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Neuroimaging in autism—from basic science to translational research

Nature Reviews Neurology volume 10, pages 82–91 (2014)Cite this article

Subjects

Key Points

  • The focus of neuroimaging in mental health research is increasingly on translational approaches

  • A multidisciplinary approach is required for clinical translation of neuroimaging findings in autism spectrum disorder (ASD)

  • Improving the specificity of neuroimaging markers will substantially enhance the translational potential of this modality

  • Novel neuroimaging markers that accurately reflect specific pathological processes in ASD are required to link with data from other scientific approaches, such as genetic or molecular studies

  • Neuroimaging findings could provide biomarkers that facilitate diagnosis and prediction of response to treatment, and enable stratification of individuals with ASD

Abstract

Over the past decade, human neuroimaging studies have provided invaluable insights into the neural substrates that underlie autism spectrum disorder (ASD). Although observations from multiple neuroimaging approaches converge in suggesting that changes in brain structure, functioning and connectivity are associated with ASD, the neurobiology of this disorder is complex, and considerable aetiological and phenotypic heterogeneity exists among individuals on the autism spectrum. Characterization of the neurobiological alterations that underlie ASD and development of novel pharmacotherapies for ASD, therefore, requires multidisciplinary collaboration. Consequently, pressure is growing to combine neuroimaging data with information provided by other disciplines to translate research findings into clinically useful biomarkers. So far, however, neuroimaging studies in patients with ASD have mainly been conducted in isolation, and the low specificity of neuroimaging measures has hindered the development of biomarkers that could aid clinical trials and/or facilitate patient identification. Novel approaches to acquiring and analysing data on brain characteristics are currently being developed to overcome these inherent limitations, and to integrate neuroimaging into translational research. Here, we discuss promising new studies of cortical pathology in patients with ASD, and outline how the novel insights thereby obtained could inform diagnosis and treatment of ASD in the future.

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Figure 1: The role of human neuroimaging in the translational research cycle of ASD.
Figure 2: Personalized diagnosis and treatment of ASD.
Figure 3: Multivariate pattern classification can discriminate between multiple subgroups of ASD on the basis of neuroimaging data.
Figure 4: The translatability of neuroimaging is directly related to its resolution.

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References

  1. Wing, L. The autistic spectrum. Lancet 350, 1761–1766 (1997).

    Article  CAS  PubMed  Google Scholar 

  2. Abrahams, B. S. & Geschwind, D. H. Advances in autism genetics: on the threshold of a new neurobiology. Nat. Rev. Genet. 9, 341–355 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Ecker, C. et al. Brain anatomy and its relationship to behavior in adults with autism spectrum disorder: a multicenter magnetic resonance imaging study. Arch. Gen. Psychiatry 69, 195–209 (2012).

    Article  PubMed  Google Scholar 

  4. Amaral, D. G., Schumann, C. M. & Nordahl, C. W. Neuroanatomy of autism. Trends Neurosci. 31, 137–145 (2008).

    Article  CAS  PubMed  Google Scholar 

  5. Lombardo, M. V., Chakrabarti, B., Bullmore, E. T., MRC AIMS Consortium & Baron-Cohen, S. Specialization of right temporo-parietal junction for mentalizing and its relation to social impairments in autism. Neuroimage 56, 1832–1838 (2011).

    Article  PubMed  Google Scholar 

  6. Langen, M., Durston, S., Staal, W. G., Palmen, S. J. & van Engeland, H. Caudate nucleus is enlarged in high-functioning medication-naive subjects with autism. Biol. Psychiatry 62, 262–266 (2007).

    Article  PubMed  Google Scholar 

  7. Waiter, G. D. et al. A voxel-based investigation of brain structure in male adolescents with autistic spectrum disorder. Neuroimage 22, 619–625 (2004).

    Article  PubMed  Google Scholar 

  8. Carper, R. A. & Courchesne, E. Localized enlargement of the frontal cortex in early autism. Biol. Psychiatry 57, 126–133 (2005).

    Article  PubMed  Google Scholar 

  9. Courchesne, E. et al. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology 57, 245–254 (2001).

    Article  CAS  PubMed  Google Scholar 

  10. Ecker, C., Spooren, W. & Murphy, D. Developing new pharmacotherapies for autism. J. Intern. Med. 274, 308–320 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Williams, K. et al. Selective serotonin reuptake inhibitors (SSRIs) for autism spectrum disorders (ASD). Cochrane Database of Systematic Reviews, Issue 8. Art. No.: CD004677 http://dx.doi.org/10.1002/14651858.CD004677.pub3.

