A Sleeping rs-fMRI Study of Preschool Children with Autism Spectrum Disorders

Author(s): Xiaomeng Li, Longlun Wang, Bin Qin, Yun Zhang, Zhiming Zhou, Yong Qin, Guangcheng Bao, Jie Huang, Jinhua Cai*

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 16 , Issue 7 , 2020


Become EABM
Become Reviewer
Call for Editor

Graphical Abstract:


Abstract:

Objectives: The brain functional network of autism spectrum disorders (ASDs) in the earlier stages of life has been almost unknown due to difficulties in obtaining a resting-state functional magnetic resonance imaging (rs-fMRI). This study aimed to perform rs-MRI under a sedated sleep state and reveal possible alterations in the brain functional network.

Methods: Rs-fMRI was performed in a group of preschool children (aged 2–6 years, 53 with ASD, 63 as controls) under a sedated sleeping state. Based on graph theoretical analysis, global and local topological metrics were calculated to investigate alterations in brain functional networks. Besides, correlation analyses were conducted between the abnormal attribute values and the Childhood Autism Rating Scale (CARS) scores.

Results: The graph theoretical analysis showed that the nodal degree of the right medial frontal gyrus and the nodal efficiency of the right lingual gyrus in the ASD group were higher than those in the control group (P<0.05). There was a statistically significant positive correlation (R=0.318, P<0.05) between the right midfrontal gyrus nodal degree values and CARS scores in the ASD patients.

Conclusion: Alterations of some nodal attributes in the brain network occurred in preschool autistic children which could serve as potential imaging biomarkers for evaluating ASD in earlier stages.

Keywords: Resting-state functional magnetic resonance imaging, graph theory analysis, autism Spectrum Disorder (ASD), preschool children, childhood Autism Rating Scale, Blood-Oxygen-Level-Dependent (BOLD).

