Skip to main content
Top
Published in: Molecular Autism 1/2016

Open Access 01-12-2016 | Research

Cortical morphological markers in children with autism: a structural magnetic resonance imaging study of thickness, area, volume, and gyrification

Authors: Daniel Y.-J. Yang, Danielle Beam, Kevin A. Pelphrey, Sebiha Abdullahi, Roger J. Jou

Published in: Molecular Autism | Issue 1/2016

Login to get access

Abstract

Background

Individuals with autism spectrum disorder (ASD) have been characterized by altered cerebral cortical structures; however, the field has yet to identify consistent markers and prior studies have included mostly adolescents and adults. While there are multiple cortical morphological measures, including cortical thickness, surface area, cortical volume, and cortical gyrification, few single studies have examined all these measures. The current study analyzed all of the four measures and focused on pre-adolescent children with ASD.

Methods

We employed the FreeSurfer pipeline to examine surface-based morphometry in 60 high-functioning boys with ASD (mean age = 8.35 years, range = 4–12 years) and 41 gender-, age-, and IQ-matched typically developing (TD) peers (mean age = 8.83 years), while testing for age-by-diagnosis interaction and between-group differences.

Results

During childhood and in specific regions, ASD participants exhibited a lack of normative age-related cortical thinning and volumetric reduction and an abnormal age-related increase in gyrification. Regarding surface area, ASD and TD exhibited statistically comparable age-related development during childhood. Across childhood, ASD relative to TD participants tended to have higher mean levels of gyrification in specific regions. Within ASD, those with higher Social Responsiveness Scale total raw scores tended to have greater age-related increase in gyrification in specific regions during childhood.

