Differences in regional brain structure in toddlers with autism are related to future language outcomes.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
13 Jun 2024
13 Jun 2024
Historique:
received:
06
01
2023
accepted:
20
05
2024
medline:
14
6
2024
pubmed:
14
6
2024
entrez:
13
6
2024
Statut:
epublish
Résumé
Language and social symptoms improve with age in some autistic toddlers, but not in others, and such outcome differences are not clearly predictable from clinical scores alone. Here we aim to identify early-age brain alterations in autism that are prognostic of future language ability. Leveraging 372 longitudinal structural MRI scans from 166 autistic toddlers and 109 typical toddlers and controlling for brain size, we find that, compared to typical toddlers, autistic toddlers show differentially larger or thicker temporal and fusiform regions; smaller or thinner inferior frontal lobe and midline structures; larger callosal subregion volume; and smaller cerebellum. Most differences are replicated in an independent cohort of 75 toddlers. These brain alterations improve accuracy for predicting language outcome at 6-month follow-up beyond intake clinical and demographic variables. Temporal, fusiform, and inferior frontal alterations are related to autism symptom severity and cognitive impairments at early intake ages. Among autistic toddlers, brain alterations in social, language and face processing areas enhance the prediction of the child's future language ability.
Identifiants
pubmed: 38871689
doi: 10.1038/s41467-024-48952-4
pii: 10.1038/s41467-024-48952-4
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5075Subventions
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
ID : R01MH118879
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
ID : R01MH104446
Organisme : U.S. Department of Health & Human Services | NIH | National Institute on Deafness and Other Communication Disorders (NIDCD)
ID : R01DC016385
Informations de copyright
© 2024. The Author(s).
Références
American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5 (American Psychiatric Association, 2017).
Courchesne, E. et al. Mapping early brain development in autism. Neuron 56, 399–413 (2007).
pubmed: 17964254
doi: 10.1016/j.neuron.2007.10.016
Maenner, M. J. et al. Prevalence and characteristics of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2018. MMWR Surveill. Summ. 70, 1 (2021).
pubmed: 34855727
pmcid: 8639027
doi: 10.15585/mmwr.ss7011a1
Tek, S., Mesite, L., Fein, D. & Naigles, L. Longitudinal analyses of expressive language development reveal two distinct language profiles among young children with autism spectrum disorders. J. Autism Dev. Disord. 44, 75–89 (2014).
pubmed: 23719855
pmcid: 4557801
doi: 10.1007/s10803-013-1853-4
Lord, C., Bishop, S. & Anderson, D. Developmental trajectories as autism phenotypes. Am. J. Med. Genet. C 169, 198–208 (2015).
doi: 10.1002/ajmg.c.31440
Campbell, D. J., Shic, F., Macari, S. & Chawarska, K. Gaze response to dyadic bids at 2 years related to outcomes at 3 years in autism spectrum disorders: a subtyping analysis. J. Autism Dev. Disord. 44, 431–442 (2014).
pubmed: 23877749
pmcid: 3900601
doi: 10.1007/s10803-013-1885-9
Weismer, S. E. & Kover, S. T. Preschool language variation, growth, and predictors in children on the autism spectrum. J. Child Psychol. Psyc. 56, 1327–1337 (2015).
doi: 10.1111/jcpp.12406
Pickles, A., Anderson, D. K. & Lord, C. Heterogeneity and plasticity in the development of language: a 17-year follow-up of children referred early for possible autism. J. Child Psychol. Psyc. 55, 1354–1362 (2014).
doi: 10.1111/jcpp.12269
Makrygianni, M. K., Gena, A., Katoudi, S. & Galanis, P. The effectiveness of applied behavior analytic interventions for children with autism spectrum disorder: a meta-analytic study. Res. Autism Spectr. Disord. 51, 18–31 (2018).
