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Quantitative susceptibility mapping shows lower brain iron content in children with autism

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Abstract

Objective

To explore the application of quantitative susceptibility mapping (QSM) of brain iron content in children with autism.

Methods

For the control group, 40 normal children aged 2–3, 3–4, 4–5, and 5–6 years were prospectively selected from June 2018 to December 2018, with equal numbers of males and females in each age group. For the study group, 40 children with autism aged 2–3, 3–4, 4–5, and 5–6 years were prospectively selected from January 2019 to October 2019; once again, there were equal numbers of males and females in each age group. All children received routine head MRI scans and enhanced T2*-weighted angiography (ESWAN) sequence scans, and the ESWAN sequence images were processed by software to obtain magnetic susceptibility maps. The regions of interest (ROIs) of the frontal white matter, frontal gray matter, thalamus, red nucleus, substantia nigra, dentate nucleus, globus pallidus, putamen nucleus, caudate nucleus, pons, and splenium of the corpus callosum were selected, and the magnetic susceptibility values were measured. The differences in magnetic susceptibility between the two groups were compared in children at the same age.

Results

For the children aged 2–3 years, the magnetic susceptibility values in the caudate nucleus, dentate nucleus, and splenium of the corpus callosum in the study group were lower than those in the control group (p < 0.05). For the children aged 3–4, 4–5, and 5–6 years, the magnetic susceptibility values in the frontal white matter, caudate nucleus, red nucleus, substantia nigra, dentate nucleus, and splenium of the corpus callosum in the study group were lower than those in the control group (p < 0.05).

Conclusion

The brain iron content of children with autism is lower than that of normal children.

Trial registration

This study protocol was registered at the Chinese clinical trial registry (registration number: ChiCTR2000029699; http://www.chictr.org.cn/searchprojen.aspx).

Key Points

• In this study, the brain iron content of normal children and children with autism was compared to identify the differences, which provided a new objective basis for the early diagnosis of children with autism.

This study examined the iron content values in various brain regions of normal children aged 2–6 years in this region and established a reference range for iron content in various brain regions of normal children in one geographical area, providing a reliable and objective standard for the diagnosis and treatment of some brain diseases in children.

The results of this study indicate that the brain iron content of preschool children with autism is lower than that of normal preschool children.

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Abbreviations

ASD:

Autism spectrum disorder

CSF:

Cerebrospinal fluid

DSM-IV:

Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition

ESWAN:

Enhanced T2*-weighted angiography

IQ:

Intelligence quotient

MCV:

Mean corpuscular volume

MRI:

Magnetic resonance imaging

QSM:

Quantitative susceptibility mapping

ROI:

Region of interest

SWI:

Susceptibility-weighted imaging

References

  1. Gabrielsen TP, Anderson JS, Stephenson KG et al (2018) Functional MRI connectivity of children with autism and low verbal and cognitive performance. Mol Autism 27(9):67–75

    Article  Google Scholar 

  2. Cox AD, Virues-Ortega J, Julio F, Martin TL (2017) Establishing motion control in children with autism and intellectual disability: applications for anatomical and functional MRI. J Appl Behav Anal 50(1):8–26

  3. Peterson BS, Zargarian A, Peterson JB et al (2019) Hyperperfusion of frontal white and subcortical gray matter in autism spectrum disorder. Biol Psychiatry 85(7):584–595

    Article  Google Scholar 

  4. Dell’Osso L, Lorenzi P, Carpita B (2019) Autistic traits and illness trajectories. Clin Pract Epidemiol Ment Health 30(15):94–98

    Article  Google Scholar 

  5. Nuntanee S, Daranee S (2019) Effect of motorized elephant-assisted therapy program on balance control of children with autism spectrum disorder. Occup Ther Int (11)18.2019:5914807

  6. Dean DC 3rd, Freeman A, Lainhart J et al (2020) The development of the social brain in baby siblings of children with autism. Curr Opin Psychiatry 33(2):110–116

    Article  Google Scholar 

  7. Sen B, Borle NC, Greiner R, Brown MRG (2018) A general prediction model for the detection of ADHD and autism using structural and functional MRI. PLoS One 13(4):e0194856

  8. Andrews DS, Lee JK, Solomon M, Rogers SJ, Amaral DG, Nordahl CW (2019) A diffusion-weighted imaging tract-based spatial statistics study of autism spectrum disorder in preschool-aged children. J Neurodev Disord 11(1):32–40

  9. Tseng PT, Cheng YS, Chen YW et al (2018) Peripheral iron levels in children with autism spectrum disorders vs controls: a systematic review and meta-analysis. Nutr Res 50(8):44–52

    Article  CAS  Google Scholar 

  10. Hrdlicka M, Sanda J, Urbanek T et al (2019) Diffusion tensor imaging and tractography in autistic, dysphasic, and healthy control children. Neuropsychiatr Dis Treat 15(3):2843–2852