  12. Geschwind, D. H. & Levitt, P. Autism spectrum disorders: developmental disconnection syndromes. Curr. Opin. Neurobiol. 17, 103–111 (2007).

    Article  CAS  PubMed  Google Scholar 

  13. Belmonte, M. K. et al. Autism and abnormal development of brain connectivity. J. Neurosci. 24, 9228–9231 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. McAlonan, G. M. et al. Differential effects on white-matter systems in high-functioning autism and Asperger's syndrome. Psychol. Med. 39, 1885–1893 (2009).

    Article  CAS  PubMed  Google Scholar 

  15. Pugliese, L. et al. The anatomy of extended limbic pathways in Asperger syndrome: a preliminary diffusion tensor imaging tractography study. Neuroimage 47, 427–434 (2009).

    Article  PubMed  Google Scholar 

  16. Langen, M. et al. Fronto-striatal circuitry and inhibitory control in autism: findings from diffusion tensor imaging tractography. Cortex 48, 183–193 (2011).

    Article  PubMed  Google Scholar 

  17. Shukla, D. K., Keehn, B. & Müller, R.-A. Tract-specific analyses of diffusion tensor imaging show widespread white matter compromise in autism spectrum disorder. J. Child Psychol. Psychiatry 52, 286–295 (2011).

    Article  PubMed  Google Scholar 

  18. Koshino, H. et al. Functional connectivity in an fMRI working memory task in high-functioning autism. Neuroimage 24, 810–821 (2005).

    Article  PubMed  Google Scholar 

  19. Koshino, H. et al. fMRI investigation of working memory for faces in autism: visual coding and underconnectivity with frontal areas. Cereb. Cortex 18, 289–300 (2008).

    Article  PubMed  Google Scholar 

  20. Lewis, D. A., Melchitzky, D. S. & Burgos, G.-G. Specificity in the functional architecture of primate prefrontal cortex. J. Neurocytol. 31, 265–276 (2002).

    Article  PubMed  Google Scholar 

  21. Melchitzky, D. S., González-Burgos, G., Barrionuevo, G. & Lewis, D. A. Synaptic targets of the intrinsic axon collaterals of supragranular pyramidal neurons in monkey prefrontal cortex. J. Comp. Neurol. 430, 209–221 (2001).

    Article  CAS  PubMed  Google Scholar 

  22. Ecker, C. et al. Intrinsic gray-matter connectivity of the brain in adults with autism spectrum disorder. Proc. Natl Acad. Sci. USA 110, 13222–13227 (2013).

    Article  PubMed  Google Scholar 

  23. Griffin, L. D. The intrinsic geometry of the cerebral cortex. J. Theor. Biol. 166, 261–273 (1994).

    Article  CAS  PubMed  Google Scholar 

  24. Klöppel, S. et al. Diagnostic neuroimaging across diseases. Neuroimage 61, 457–463 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Schumann, C. M. et al. Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. J. Neurosci. 30, 4419–4427 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hazlett, H. C. et al. Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. Arch. Gen. Psychiatry 68, 467–476 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  27. 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).

    Article  PubMed  Google Scholar 

  28. Hardan, A. Y., Libove, R. A., Keshavan, M. S., Melhem, N. M. & Minshew, N. J. A preliminary longitudinal magnetic resonance imaging study of brain volume and cortical thickness in autism. Biol. Psychiatry 66, 320–326 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Courchesne, E. Abnormal early brain development in autism. Mol. Psychiatry 7 (Suppl. 2), S21–S23 (2002).

    Article  PubMed  Google Scholar 

  30. Herbert, M. R. Large brains in autism: the challenge of pervasive abnormality. Neuroscientist 11, 417–440 (2005).

    Article  PubMed  Google Scholar 

  31. Ecker, C. et al. 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, 10612–10623 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ecker, C. et al. Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage 49, 44–56 (2010).

    Article  PubMed  Google Scholar 

  33. Jiao, Y. et al. Predictive models of autism spectrum disorder based on brain regional cortical thickness. Neuroimage 50, 589–599 (2010).

    Article  PubMed  Google Scholar 

  34. Uddin, L. Q. et al. Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism. Biol. Psychiatry 70, 833–841 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Anderson, J. S. et al. Functional connectivity magnetic resonance imaging classification of autism. Brain 134, 3739–3751 (2011).

