Dwyer P. Stigma, Incommensurability, or Both? Pathology-First, Person-First, and Identity-First Language and the Challenges of Discourse in Divided Autism Communities. J Dev Behav Pediatr. 2022;43(2):111–13. https://doi.org/10.1097/DBP.0000000000001054.
Gernsbacher MA. Editorial perspective: the use of person-first language in scholarly writing may accentuate stigma. J Child Psychol Psychiatry. 2017;58(7):859–61 [cited 2022 Jul 20]. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/jcpp.12706.
Article
PubMed
PubMed Central
Google Scholar
Sinclair J. Why I dislike “person first” Language. Auton Crit J Interdiscip Autism Stud. 2013;1(2) [cited 2022 Jul 20]. Available from: http://www.larry-arnold.net/Autonomy/index.php/autonomy/article/view/OP1.
Maenner MJ. Prevalence of autism spectrum disorder among children aged 8 years — autism and developmental disabilities monitoring network, 11 sites, United States, 2016. MMWR Surveill Summ. 2020;69 [cited 2020 Aug 18]. Available from: https://www.cdc.gov/mmwr/volumes/69/ss/ss6904a1.htm.
Pickles A, Anderson DK, 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 Psychiatry. 2014;55(12):1354–62 [cited 2021 Oct 27]. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/jcpp.12269.
Article
PubMed
Google Scholar
Fountain C, Winter AS, Bearman PS. Six developmental trajectories characterize children with autism. Pediatrics. 2012;129(5):e1112–20 [cited 2021 Oct 27]. Available from: https://pediatrics.aappublications.org/content/129/5/e1112.
Article
PubMed
PubMed Central
Google Scholar
Baghdadli A, Assouline B, Sonié S, Pernon E, Darrou C, Michelon C, et al. Developmental trajectories of adaptive behaviors from early childhood to adolescence in a cohort of 152 children with autism spectrum disorders. J Autism Dev Disord. 2012;42(7):1314–25. https://doi.org/10.1007/s10803-011-1357-z [cited 2021 Oct 27].
Article
PubMed
Google Scholar
Solomon M, Iosif AM, Reinhardt VP, Libero LE, Nordahl CW, Ozonoff S, et al. What will my child’s future hold? phenotypes of intellectual development in 2–8-year-olds with autism spectrum disorder. Autism Res. 2018;11(1):121–32.
Article
PubMed
Google Scholar
Jack A, Pelphrey KA. Annual research review: understudied populations within the autism spectrum – current trends and future directions in neuroimaging research. J Child Psychol Psychiatry. 2017;58(4):411–35 [cited 2021 Oct 8]. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/jcpp.12687.
Article
PubMed
PubMed Central
Google Scholar
Reiter MA, Mash LE, Linke AC, Fong CH, Fishman I, Müller RA. Distinct patterns of atypical functional connectivity in lower-functioning autism. Biol Psychiatry Cogn Neurosci Neuroimaging. 2019;4(3):251–9.
PubMed
Google Scholar
Gabrielsen TP, Anderson JS, Stephenson KG, Beck J, King JB, Kellems R, et al. Functional MRI connectivity of children with autism and low verbal and cognitive performance. Mol Autism. 2018;9:67.
Article
PubMed
PubMed Central
Google Scholar
Lee JJ, McGue M, Iacono WG, Michael AM, Chabris CF. The causal influence of brain size on human intelligence: evidence from within-family phenotypic associations and GWAS modeling. Intelligence. 2019;75(48) [cited 2022 Jan 31]. Available from: https://www.ncbi.nlm.nih.gov/labs/pmc/articles/PMC7440690/.
Nave G, Jung WH, Karlsson Linnér R, Kable JW, Koellinger PD. Are Bigger Brains Smarter? Evidence from a large-scale preregistered study. Psychol Sci. 2019;30(1):43–54. https://doi.org/10.1177/0956797618808470 [cited 2022 Feb 1].
Article
PubMed
Google Scholar
Campbell LE, Daly E, Toal F, Stevens A, Azuma R, Karmiloff-Smith A, et al. Brain structural differences associated with the behavioural phenotype in children with Williams syndrome. Brain Res. 2009;1258:96–107 [cited 2022 Feb 1]. Available from: https://www.sciencedirect.com/science/article/pii/S0006899308028515.
