Hsiao, E. Y., McBride, S. W., Chow, J., Mazmanian, S. K. & Patterson, P. H. Modeling an autism risk factor in mice leads to permanent immune dysregulation. Proc. Natl. Acad. Sci. USA 109, 12776\u201312781 (2012).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"86320_CR61","doi-asserted-by":"publisher","first-page":"10695","DOI":"10.1523\/JNEUROSCI.2178-07.2007","volume":"27","author":"SEP Smith","year":"2007","unstructured":"Smith, S. E. P., Li, J., Garbett, K., Mirnics, K. & Patterson, P. H. Maternal immune activation alters fetal brain development through interleukin-6. J. Neurosci. 27, 10695\u201310702 (2007).","journal-title":"J. Neurosci."},{"key":"86320_CR62","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1016\/j.biopsych.2017.09.030","volume":"83","author":"I Corradini","year":"2018","unstructured":"Corradini, I. et al. Maternal immune activation delays excitatory-to-inhibitory gamma-aminobutyric acid switch in offspring. Biol. Psychiatry 83, 680\u2013691 (2018).","journal-title":"Biol. Psychiatry"},{"key":"86320_CR63","doi-asserted-by":"publisher","first-page":"14420","DOI":"10.1073\/pnas.1604288113","volume":"113","author":"LJ Chareyron","year":"2016","unstructured":"Chareyron, L. J., Amaral, D. G., Lavenex, P. & Rakic, P. Selective lesion of the hippocampus increases the differentiation of immature neurons in the monkey amygdala. Proc. Natl. Acad. Sci. USA 113, 14420\u201314425 (2016).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"86320_CR64","doi-asserted-by":"publisher","first-page":"3710","DOI":"10.1073\/pnas.1801912115","volume":"115","author":"TA Avino","year":"2018","unstructured":"Avino, T. A. et al. Neuron numbers increase in the human amygdala from birth to adulthood, but not in autism. Proc. Natl. Acad. Sci. USA 115, 3710\u20133715 (2018).","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"86320_CR65","doi-asserted-by":"publisher","first-page":"2748","DOI":"10.1038\/s41467-019-10765-1","volume":"10","author":"SF Sorrells","year":"2019","unstructured":"Sorrells, S. F. et al. Immature excitatory neurons develop during adolescence in the human amygdala. Nat. Commun. 10, 2748 (2019).","journal-title":"Nat. Commun."},{"key":"86320_CR66","doi-asserted-by":"publisher","first-page":"626","DOI":"10.1016\/j.tins.2008.09.002","volume":"31","author":"Y Ben-Ari","year":"2008","unstructured":"Ben-Ari, Y. Neuro-archaeology: Pre-symptomatic architecture and signature of neurological disorders. Trends Neurosci. 31, 626\u2013636 (2008).","journal-title":"Trends Neurosci."},{"key":"86320_CR67","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1523\/JNEUROSCI.4447-13.2014","volume":"34","author":"Q He","year":"2014","unstructured":"He, Q., Nomura, T., Xu, J. & Contractor, A. The developmental switch in GABA polarity is delayed in fragile X mice. J. Neurosci. 34, 446\u2013450 (2014).","journal-title":"J. Neurosci."},{"key":"86320_CR68","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1126\/science.1133212","volume":"314","author":"R Tyzio","year":"2006","unstructured":"Tyzio, R. et al. Maternal oxytocin triggers a transient inhibitory switch in GABA signaling in the fetal brain during delivery. Science (80-. ) 314, 1788\u20131792 (2006).","journal-title":"Science (80-. )"},{"key":"86320_CR69","doi-asserted-by":"publisher","first-page":"1215","DOI":"10.1152\/physrev.00017.2006","volume":"87","author":"Y Ben-Ari","year":"2007","unstructured":"Ben-Ari, Y., Gaiarsa, J. L., Tyzio, R. & Khazipov, R. GABA: A pioneer transmitter that excites immature neurons and generates primitive oscillations. Physiol. Rev. 87, 1215\u20131284 (2007).","journal-title":"Physiol. Rev."