Conclusions

ASD is characterized by cortical neuroanatomical abnormalities that are age-, measure-, statistical model-, and region-dependent. The current study is the first to examine the development of all four cortical measures in one of the largest pre-adolescent samples. Strikingly, Neurosynth-based quantitative reverse inference of the surviving clusters suggests that many of the regions identified above are related to social perception, language, self-referential, and action observation networks—those frequently found to be functionally altered in individuals with ASD. The comprehensive, multilevel analyses across a wide range of cortical measures help fill a knowledge gap and present a complex but rich picture of neuroanatomical developmental differences in children with ASD.
Appendix
Available only for authorised users
Literature
1.
go back to reference Blumberg SJ, Bramlett MD, Kogan MD, Schieve LA, Jones JR, Lu MC. Changes in prevalence of parent-reported autism spectrum disorder in school-aged U.S. children: 2007 to 2011-2012. Natl Health Stat Rep. 2013;65:1–11. 1 p following. Blumberg SJ, Bramlett MD, Kogan MD, Schieve LA, Jones JR, Lu MC. Changes in prevalence of parent-reported autism spectrum disorder in school-aged U.S. children: 2007 to 2011-2012. Natl Health Stat Rep. 2013;65:1–11. 1 p following.
2.
go back to reference Abrahams BS, Geschwind DH. Advances in autism genetics: on the threshold of a new neurobiology (vol 9, pg 341, 2008). Nat Rev Genet. 2008;9(6):493.CrossRef Abrahams BS, Geschwind DH. Advances in autism genetics: on the threshold of a new neurobiology (vol 9, pg 341, 2008). Nat Rev Genet. 2008;9(6):493.CrossRef
3.
go back to reference APA. Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Washington, D.C: American Psychiatric Publishing; 2013. APA. Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Washington, D.C: American Psychiatric Publishing; 2013.
12.
go back to reference Schultz RT, Gauthier I, Klin A, Fulbright RK, Anderson AW, Volkmar F, et al. Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and Asperger syndrome. Arch Gen Psychiatry. 2000;57(4):331–40.CrossRefPubMed Schultz RT, Gauthier I, Klin A, Fulbright RK, Anderson AW, Volkmar F, et al. Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and Asperger syndrome. Arch Gen Psychiatry. 2000;57(4):331–40.CrossRefPubMed
14.
15.
go back to reference Courchesne E, Karns CM, Davis HR, Ziccardi R, Carper RA, Tigue ZD, et al. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology. 2001;57(2):245–54.CrossRefPubMed Courchesne E, Karns CM, Davis HR, Ziccardi R, Carper RA, Tigue ZD, et al. Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology. 2001;57(2):245–54.CrossRefPubMed
20.
go back to reference Lin HY, Ni HC, Lai MC, Tseng WYI, Gau SSF. Regional brain volume differences between males with and without autism spectrum disorder are highly age-dependent. Molecular Autism. 2015;6. doi:10.1186/s13229-015-0022-3. Lin HY, Ni HC, Lai MC, Tseng WYI, Gau SSF. Regional brain volume differences between males with and without autism spectrum disorder are highly age-dependent. Molecular Autism. 2015;6. doi:10.​1186/​s13229-015-0022-3.
24.
go back to reference Ecker C, Marquand A, Mourao-Miranda J, Johnston P, Daly EM, Brammer MJ, 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. 2010;30(32):10612–23. doi:10.1523/JNEUROSCI.5413-09.2010.CrossRefPubMed Ecker C, Marquand A, Mourao-Miranda J, Johnston P, Daly EM, Brammer MJ, 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. 2010;30(32):10612–23. doi:10.​1523/​JNEUROSCI.​5413-09.​2010.CrossRefPubMed
25.
go back to reference Hyde KL, Samson F, Evans AC, 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. 2010;31(4):556–66. doi:10.1002/hbm.20887.PubMed Hyde KL, Samson F, Evans AC, 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. 2010;31(4):556–66. doi:10.​1002/​hbm.​20887.PubMed
26.
29.
40.
41.
go back to reference Rakic P. A small step for the cell, a giant leap for mankind: a hypothesis of neocortical expansion during evolution. Trends Neurosci. 1995;18(9):383–8.CrossRefPubMed Rakic P. A small step for the cell, a giant leap for mankind: a hypothesis of neocortical expansion during evolution. Trends Neurosci. 1995;18(9):383–8.CrossRefPubMed
42.
go back to reference Kriegstein A, Noctor S, Martinez-Cerdeno V. Patterns of neural stem and progenitor cell division may underlie evolutionary cortical expansion. Nat Rev Neurosci. 2006;7(11):883–90. doi:10.1038/nrn2008.CrossRefPubMed Kriegstein A, Noctor S, Martinez-Cerdeno V. Patterns of neural stem and progenitor cell division may underlie evolutionary cortical expansion. Nat Rev Neurosci. 2006;7(11):883–90. doi:10.​1038/​nrn2008.CrossRefPubMed
45.
go back to reference Lange N, Travers BG, Bigler ED, Prigge MB, Froehlich AL, Nielsen JA, et al. Longitudinal volumetric brain changes in autism spectrum disorder ages 6–35 years. Autism Res. 2015;8(1):82–93. doi:10.1002/aur.1427.CrossRefPubMed Lange N, Travers BG, Bigler ED, Prigge MB, Froehlich AL, Nielsen JA, et al. Longitudinal volumetric brain changes in autism spectrum disorder ages 6–35 years. Autism Res. 2015;8(1):82–93. doi:10.​1002/​aur.​1427.CrossRefPubMed
47.
go back to reference Schüz A, Miller R. Cortical areas : unity and diversity. London. New York: Taylor & Francis; 2002.CrossRef Schüz A, Miller R. Cortical areas : unity and diversity. London. New York: Taylor & Francis; 2002.CrossRef
53.
go back to reference Richman DP, Stewart RM, Hutchinson JW, Caviness VS. Mechanical model of brain convolutional development. Science. 1975;189(4196):18–21.CrossRefPubMed Richman DP, Stewart RM, Hutchinson JW, Caviness VS. Mechanical model of brain convolutional development. Science. 1975;189(4196):18–21.CrossRefPubMed
57.
go back to reference Elliott CD. Differential Ability Scale—Second Edition (DAS-II). San Antonio, TX: The Psychological Corporation; 2007. Elliott CD. Differential Ability Scale—Second Edition (DAS-II). San Antonio, TX: The Psychological Corporation; 2007.
58.
go back to reference APA. Diagnostic and Statistical Manual of Mental Disorders, 4th ed, text revision (DSM-IV-TR). Washington, DC: American Psychiatric Association; 2000. APA. Diagnostic and Statistical Manual of Mental Disorders, 4th ed, text revision (DSM-IV-TR). Washington, DC: American Psychiatric Association; 2000.