doi: 10.1016/j.rasd.2018.03.006
Shkedy, G., Shkedy, D. & Sandoval-Norton, A. H. Treating self-injurious behaviors in autism spectrum disorder. Cogent Psychol. 6, 1682766 (2019).
doi: 10.1080/23311908.2019.1682766
Shkedy, G., Shkedy, D. & Sandoval-Norton, A. H. Long-term ABA therapy is abusive: a response to Gorycki, Ruppel, and Zane. Adv. Neurodev. Disord. 5, 126–134 (2021).
doi: 10.1007/s41252-021-00201-1
Courchesne, E., Carper, R. & Akshoomoff, N. Evidence of brain overgrowth in the first year of life in autism. JAMA 290, 337–344 (2003).
pubmed: 12865374
doi: 10.1001/jama.290.3.337
Courchesne, E. et al. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology. 57, 245–254, https://doi.org/10.1212/WNL.57.2.245 (2001).
Dementieva, Y. A. et al. Accelerated head growth in early development of individuals with autism. Pediatr. Neurol. 32, 102–108 (2005).
pubmed: 15664769
doi: 10.1016/j.pediatrneurol.2004.08.005
Vaccarino, F. M. & Smith, K. M. Increased brain size in autism-what it will take to solve a mystery. Biol. Psychiatry 66, 313–315 (2009).
pubmed: 19643218
pmcid: 2803090
doi: 10.1016/j.biopsych.2009.06.013
Sacco, R., Gabriele, S. & Persico, A. M. Head circumference and brain size in autism spectrum disorder: a systematic review and meta-analysis. Psychiatry Res. 234, 239–251 (2015).
pubmed: 26456415
doi: 10.1016/j.pscychresns.2015.08.016
Amaral, D. G., Schumann, C. M. & Nordahl, C. W. Neuroanatomy of autism. Trends Neurosci. 31, 137–145 (2008).
pubmed: 18258309
doi: 10.1016/j.tins.2007.12.005
Schumann, C. M. et al. Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. J. Neurosci. 30, 4419–4427 (2010).
pubmed: 20335478
pmcid: 2859218
doi: 10.1523/JNEUROSCI.5714-09.2010
Courchesne, E. Abnormal early brain development in autism. Mol. Psychiatry 7, S21–S23 (2002).
pubmed: 12142938
doi: 10.1038/sj.mp.4001169
Courchesne, E., Campbell, K. & Solso, S. Brain growth across the life span in autism: age-specific changes in anatomical pathology. Brain Res. 1380, 138–145 (2011).
pubmed: 20920490
doi: 10.1016/j.brainres.2010.09.101
Carper, R. A., Moses, P., Tigue, Z. D. & Courchesne, E. Cerebral lobes in autism: early hyperplasia and abnormal age effects. Neuroimage 16, 1038–1051 (2002).
pubmed: 12202091
doi: 10.1006/nimg.2002.1099
Courchesne, E. et al. Embryonic origin of two ASD subtypes of social symptom severity: the larger the brain cortical organoid size, the more severe the social symptoms. Mol Autism 15, 22 (2024).
Mosconi, M. W. et al. Longitudinal study of amygdala volume and joint attention in 2- to 4-year-old children with autism. Arch. Gen. Psychiatry 66, 509–516 (2009).
pubmed: 19414710
pmcid: 3156446
doi: 10.1001/archgenpsychiatry.2009.19
Schumann, C. M. et al. The amygdala is enlarged in children but not adolescents with autism; the hippocampus is enlarged at all ages. J. Neurosci. 24, 6392–6401 (2004).
pubmed: 15254095
pmcid: 6729537
doi: 10.1523/JNEUROSCI.1297-04.2004
Van Rooij, D. et al. Cortical and subcortical brain morphometry differences between patients with autism spectrum disorder and healthy individuals across the lifespan: results from the ENIGMA ASD Working Group. Am. J. Psychiatry 175, 359–369 (2018).
