Advertisement

Journal of Autism and Developmental Disorders

, Volume 44, Issue 7, pp 1788–1792 | Cite as

Why Autism Must be Taken Apart

  • Lynn WaterhouseEmail author
  • Christopher Gillberg
Letter to the Editor

Abstract

Although accumulated evidence has demonstrated that autism is found with many varied brain dysfunctions, researchers have tried to find a single brain dysfunction that would provide neurobiological validity for autism. However, unitary models of autism brain dysfunction have not adequately addressed conflicting evidence, and efforts to find a single unifying brain dysfunction have led the field away from research to explore individual variation and micro-subgroups. Autism must be taken apart in order to find neurobiological treatment targets. Three research changes are needed. The belief that there is a single defining autism spectrum disorder brain dysfunction must be relinquished. The noise caused by the thorny brain-symptom inference problem must be reduced. Researchers must explore individual variation in brain measures within autism.

Keywords

Autism ASD Brain dysfunction DSM-5 Pathophysiology RDoC 

References

  1. Allely, C. S., Gillberg, C., & Wilson, P. (2013). Neurobiological abnormalities in the first few years of life in individuals later diagnosed with autistic spectrum disorder: A review of recent data. Behavioural Neurology. doi: 10.3233/BEN-130350.Google Scholar
  2. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Publishing.Google Scholar
  3. Aoki, Y., Abe, O., Nippashi, Y., & Yamasue, H. (2013). Comparison of white matter integrity between autism spectrum disorder subjects and typically developing individuals: A meta-analysis of diffusion tensor imaging tractography studies. Molecular Autism, 4(1), 25. doi: 10.1.1186/2040-2392-4-25.PubMedCentralPubMedCrossRefGoogle Scholar
  4. Blumberg, S. J., Bramlett, M. D., Kogan, M. D., Schieve, L. A., Jones, J. R., & Lu, M. C. (2013). Changes in prevalence of parent-reported autism spectrum disorder in school-aged US children: 2007 to 2011–2012. National health statistics reports, 65, 1–11. Hyattsville, MD: National Center for Health Statistics.Google Scholar
  5. Campbell, M. G., Kohane, I. S., & Kong, S. W. (2013). Pathway-based outlier method reveals heterogeneous genomic structure of autism in blood transcriptome. BMC Medical Genomics, 6(1), 34. doi: 10.1186/1755-8794-6-34.PubMedCentralPubMedCrossRefGoogle Scholar
  6. Chaste, P., Klei, L., Sanders, S. J., Murtha, M. T., Hus, V., Lowe, J. K., et al. (2013). Adjusting head circumference for covariates in autism: clinical correlates of a highly heritable continuous trait. Biological Psychiatry, 74(8), 576–584. doi: 10.1016/j.biopsych.2013.04.018.PubMedCrossRefGoogle Scholar
  7. Ciesielski, K. T., Harris, R. J., Hart, B. L., & Pabst, H. F. (1997). Cerebellar hypoplasia and frontal lobe cognitive deficits in disorders of early childhood. Neuropsychologia, 35(5), 643–655. doi: 10.1016/S0028-3932(96)00119-4.PubMedCrossRefGoogle Scholar
  8. Ciesielski, K. T., & Knight, J. E. (1994). Cerebellar abnormality in autism: a nonspecific effect of early brain damage? Acta Neurobiologiae Experimentalis, 54, 151–154.PubMedGoogle Scholar
  9. Coleman, M., & Gillberg, C. (2012). The autisms. Oxford: Oxford University Press.Google Scholar
  10. Courchesne, E., Yeung-Courchesne, R., Hesselink, J. R., & Jernigan, T. L. (1988). Hypoplasia of cerebellar vermal lobules VI and VII in autism. New England Journal of Medicine, 318(21), 1349–1354.PubMedCrossRefGoogle Scholar
  11. Cuthbert, B. N., & Insel, T. R. (2013). Toward the future of psychiatric diagnosis: the seven pillars of RDoC. BMC Medicine, 11(1), 126. doi: 10.1186/1741-7015-11-126.PubMedCentralPubMedCrossRefGoogle Scholar
