Using Pattern Classification to Identify Brain Imaging Markers in Autism Spectrum Disorder

Part of the Current Topics in Behavioral Neurosciences book series (CTBN, volume 40)


Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by deficits in social interaction and communication, as well as repetitive and restrictive behaviours. The etiological and phenotypic complexity of ASD has so far hindered the development of clinically useful biomarkers for the condition. Neuroimaging studies have been valuable in establishing a biological basis for ASD. Increasingly, neuroimaging has been combined with ‘machine learning’-based pattern classification methods to make individual diagnostic predictions. Moving forward, the hope is that these techniques may not only facilitate the diagnostic process but may also aid in fractionating the ASD phenotype into more biologically homogeneous sub-groups, with defined pathophysiology, predictable outcomes and/or responses to targeted treatments and/or interventions. This review chapter will first introduce ‘machine learning’ and pattern recognition methods in general, with a focus on their application to diagnostic classification. It will highlight why such approaches to biomarker discovery may have advantages over more conventional analytical methods. Magnetic resonance imaging (MRI) findings of atypical brain structure, function and connectivity in ASD will be briefly reviewed before we describe how pattern recognition has been applied to generate predictive models for ASD. Last, we will discuss some limitations and pitfalls of pattern recognition analyses in ASD and consider how the field can advance beyond the prediction of binary outcomes.


Autism Imaging Pattern recognition 



The authors would like to acknowledge all the participants, families and researchers who contributed to the studies reviewed in this chapter. The authors acknowledge support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no 115300, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contribution. DA and GM are partly funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. DA acknowledges support from the National Institutes of Health (NIH) grants MH103371 and MH104438. CE acknowledges support by grants EC480/1-1 and EC480/2-1 from the German Research Foundation under the Heisenberg Programme. AFM gratefully acknowledges support from the Dutch Organisation for Scientific Research (NWO), under a Vernieuwingsimpuls ‘VIDI’ Fellowship (grant number 016.156.415).


  1. Adams JB, Baral M, Geis E, Mitchell J, Ingram J, Hensley A, Zappia I, Newmark S, Gehn E, Rubin RA, Mitchell K, Bradstreet J, EL-Dahr JM (2009) The severity of autism is associated with toxic metal body burden and red blood cell glutathione levels. J Toxicol 2009:7Google Scholar
  2. Akshoomoff N, Lord C, Lincoln AJ, Courchesne RY, Carper RA, Townsend J, Courchesne E (2004) Outcome classification of preschool children with autism spectrum disorders using MRI brain measures. J Am Acad Child Adolesc Psychiatry 43:349–357PubMedGoogle Scholar
  3. Alexander AL, Lee JE, Lazar M, Boudos R, Dubray MB, Oakes TR, Miller JN, Lu J, Jeong E-K, McMahon WM (2007) Diffusion tensor imaging of the corpus callosum in autism. NeuroImage 34:61–73PubMedGoogle Scholar
  4. Amaral DG, Schumann CM, Nordahl CW (2008) Neuroanatomy of autism. Trends Neurosci 31:137–145PubMedGoogle Scholar
  5. Anderson JS, Nielsen JA, Froehlich AL, Dubray MB, Druzgal TJ, Cariello AN, Cooperrider JR, Zielinski BA, Ravichandran C, Fletcher PT (2011) Functional connectivity magnetic resonance imaging classification of autism. Brain 134:3742–3754PubMedGoogle Scholar
