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• Clinical Article •
Early brain overgrowth in children with autism spectrum disorders based on structural magnetic resonance imaging
LIU Yang  ZHU Zhijun  CAO Manrui  LIU Bingguang  GUO Jimin  WAN Guobin 

Cite this article as: Liu Y, Zhu ZJ, Cao MR, et al. Early brain overgrowth in children with autism spectrum disorders based on structural magnetic resonance imaging. Chin J Magn Reson Imaging, 2020, 11(4): 264-269. DOI:10.12015/issn.1674-8034.2020.04.005.


[Abstract] Objective: Autism spectrum disorders (ASD) is a severe and pervasive neurodevelopmental disorder. At present, the etiology of ASD is still an unsolved problem in the world of medicine, and remains poorly understood. In this study, regional features extracted by structural magnetic resonance imaging were used to study brain dysplasia in ASD.Materials and Methods: This study investigates brain developmental abnormalities in patients with ASD by utilizing regional features extracted from structural magnetic resonance imaging (sMRI). Regional features include cerebral cortex thickness, gray matter and white matter region volume, and several subcortical structures extracted from a predefined regions of interest (ROI).Results: Through the analysis of 150 items of cerebral cortex and volume by t test, this study found that there were significant differences in 66 items among children with ASD and the normal control group (P<0.05), and the brains of children with ASD showed significant patterns of excessive growth compared with older, normal-developing children, especially in the parietal, occipital, frontal, temporal and precuneus region.Conclusions: This study confirms the existence of over-development of brain structure in children with ASD. Increased brain size is an important structural feature of early brain development in children with ASD, which will provide a powerful tool for the early diagnosis and assessment of ASD.
[Keywords] autism spectrum disorders；magnetic resonance imaging；excessive growth；cerebral cortex

LIU Yang Department of Radiology, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen 518028, China

ZHU Zhijun* Department of Radiology, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen 518028, China

CAO Manrui Department of Radiology, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen 518028, China

LIU Bingguang Department of Radiology, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen 518028, China

GUO Jimin Department of Radiology, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen 518028, China

WAN Guobin Department of Children Psychology, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen 518028, China

*Correspondence to: Zhu ZJ, E-mail: zhuzhijun72@126.com

Conflicts of interest   None.

Received  2019-12-13
Accepted  2020-03-13
DOI: 10.12015/issn.1674-8034.2020.04.005
Cite this article as: Liu Y, Zhu ZJ, Cao MR, et al. Early brain overgrowth in children with autism spectrum disorders based on structural magnetic resonance imaging. Chin J Magn Reson Imaging, 2020, 11(4): 264-269. DOI:10.12015/issn.1674-8034.2020.04.005.