  12. Delorme, R., Ey, E., Toro, R., Leboyer, M., Gillberg, C., & Bourgeron, T. (2013). Progress toward treatments for synaptic defects in autism. Nature Medicine, 19(6), 685–694. doi: 10.1038/nm.3193.PubMedCrossRefGoogle Scholar
  13. Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., et al. (2013). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry,. doi: 10.1038/mp.2013.78.PubMedGoogle Scholar
  14. Dove, D., Warren, Z., McPheeters, M. L., Taylor, J. L., Sathe, N. A., & Van der Veenstra-Weele, J. (2012). Medications for adolescents and young adults with autism spectrum disorders: A systematic review. Pediatrics, 130(4), 717–726. doi: 10.1542/peds.2012-0683.PubMedCrossRefGoogle Scholar
  15. Doyle, C. A., & McDougle, C. J. (2012). Pharmacologic treatments for the behavioral symptoms associated with autism spectrum disorders across the lifespan. Dialogues in Clinical Neuroscience, 14(3), 263–279.PubMedCentralPubMedGoogle Scholar
  16. Fatemi, S. H. (2013). Cerebellum and autism. Cerebellum, 12, 778–779. doi: 10.1007/s12311-013-0484-9.PubMedCrossRefGoogle Scholar
  17. Gillberg, G. (2010). The ESSENCE in child psychiatry: Early symptomatic syndromes eliciting neurodevelopmental clinical examinations. Research in Developmental Disabilities, 31(6), 1543–1555. doi: 10.1016/j.ridd.2010.06.002.PubMedCrossRefGoogle Scholar
  18. Gordon, I., Van der Wyk, B. C., Bennett, R. H., Cordeaux, C., Lucas, M. V., Eilbott, J. A., & Pelphrey, K. A. (2013). Oxytocin enhances brain function in children with autism. Proceedings of the National Academy of Sciences, 201312857. doi: 10.1073/pnas.1312857110.
  19. Horder, J., Lavender, T., Mendez, M. A., O’Gorman, R., Daly, E., Craig, M. C., et al. (2013). Reduced subcortical glutamate/glutamine in adults with autism spectrum disorders: A 1HMRS study. Translational psychiatry, 3(7), e279. doi: 10.1038/tp.2013.53.PubMedCentralPubMedCrossRefGoogle Scholar
  20. Insel, T. (2013). Transforming diagnosis, Director’s blog, April 29 2013, National Institutes of Mental Health. Available from: http://www.nimh.nih.gov/about/director/2013/transforming-diagnosis.shtml.
  21. Jonas, R. K., Montojo, C. A., & Bearden, C. E. (2013). The 22q11.2 deletion syndrome as a window into complex neuropsychiatric disorders over the lifespan. Biological Psychiatry,. doi: 10.1016/j.biopsych.2013.07.019.PubMedGoogle Scholar
  22. Jones, W., & Klin, A. (2013). Attention to eyes is present but in decline in 2–6-month-old infants later diagnosed with autism. Nature,. doi: 10.1038/nature12715.Google Scholar
  23. Kell, A. J., Koldewyn, K., & Kanwisher, N. G. (2013). The functional organization of the ventral visual pathway in adults with autism. Journal of Vision, 13(9), 832. doi: 10.1167/13.9.832.CrossRefGoogle Scholar
  24. Kupfer, D. J., & Regier, D. A. (2011). Neuroscience, clinical evidence, and the future of psychiatric classification in DSM-5. American Journal of Psychiatry, 168(7), 672–674. doi: 10.1176/appi.ajp.2011.11020219.PubMedCrossRefGoogle Scholar
  25. Lai, M.-C., Lombardo, M. V., Chakrabarti, B., & Baron-Cohen, S. (2013). Subgrouping the autism ‘‘spectrum’’: Reflections on DSM-5. PLoS Biology, 11(4), e1001544. doi: 10.1371/journal.pbio.1001544.PubMedCentralPubMedCrossRefGoogle Scholar
  26. Lee, M., Martin-Ruiz, C., Graham, A., Jaros, E., Perry, R., Iversen, P., et al. (2002). Nicotinic receptor abnormalities in the cerebellar cortex in autism. Brain, 125(7), 1483–1495. doi: 10.1093/brain/awf160.PubMedCrossRefGoogle Scholar
  27. Levitt, J. G., O’Neill, J., & Alger, J. R. (2013). Magnetic resonance spectroscopy studies of autistic spectrum disorders. In S. Blüml & A. Panigrahy (Eds.), MR spectroscopy of pediatric brain disorders (pp. 213–227). New York: Springer.CrossRefGoogle Scholar