  6. Andrews-Hanna JR (2012) The brain’s default network and its adaptive role in internal mentation. Neuroscientist 18:251–270PubMedGoogle Scholar
  7. Arbabshirani MR, Plis S, Sui J, Calhoun VD (2016) Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. NeuroImage 145:137–165PubMedPubMedCentralGoogle Scholar
  8. Ashburner J, Friston KJ (2000) Voxel-based morphometry – the methods. NeuroImage 11:805–821PubMedGoogle Scholar
  9. Ashwood K, Gillan N, Horder J, Hayward H, Woodhouse E, McEwen F, Findon J, Eklund H, Spain D, Wilson C (2016) Predicting the diagnosis of autism in adults using the Autism-Spectrum Quotient (AQ) questionnaire. Psychol Med 46:2595–2604PubMedPubMedCentralGoogle Scholar
  10. Assaf M, Jagannathan K, Calhoun VD, Miller L, Stevens MC, Sahl R, O’Boyle JG, Schultz RT, Pearlson GD (2010) Abnormal functional connectivity of default mode sub-networks in autism spectrum disorder patients. NeuroImage 53:247–256PubMedPubMedCentralGoogle Scholar
  11. Atladóttir HÓ, Thorsen P, Østergaard L, Schendel DE, Lemcke S, Abdallah M, Parner ET (2010) Maternal infection requiring hospitalization during pregnancy and autism spectrum disorders. J Autism Dev Disord 40:1423–1430PubMedGoogle Scholar
  12. Barnea-Goraly N, Kwon H, Menon V, Eliez S, Lotspeich L, Reiss AL (2004) White matter structure in autism: preliminary evidence from diffusion tensor imaging. Biol Psychiatry 55:323–326PubMedGoogle Scholar
  13. Betzel RF, Byrge L, He Y, Goñi J, Zuo X-N, Sporns O (2014) Changes in structural and functional connectivity among resting-state networks across the human lifespan. NeuroImage 102:345–357PubMedGoogle Scholar
  14. Buckley PF, Miller BJ, Lehrer DS, Castle DJ (2008) Psychiatric comorbidities and schizophrenia. Schizophr Bull 35(2):383–402PubMedPubMedCentralGoogle Scholar
  15. Buescher AS, Cidav Z, Knapp M, Mandell DS (2014) Costs of autism spectrum disorders in the United Kingdom and the United States. JAMA Pediatr 168:721–728PubMedGoogle Scholar
  16. C Yuen RK, Merico D, Bookman M, L Howe J, Thiruvahindrapuram B, Patel RV, Whitney J, Deflaux N, Bingham J, Wang Z, Pellecchia G, Buchanan JA, Walker S, Marshall CR, Uddin M, Zarrei M, Deneault E, D’Abate L, Chan AJS, Koyanagi S, Paton T, Pereira SL, Hoang N, Engchuan W, Higginbotham EJ, Ho K, Lamoureux S, Li W, MacDonald JR, Nalpathamkalam T, Sung WWL, Tsoi FJ, Wei J, Xu L, Tasse A-M, Kirby E, Van Etten W, Twigger S, Roberts W, Drmic I, Jilderda S, Modi BM, Kellam B, Szego M, Cytrynbaum C, Weksberg R, Zwaigenbaum L, Woodbury-Smith M, Brian J, Senman L, Iaboni A, Doyle-Thomas K, Thompson A, Chrysler C, Leef J, Savion-Lemieux T, Smith IM, Liu X, Nicolson R, Seifer V, Fedele A, Cook EH, Dager S, Estes A, Gallagher L, Malow BA, Parr JR, Spence SJ, Vorstman J, Frey BJ, Robinson JT, Strug LJ, Fernandez BA, Elsabbagh M, Carter MT, Hallmayer J, Knoppers BM, Anagnostou E, Szatmari P, Ring RH, Glazer D, Pletcher MT, Scherer SW (2017) Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder. Nat Neurosci 20(4):602–611. CrossRefPubMedGoogle Scholar
  17. Calderoni S, Retico A, Biagi L, Tancredi R, Muratori F, Tosetti M (2012) Female children with autism spectrum disorder: an insight from mass-univariate and pattern classification analyses. NeuroImage 59:1013–1022PubMedGoogle Scholar
  18. Carroll LS, Owen MJ (2009) Genetic overlap between autism, schizophrenia and bipolar disorder. Genome Med 1(10):102PubMedPubMedCentralGoogle Scholar
  19. Carter M, Scherer S (2013) Autism spectrum disorder in the genetics clinic: a review. Clin Genet 83:399–407PubMedGoogle Scholar
  20. Cerliani L, Mennes M, Thomas RM, DI Martino A, Thioux M, Keysers C (2015) Increased functional connectivity between subcortical and cortical resting-state networks in autism spectrum disorder. JAMA Psychiat 72:767–777Google Scholar