[1]
Schroeder CS, Smith-Boydston JM, American Psychiatric Association. Diagnostic & statistical manual of mental disorders (DSM-5). Washington: American Psychiatric Publishing, 2013: 1-39.
[2]
Sharma SR, Gonda X, Tarazi FI. Autism spectrum disorder: classification, diagnosis and therapy. Pharmacology & Therapeutics, 2018, 190: 91-104.
[3]
Christensen DL, Braun KVN, Baio J, et al. Prevalence and characteristics of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network, 11 sites, United States, 2012. MMWR Surveillance Summaries, 2018, 65(13): 101-103.
[4]
Baio J. Prevalence of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network, 11 sites, United States, 2010. Morbidity and Mortality Weekly Report, 2014, 63(2): 560-562.
[5]
Geschwind D. Autism: searching for coherence. Biological Psychiatry, 2007, 62(9): 949-950.
[6]
McAlonan GM, Daly E, Kumari V, et al. Brain anatomy and sensorimotor gating in Asperger' s syndrome. Brain, 2002, 125(7): 1594-1606.
[7]
Webb SJ, Jones EJH, Kelly J, et al. The motivation for very early intervention for infants at high risk for autism spectrum disorders. Inter J Speech-Language Pathology, 2014, 16(1): 36-42.
[8]
Lenroot RK, Gogtay N, Greenstein DK, et al. Sexual dimorphism of brain developmental trajectories during childhood and adolescence. Neuroimage, 2007, 36(4): 1065-1073.
[9]
Hazlett HC, Poe M, Gerig G, et al. Magnetic resonance imaging and head circumference study of brain size in autism: birth through age 2 years. Arch Gen Psychiatry, 2005, 62(12): 1366-1376.
[10]
Redcay E, Courchesne E. When is the brain enlarged in autism? A meta-analysis of all brain size reports. Biological Psychiatry, 2005, 58(1): 1-9.
[11]
Schumann CM, Bloss CS, Barnes CC, et al. Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. J Neuroscience, 2010, 30(12): 4419-4427.
[12]
World Health Organization. International statistical classification of diseases and related health problems. World Health Organization, 2004: F80-F89.
[13]
Achenback TM. Manual for the child behavior checklist/4-18 and 1991 profile. Department of Psychiatry, University of Vermont, 1991: 1-288.
[14]
Siegel S. Nonparametric statistics. American Statistician, 1957, 11(3): 13-19.
[15]
Ecker C, Andrews DS, Gudbrandsen CM, et al. Association between the probability of autism spectrum disorder and normative sex-related phenotypic diversity in brain structure. JAMA Psychiatry, 2017, 74(4): 329-338.
[16]
Libero LE, Nordahl CW, Li DD, et al. Persistence of megalencephaly in a subgroup of young boys with autism spectrum disorder. Autism Res, 2016, 9(11): 1169-1182.
[17]
Riddle K, Cascio CJ, Woodward ND. Brain structure in autism: a voxel-based morphometry analysis of the autism brain imaging database exchange(ABIDE). Brain Imaging Behavior, 2017, 11(2): 541-551.
[18]
Turner AH, Greenspan KS, van Erp TGM. Pallidum and lateral ventricle volume enlargement in autism spectrum disorder. Psychiat Res, 2016, 252: 40-45.
[19]
Haar S, Berman S, Behrmann M, et al. Anatomical abnormalities in autism?. Cerebral Cortex, 2016, 26(4): 1440-1452.
[20]
Brun CC, Nicolson R, Leporé N, et al. Mapping brain abnormalities in boys with autism. Hum Brain Mapp, 2009, 30(12): 3887-3900.
[21]
Herbert MR, Ziegler DA, Makris N, et al. Localization of white matter volume increase in autism and developmental language disorder. Ann Neurol, 2004, 55(4): 530-540.
[22]
Bachevalier J. Medial temporal lobe structures and autism: a review of clinical and experimental findings. Neuropsychologia, 1994, 32(6): 627-648.
[23]
Pagnozzi AM, Conti E, Calderoni S, et al. A systematic review of structural MRI biomarkers in autism spectrum disorder: a machine learning perspective. Int J Dev Neurosci, 2018, 71: 68-82.
[24]
Katuwal GJ, Baum SA, Cahill ND, et al. Inter-method discrepancies in brain volume estimation may drive inconsistent findings in autism. Front Neurosci, 2016, 10: 439.
[25]
Li G, Liu M, Sun Q, et al. Early diagnosis of autism disease by multi-channel CNNs. International Workshop on Machine Learning in Medical Imaging. Springer, Cham, 2018: 303-309.
[26]
Hampton T. Early brain imaging in infants may help predict autism. JAMA, 2017, 318(13): 1211-1212.
[27]
Hazlett HC, Gu H, Munsell BC, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature, 2017, 542(7641): 348-351.
[28]
Emerson RW, Adams C, Nishino T, et al. Functional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age. Sci Transl Med, 2017, 9(393): 2882.
[29]
Heinsfeld AS, Franco AR, Craddock RC, et al. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage, 2018, 17: 16-23.
[30]
Sherkatghanad Z, Akhondzadeh M, Salari S, et al. Automated detection of autism spectrum disorder using a convolutional neural network. Front Neurosci, 2019, 13: 1325.

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