    PubMed Central  Google Scholar 

  36. Ingalhalikar, M., Parker, D., Bloy, L., Roberts, T. P. & Verma, R. Diffusion based abnormality markers of pathology: toward learned diagnostic prediction of ASD. Neuroimage 57, 918–927 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Lange, N. et al. Atypical diffusion tensor hemispheric asymmetry in autism. Autism Res. 3, 350–358 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Coutanche, M. N., Thompson-Schill, S. L. & Schultz, R. T. Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity. Neuroimage 57, 113–123 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Calderoni, S. et al. Female children with autism spectrum disorder: an insight from mass-univariate and pattern classification analyses. Neuroimage 59, 1013–1022 (2012).

    Article  PubMed  Google Scholar 

  40. Corrigan, N. M. et al. Atypical developmental patterns of brain chemistry in children with autism spectrum disorder. JAMA Psychiatry 70, 964–974 (2013).

    Article  PubMed  Google Scholar 

  41. Ecker, C. Autism biomarkers for more efficacious diagnosis. Biomark. Med. 5, 193–195 (2011).

    Article  PubMed  Google Scholar 

  42. Mak-Fan, K. M., Taylor, M. J., Roberts, W. & Lerch, J. P. Measures of cortical grey matter structure and development in children with autism spectrum disorder. J. Autism Dev. Disord. 42, 419–427 (2011).

    Article  Google Scholar 

  43. Hardan, A. Y., Muddasani, S., Vemulapalli, M., Keshavan, M. S. & Minshew, N. J. An MRI study of increased cortical thickness in autism. Am. J. Psychiatry 163, 1290–1292 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Scheel, C. et al. Imaging derived cortical thickness reduction in high-functioning autism: key regions and temporal slope. Neuroimage 58, 391–400 (2011).

    Article  PubMed  Google Scholar 

  45. Hyde, K. L., Samson, F., Evans, A. C. & Mottron, L. Neuroanatomical differences in brain areas implicated in perceptual and other core features of autism revealed by cortical thickness analysis and voxel-based morphometry. Hum. Brain Mapp. 31, 556–566 (2010).

    PubMed  Google Scholar 

  46. Wallace, G. L., Dankner, N., Kenworthy, L., Giedd, J. N. & Martin, A. Age-related temporal and parietal cortical thinning in autism spectrum disorders. Brain 133, 3745–3754 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Lainhart, J. E. et al. Head circumference and height in autism: a study by the Collaborative Program of Excellence in Autism. Am. J. Med. Genet. A 140, 2257–2274 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  48. 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).

    Article  PubMed  Google Scholar 

  49. Pontious, A., Kowalczyk, T., Englund, C. & Hevner, R. F. Role of intermediate progenitor cells in cerebral cortex development. Dev. Neurosci. 30, 24–32 (2008).

    Article  CAS  PubMed  Google Scholar 

  50. Miyata, T. et al. Asymmetric production of surface-dividing and non-surface-dividing cortical progenitor cells. Development 131, 3133–3145 (2004).

    Article  CAS  PubMed  Google Scholar 

  51. Noctor, S. C., Martínez-Cerdeño, V., Ivic, L. & Kriegstein, A. R. Cortical neurons arise in symmetric and asymmetric division zones and migrate through specific phases. Nat. Neurosci. 7, 136–144 (2004).

    Article  CAS  PubMed  Google Scholar 

  52. Rakic, P. Defects of neuronal migration and the pathogenesis of cortical malformations. Prog. Brain Res. 73, 15–37 (1988).

    Article  CAS  PubMed  Google Scholar 

  53. Joyner, A. H. et al. A common MECP2 haplotype associates with reduced cortical surface area in humans in two independent populations. Proc. Natl Acad. Sci. USA 106, 15483–15488 (2009).

    Article  PubMed  Google Scholar 

  54. Crawley, J. N. Translational animal models of autism and neurodevelopmental disorders. Dialogues Clin. Neurosci. 14, 293–305 (2012).

    PubMed  PubMed Central  Google Scholar 

  55. Bailey, A. et al. Autism as a strongly genetic disorder: evidence from a British twin study. Psychol. Med. 25, 63–77 (1995).

    Article  CAS  PubMed  Google Scholar 

  56. Hallmayer, J. et al. Genetic heritability and shared environmental factors among twin pairs with autism. Arch. Gen. Psychiatry 68, 1095–1102 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Lichtenstein, P., Carlström, E., Råstam, M., Gillberg, C. & Anckarsäter, H. The genetics of autism spectrum disorders and related neuropsychiatric disorders in childhood. Am. J. Psychiatry 167, 1357–1363 (2010).