  21. Chanel G, Pichon S, Conty L, Berthoz S, Chevallier C, Grèzes J (2016) Classification of autistic individuals and controls using cross-task characterization of fMRI activity. NeuroImage Clin 10:78–88PubMedGoogle Scholar
  22. Chang YC, Quan J, Wood JJ (2012) Effects of anxiety disorder severity on social functioning in children with autism spectrum disorders. J Dev Phys Disabil 24(3):235–245Google Scholar
  23. Chen CP, Keown CL, Jahedi A, Nair A, Pflieger ME, Bailey BA, Müller RA (2015) Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. NeuroImage Clin 8:238–245PubMedPubMedCentralGoogle Scholar
  24. Chen H, Duan X, Liu F, Lu F, Ma X, Zhang Y, Uddin LQ, Chen H (2016) Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity – a multi-center study. Prog Neuro-Psychopharmacol Biol Psychiatry 64:1–9Google Scholar
  25. Courchesne E, Campbell K, Solso S (2011a) Brain growth across the life span in autism: age-specific changes in anatomical pathology. Brain Res 1380:138–145PubMedGoogle Scholar
  26. Courchesne E, Mouton PR, Calhoun ME, Semendeferi K, Ahrens-Barbeau C, Hallet MJ, Barnes CC, Pierce K (2011b) Neuron number and size in prefrontal cortex of children with autism. JAMA 306:2001–2010PubMedPubMedCentralGoogle Scholar
  27. Croen LA, Grether JK, Selvin S (2002) Descriptive epidemiology of autism in a California population: who is at risk? J Autism Dev Disord 32:217–224PubMedGoogle Scholar
  28. Deshpande G, Libero LE, Sreenivasan KR, Deshpande HD, Kana RK (2013) Identification of neural connectivity signatures of autism using machine learning. Front Hum Neurosci 7:670PubMedPubMedCentralGoogle Scholar
  29. Di Martino A, Kelly C, Grzadzinski R, Zuo X-N, Mennes M, Mairena MA, Lord C, Castellanos FX, Milham MP (2011) Aberrant striatal functional connectivity in children with autism. Biol Psychiatry 69:847–856PubMedGoogle Scholar
  30. Ebisch SJ, Gallese V, Willems RM, Mantini D, Groen WB, Romani GL, Buitelaar JK, Bekkering H (2011) Altered intrinsic functional connectivity of anterior and posterior insula regions in high-functioning participants with autism spectrum disorder. Hum Brain Mapp 32:1013–1028PubMedGoogle Scholar
  31. Ecker C, Murphy D (2014) Neuroimaging in autism – from basic science to translational research. Nat Rev Neurol 10:82–91PubMedGoogle Scholar
  32. Ecker C, Marquand A, Mourão-Miranda J, Johnston P, Daly EM, Brammer MJ, Maltezos S, Murphy CM, Robertson D, Williams SC (2010a) Describing the brain in autism in five dimensions – magnetic resonance imaging-assisted diagnosis of autism spectrum disorder using a multiparameter classification approach. J Neurosci 30:10612–10623PubMedGoogle Scholar
  33. Ecker C, Rocha-Rego V, Johnston P, Mourao-Miranda J, Marquand A, Daly EM, Brammer MJ, Murphy C, Murphy DG (2010b) Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. NeuroImage 49:44–56PubMedGoogle Scholar
  34. Ecker C, Suckling J, Deoni SC, Lombardo MV, Bullmore ET, Baron-Cohen S, Catani M, Jezzard P, Barnes A, Bailey AJ, Williams SC, Murphy DGM, MRC AIMS Consortium (2012) Brain anatomy and its relationship to behavior in adults with autism spectrum disorder. Arch Gen Psychiatry 69:195–209PubMedGoogle Scholar
  35. Ecker C, Shahidiani A, Feng Y, Daly E, Murphy C, D’Almeida V, Deoni S, Williams SC, Gillan N, Gudbrandsen M, Wichers R, Andrews D, Van Hemert L, Murphy DGM (2014) The effect of age, diagnosis, and their interaction on vertex-based measures of cortical thickness and surface area in autism spectrum disorder. J Neural Transm 121:1157–1170PubMedGoogle Scholar