Article
CAS
PubMed
Google Scholar
Basten U, Hilger K, Fiebach CJ. Where smart brains are different: a quantitative meta-analysis of functional and structural brain imaging studies on intelligence. Intelligence. 2015;51:10–27 [cited 2022 Feb 1]. Available from: https://www.sciencedirect.com/science/article/pii/S0160289615000562.
Article
Google Scholar
Tamnes CK, Walhovd KB, Grydeland H, Holland D, Østby Y, Dale AM, et al. Longitudinal working memory development is related to structural maturation of frontal and parietal cortices. J Cogn Neurosci. 2013;25(10):1611–23. [cited 2022 Jan 27]. https://doi.org/10.1162/jocn_a_00434.
Article
PubMed
Google Scholar
Friedman NP, Miyake A. Unity and diversity of executive functions: individual differences as a window on cognitive structure. Cortex. 2017;86:186–204 [cited 2022 Jan 27]. Available from: https://www.sciencedirect.com/science/article/pii/S0010945216301071.
Article
PubMed
Google Scholar
Wendelken C, Ferrer E, Ghetti S, Bailey SK, Cutting L, Bunge SA. Frontoparietal structural connectivity in childhood predicts development of functional connectivity and reasoning ability: a large-scale longitudinal investigation. J Neurosci. 2017;37(35):8549–58 [cited 2022 Jan 27]. Available from: https://www.jneurosci.org/content/37/35/8549.
Article
CAS
PubMed
PubMed Central
Google Scholar
Assem M, Blank IA, Mineroff Z, Ademoğlu A, Fedorenko E. Activity in the fronto-parietal multiple-demand network is robustly associated with individual differences in working memory and fluid intelligence. Cortex. 2020;131:1–16 [cited 2022 Jan 27]. Available from: https://www.sciencedirect.com/science/article/pii/S0010945220302720.
Article
PubMed
PubMed Central
Google Scholar
Wright S, Matlen B, Baym C, Ferrer E, Bunge S. Neural correlates of fluid reasoning in children and adults. Front Hum Neurosci. 2008;2 [cited 2022 Jan 27]. Available from: https://www.frontiersin.org/article/10.3389/neuro.09.008.2007.
Jung RE, Haier RJ. The Parieto-Frontal Integration Theory (P-FIT) of intelligence: converging neuroimaging evidence. Behav Brain Sci. 2007;30(2):135–54 discussion 154-187.
Article
PubMed
Google Scholar
Langeslag SJE, Schmidt M, Ghassabian A, Jaddoe VW, Hofman A, van der Lugt A, et al. Functional connectivity between parietal and frontal brain regions and intelligence in young children: the Generation R study. Hum Brain Mapp. 2013;34(12):3299–307.
Article
PubMed
Google Scholar
Donovan APA, Basson MA. The neuroanatomy of autism - a developmental perspective. J Anat. 2017;230(1):4–15.
Article
PubMed
Google Scholar
Harrison BJ, Pujol J, López-Solà M, Hernández-Ribas R, Deus J, Ortiz H, et al. Consistency and functional specialization in the default mode brain network. Proc Natl Acad Sci. 2008;105(28):9781–6 [cited 2021 Oct 26]. Available from: https://www.pnas.org/content/105/28/9781.
Article
CAS
PubMed
PubMed Central
Google Scholar
Hearne LJ, Mattingley JB, Cocchi L. Functional brain networks related to individual differences in human intelligence at rest. Sci Rep. 2016;6:32328 [cited 2021 Aug 28]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4999800/.
Article
CAS
PubMed
PubMed Central
Google Scholar
Jung T, Wickrama KAS. An introduction to latent class growth analysis and growth mixture modeling. Soc Personal Psychol Compass. 2008;2(1):302–17 [cited 2021 Oct 10]. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1751-9004.2007.00054.x.
Article
Google Scholar
Dixon ML, Vega ADL, Mills C, Andrews-Hanna J, Spreng RN, Cole MW, et al. Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc Natl Acad Sci. 2018;115(7):E1598–607 [cited 2021 Aug 28]. Available from: https://www.pnas.org/content/115/7/E1598.