},{"key":"86320_CR70","doi-asserted-by":"publisher","first-page":"9276","DOI":"10.1038\/s41598-019-45635-9","volume":"9","author":"N Lozovaya","year":"2019","unstructured":"Lozovaya, N. et al. Early alterations in a mouse model of Rett syndrome: The GABA developmental shift is abolished at birth. Sci. Rep. 9, 9276 (2019).","journal-title":"Sci. Rep."},{"key":"86320_CR71","doi-asserted-by":"publisher","first-page":"176","DOI":"10.1126\/science.1256009","volume":"346","author":"S Eftekhari","year":"2014","unstructured":"Eftekhari, S. et al. Response to comment on \u2018Oxytocin-mediated GABA inhibition during delivery attenuates autism pathogenesis in rodent offspring\u2019. Science (80-. ) 346, 176 (2014).","journal-title":"Science (80-. )"},{"key":"86320_CR72","doi-asserted-by":"publisher","first-page":"e1056","DOI":"10.1038\/tp.2017.10","volume":"7","author":"E Lemonnier","year":"2017","unstructured":"Lemonnier, E. et al. Effects of bumetanide on neurobehavioral function in children and adolescents with autism spectrum disorders. Transl. Psychiatry 7, e1056 (2017).","journal-title":"Transl. Psychiatry"},{"key":"86320_CR73","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41398-019-0665-5","volume":"10","author":"L Zhang","year":"2020","unstructured":"Zhang, L. et al. Symptom improvement in children with autism spectrum disorder following bumetanide administration is associated with decreased GABA\/glutamate ratios. Transl. Psychiatry 10, 1\u201312 (2020).","journal-title":"Transl. Psychiatry"},{"key":"86320_CR74","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1177\/1362361313514141","volume":"19","author":"N Hadjikhani","year":"2015","unstructured":"Hadjikhani, N. et al. Improving emotional face perception in autism with diuretic bumetanide: A proof-of-concept behavioral and functional brain imaging pilot study. Autism 19, 149\u2013157 (2015).","journal-title":"Autism"},{"key":"86320_CR75","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-21958-x","volume":"8","author":"N Hadjikhani","year":"2018","unstructured":"Hadjikhani, N. et al. Bumetanide for autism: More eye contact, less amygdala activation. Sci. Rep. 8, 1\u20138 (2018).","journal-title":"Sci. Rep."},{"key":"86320_CR76","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1186\/s13229-020-00335-4","volume":"11","author":"DM Van Andel","year":"2020","unstructured":"Van Andel, D. M. et al. Effects of bumetanide on neurodevelopmental impairments in patients with tuberous sclerosis complex: An open-label pilot study. Mol. Autism 11, 30 (2020).","journal-title":"Mol. Autism"},{"key":"86320_CR77","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1186\/s40537-020-00305-w","volume":"7","author":"JT Hancock","year":"2020","unstructured":"Hancock, J. T. & Khoshgoftaar, T. M. Survey on categorical data for neural networks. J. Big Data 7, 28 (2020).","journal-title":"J. Big Data"},{"key":"86320_CR78","doi-asserted-by":"publisher","first-page":"8346","DOI":"10.1038\/s41598-020-65384-4","volume":"10","author":"A Vabalas","year":"2020","unstructured":"Vabalas, A., Gowen, E., Poliakoff, E. & Casson, A. J. applying machine learning to kinematic and eye movement features of a movement imitation task to predict autism diagnosis. Sci. Rep. 10, 8346 (2020).","journal-title":"Sci. Rep."},{"key":"86320_CR79","doi-asserted-by":"publisher","first-page":"126","DOI":"10.1016\/j.jneumeth.2015.01.010","volume":"250","author":"E Combrisson","year":"2015","unstructured":"Combrisson, E. & Jerbi, K. Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. J. Neurosci. Methods 250, 126\u2013136 (2015).","journal-title":"J. Neurosci. Methods"},{"key":"86320_CR80","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1109\/34.75512","volume":"13","author":"SJ Raudys","year":"1991","unstructured":"Raudys, S. J. & Jain, A. K. Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Trans. Pattern Anal. Mach. Intell. 13, 252\u2013264 (1991).