  36. Ecker C, Andrews DS, Gudbrandsen CM et al (2017) Association between the probability of autism spectrum disorder and normative sex-related phenotypic diversity in brain structure. JAMA Psychiat 74(4):329–338Google Scholar
  37. Emerson RW, Adams C, Nishino T, Hazlett HC, Wolff JJ, Zwaigenbaum L, Constantino JN, Shen MD, Swanson MR, Elison JT (2017) Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. Sci Transl Med 9:eaag2882PubMedPubMedCentralGoogle Scholar
  38. Frazier T, Hardan A (2009) A meta-analysis of the corpus callosum in autism. Biol Psychiatry 66:935–941PubMedPubMedCentralGoogle Scholar
  39. Frith C (2004) Is autism a disconnection disorder? Lancet Neurol 3:577PubMedGoogle Scholar
  40. Gandal MJ, Haney J, Parikshak N, Leppa V, Horvath S, Geschwind DH (2018) Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359:693–697PubMedPubMedCentralGoogle Scholar
  41. Gardener H, Spiegelman D, Buka SL (2009) Prenatal risk factors for autism: comprehensive meta-analysis. Br J Psychiatry 195:7–14PubMedPubMedCentralGoogle Scholar
  42. Geschwind DH, Levitt P (2007) Autism spectrum disorders: developmental disconnection syndromes. Curr Opin Neurobiol 17:103–111PubMedGoogle Scholar
  43. Gori I, Giuliano A, Muratori F, Saviozzi I, Oliva P, Tancredi R, Cosenza A, Tosetti M, Calderoni S, Retico A (2015) Gray matter alterations in young children with autism spectrum disorders: comparing morphometry at the voxel and regional level. J Neuroimaging 25(6):866–874PubMedGoogle Scholar
  44. Guo X, Dominick KC, Minai AA, Li H, Erickson CA, Lu LJ (2017) Diagnosing autism spectrum disorder from brain resting-state functional connectivity patterns using a deep neural network with a novel feature selection method. Front Neurosci 11:460PubMedPubMedCentralGoogle Scholar
  45. Hallmayer J, Cleveland S, Torres A, Phillips J, Cohen B, Torigoe T (2011) Genetic heritability and shared environmental factors among twin pairs with autism. Arch Gen Psychiatry 68:1095–1102PubMedPubMedCentralGoogle Scholar
  46. Hazlett HC, Gu H, Munsell BC, Kim SH, Styner M, Wolff JJ, Elison JT, Swanson MR, Zhu H, Botteron KN (2017) Early brain development in infants at high risk for autism spectrum disorder. Nature 542:348–351PubMedPubMedCentralGoogle Scholar
  47. Herrington JD, Miller JS, Pandey J, Schultz RT (2016) Anxiety and social deficits have distinct relationships with amygdala function in autism spectrum disorder. Soc Cogn Affect Neurosci 11(6):907–914PubMedPubMedCentralGoogle Scholar
  48. Hong S, Ke X, Tang T, Hang Y, Chu K, Huang H, Ruan Z, Lu Z, Tao G, Liu Y (2011) Detecting abnormalities of corpus callosum connectivity in autism using magnetic resonance imaging and diffusion tensor tractography. Psychiatry Res Neuroimaging 194:333–339Google Scholar
  49. Huguet G, Ey E, Bourgeron T (2013) The genetic landscapes of autism spectrum disorders. Annu Rev Genomics Hum Genet 14:191–213PubMedGoogle Scholar
  50. Hull JV, Jacokes ZJ, Torgerson CM, Irimia A, van Horn JD (2016) Resting-state functional connectivity in autism spectrum disorders: a review. Front Psych 7:205Google Scholar
  51. Iidaka T (2015) Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex 63:55–67PubMedGoogle Scholar
  52. Ingalhalikar M, Kanterakis S, Gur R, Roberts TP, Verma R (2010) DTI based diagnostic prediction of a disease via pattern classification. Med Image Comput Comput Assist Interv 13(Pt 1):558–565PubMedGoogle Scholar
  53. Ingalhalikar M, Parker D, Bloy L, Roberts TPL, Verma R (2011) Diffusion based abnormality markers of pathology: toward learned diagnostic prediction of ASD. NeuroImage 57:918–927PubMedPubMedCentralGoogle Scholar