Article
CAS
PubMed
PubMed Central
Google Scholar
Zielinski BA, Gennatas ED, Zhou J, Seeley WW. Network-level structural covariance in the developing brain. Proc Natl Acad Sci U S A. 2010;107(42):18191–6 Available from: http://www.ncbi.nlm.nih.gov/pubmed/20921389.
Article
CAS
PubMed
PubMed Central
Google Scholar
Paquola C, Bennett MR, Lagopoulos J. Structural and functional connectivity underlying gray matter covariance: impact of developmental insult. Brain Connect. 2018;8(5):299–310 [cited 2022 Jan 31]. Available from: https://www.liebertpub.com/doi/full/10.1089/brain.2018.0584.
Article
PubMed
Google Scholar
Raznahan A, Lerch JP, Lee N, Greenstein D, Wallace GL, Stockman M, et al. Patterns of coordinated anatomical change in human cortical development: a longitudinal neuroimaging study of maturational coupling. Neuron. 2011;72(5):873–84 [cited 2022 Jul 20]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4870892/.
Article
CAS
PubMed
PubMed Central
Google Scholar
Solomon M. IQ phenotypes of autistic youth from early childhood to preadolescence. In: Virtual Keynote; 2021.
Google Scholar
Zapala MA, Schork NJ. Statistical properties of multivariate distance matrix regression for high-dimensional data analysis. Front Genet. 2012;3 [cited 2018 Aug 27]. Available from: http://journal.frontiersin.org/article/10.3389/fgene.2012.00190/abstract.
McArtor DB, Lubke GH, Bergeman CS. Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic. Psychometrika. 2017;82(4):1052–77 [cited 2018 Aug 27]. Available from: http://link.springer.com/10.1007/s11336-016-9527-8.
Article
PubMed
Google Scholar
Mullen EM. Mullen scales of early learning. Minnesota: AGS Circle Pines; 1995.
Google Scholar
Beran TN, Elliott CD. Differential ability scales. 2nd ed. San Antonio: Harcourt Assessment; 2007. Can J Sch Psychol. 2007 ;22(1):128–32. [cited 2018 Aug 27]. Available from: http://journals.sagepub.com/doi/10.1177/0829573507302967
Google Scholar
Gotham K, Pickles A, Lord C. Standardizing ADOS scores for a measure of severity in autism spectrum disorders. J Autism Dev Disord. 2009;39(5):693–705.
Article
PubMed
Google Scholar
Lord C, Rutter M, Le Couteur A. Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord. 1994;24(5):659–85.
Article
CAS
PubMed
Google Scholar
Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC, 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. 2000;30(3):205–23.
Lord C, Rutter M, DiLavore PC, Risi GK, Bishop S. Autism diagnostic observation schedule. 2nd ed. Torrance: Western Psychological Services; 2012.
Google Scholar
Nordahl CW, Simon TJ, Zierhut C, Solomon M, Rogers SJ, Amaral DG. Brief report: methods for acquiring structural MRI data in very young children with autism without the use of sedation. J Autism Dev Disord. 2008;38(8):1581–90.
Article
PubMed
Google Scholar
Nordahl CW, Scholz R, Yang X, Buonocore MH, Simon T, Rogers S, et al. Increased rate of amygdala growth in children aged 2 to 4 years with autism spectrum disorders: a longitudinal study. Arch Gen Psychiatry. 2012;69(1):53–61.
Article
PubMed
PubMed Central
Google Scholar
Nordahl CW, Mello M, Shen AM, Shen MD, Vismara LA, Li D, et al. Methods for acquiring MRI data in children with autism spectrum disorder and intellectual impairment without the use of sedation. J Neurodev Disord. 2016;8(1):20.
Article
PubMed
PubMed Central
Google Scholar
Mori S, Wu D, Ceritoglu C, Li Y, Kolasny A, Vaillant MA, et al. MRICloud: delivering high-throughput MRI neuroinformatics as cloud-based software as a service. Comput Sci Eng. 2016;18(5):21–35.
Article
Google Scholar
Oishi K, Faria A, Jiang H, Li X, Akhter K, Zhang J, et al. Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer’s disease participants. Neuroimage. 2009;46(2):486–99 Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19385016.
Article
PubMed
Google Scholar
Shattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, Narr KL, et al. Construction of a 3D probabilistic atlas of human cortical structures. NeuroImage. 2008;39(3):1064–80.