  28. Licinio, J., & Wong, M.-L. (2013). A novel conceptual framework for psychiatry: Vertically and horizontally integrated approaches to redundancy and pleiotropism that co-exist with a classification of symptom clusters based on DSM-5. Molecular Psychiatry, 18, 846–848. doi: 10.1038/mp.2013.90.PubMedCrossRefGoogle Scholar
  29. Marcotte, L., Aronica, E., Baybis, M., & Crino, P. B. (2012). Cytoarchitectural alterations are widespread in cerebral cortex in tuberous sclerosis complex. Acta Neuropathologica, 123(5), 685–693. doi: 10.1007/s00401-012-0950-3.PubMedCrossRefGoogle Scholar
  30. Moreno-De-Luca, A., Myers, S. M., Challman, T. D., Moreno-De-Luca, D., Evans, D. W., & Ledbetter, D. H. (2013). Developmental brain dysfunction: Revival and expansion of old concepts based on new genetic evidence. The Lancet Neurology, 12(4), 406–414. doi: 10.1016/S1474-4422(13)70011-5.CrossRefGoogle Scholar
  31. Murdoch, J. D., & State, M. W. (2013). Recent developments in the genetics of autism spectrum disorders. Current Opinion in Genetics and Development, 23(3), 310–315. doi: 10.1016/j.gde.2013.02.003.PubMedCrossRefGoogle Scholar
  32. Peters, J. M., Taquet, M., Vega, C., Jeste, S. S., Sanchez Fernandez, I., Tan, J., et al. (2013). Brain functional networks in syndromic and non-syndromic autism: A graph theoretical study of EEG connectivity. BMC Medicine, 11(1), 54. doi: 10.1186/1741-7015-11-54.PubMedCentralPubMedCrossRefGoogle Scholar
  33. Philip, R., Dauvermann, M. R., Whalley, H. C., Baynham, K., Lawrie, S. M., & Stanfield, A. C. (2012). A systematic review and meta-analysis of the fMRI investigation of autism spectrum disorders. Neuroscience and Biobehavioral Reviews, 36(2), 901–942. doi: 10.1016/j.neubiorev.2011.10.008.PubMedCrossRefGoogle Scholar
  34. Pina-Camacho, L., Villero, S., Fraguas, D., Joost Boada, L., Janssen, J., Navas-Sánchez, F. J., et al. (2012). Autism spectrum disorder: Does neuroimaging support the DSM-5 proposal for a symptom dyad? A systematic review of functional magnetic resonance imaging and diffusion tensor imaging studies. Journal of Autism and Developmental Disorders, 42(7), 1326–1341. doi: 10.1007/s10803-011-1360-4.PubMedCrossRefGoogle Scholar
  35. Raznahan, A., Wallace, G. L., Antezana, L., Greenstein, D., Lenroot, R., Thurm, A., et al. (2013). Compared to what? Early brain overgrowth in autism and the perils of population norms. Biological Psychiatry, 74(8), 563–575. doi: 10.1016/j.biopsych.2013.03.022.PubMedCrossRefGoogle Scholar
  36. Redcay, E., Moran, J. M., Mavros, P. L., Tager-Flusberg, H., Gabrieli, J. D., & Whitfield-Gabrieli, S. (2013). Intrinsic functional network organization in high-functioning adolescents with autism spectrum disorder. Frontiers in Human Neuroscience, 7(573), 2013. doi: 10.3389/fnhum.2013.00573.Google Scholar
  37. Roullet, F. I., Lai, J. K., & Foster, J. A. (2013). In utero exposure to valproic acid and autism: A current review of clinical and animal studies. Neurotoxicology and Teratology, 36, 47–56. doi: 10.1016/j.ntt.2013.01.004.PubMedCrossRefGoogle Scholar
  38. Saygin, A. P., Cook, J., & Blakemore, S. J. (2010). Unaffected perceptual thresholds for biological and non-biological form-from-motion perception in autism spectrum conditions. PloS One, 5(10), e13491. doi: 10.1371/journal.pone.0013491.
  39. Schumann, C. M., & Nordahl, C. W. (2011). Bridging the gap between MRI and postmortem research in autism. Brain Research, 1380, 175–186. doi: 10.1016/j.brainres.2010.09.061.PubMedCentralPubMedCrossRefGoogle Scholar