  54. Ingalhalikar M, Parker WA, Bloy L, Roberts TP, Verma R (2014) Creating multimodal predictors using missing data: classifying and subtyping autism spectrum disorder. J Neurosci Methods 235:1–9PubMedGoogle Scholar
  55. Jiao Y, Chen R, Ke X, Chu K, Lu Z, Herskovits EH (2010) Predictive models of autism spectrum disorder based on brain regional cortical thickness. NeuroImage 50:589–599PubMedGoogle Scholar
  56. Jung M, Kosaka H, Saito DN, Ishitobi M, Morita T, Inohara K, Asano M, Arai S, Munesue T, Tomoda A (2014) Default mode network in young male adults with autism spectrum disorder: relationship with autism spectrum traits. Mol Autism 5:35PubMedPubMedCentralGoogle Scholar
  57. Just MA, Keller TA, Malave VL, Kana RK, Varma S (2012) Autism as a neural systems disorder: a theory of frontal-posterior underconnectivity. Neurosci Biobehav Rev 36:1292–1313PubMedPubMedCentralGoogle Scholar
  58. Just MA, Cherkassky VL, Buchweitz A, Keller TA, Mitchell TM (2014) Identifying autism from neural representations of social interactions: neurocognitive markers of autism. PLoS One 9:e113879PubMedPubMedCentralGoogle Scholar
  59. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA (2015) Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiat 72(6):603–611Google Scholar
  60. Kanner L (1943) Autistic disturbances of affective contact. Nerv Child 2:217–250Google Scholar
  61. Lai M-C, Lombardo MV, Auyeung B, Chakrabarti B, Baron-Cohen S (2015) Sex/gender differences and autism: setting the scene for future research. J Am Acad Child Adolesc Psychiatry 54:11–24PubMedPubMedCentralGoogle Scholar
  62. Lebel C, Gee M, Camicioli R, Wieler M, Martin W, Beaulieu C (2012) Diffusion tensor imaging of white matter tract evolution over the lifespan. NeuroImage 60:340–352PubMedGoogle Scholar
  63. Lee BK, Magnusson C, Gardner RM, Blomström Å, Newschaffer CJ, Burstyn I, Karlsson H, Dalman C (2015) Maternal hospitalization with infection during pregnancy and risk of autism spectrum disorders. Brain Behav Immun 44:100–105Google Scholar
  64. Libero LE, Deramus TP, Lahti AC, Deshpande G, Kana RK (2015) Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates. Cortex 66:46–59PubMedPubMedCentralGoogle Scholar
  65. Libero LE, Nordahl CW, Li DD, Ferrer E, Rogers SJ, Amaral DG (2016) Persistence of megalencephaly in a subgroup of young boys with autism spectrum disorder. Autism Res 9:1169–1182PubMedPubMedCentralGoogle Scholar
  66. Lichtenstein P, Carlstrom E, Rastam M, Gillberg C, Anckarsater H (2010) The genetics of autism spectrum disorders and related neuropsychiatric disorders in childhood. Am J Psychiatry 167:1357–1363PubMedGoogle Scholar
  67. Lim L, Marquand A, Cubillo AA, Smith AB, Chantiluke K, Simmons A, Mehta M, Rubia K (2013) Disorder-specific predictive classification of adolescents with attention deficit hyperactivity disorder (ADHD) relative to autism using structural magnetic resonance imaging. PLoS One 8:e63660PubMedPubMedCentralGoogle Scholar
  68. Lord C, Rutter M, Couteur A (1994) Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord 24:659–685PubMedGoogle Scholar
  69. Lord C, Risi S, Lambrecht L, Cook JR EH, Leventhal BL, Dilavore PC, 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. J Autism Dev Disord 30:205–223PubMedGoogle Scholar
  70. Marquand AF, Rezek I, Buitelaar J, Beckmann CF (2016) Understanding heterogeneity in clinical cohorts using normative models: beyond case-control studies. Biol Psychiatry 80:552–561PubMedPubMedCentralGoogle Scholar
  71. McAlonan GM, Suckling J, Wong N, Cheung V, Lienenkaemper N, Cheung C, Chua SE (2008) Distinct patterns of grey matter abnormality in high-functioning autism and Asperger’s syndrome. J Child Psychol Psychiatry 49:1287–1295PubMedGoogle Scholar