Article
PubMed
Google Scholar
Ceritoglu C, Oishi K, Li X, Chou MC, Younes L, Albert M, et al. Multi-contrast large deformation diffeomorphic metric mapping for diffusion tensor imaging. Neuroimage. 2009;47(2):618–27 Available from: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=19398016.
Article
PubMed
Google Scholar
Tang X, Oishi K, Faria AV, Hillis AE, Albert MS, Mori S, et al. Bayesian parameter estimation and segmentation in the multi-atlas random orbit model. Rapallo F, editor. PLoS One. 2013;8(6):e65591.
Article
CAS
PubMed
PubMed Central
Google Scholar
Wang H, Yushkevich PA. Multi-atlas segmentation with joint label fusion and corrective learning—an open source implementation. Front Neuroinform. 2013;7 [cited 2018 Nov 8]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3837555/.
Andrews DS, Aksman L, Kerns CM, Lee JK, Winder-Patel BM, Harvey DJ, et al. Association of amygdala development with different forms of anxiety in autism spectrum disorder. Biol Psychiatry. 2022;0(0) [cited 2022 Feb 9]. Available from: https://www.biologicalpsychiatryjournal.com/article/S0006-3223(22)00058-0/fulltext.
Lee JK, Fandakova Y, Johnson EG, Cohen NJ, Bunge SA, Ghetti S. Changes in anterior and posterior hippocampus differentially predict item-space, item-time, and item-item memory improvement. Dev Cogn Neurosci. 2020;41:100741. https://doi.org/10.1016/j.dcn.2019.100741.
Article
PubMed
Google Scholar
Reinhardt VP, Iosif AM, Libero L, Heath B, Rogers SJ, Ferrer E, et al. Understanding Hippocampal Development in Young Children With Autism Spectrum Disorder. J Am Acad Child Adolesc Psychiatry. 2020;59(9):1069–79. https://doi.org/10.1016/j.jaac.2019.08.008. Epub 2019 Aug 23.
Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE, et al. Correspondence of the brain’s functional architecture during activation and rest. Proc Natl Acad Sci U S A. 2009;106(31):13040–5.
Article
CAS
PubMed
PubMed Central
Google Scholar
Lee JK, Amaral DG, Solomon M, Rogers SJ, Ozonoff S, Nordahl CW. Sex differences in the amygdala resting-state connectome of children with autism spectrum disorder. Biol Psychiatry Cogn Neurosci Neuroimaging. 2020;5(3):320–9.
PubMed
Google Scholar
Satterthwaite TD, Vandekar SN, Wolf DH, Bassett DS, Ruparel K, Shehzad Z, et al. Connectome-wide network analysis of youth with psychosis-spectrum symptoms. Mol Psychiatry. 2015;20(12):1508–15 [cited 2018 Aug 27]. Available from: http://www.nature.com/articles/mp201566.
Article
CAS
PubMed
PubMed Central
Google Scholar
Gower JC. Some distance properties of latent root and vector methods used in multivariate analysis. 1966;53(3/4):325–38 [cited 2018 Aug 27]. Available from: https://www.jstor.org/stable/2333639.
Aggarwal CC, Hinneburg A, Keim DA. On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche J, Vianu V, editors. Database Theory — ICDT 2001. Berlin: Springer; 2001. p. 420–34. (Lecture Notes in Computer Science).
Chapter
Google Scholar
Anderson MJ, Willis TJ. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology. 2003;84(2):511–25 [cited 2019 Jun 1]. Available from: https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1890/0012-9658%282003%29084%5B0511%3ACAOPCA%5D2.0.CO%3B2.
Article
Google Scholar
Legendre P, Anderson MJ. Distance-based redundancy analysis: testing multispecies responses in multifactorial ecological experiments. Ecol Monogr. 1999;69(1):1–24 [cited 2021 Aug 10]. Available from: https://www.jstor.org/stable/2657192.
Article
Google Scholar
Apostol TM, Mnatsakanian MA. Sums of squares of distances in m-space. Am Math Mon. 2003;110(6):516–26. [cited 2021 Feb 26]. https://doi.org/10.1080/00029890.2003.11919989.
Article
Google Scholar
Duncan J. The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour. Trends Cogn Sci. 2010;14(4):172–9.