  40. Shen, M. D., Nordahl, C. W., Young, G. S., Wootton-Gorges, S. L., Lee, A., Liston, S. E., et al. (2013). Early brain enlargement and elevated extra-axial fluid in infants who develop autism spectrum disorder. Brain, 136(9), 2825–2835. doi: 10.1093/brain/awt166.PubMedCrossRefGoogle Scholar
  41. Silver, W. G., & Rapin, I. (2012). Neurobiological basis of autism. Pediatric Clinics of North America, 59(1), 45–61. doi: 10.1016/j.pcl.2011.10.010.PubMedCrossRefGoogle Scholar
  42. Skudlarski, P., Schretlen, D. J., Thaker, G. K., Stevens, M. C., Keshavan, M. S., Sweeney, J. A., et al. (2013). Diffusion tensor imaging white matter endophenotypes in patients with schizophrenia or psychotic bipolar disorder and their relatives. American Journal of Psychiatry, 170(8), 886–898. doi: 10.1176/appi.ajp.2013.12111448.PubMedCrossRefGoogle Scholar
  43. Stigler, K. A., McDonald, B. C., Anand, A., Saykin, A. J., & McDougle, C. J. (2011). Structural and functional magnetic resonance imaging of autism spectrum disorders. Brain Research, 1380, 146–161. doi: 10.1016/j.brainres.2010.11.076.PubMedCentralPubMedCrossRefGoogle Scholar
  44. Tsai, P. T., Chu, Y., Greene-Colozzi, E., Sadowski, A. R., Leech, J. M., Steinberg, J., et al. (2012). Autistic-like behaviour and cerebellar dysfunction in Purkinje cell Tsc1 mutant mice. Nature, 488(7413), 647–651. doi: 10.1038/nature11310.PubMedCentralPubMedCrossRefGoogle Scholar
  45. Tye, C., & Bolton, P. (2013). Neural connectivity abnormalities in autism: Insights from the tuberous sclerosis model. BMC Medicine, 11, 55. doi: 10.1186/1741-7015-11-55.PubMedCentralPubMedCrossRefGoogle Scholar
  46. Tyszka, J. M., Kennedy, D. P., Paul, L. K., & Adolphs, R. (2013). Largely typical patterns of resting-state functional connectivity in high-functioning adults with autism. Cerebral Cortex,. doi: 10.1093/cercor/bht040.PubMedGoogle Scholar
  47. Unwin, L. M., Maybery, M. T., Wray, J. A., & Whitehouse, A. J. (2013). A “bottom-up” approach to aetiological research in autism spectrum disorders. Frontiers in human neuroscience, 7(606), 2013. doi: 10.3389/fnhum.2013.00606.Google Scholar
  48. Vasa, R. A., Ranta, M., Huisman, T. A., Pinto, P. S., Tillman, R. M., & Mostofsky, S. H. (2012). Normal rates of neuroradiological findings in children with high functioning autism. Journal of Autism and Developmental Disorders, 42(8), 1662–1670. doi: 10.1007/s10803-001-1407-6.PubMedCentralPubMedCrossRefGoogle Scholar
  49. Washington, S. D., Gordon, E. M., Brar, J., Warburton, S., Sawyer, A. T., Wolfe, A., et al. (2013). Dysmaturation of the default mode network in autism. Human Brain Mapping,. doi: 10.1002/hbm.22252.PubMedCentralPubMedGoogle Scholar
  50. Waterhouse, L. (2008). Autism overflows: Increasing prevalence and proliferating theories. Neuropsychology Review, 18(4), 273–286. doi: 10.1007/s11065-008-9074-x.PubMedCrossRefGoogle Scholar
  51. Waterhouse, L. (2013). Rethinking autism: Variation and complexity. Waltham: Academic Press.Google Scholar
  52. Weisenfeld, N. I., Peters, J. M., Tsai, P. T., Prabhu, S. P., Dies, K. A., Sahin, M., et al. (2013). A magnetic resonance imaging study of cerebellar volume in tuberous sclerosis complex. Pediatric Neurology, 48(2), 105–110. doi: 10.1016/j.pediatrneurol.2012.10.011.PubMedCentralPubMedCrossRefGoogle Scholar
  53. Ziats, M. N., & Rennert, O. M. (2013). The cerebellum in autism: Pathogenic or an anatomical beacon? Cerebellum, 12(5), 776–777. doi: 10.1007/s12311-013-0483-x.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Global Graduate Programs, Child Behavior StudyThe College of New JerseyEwingUSA
  2. 2.Kailua KonaUSA
  3. 3.Gillberg Neuropsychiatry CentreGothenburg UniversityGöteborgSweden

Personalised recommendations