    Article  PubMed  Google Scholar 

  58. Betancur, C., Sakurai, T. & Buxbaum, J. D. The emerging role of synaptic cell-adhesion pathways in the pathogenesis of autism spectrum disorders. Trends Neurosci. 32, 402–412 (2009).

    Article  CAS  PubMed  Google Scholar 

  59. Betancur, C. Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res. 1380, 42–77 (2011).

    Article  CAS  PubMed  Google Scholar 

  60. Spooren, W., Lindemann, L., Ghosh, A. & Santarelli, L. Synapse dysfunction in autism: a molecular medicine approach to drug discovery in neurodevelopmental disorders. Trends Pharmacol. Sci. 33, 669–684 (2012).

    Article  CAS  PubMed  Google Scholar 

  61. Baudouin, S. J. et al. Shared synaptic pathophysiology in syndromic and nonsyndromic rodent models of autism. Science 338, 128–132 (2012).

    Article  CAS  PubMed  Google Scholar 

  62. Gilman, S. R. et al. Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70, 898–907 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Dalva, M. B., McClelland, A. C. & Kayser, M. S. Cell adhesion molecules: signalling functions at the synapse. Nat. Rev. Neurosci. 8, 206–220 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Lowery, L. A. & Van Vactor, D. The trip of the tip: understanding the growth cone machinery. Nat. Rev. Mol. Cell Biol. 10, 332–343 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Jamain, S. et al. Mutations of the X-linked genes encoding neuroligins NLGN3 and NLGN4 are associated with autism. Nat. Genet. 34, 27–29 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Laumonnier, F. et al. X-linked mental retardation and autism are associated with a mutation in the NLGN4 gene, a member of the neuroligin family. Am. J. Hum. Genet. 74, 552–557 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Chih, B., Engelman, H. & Scheiffele, P. Control of excitatory and inhibitory synapse formation by neuroligins. Science 307, 1324–1328 (2005).

    Article  CAS  PubMed  Google Scholar 

  68. Graf, E. R., Zhang, X., Jin, S.-X., Linhoff, M. W. & Craig, A. M. Neurexins induce differentiation of GABA and glutamate postsynaptic specializations via neuroligins. Cell 119, 1013–1026 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Rubenstein, J. L. & Merzenich, M. M. Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes Brain Behav. 2, 255–267 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Page, L. A. et al. In vivo1H-magnetic resonance spectroscopy study of amygdala-hippocampal and parietal regions in autism. Am. J. Psychiatry 163, 2189–2192 (2006).

    Article  PubMed  Google Scholar 

  71. Mendez, M. A. et al. The brain GABA-benzodiazepine receptor alpha-5 subtype in autism spectrum disorder: a pilot [11C]Ro15-4513 positron emission tomography study. Neuropharmacology 68, 195–201 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Poliak, S. et al. Juxtaparanodal clustering of Shaker-like K+ channels in myelinated axons depends on Caspr2 and TAG-1. J. Cell Biol. 162, 1149–1160 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Corfas, G., Velardez, M. O., Ko, C.-P., Ratner, N. & Peles, E. Mechanisms and roles of axon–Schwann cell interactions. J. Neurosci. 24, 9250–9260 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Peñagarikano, O. et al. Absence of CNTNAP2 leads to epilepsy, neuronal migration abnormalities, and core autism-related deficits. Cell 147, 235–246 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Tuchman, R. & Rapin, I. Epilepsy in autism. Lancet Neurol. 1, 352–358 (2002).

    Article  PubMed  Google Scholar 

  76. Strauss, K. A. et al. Recessive symptomatic focal epilepsy and mutant contactin-associated protein-like 2. N. Engl. J. Med. 354, 1370–1377 (2006).

    Article  CAS  PubMed  Google Scholar 

  77. Peippo, M. M. et al. Pitt–Hopkins syndrome in two patients and further definition of the phenotype. Clin. Dysmorphol. 15, 47–54 (2006).

    Article  PubMed  Google Scholar 

  78. Dennis, E. L. et al. Altered structural brain connectivity in healthy carriers of the autism risk gene, CNTNAP2. Brain Connect. 1, 447–459 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Scott-Van Zeeland, A. A. et al. Altered functional connectivity in frontal lobe circuits is associated with variation in the autism risk gene CNTNAP2. Sci. Transl. Med. 2, 56ra80 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Guilmatre, A., Huguet, G., Delorme, R. & Bourgeron, T. The emerging role of SHANK genes in neuropsychiatric disorders. Dev. Neurobiol. http://dx.doi.org/10.1002/dneu.22128.