[1]
Bralten J, van Hulzen KJ, Martens MB, et al. Autism spectrum disorders and autistic traits share genetics and biology. Mol Psychiatry 2018; 23(5): 1205-12.
[http://dx.doi.org/10.1038/mp.2017.98]
[2]
Autism BR. Lancet 2016; 387(10033): 2082.
[http://dx.doi.org/10.1016/S0140-6736(16)30530-X]
[3]
Crane L, Batty R, Adeyinka H, Goddard L, Henry LA, Hill EL. Autism diagnosis in the United Kingdom: perspectives of autistic adults, parents and professionals. J Autism Dev Disord 2018; 48(11): 3761-72.
[http://dx.doi.org/10.1007/s10803-018-3639-1]
[4]
Vissers ME, Cohen MX, Geurts HM. Brain connectivity and high functioning autism: a promising path of research that needs refined models, methodological convergence, and stronger behavioral links. Neurosci Biobehav Rev 2012; 36(1): 604-25.
[http://dx.doi.org/10.1016/j.neubiorev.2011.09.003]
[5]
Hull JV, Dokovna LB, Jacokes ZJ, Torgerson CM, Irimia A, Van Horn JD. GENDAAR research consortium. corrigendum: resting-state functional connectivity in autism spectrum disorders: a review. Front Psychiatry 2018; 9: 268.
[http://dx.doi.org/10.3389/fpsyt.2018.00268]
[6]
Belmonte MK, Allen G, Beckel-Mitchener A, Boulanger LM, Carper RA, Webb SJ. Autism and abnormal development of brain connectivity. J Neurosci 2004; 24(42): 9228-31.
[http://dx.doi.org/10.1523/JNEUROSCI.3340-04.2004]
[7]
Yamasaki S, Yamasue H, Abe O, et al. Reduced gray matter volume of pars opercularis is associated with impaired social communication in high-functioning autism spectrum disorders. Biol Psychiatry 2010; 68(12): 1141-7.
[http://dx.doi.org/10.1016/j.biopsych.2010.07.012]
[8]
Pierce K. Early functional brain development in autism and the promise of sleep fMRI. Brain Res 2011; 1380: 162-74.
[http://dx.doi.org/10.1016/j.brainres.2010.09.028]
[9]
Jiang L, Stocco A, Losey DM, Abernethy JA, Prat CS, Rao RPN. BrainNet: a multi-person brain-to-brain interface for direct collaboration between brains. Sci Rep 2019; 9(1): 6115.
[http://dx.doi.org/10.1038/s41598-019-41895-7]
[10]
Xia M, Wang J, He Y. BrainNet viewer: a network visualization tool for human brain connectomics. PLoS One 2013; 8(7): e68910.
[http://dx.doi.org/10.1371/journal.pone.0068910]
[11]
Lawrence KE, Hernandez LM, Bookheimer SY, et al. Atypical longitudinal development of functional connectivity in adolescents with autism spectrum disorder. Autism Res 2019; 12(1): 53-65.
[http://dx.doi.org/10.1002/aur.1971]
[12]
Raichle ME. Two views of brain function. Trends Cogn Sci 2010; 14(4): 180-90.
[http://dx.doi.org/10.1016/j.tics.2010.01.008]
[13]
Reeb-Sutherland BC, Fifer WP, Byrd DL, Hammock EA, Levitt P, Fox NA. One-month-old human infants learn about the social world while they sleep. Dev Sci 2011; 14(5): 1134-41.
[http://dx.doi.org/10.1111/j.1467-7687.2011.01062.x]
[14]
Graham AM, Pfeifer JH, Fisher PA, Lin W, Gao W, Fair DA. The potential of infant fMRI research and the study of early life stress as a promising exemplar. Dev Cogn Neurosci 2015; 12: 12-39.
[http://dx.doi.org/10.1016/j.dcn.2014.09.005]
[15]
Itahashi T, Yamada T, Watanabe H, et al. Altered network topologies and hub organization in adults with autism: a resting-state fMRI study. PLoS One 2014; 9(4): e94115.
[http://dx.doi.org/10.1371/journal.pone.0094115]
[16]
Jakab A, Emri M, Spisak T, et al. Autistic traits in neurotypical adults: correlates of graph theoretical functional network topology and white matter anisotropy patterns. PLoS One 2013; 8(4): e60982.
[http://dx.doi.org/10.1371/journal.pone.0060982]
[17]
Courchesne E, Campbell K, Solso S. Brain growth across the life span in autism: age-specific changes in anatomical pathology. Brain Res 2011; 1380: 138-45.
[http://dx.doi.org/10.1016/j.brainres.2010.09.101]
[18]
Courchesne E, Mouton PR, Calhoun ME, et al. Neuron number and size in prefrontal cortex of children with autism. JAMA 2011; 306(18): 2001-10.
[http://dx.doi.org/10.1001/jama.2011.1638]
[19]
Campbell DJ, Chang J, Chawarska K. Early generalized overgrowth in autism spectrum disorder: prevalence rates, gender effects, and clinical outcomes. J Am Acad Child Adolesc Psychiatry 2014; 53(10): 1063-73.e5.
[http://dx.doi.org/10.1016/j.jaac.2014.07.008]
[20]
Hazlett HC, Poe MD, Gerig G, et al. Early brain overgrowth in autism associated with an increase in cortical surface area before age 2 years. Arch Gen Psychiatry 2011; 68(5): 467-76.
[http://dx.doi.org/10.1001/archgenpsychiatry.2011.39]
[21]
Testa-Silva G, Loebel A, Giugliano M, et al. Hyperconnectivity and slow synapses during early development of medial prefrontal cortex in a mouse model for mental retardation and autism. Cereb Cortex 2012; 22(6): 1333-42.
[http://dx.doi.org/10.1093/cercor/bhr224]
[22]
Rubenstein JL, Merzenich MM. Model of autism: increased ratio of excitation/inhibition in key neural systems. Genes Brain Behav 2003; 2(5): 255-67.
[http://dx.doi.org/10.1034/j.1601-183X.2003.00037.x]
[23]
Supekar K, Uddin LQ, Prater K, Amin H, Greicius MD, Menon V. Development of functional and structural connectivity within the default mode network in young children. Neuroimage 2010; 52(1): 290-301.
[http://dx.doi.org/10.1016/j.neuroimage.2010.04.009]
[24]
Dehaene-Lambertz G, Hertz-Pannier L, Dubois J. Nature and nurture in language acquisition: anatomical and functional brain-imaging studies in infants. Trends Neurosci 2006; 29(7): 367-73.
[http://dx.doi.org/10.1016/j.tins.2006.05.011]
[25]
Flagg EJ, Cardy JE, Roberts W, Roberts TP. Language lateralization development in children with autism: insights from the late field magnetoencephalogram. Neurosci Lett 2005; 386(2): 82-7.
[http://dx.doi.org/10.1016/j.neulet.2005.05.037]
[26]
Boddaert N, Belin P, Chabane N, et al. Perception of complex sounds: abnormal pattern of cortical activation in autism. Am J Psychiatry 2003; 160(11): 2057-60.
[http://dx.doi.org/10.1176/appi.ajp.160.11.2057]
[27]
Garavan H, Ross TJ, Stein EA. Right hemispheric dominance of inhibitory control: an event-related functional MRI study. Proc Natl Acad Sci USA 1999; 96(14): 8301-6.
[http://dx.doi.org/10.1073/pnas.96.14.8301]
[28]
Konishi S, Nakajima K, Uchida I, Kikyo H, Kameyama M, Miyashita Y. Common inhibitory mechanism in human inferior prefrontal cortex revealed by event-related functional MRI. Brain 1999; 122(Pt 5): 981-91.
[http://dx.doi.org/10.1093/brain/122.5.981]
[29]
Wrzesińska M, Kapias J, Nowakowska-Domagała K, Kocur J. Visual impairment and traits of autism in children. Psychiatr Pol 2017; 51(2): 349-58.
[http://dx.doi.org/10.12740/PP/OnlineFirst/61352]
[30]
Tewolde FG, Bishop DVM, Manning C. Visual motion prediction and verbal false memory performance in autistic children. Autism Res 2018; 11(3): 509-18.
[http://dx.doi.org/10.1002/aur.1915]
[31]
Seymour RA, Rippon G, Gooding-Williams G, Schoffelen JM, Kessler K. Dysregulated oscillatory connectivity in the visual system in autism spectrum disorder. Brain 2019; 142(10): 3294-305.
[http://dx.doi.org/10.1093/brain/awz214]
[32]
Zhou Y, Yu F, Duong T. Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning. PLoS One 2014; 9(6): e90405.
[http://dx.doi.org/10.1371/journal.pone.0090405]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 16
ISSUE: 7
Year: 2020
Published on: 09 May, 2020
Page: [921 - 927]
Pages: 7
DOI: 10.2174/1573405616666200510003144
Price: $65

Article Metrics

PDF: 34
HTML: 2