pubmed: 29145754
doi: 10.1176/appi.ajp.2017.17010100
Prigge, M. B. D. et al. A 16-year study of longitudinal volumetric brain development in males with autism. Neuroimage 236, 118067 (2021).
pubmed: 33878377
doi: 10.1016/j.neuroimage.2021.118067
Wolff, J. J. et al. Altered corpus callosum morphology associated with autism over the first 2 years of life. Brain 138, 2046–2058 (2015).
pubmed: 25937563
pmcid: 4492413
doi: 10.1093/brain/awv118
Piven, J., Bailey, J., Ranson, B. J. & Arndt, S. An MRI study of the corpus callosum in autism. Am. J. Psychiatry 154, 1051–1056 (1997).
pubmed: 9247388
doi: 10.1176/ajp.154.8.1051
Frazier, T. W. & Hardan, A. Y. A meta-analysis of the corpus callosum in autism. Biol. Psychiatry 66, 935–941 (2009).
pubmed: 19748080
pmcid: 2783565
doi: 10.1016/j.biopsych.2009.07.022
Rojas, D. C. et al. Regional gray matter volumetric changes in autism associated with social and repetitive behavior symptoms. BMC Psychiatry 6, 56 (2006).
pubmed: 17166273
pmcid: 1770914
doi: 10.1186/1471-244X-6-56
Liu, J. et al. Gray matter abnormalities in pediatric autism spectrum disorder: a meta-analysis with signed differential mapping. Eur. Child Adolesc. Psychiatry 26, 933–945 (2017).
pubmed: 28233073
doi: 10.1007/s00787-017-0964-4
Courchesne, E., Yeung-Courchesne, R., Press, G. A., Hesselink, J. R. & Jernigan, T. L. Hypoplasia of cerebellar vermal lobules VI and VII in autism. N. Engl. J. Med. 318, 1349–1354 (1988).
pubmed: 3367935
doi: 10.1056/NEJM198805263182102
Piven, J., Saliba, K., Bailey, J. & Arndt, S. An MRI study of autism: the cerebellum revisited. Neurology 49, 546–551 (1997).
pubmed: 9270594
doi: 10.1212/WNL.49.2.546
Geuze, E., Vermetten, E. & Bremner, J. D. MR-based in vivo hippocampal volumetrics: 1. Review of methodologies currently employed. Mol. Psychiatry 10, 147–159 (2005).
pubmed: 15340353
doi: 10.1038/sj.mp.4001580
Lombardo, M. V. et al. Atypical genomic cortical patterning in autism with poor early language outcome. Sci. Adv. 7, eabh1663 (2021).
pubmed: 34516910
pmcid: 8442861
doi: 10.1126/sciadv.abh1663
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).
pubmed: 32353336
pmcid: 7373219
doi: 10.1016/j.tins.2020.03.005
Gandal, M. J. et al. Broad transcriptomic dysregulation occurs across the cerebral cortex in ASD. Nature 611, 532–539 (2022).
pubmed: 36323788
pmcid: 9668748
doi: 10.1038/s41586-022-05377-7
Panizzon, M. S. et al. Distinct genetic influences on cortical surface area and cortical thickness. Cereb. Cortex 19, 2728–2735 (2009).
pubmed: 19299253
pmcid: 2758684
doi: 10.1093/cercor/bhp026
Hazlett, H. C. et al. Early brain development in infants at high risk for autism spectrum disorder. Nature 542, 348–351 (2017).
pubmed: 28202961
pmcid: 5336143
doi: 10.1038/nature21369
Grecucci, A., Rubicondo, D., Siugzdaite, R., Surian, L. & Job, R. Uncovering the social deficits in the autistic brain. A source-based morphometric study. Front. Neurosci. 10, 388 (2016).