  81. Renner, M., Specht, C. G. & Triller, A. Molecular dynamics of postsynaptic receptors and scaffold proteins. Curr. Opin. Neurobiol. 18, 532–540 (2008).

    Article  CAS  PubMed  Google Scholar 

  82. Durand, C. M. et al. Mutations in the gene encoding the synaptic scaffolding protein SHANK3 are associated with autism spectrum disorders. Nat. Genet. 39, 25–27 (2007).

    Article  CAS  PubMed  Google Scholar 

  83. Durand, C. M. et al. SHANK3 mutations identified in autism lead to modification of dendritic spine morphology via an actin-dependent mechanism. Mol. Psychiatry 17, 71–84 (2012).

    Article  CAS  PubMed  Google Scholar 

  84. Peça, J. et al. Shank3 mutant mice display autistic-like behaviours and striatal dysfunction. Nature 472, 437–442 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Anderson, G. M., Horne, W. C., Chatterjee, D. & Cohen, D. J. The hyperserotonemia of autism. Ann. N. Y. Acad. Sci. 600, 331–342 (1990).

    Article  CAS  PubMed  Google Scholar 

  86. Hranilovic, D. et al. Hyperserotonemia in adults with autistic disorder. J. Autism Dev. Disord. 37, 1934–1940 (2007).

    Article  PubMed  Google Scholar 

  87. Schain, R. J. & Freedman, D. X. Studies on 5-hydroxyindole metabolism in autistic and other mentally retarded children. J. Pediatr. 58, 315–320 (1961).

    Article  CAS  PubMed  Google Scholar 

  88. Chugani, D. C. et al. Developmental changes in brain serotonin synthesis capacity in autistic and nonautistic children. Ann. Neurol. 45, 287–295 (1999).

    Article  CAS  PubMed  Google Scholar 

  89. Murphy, D. G. et al. Cortical serotonin 5-HT2A receptor binding and social communication in adults with Asperger's syndrome: an in vivo SPECT study. Am. J. Psychiatry 163, 934–936 (2006).

    Article  PubMed  Google Scholar 

  90. Fatemi, S. H. The hyperglutamatergic hypothesis of autism. Prog. Neuropsychopharmacol. Biol. Psychiatry 32, 911 (2008).

    Article  CAS  PubMed  Google Scholar 

  91. Fatemi, S. H. et al. Glutamic acid decarboxylase 65 and 67 kDa proteins are reduced in autistic parietal and cerebellar cortices. Biol. Psychiatry 52, 805–810 (2002).

    Article  CAS  PubMed  Google Scholar 

  92. Horder, J. et al. Reduced subcortical glutamate/glutamine in adults with autism spectrum disorders: a [1H]MRS study. Transl. Psychiatry 3, e279 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Herbert, M. R. et al. Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys. Brain 126, 1182–1192 (2003).

    Article  CAS  PubMed  Google Scholar 

  94. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Waiter, G. D. et al. Structural white matter deficits in high-functioning individuals with autistic spectrum disorder: a voxel-based investigation. Neuroimage 24, 455–461 (2005).

    Article  PubMed  Google Scholar 

  96. Nordahl, C. W. et al. Cortical folding abnormalities in autism revealed by surface-based morphometry. J. Neurosci. 27, 11725–11735 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Wallace, G. L. et al. Increased gyrification, but comparable surface area in adolescents with autism spectrum disorders. Brain 136, 1956–1967 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors' work is supported by the Autism Imaging Multicentre Study Consortium, Medical Research Council UK Grant G0400061, and by European Autism Interventions—A Multicentre Study for Developing New Medications (EU-AIMS), which receives support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115300. The latter includes financial contributions from the European Union Seventh Framework Programme (FP7/2007–2013), the European Federation of Pharmaceutical Industries and Associations companies (in kind), and from Autism Speaks. We thank the National Institute for Health Research Biomedical Research Centre for Mental Health, and the Dr Mortimer and Theresa Sackler Foundation for their financial support.

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  1. Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, King's College, PO50, De Crespigny Park, London, SE5 8AF, UK

    Christine Ecker & Declan Murphy

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Ecker, C., Murphy, D. Neuroimaging in autism—from basic science to translational research. Nat Rev Neurol 10, 82–91 (2014). https://doi.org/10.1038/nrneurol.2013.276

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