  72. McAlonan G, Cheung C, Cheung V, Wong N, Suckling J, Chua S (2009) Differential effects on white-matter systems in high-functioning autism and Asperger’s syndrome. Psychol Med 39:1885–1893PubMedGoogle Scholar
  73. Mitchell TM (1997) Machine learning, vol 45. McGraw-Hill, Burr Ridge, p 37Google Scholar
  74. Mourão-Miranda J, Hardoon DR, Hahn T, Marquand AF, Williams SC, Shawe-Taylor J, Brammer M (2011) Patient classification as an outlier detection problem: an application of the one-class support vector machine. NeuroImage 58:793–804PubMedPubMedCentralGoogle Scholar
  75. Murdaugh DL, Shinkareva SV, Deshpande HR, Wang J, Pennick MR, Kana RK (2012) Differential deactivation during mentalizing and classification of autism based on default mode network connectivity. PLoS One 7:e50064PubMedPubMedCentralGoogle Scholar
  76. Mwangi B, Tian TS, Soares JC (2014) A review of feature reduction techniques in neuroimaging. Neuroinformatics 12:229–244PubMedPubMedCentralGoogle Scholar
  77. Nickl-Jockschat T, Habel U, Maria Michel T, Manning J, Laird AR, Fox PT, Schneider F, Eickhoff SB (2012) Brain structure anomalies in autism spectrum disorder – a meta-analysis of VBM studies using anatomic likelihood estimation. Hum Brain Mapp 33:1470–1489PubMedGoogle Scholar
  78. Nordahl CW, Scholz R, Yang X, Buonocore MH, Simon T, Rogers S, Amaral DG (2012) Increased rate of amygdala growth in children aged 2 to 4 years with autism spectrum disorders: a longitudinal study. Arch Gen Psychiatry 69(1):53–61PubMedPubMedCentralGoogle Scholar
  79. Orrú G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A (2012) Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev 36(4):1140–1152PubMedGoogle Scholar
  80. Packer A (2016) Neocortical neurogenesis and the etiology of autism spectrum disorder. Neurosci Biobehav Rev 64:185–195PubMedGoogle Scholar
  81. Panizzon MS, Fennema-Notestine C, Eyler LT, Jernigan TL, Prom-Wormley E, Neale M, Jacobson K, Lyons MJ, Grant MD, Franz CE (2009) Distinct genetic influences on cortical surface area and cortical thickness. Cereb Cortex 19(11):2728–2735PubMedPubMedCentralGoogle Scholar
  82. Philip RC, Dauvermann MR, Whalley HC, Baynham K, Lawrie SM, Stanfield AC (2012) A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders. Neurosci Biobehav Rev 36:901–942PubMedGoogle Scholar
  83. Piven J, Arndt S, Bailey J, Havercamp S, Andreasen NC, Palmer P (1995) An MRI study of brain size in autism. Am J Psychiatr 152:1145–1149PubMedGoogle Scholar
  84. Piven J, Arndt S, Bailey J, Andreasen N (1996) Regional brain enlargement in autism: a magnetic resonance imaging study. J Am Acad Child Adolesc Psychiatry 35:530–536PubMedGoogle Scholar
  85. Plitt M, Barnes KA, Martin A (2015) Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage Clin 7:359–366PubMedGoogle Scholar
  86. Raichle ME, MaCleod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001) A default mode of brain function. Proc Natl Acad Sci 98:676–682PubMedGoogle Scholar
  87. Rakic P (1995) A small step for the cell, a giant leap for mankind: a hypothesis of neocortical expansion during evolution. Trends Neurosci 18:383–388PubMedGoogle Scholar
  88. Ruggeri B, Sarkans U, Schumann G, Persico AM (2013) Biomarkers in autism spectrum disorder: the old and the new. Psychopharmacology 231(6):1–16Google Scholar
  89. Sabuncu MR, Konukoglu E (2015) Clinical prediction from structural brain MRI scans: a large-scale empirical study. Neuroinformatics 13(1):31–46PubMedPubMedCentralGoogle Scholar
  90. Sandin S, Lichtenstein P, Kuja-Halkola R, Larsson H, Hultman CM, Reichenberg A (2014) The familial risk of autism. JAMA 311:1770–1777PubMedPubMedCentralGoogle Scholar