pubmed: 27630538
pmcid: 5005369
doi: 10.3389/fnins.2016.00388
Dziobek, I., Bahnemann, M., Convit, A. & Heekeren, H. R. The role of the fusiform-amygdala system in the pathophysiology of autism. Arch. Gen. Psychiatry 67, 397–405 (2010).
pubmed: 20368515
doi: 10.1001/archgenpsychiatry.2010.31
Kanwisher, N. & Yovel, G. The fusiform face area: a cortical region specialized for the perception of faces. Philos. Trans. R. Soc. Lond. B Biol. Sci. 361, 2109–2128 (2006).
pubmed: 17118927
pmcid: 1857737
doi: 10.1098/rstb.2006.1934
Schultz, R. T. et al. The role of the fusiform face area in social cognition: implications for the pathobiology of autism. Philos. Trans. R. Soc. Lond. B Biol. Sci. 358, 415–427 (2003).
pubmed: 12639338
pmcid: 1693125
doi: 10.1098/rstb.2002.1208
Schultz, R. T. Developmental deficits in social perception in autism: the role of the amygdala and fusiform face area. Int. J. Dev. Neurosci. 23, 125–141 (2005).
pubmed: 15749240
doi: 10.1016/j.ijdevneu.2004.12.012
Pierce, K., Muller, R. A., Ambrose, J., Allen, G. & Courchesne, E. Face processing occurs outside the fusiform ‘face area’ in autism: evidence from functional MRI. Brain 124, 2059–2073 (2001).
pubmed: 11571222
doi: 10.1093/brain/124.10.2059
Bedford, S. A. et al. Large-scale analyses of the relationship between sex, age and intelligence quotient heterogeneity and cortical morphometry in autism spectrum disorder. Mol. Psychiatry 25, 614–628 (2020).
pubmed: 31028290
doi: 10.1038/s41380-019-0420-6
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: 18672231
pmcid: 2879340
doi: 10.1016/j.biopsych.2008.05.020
Eyler, L. T., Pierce, K. & Courchesne, E. 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: 22350062
pmcid: 3286331
doi: 10.1093/brain/awr364
Lombardo, M. V. et al. Different functional neural substrates for good and poor language outcome in autism. Neuron 86, 567–577 (2015).
pubmed: 25864635
pmcid: 4610713
doi: 10.1016/j.neuron.2015.03.023
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).
pubmed: 30482947
pmcid: 6445349
doi: 10.1038/s41593-018-0281-3
Xiao, Y. 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).
pubmed: 34980898
doi: 10.1038/s41562-021-01237-y
Redcay, E. The superior temporal sulcus performs a common function for social and speech perception: implications for the emergence of autism. Neurosci. Biobehav. Rev. 32, 123–142 (2008).
pubmed: 17706781
doi: 10.1016/j.neubiorev.2007.06.004
Hein, G. & Knight, R. T. Superior temporal sulcus-It’s my area: or is it? J. Cogn. Neurosci. 20, 2125–2136 (2008).
pubmed: 18457502
doi: 10.1162/jocn.2008.20148
Hickok, G. The functional neuroanatomy of language. Phys. Life Rev. 6, 121–143 (2009).
pubmed: 20161054
pmcid: 2747108
doi: 10.1016/j.plrev.2009.06.001
Kennedy, D. P. & Adolphs, R. The social brain in psychiatric and neurological disorders. Trends Cogn. Sci. 16, 559–572 (2012).
pubmed: 23047070
pmcid: 3606817
doi: 10.1016/j.tics.2012.09.006
Beauchamp, M. S. The social mysteries of the superior temporal sulcus. Trends Cogn. Sci. 19, 489–490 (2015).
pubmed: 26208834
pmcid: 4556565
doi: 10.1016/j.tics.2015.07.002
Deen, B., Koldewyn, K., Kanwisher, N. & Saxe, R. Functional organization of social perception and cognition in the superior temporal sulcus. Cereb. Cortex 25, 4596–4609 (2015).