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"86320_CR81","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/0031-3203(71)90013-6","volume":"3","author":"L Kanal","year":"1971","unstructured":"Kanal, L. & Chandrasekaran, B. On dimensionality and sample size in statistical pattern classification. Pattern Recognit. 3, 225\u2013234 (1971).","journal-title":"Pattern Recognit."},{"key":"86320_CR82","doi-asserted-by":"publisher","unstructured":"Ng, A. Y. Feature selection, L1 vs. L2 regularization, and rotational invariance. In Proceedings, Twenty-First International Conference on Machine Learning, ICML 2004 (2004). https:\/\/doi.org\/10.1145\/1015330.1015435.","DOI":"10.1145\/1015330.1015435"},{"key":"86320_CR83","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman, J. H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 29, 1189\u20131232 (2001).","journal-title":"Ann. Stat."},{"key":"86320_CR84","doi-asserted-by":"publisher","first-page":"398","DOI":"10.1016\/j.jclinepi.2012.11.008","volume":"66","author":"PC Austin","year":"2013","unstructured":"Austin, P. C., Tu, J. V., Ho, J. E., Levy, D. & Lee, D. S. Using methods from the data-mining and machine-learning literature for disease classification and prediction: A case study examining classification of heart failure subtypes. J. Clin. Epidemiol. 66, 398\u2013407 (2013).","journal-title":"J. Clin. Epidemiol."},{"key":"86320_CR85","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/6915850","volume":"2019","author":"X Tian","year":"2019","unstructured":"Tian, X. et al. Using machine learning algorithms to predict hepatitis B surface antigen seroclearance. Comput. Math. Methods Med. 2019, 1\u20137 (2019).","journal-title":"Comput. Math. Methods Med."},{"key":"86320_CR86","doi-asserted-by":"publisher","unstructured":"Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016). https:\/\/doi.org\/10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"key":"86320_CR87","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s41551-018-0304-0","volume":"2","author":"SM Lundberg","year":"2018","unstructured":"Lundberg, S. M. et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2, 749\u2013760 (2018).","journal-title":"Nat. Biomed. Eng."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-86320-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-86320-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-86320-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T04:47:44Z","timestamp":1675054064000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-021-86320-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,25]]},"references-count":87,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["86320"],"URL":"https:\/\/doi.org\/10.1038\/s41598-021-86320-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,25]]},"assertion":[{"value":"10 August 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Dr. S. Alain reported being a Scientific Expert for GSK, Shire, Sanofi, MSD, Merck, Biotest, BioM\u00e9rieux and Hologic, and IP France Solstice study (Takeda). Dr. Y. Ben-Ari reported being the CEO and shareholder of BABiomedical, a company dedicated to the use of artificial intelligence in biomedical research, and Neurochlore, a company dedicated to treat autism. Dr. S. Hantz reported receiving a congress fee, travel and accommodation by MSD vaccines, a congress fee from Diasorin, and a remuneration as speaker for \u201cExpert days in virology\u201d from Roche Diagnostics. Dr. E. Lemonnier reported being a shareholder of Neurochlore. Dr. H. Rabiei reported his salary is paid by Neurochlore and BABiomedical company. Dr. N. Hadjikhani reported that she is a shareholder of Neurochlore. The other authors (P. Coste-Mazeau, T. Chianea, J-L Eyraud, H. Caly, C. Caly and D. Makowski) have no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6877"}}