59.
go back to reference 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.CrossRefPubMed 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.CrossRefPubMed
60.
go back to reference Lord C, Rutter M, Goode S, Heemsbergen J, Jordan H, Mawhood L, et al. Autism diagnostic observation schedule: a standardized observation of communicative and social behavior. J Autism Dev Disord. 1989;19(2):185–212.CrossRefPubMed Lord C, Rutter M, Goode S, Heemsbergen J, Jordan H, Mawhood L, et al. Autism diagnostic observation schedule: a standardized observation of communicative and social behavior. J Autism Dev Disord. 1989;19(2):185–212.CrossRefPubMed
61.
go back to reference Constantino JN. The Social Responsiveness Scale. Western Psychological Services: Los Angeles; 2002. Constantino JN. The Social Responsiveness Scale. Western Psychological Services: Los Angeles; 2002.
64.
go back to reference Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14(1):11–22.CrossRefPubMed Fischl B, van der Kouwe A, Destrieux C, Halgren E, Segonne F, Salat DH, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14(1):11–22.CrossRefPubMed
65.
66.
go back to reference Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr. 1994;18(2):192–205.CrossRefPubMed Collins DL, Neelin P, Peters TM, Evans AC. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr. 1994;18(2):192–205.CrossRefPubMed
69.
go back to reference Zilles K, Armstrong E, Schleicher A, Kretschmann HJ. The human pattern of gyrification in the cerebral cortex. Anat Embryol (Berl). 1988;179(2):173–9.CrossRef Zilles K, Armstrong E, Schleicher A, Kretschmann HJ. The human pattern of gyrification in the cerebral cortex. Anat Embryol (Berl). 1988;179(2):173–9.CrossRef
70.
go back to reference Schaer M, Cuadra MB, Schmansky N, Fischl B, Thiran JP, Eliez S. How to measure cortical folding from MR images: a step-by-step tutorial to compute local gyrification index. Jove-J Vis Exp. 2012(59). doi:10.3791/3417. Schaer M, Cuadra MB, Schmansky N, Fischl B, Thiran JP, Eliez S. How to measure cortical folding from MR images: a step-by-step tutorial to compute local gyrification index. Jove-J Vis Exp. 2012(59). doi:10.​3791/​3417.
71.
go back to reference Rosas HD, Liu AK, Hersch S, Glessner M, Ferrante RJ, Salat DH, et al. Regional and progressive thinning of the cortical ribbon in Huntington's disease. Neurology. 2002;58(5):695–701.CrossRefPubMed Rosas HD, Liu AK, Hersch S, Glessner M, Ferrante RJ, Salat DH, et al. Regional and progressive thinning of the cortical ribbon in Huntington's disease. Neurology. 2002;58(5):695–701.CrossRefPubMed
80.
go back to reference Wallace GL, Eisenberg IW, Robustelli B, Dankner N, Kenworthy L, Giedd JN, et al. Longitudinal cortical development during adolescence and young adulthood in autism spectrum disorder: increased cortical thinning but comparable surface area changes. J Am Acad Child Adolesc Psychiatry. 2015;54(6):464–9. doi:10.1016/j.jaac.2015.03.007.CrossRefPubMedPubMedCentral Wallace GL, Eisenberg IW, Robustelli B, Dankner N, Kenworthy L, Giedd JN, et al. Longitudinal cortical development during adolescence and young adulthood in autism spectrum disorder: increased cortical thinning but comparable surface area changes. J Am Acad Child Adolesc Psychiatry. 2015;54(6):464–9. doi:10.​1016/​j.​jaac.​2015.​03.​007.CrossRefPubMedPubMedCentral
82.
go back to reference Lyall AE, Shi F, Geng X, Woolson S, Li G, Wang L et al. dynamic development of regional cortical thickness and surface area in early childhood. Cereb Cortex. 2014. doi:10.1093/cercor/bhu027. Lyall AE, Shi F, Geng X, Woolson S, Li G, Wang L et al. dynamic development of regional cortical thickness and surface area in early childhood. Cereb Cortex. 2014. doi:10.​1093/​cercor/​bhu027.
84.
go back to reference Ecker C, Shahidiani A, Feng Y, Daly E, Murphy C, D'Almeida V, et al. 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. 2014;121(9):1157–70. doi:10.1007/s00702-014-1207-1.CrossRefPubMed Ecker C, Shahidiani A, Feng Y, Daly E, Murphy C, D'Almeida V, et al. 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. 2014;121(9):1157–70. doi:10.​1007/​s00702-014-1207-1.CrossRefPubMed
86.
go back to reference Armstrong E, Schleicher A, Omran H, Curtis M, Zilles K. The ontogeny of human gyrification. Cereb Cortex. 1995;5(1):56–63.CrossRefPubMed Armstrong E, Schleicher A, Omran H, Curtis M, Zilles K. The ontogeny of human gyrification. Cereb Cortex. 1995;5(1):56–63.CrossRefPubMed
88.
go back to reference Tisdall MD, Hess AT, Reuter M, Meintjes EM, Fischl B, van der Kouwe AJ. Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI. Magn Reson Med. 2012;68(2):389–99. doi:10.1002/mrm.23228.CrossRefPubMed Tisdall MD, Hess AT, Reuter M, Meintjes EM, Fischl B, van der Kouwe AJ. Volumetric navigators for prospective motion correction and selective reacquisition in neuroanatomical MRI. Magn Reson Med. 2012;68(2):389–99. doi:10.​1002/​mrm.​23228.CrossRefPubMed
92.
go back to reference Willett JB, Singer JD, Martin NC. The design and analysis of longitudinal studies of development and psychopathology in context: statistical models and methodological recommendations. Dev Psychopathol. 1998;10(2):395–426.CrossRefPubMed Willett JB, Singer JD, Martin NC. The design and analysis of longitudinal studies of development and psychopathology in context: statistical models and methodological recommendations. Dev Psychopathol. 1998;10(2):395–426.CrossRefPubMed
94.
go back to reference Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013;14(5):365–76. doi:10.1038/nrn3475.CrossRefPubMed Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES, et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci. 2013;14(5):365–76. doi:10.​1038/​nrn3475.CrossRefPubMed
Metadata
Title
Cortical morphological markers in children with autism: a structural magnetic resonance imaging study of thickness, area, volume, and gyrification
Authors
Daniel Y.-J. Yang
Danielle Beam
Kevin A. Pelphrey
Sebiha Abdullahi
Roger J. Jou
Publication date
01-12-2016
Publisher
BioMed Central
Published in
Molecular Autism / Issue 1/2016
Electronic ISSN: 2040-2392
DOI
https://doi.org/10.1186/s13229-016-0076-x

Other articles of this Issue 1/2016

Molecular Autism 1/2016 Go to the issue