pubmed: 26048954
pmcid: 4816802
doi: 10.1093/cercor/bhv111
Patriquin, M. A., DeRamus, T., Libero, L. E., Laird, A. & Kana, R. K. Neuroanatomical and neurofunctional markers of social cognition in autism spectrum disorder. Hum. Brain Mapp. 37, 3957–3978 (2016).
pubmed: 27329401
pmcid: 5053857
doi: 10.1002/hbm.23288
Schmalzle, R. et al. Brain connectivity dynamics during social interaction reflect social network structure. Proc. Natl Acad. Sci. USA 114, 5153–5158 (2017).
pubmed: 28465434
pmcid: 5441802
doi: 10.1073/pnas.1616130114
Falk, E. B. & Bassett, D. S. Brain and social networks: fundamental building blocks of human experience. Trends Cogn. Sci. 21, 674–690 (2017).
pubmed: 28735708
pmcid: 8590886
doi: 10.1016/j.tics.2017.06.009
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: 17904385
doi: 10.1016/j.neuroimage.2007.08.005
Beauchemin, M. et al. Mother and stranger: an electrophysiological study of voice processing in newborns. Cereb. Cortex 21, 1705–1711 (2011).
pubmed: 21149849
doi: 10.1093/cercor/bhq242
Dehaene-Lambertz, G., Dehaene, S. & Hertz-Pannier, L. Functional neuroimaging of speech perception in infants. Science 298, 2013–2015 (2002).
pubmed: 12471265
doi: 10.1126/science.1077066
Hertrich, I., Dietrich, S. & Ackermann, H. The margins of the language network in the brain. Front. Commun. 5, 519955 (2020).
doi: 10.3389/fcomm.2020.519955
Perani, D. et al. Neural language networks at birth. Proc. Natl Acad. Sci. USA 108, 16056–16061 (2011).
pubmed: 21896765
pmcid: 3179044
doi: 10.1073/pnas.1102991108
Braga, R. M., DiNicola, L. M., Becker, H. C. & Buckner, R. L. Situating the left-lateralized language network in the broader organization of multiple specialized large-scale distributed networks. J. Neurophysiol. 124, 1415–1448 (2020).
pubmed: 32965153
pmcid: 8356783
doi: 10.1152/jn.00753.2019
Verly, M. et al. Structural and functional underconnectivity as a negative predictor for language in autism. Hum. Brain Mapp. 35, 3602–3615 (2014).
pubmed: 24375710
doi: 10.1002/hbm.22424
Peer, M., Hayman, M., Tamir, B. & Arzy, S. Brain coding of social network structure. J. Neurosci. 41, 4897–4909 (2021).
pubmed: 33903220
pmcid: 8260169
doi: 10.1523/JNEUROSCI.2641-20.2021
Tie, Y. et al. Defining language networks from resting-state fMRI for surgical planning-a feasibility study. Hum. Brain Mapp. 35, 1018–1030 (2014).
pubmed: 23288627
doi: 10.1002/hbm.22231
Bloom, J. S. & Hynd, G. W. The role of the corpus callosum in interhemispheric transfer of information: excitation or inhibition? Neuropsychol. Rev. 15, 59–71 (2005).
pubmed: 16211466
doi: 10.1007/s11065-005-6252-y
Gazzaniga, M. S. Cerebral specialization and interhemispheric communication: does the corpus callosum enable the human condition? Brain 123, 1293–1326 (2000).
pubmed: 10869045
doi: 10.1093/brain/123.7.1293
Manto, M. et al. Consensus paper: roles of the cerebellum in motor control-the diversity of ideas on cerebellar involvement in movement. Cerebellum 11, 457–487 (2012).
pubmed: 22161499
pmcid: 4347949
doi: 10.1007/s12311-011-0331-9
Starowicz-Filip, A. et al. The role of the cerebellum in the regulation of language functions. Psychiatr. Pol. 51, 661–671 (2017).