Article
PubMed
Google Scholar
Song M, Zhou Y, Li J, Liu Y, Tian L, Yu C, et al. Brain spontaneous functional connectivity and intelligence. NeuroImage. 2008;41(3):1168–76.
Article
PubMed
Google Scholar
Dryburgh E, McKenna S, Rekik I. Predicting full-scale and verbal intelligence scores from functional connectomic data in individuals with autism spectrum disorder. Brain Imaging Behav. 2020;14(5):1769–78. [cited 2021 Oct 27]. https://doi.org/10.1007/s11682-019-00111-w.
Article
PubMed
Google Scholar
Pua EPK, Malpas CB, Bowden SC, Seal ML. Different brain networks underlying intelligence in autism spectrum disorders; 2018. p. 143891. [cited 2021 Oct 27]. Available from: https://www.biorxiv.org/content/10.1101/143891v2
Google Scholar
Ma X, Tan J, Jiang L, Wang X, Cheng B, Xie P, et al. Aberrant structural and functional developmental trajectories in children with intellectual disability. Front. Psychiatry. 2021;12(53) [cited 2021 Aug 25]. Available from: https://www.frontiersin.org/article/10.3389/fpsyt.2021.634170.
Vega JN, Hohman TJ, Pryweller JR, Dykens EM, Thornton-Wells TA. Resting-state functional connectivity in individuals with Down Syndrome and Williams syndrome compared with typically developing controls. brain. Connect. 2015;5(8):461–75 [cited 2021 Oct 26]. Available from: https://www.liebertpub.com/doi/10.1089/brain.2014.0266.
Google Scholar
Visser M, Jefferies E, Embleton KV, Lambon Ralph MA. Both the middle temporal gyrus and the ventral anterior temporal area are crucial for multimodal semantic processing: distortion-corrected fMRI evidence for a double gradient of information convergence in the temporal lobes. J Cogn Neurosci. 2012;24(8):1766–78. [cited 2022 Jul 20]. https://doi.org/10.1162/jocn_a_00244.
Article
PubMed
Google Scholar
Friederici AD, Gierhan SM. The language network. Curr Opin Neurobiol. 2013;23(2):250–4 [cited 2022 Jul 20]. Available from: https://www.sciencedirect.com/science/article/pii/S0959438812001614.
Article
CAS
PubMed
Google Scholar
Doshi-Velez F, Ge Y, Kohane I. Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis. Pediatrics. 2014;133(1):e54–63 [cited 2021 Oct 29]. Available from: https://pediatrics.aappublications.org/content/133/1/e54.
Article
PubMed
PubMed Central
Google Scholar
Kim SH, Macari S, Koller J, Chawarska K. Examining the phenotypic heterogeneity of early autism spectrum disorder: subtypes and short-term outcomes. J Child Psychol Psychiatry. 2016;57(1):93–102 [cited 2021 Oct 29]. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/jcpp.12448.
Article
PubMed
Google Scholar
Lai MC, Kassee C, Besney R, Bonato S, Hull L, Mandy W, et al. Prevalence of co-occurring mental health diagnoses in the autism population: a systematic review and meta-analysis. Lancet Psychiatry. 2019;6(10):819–29 [cited 2020 Aug 21]. Available from: https://www.thelancet.com/journals/lanpsy/article/PIIS2215-0366(19)30289-5/abstract.
Article
PubMed
Google Scholar
Chen B, Xu T, Zhou C, Wang L, Yang N, Wang Z, et al. Individual variability and test-retest reliability revealed by ten repeated resting-state brain scans over one month. PLoS One. 2015;10(12):e0144963 [cited 2022 Jan 25]. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0144963.
Article
PubMed
PubMed Central
CAS
Google Scholar
Marek S, Tervo-Clemmens B, Calabro FJ, Montez DF, Kay BP, Hatoum AS, et al. Towards reproducible brain-wide association studies. bioRxiv. 2020:2020.08.21.257758 [cited 2022 Jan 26]. Available from: https://www.biorxiv.org/content/10.1101/2020.08.21.257758v1.
Smith SM, Nichols TE. Statistical challenges in “big data” human neuroimaging. Neuron. 2018;97(2):263–8.
Article
CAS
PubMed
Google Scholar