  91. Schendel D, Bhasin TK (2008) Birth weight and gestational age characteristics of children with autism, including a comparison with other developmental disabilities. Pediatrics 121:1155–1164PubMedGoogle Scholar
  92. Segovia F, Holt R, Spencer M, Górriz JM, Ramírez J, Puntonet CG, Phillips C, Chura L, Baron-Cohen S, Suckling J (2014) Identifying endophenotypes of autism: a multivariate approach. Front Comput Neurosci 8:60PubMedPubMedCentralGoogle Scholar
  93. Shen MD, Li DD, Keown CL, Lee A, Johnson RT, Angkustsiri K, Rogers SJ, Müller RA, Amaral DG, Nordahl CW (2016) Functional connectivity of the amygdala is disrupted in preschool-aged children with autism spectrum disorder. J Am Acad Child Adolesc Psychiatry 55(9):817–824PubMedPubMedCentralGoogle Scholar
  94. Shen MD, Kim SH, McKinstry RC, Gu H, Hazlett HC, Nordahl CW, Emerson RE, Shaw D, Elison JT, Swanson MR, Fonov VS, Gerig G, Dager SR, Botteron KN, Paterson S, Schultz RT, Evans AC, Estes AM, Zwaigenbaum L, Styner MA, Amaral DG, Piven J (2017) Increased extra-axial cerebrospinal fluid in high-risk infants who later develop autism. Biol Psychiatry 82(3):186–193PubMedPubMedCentralGoogle Scholar
  95. Shukla DK, Keehn B, Lincoln AJ, Müller R-A (2010) White matter compromise of callosal and subcortical fiber tracts in children with autism spectrum disorder: a diffusion tensor imaging study. J Am Acad Child Adolesc Psychiatry 49:1269–1278.e2PubMedPubMedCentralGoogle Scholar
  96. Sowell ER, Thompson PM, Leonard CM, Welcome SE, Kan E, Toga AW (2004) Longitudinal mapping of cortical thickness and brain growth in normal children. J Neurosci 24:8223–8231PubMedGoogle Scholar
  97. Starck T, Nikkinen J, Rahko J, Remes J, Hurtig T, Haapsamo H, Jussila K, Kuusikko-Gauffin S, Mattila M-L, Jansson-Verkasalo E (2013) Resting state fMRI reveals a default mode dissociation between retrosplenial and medial prefrontal subnetworks in ASD despite motion scrubbing. Front Hum Neurosci 7:802PubMedPubMedCentralGoogle Scholar
  98. Sukhodolsky DG, Scahill L, Gadow KD, Arnold LE, Aman MG, McDougle CJ, McCracken JT, Tierney E, White SW, Lecavalier L, Vitiello B (2008) Parent-rated anxiety symptoms in children with pervasive developmental disorders: frequency and association with core autism symptoms and cognitive functioning. J Abnorm Child Psychol 36(1):117–128PubMedGoogle Scholar
  99. Tammimies K, Marshall CR, Walker S, Kaur G, Thiruvahindrapuram B, Lionel AC, Yuen RK, Uddin M, Roberts W, Weksberg R (2015) Molecular diagnostic yield of chromosomal microarray analysis and whole-exome sequencing in children with autism spectrum disorder. JAMA 314:895–903PubMedGoogle Scholar
  100. Tang G, Gudsnuk K, Kuo S-H, Cotrina ML, Rosoklija G, Sosunov A, Sonders MS, Kanter E, Castagna C, Yamamoto A, Yue Z, Arancio O, Peterson BS, Champagne F, Dwork AJ, Goldman J, Sulzer D (2014) Loss of mTOR-dependent macroautophagy causes autistic-like synaptic pruning deficits. Neuron 83(5):1131–1143PubMedPubMedCentralGoogle Scholar
  101. Taylor LE, Swerdfeger AL, Eslick GD (2014) Vaccines are not associated with autism: an evidence-based meta-analysis of case-control and cohort studies. Vaccine 32(29):3623–3629PubMedGoogle Scholar
  102. Uddin LQ, Menon V, Young CB, Ryali S, Chen T, Khouzam A, Minshew NJ, Hardan AY (2011) Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism. Biol Psychiatry 70:833–841PubMedPubMedCentralGoogle Scholar
  103. Uddin LQ, Supekar K, Lynch CJ, Khouzam A, Phillips J, Feinstein C, Ryali S, Menon V (2013) Salience network–based classification and prediction of symptom severity in children with autism. JAMA Psychiat 70:869–879Google Scholar