pubmed: 28987056
doi: 10.12740/PP/68547
De Smet, H. J., Paquier, P., Verhoeven, J. & Marien, P. The cerebellum: its role in language and related cognitive and affective functions. Brain Lang. 127, 334–342 (2013).
pubmed: 23333152
doi: 10.1016/j.bandl.2012.11.001
Yuan, Q. et al. The cerebellum and cognition: further evidence for its role in language control. Cereb. Cortex 33, 35–49 (2022).
pubmed: 35226917
doi: 10.1093/cercor/bhac051
Hazlett, H. C., Poe, M. D., Gerig, G., Smith, R. G. & Piven, J. Cortical gray and white brain tissue volume in adolescents and adults with autism. Biol. Psychiatry 59, 1–6 (2006).
pubmed: 16139816
doi: 10.1016/j.biopsych.2005.06.015
Yankowitz, L. D., Yerys, B. E., Herrington, J. D., Pandey, J. & Schultz, R. T. Dissociating regional gray matter density and gray matter volume in autism spectrum condition. Neuroimage Clin. 32, 102888 (2021).
pubmed: 34911194
pmcid: 8633367
doi: 10.1016/j.nicl.2021.102888
Bethlehem, R. A. I. et al. A normative modelling approach reveals age-atypical cortical thickness in a subgroup of males with autism spectrum disorder. Commun. Biol. 3, 486 (2020).
pubmed: 32887930
pmcid: 7474067
doi: 10.1038/s42003-020-01212-9
Bethlehem, R. A. I. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).
pubmed: 35388223
pmcid: 9021021
doi: 10.1038/s41586-022-04554-y
Ritvo, E. R. et al. Lower Purkinje cell counts in the cerebella of four autistic subjects: initial findings of the UCLA-NSAC Autopsy Research Report. Am. J. Psychiatry 143, 862–866 (1986).
pubmed: 3717426
doi: 10.1176/ajp.143.7.862
Fatemi, S. H. et al. Purkinje cell size is reduced in cerebellum of patients with autism. Cell Mol. Neurobiol. 22, 171–175 (2002).
pubmed: 12363198
doi: 10.1023/A:1019861721160
Ecker, C. et al. Brain surface anatomy in adults with autism: the relationship between surface area, cortical thickness, and autistic symptoms. JAMA Psychiatry 70, 59–70 (2013).
pubmed: 23404046
doi: 10.1001/jamapsychiatry.2013.265
Zielinski, B. A. et al. Longitudinal changes in cortical thickness in autism and typical development. Brain 137, 1799–1812 (2014).
pubmed: 24755274
pmcid: 4032101
doi: 10.1093/brain/awu083
Courchesne, E. et al. The ASD living biology: from cell proliferation to clinical phenotype. Mol. Psychiatry 24, 88–107 (2019).
pubmed: 29934544
doi: 10.1038/s41380-018-0056-y
Gazestani, V. H. et al. A perturbed gene network containing PI3K-AKT, RAS-ERK and WNT-beta-catenin pathways in leukocytes is linked to ASD genetics and symptom severity. Nat. Neurosci. 22, 1624–1634 (2019).
pubmed: 31551593
pmcid: 6764590
doi: 10.1038/s41593-019-0489-x
Baranova, J. et al. Autism spectrum disorder: signaling pathways and prospective therapeutic targets. Cell Mol. Neurobiol. 41, 619–649 (2021).
pubmed: 32468442
doi: 10.1007/s10571-020-00882-7
Upadhyay, J. et al. Dysregulation of multiple signaling neurodevelopmental pathways during embryogenesis: a possible cause of autism spectrum disorder. Cells 10, 958 (2021).
pubmed: 33924211
pmcid: 8074600
doi: 10.3390/cells10040958
Kumar, S. et al. Impaired neurodevelopmental pathways in autism spectrum disorder: a review of signaling mechanisms and crosstalk. J. Neurodev. Disord. 11, 10 (2019).
pubmed: 31202261
pmcid: 6571119
doi: 10.1186/s11689-019-9268-y
Salcedo-Arellano, M. J. et al. Overlapping molecular pathways leading to autism spectrum disorders, fragile X syndrome, and targeted treatments. Neurotherapeutics 18, 265–283 (2021).
pubmed: 33215285
doi: 10.1007/s13311-020-00968-6
Wen, Y., Alshikho, M. J. & Herbert, M. R. Pathway network analyses for autism reveal multisystem involvement, major overlaps with other diseases and convergence upon MAPK and calcium signaling. PLos ONE 11, e0153329 (2016).
pubmed: 27055244
pmcid: 4824422
doi: 10.1371/journal.pone.0153329
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: 33915154
doi: 10.1016/j.jpeds.2021.04.041
Pierce, K. et al. Detecting, studying, and treating autism early: the one-year well-baby check-up approach. J. Pediatr. 159, 458–U326 (2011).
pubmed: 21524759
pmcid: 3157595
doi: 10.1016/j.jpeds.2011.02.036
Pierce, K., Gazestani, V. H. & Bacon, E. Evaluation of the diagnostic stability of the early autism spectrum disorder phenotype in the general population starting at 12 months. JAMA Pediatr. 173, 801 (2019).
doi: 10.1001/jamapediatrics.2019.0624
Lord, C., Rutter, M. & DiLavore, P. C. Dissertation Abstracts International Section A: Humanities and Social Sciences (University Microfilms, 1999).
Lord, C. et al. The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J. Autism Dev. Disord. 30, 205–223 (2000).
pubmed: 11055457
doi: 10.1023/A:1005592401947
Lord, C, Rutter, M., DiLavore, P. C. & Risi, S. Autism Diagnostic Observation Schedule 2nd edn (WPS, 2012).
Mullen, E. M. Mullen Scales of Early Learning (American Guidance Service, 1995).
Sparrow, S. S., Balla, D. A. & Cicchetti, D. V. Vineland-II, Vineland Adaptive Behavior Scales: Survey Forms Manual. (AGS Publishing, 2005).
American Psychiatric A. Diagnostic and Statistical Manual of Mental Disorders: DMS-IV. (APA, 1994).
Lord, C., Elsabbagh, M., Baird, G. & Veenstra-Vanderweele, J. Autism spectrum disorder. Lancet 392, 508–520 (2018).
pubmed: 30078460
pmcid: 7398158
doi: 10.1016/S0140-6736(18)31129-2
Bishop, S. L., Guthrie, W., Coffing, M. & Lord, C. Convergent validity of the Mullen Scales of Early Learning and the differential ability scales in children with autism spectrum disorders. Am. J. Intellect. Dev. Disabil. 116, 331–343 (2011).
pubmed: 21905802
pmcid: 7398154
doi: 10.1352/1944-7558-116.5.331
Farmer, C., Golden, C. & Thurm, A. Concurrent validity of the differential ability scales, second edition with the Mullen Scales of Early Learning in young children with and without neurodevelopmental disorders. Child Neuropsychol. 22, 556–569 (2016).
pubmed: 25833070
doi: 10.1080/09297049.2015.1020775
Baranek, G. T. et al. Hyporesponsiveness to social and nonsocial sensory stimuli in children with autism, children with developmental delays, and typically developing children. Dev. Psychopathol. 25, 307–320 (2013).
pubmed: 23627946
pmcid: 3641693
doi: 10.1017/S0954579412001071
Munson, J. et al. Evidence for latent classes of IQ in young children with autism spectrum disorder. Am. J. Ment. Retard. 113, 439–452 (2008).
pubmed: 19127655
pmcid: 2991056
doi: 10.1352/2008.113:439-452
Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis—I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).
pubmed: 9931268
doi: 10.1006/nimg.1998.0395
Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).