  104. Verly M, Verhoeven J, Zink I, Mantini D, Peeters R, Deprez S, Emsell L, Boets B, Noens I, Steyaert J (2014) Altered functional connectivity of the language network in ASD: role of classical language areas and cerebellum. NeuroImage Clin 4:374–382PubMedPubMedCentralGoogle Scholar
  105. von dem Hagen EA, Stoyanova RS, Baron-Cohen S, Calder AJ (2012) Reduced functional connectivity within and between ‘social’ resting state networks in autism spectrum conditions. Soc Cogn Affect Neurosci 8:694–701Google Scholar
  106. Wang H, Zeng LL, Chen Y, Yin H, Tan Q, Hu D (2015) Evidence of a dissociation pattern in default mode subnetwork functional connectivity in schizophrenia. Sci Rep 5:14655PubMedPubMedCentralGoogle Scholar
  107. Wee CY, Wang L, Shi F, Yap PT, Shen D (2014) Diagnosis of autism spectrum disorders using regional and interregional morphological features. Hum Brain Mapp 35(7):3414–3430PubMedGoogle Scholar
  108. Weng S-J, Wiggins JL, Peltier SJ, Carrasco M, Risi S, Lord C, Monk CS (2010) Alterations of resting state functional connectivity in the default network in adolescents with autism spectrum disorders. Brain Res 1313:202–214PubMedGoogle Scholar
  109. Werling DM (2016) The role of sex-differential biology in risk for autism spectrum disorder. Biol Sex Differ 7:58PubMedPubMedCentralGoogle Scholar
  110. Werling DM, Parikshak NN, Geschwind DH (2016) Gene expression in human brain implicates sexually dimorphic pathways in autism spectrum disorders. Nat Commun 7:10717PubMedPubMedCentralGoogle Scholar
  111. Wiggins JL, Peltier SJ, Ashinoff S, Weng S-J, Carrasco M, Welsh RC, Lord C, Monk CS (2011) Using a self-organizing map algorithm to detect age-related changes in functional connectivity during rest in autism spectrum disorders. Brain Res 1380:187–197PubMedGoogle Scholar
  112. Wolfers T, Buitelaar JK, Beckmann CF, Franke B, Marquand AF (2015) From estimating activation locality to predicting disorder: a review of pattern recognition for neuroimaging-based psychiatric diagnostics. Neurosci Biobehav Rev 57:328–349PubMedGoogle Scholar
  113. World Health Organization (2004) International statistical classification of diseases and related health problems. World Health Organization, GenevaGoogle Scholar
  114. Xiao X, Fang H, Wu J, Xiao C, Xiao T, Qian L, Liang F, Xiao Z, Chu KK, Ke X (2017) Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder. Autism Res 10:620–630PubMedGoogle Scholar
  115. Yahata N, Morimoto J, Hashimoto R, Lisi G, Shibata K, Kawakubo Y, Kuwabara H, Kuroda M, Yamada T, Megumi F, Imamizu H (2016) A small number of abnormal brain connections predicts adult autism spectrum disorder. Nat Commun 7:11254PubMedPubMedCentralGoogle Scholar
  116. Zhou Y, Yu F, Duong T (2014) Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning. PLoS One 9(6):e90405PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Medical Investigation of Neurodevelopmental Disorders (MIND) Institute and Department of Psychiatry and Behavioural SciencesUC Davis School of Medicine, University of California DavisSacramentoUSA
  2. 2.Department of Forensic and Neurodevelopmental SciencesThe Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology and Neuroscience, King’s College LondonLondonUK
  3. 3.Donders Institute for Brain, Cognition and Behaviour, Radboud UniversityNijmegenThe Netherlands
  4. 4.Centre for Neuroimaging SciencesInstitute of Psychiatry, Psychology and Neuroscience, King’s College LondonLondonUK
  5. 5.Department of Child and Adolescent Psychiatry, Psychosomatics and PsychotherapyUniversitätsklinikum Frankfurt am Main, Goethe-University Frankfurt am MainFrankfurtGermany
  6. 6.South London and Maudsley NHS Foundation TrustLondonUK

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