pubmed: 16530430
doi: 10.1016/j.neuroimage.2006.01.021
Fischl, B., Sereno, M. I. & Dale, A. M. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. Neuroimage 9, 195–207 (1999).
pubmed: 9931269
doi: 10.1006/nimg.1998.0396
Ghosh, S. S. et al. Evaluating the validity of volume-based and surface-based brain image registration for developmental cognitive neuroscience studies in children 4 to 11 years of age. Neuroimage 53, 85–93 (2010).
pubmed: 20621657
doi: 10.1016/j.neuroimage.2010.05.075
Jernigan, T. L. et al. The pediatric imaging, neurocognition, and genetics (PING) data repository. Neuroimage 124, 1149–1154 (2016).
pubmed: 25937488
doi: 10.1016/j.neuroimage.2015.04.057
Levman, J., MacDonald, P., Lim, A. R., Forgeron, C. & Takahashi, E. A pediatric structural MRI analysis of healthy brain development from newborns to young adults. Hum. Brain Mapp. 38, 5931–5942 (2017).
pubmed: 28898497
pmcid: 5696794
doi: 10.1002/hbm.23799
Shrout, P. E. & Fleiss, J. L. Intraclass correlations: uses in assessing rater reliability. Psychol. Bull. 86, 420 (1979).
pubmed: 18839484
doi: 10.1037/0033-2909.86.2.420
Koo, T. K. & Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15, 155–163 (2016).
pubmed: 27330520
pmcid: 4913118
doi: 10.1016/j.jcm.2016.02.012
Houston, S. M., Herting, M. M. & Sowell, E. R. The neurobiology of childhood structural brain development: conception through adulthood. Curr. Top. Behav. Neurosci. 16, 3–17 (2014).
pubmed: 24357437
pmcid: 4114219
doi: 10.1007/978-3-662-45758-0_265
Napolitano, A. et al. Sex differences in autism spectrum disorder: diagnostic, neurobiological, and behavioral features. Front. Psychiatry 13, 889636 (2022).
pubmed: 35633791
pmcid: 9136002
doi: 10.3389/fpsyt.2022.889636
Cauvet, E. et al. Sex differences along the autism continuum: a twin study of brain structure. Cereb. Cortex 29, 1342–1350 (2019).
pubmed: 30566633
doi: 10.1093/cercor/bhy303
Hernandez, L. M. Sex-differential neuroanatomy in autism: a shift toward male-characteristic brain structure. Am. J. Psychiatry 180, 8–10 (2023).
pubmed: 36587268
doi: 10.1176/appi.ajp.20220939
van’t Westeinde, A. et al. Sex differences in brain structure: a twin study on restricted and repetitive behaviors in twin pairs with and without autism. Mol. Autism 11, 1 (2019).
pubmed: 31893022
pmcid: 6937723
doi: 10.1186/s13229-019-0309-x
Walsh, M. J. M., Wallace, G. L., Gallegos, S. M. & Braden, B. B. Brain-based sex differences in autism spectrum disorder across the lifespan: a systematic review of structural MRI, fMRI, and DTI findings. Neuroimage Clin. 31, 102719 (2021).
pubmed: 34153690
pmcid: 8233229
doi: 10.1016/j.nicl.2021.102719
Duan, K. et al. Neural correlates of cognitive function and symptoms in attention-deficit/hyperactivity disorder in adults. Neuroimage Clin. 19, 374–383 (2018).
pubmed: 30013920
pmcid: 6044210
doi: 10.1016/j.nicl.2018.04.035
Bedford, S. A. et al. Brain-charting autism and attention deficit hyperactivity disorder reveals distinct and overlapping neurobiology. Preprint at medRxiv (2023).
Duan, K. et al. Dataset of paper “Differences in regional brain structure in toddlers with autism are related to future language outcomes”. Zenodo https://doi.org/10.5281/zenodo.11200676 (2024).