    Article  CAS  Google Scholar 

  11. Wei H, Dibb R, Zhou Y et al (2015) Streaking artifact reduction for quantitative susceptibility mapping of sources with large dynamic range. NMR Biomed 28(10):1294–1303

    Article  Google Scholar 

  12. Wei H, Xie L, Dibb R et al (2016) Imaging whole-brain cytoarchitecture of mouse with MRI-based quantitative susceptibility mapping. Neuroimage 15(137):107–115

    Article  Google Scholar 

  13. Uchida Y, Kan H, Sakurai K et al (2019) Voxel-based quantitative susceptibility mapping in Parkinson’s disease with mild cognitive impairment. Mov Disord 34(8):1164–1173

    Article  CAS  Google Scholar 

  14. Fang J, Bao L, Li X, van Zijl PCM, Chen Z (2019) Background field removal for susceptibility mapping of human brain with large susceptibility variations. Magn Reson Med 81(3):2025–2037

  15. Liu C, Li W, Tong KA, Yeom KW, Kuzminski S (2015) Susceptibility-weighted imaging and quantitative susceptibility mapping in the brain. J Magn Reson Imaging 42(1):23–41

  16. Lancione M, Tosetti M, Donatelli G, Cosottini M, Costagli M (2017) The impact of white matter fiber orientation in single-acquisition quantitative susceptibility mapping. NMR Biomed 30(11):e3798

  17. Bener A, Khattab AO, Bhugra D, Hoffmann GF (2017) Iron and vitamin D levels among autism spectrum disorders children. Ann Afr Med 16(4):186–191

  18. Yan F, He N, Lin H Li R (2018) Iron deposition quantification: applications in the brain and liver. J Magn Reson Imaging 48(2):301–317

  19. Pivina L, Semenova Y, Doşa MD, Dauletyarova M, Bjørklund G (2019) Iron deficiency, cognitive functions, and neurobehavioral disorders in children. J Mol Neurosci 68(1):1–10

  20. Perrin JM, Coury DL, Hyman SL, Cole L, Reynolds AM, Clemons T (2012) Complementary and alternative medicine use in a large pediatric autism sample. Pediatrics. 130(2):S77–S82

  21. Gunes S, Ekinci O, Celik T (2017) Iron deficiency parameters in autism spectrum disorder: clinical correlates and associated factors. Ital J Pediatr 43(1):86–92

    Article  Google Scholar 

  22. Azuma M, Hirai T, Nakaura T et al (2019) Combining quantitative susceptibility mapping to the morphometric index in differentiating between progressive supranuclear palsy and Parkinson’s disease. J Neurol Sci 15(406):116443

    Article  Google Scholar 

  23. Cheng Z, Zhang J, He N et al (2019) Radiomic features of the nigrosome-1 region of the substantia nigra: using quantitative susceptibility mapping to assist the diagnosis of idiopathic Parkinson’s disease. Front Aging Neurosci 16(11):167–178

    Article  Google Scholar 

  24. Zhang S, Nguyen TD, Hurtado Rúa SM et al (2019) Quantitative susceptibility mapping of time-dependent susceptibility changes in multiple sclerosis lesions. AJNR Am J Neuroradiol 40(6):987–993

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Kaunzner UW, Kang Y, Zhang S et al (2019) Quantitative susceptibility mapping identifies inflammation in a subset of chronic multiple sclerosis lesions. Brain 142(1):133–145

    Article  Google Scholar 

  26. Arabi M, Saberi Kakhki A, Sohrabi M, Kouhbanani SS, Nooghabi MJ (2019) Is visuomotor training an effective intervention for children with autism spectrum disorders? Neuropsychiatr Dis Treat 8(15):3089–3102

  27. Gong NJ, Dibb R, Bulk M, van der Weerd L, Liu C (2019) Imaging beta amyloid aggregation and iron accumulation in Alzheimer’s disease using quantitative susceptibility mapping MRI. Neuroimage. 1(191):176–185

  28. Du L, Zhao Z, Cui A et al (2018) Increased iron deposition on brain quantitative susceptibility mapping correlates with decreased cognitive function in Alzheimer’s disease. ACS Chem Nerosci 9(7):1849–1857

    Article  CAS  Google Scholar 

  29. Zhang Y, Rauscher A, Kames C, Weber AM (2019) Quantitative analysis of punctate white matter lesions in neonates using quantitative susceptibility mapping and R2* relaxation. AJNR Am J Neuroradiol 40(7):1221–1226

  30. Li SJ, Ren YD, Li J et al (2020) The role of iron in Parkinson’s disease monkeys assessed by susceptibility weighted imaging and inductively coupled plasma mass spectrometry. Life Sci 1(240):117091

    Article  Google Scholar 

  31. Kor D, Birkl C, Ropele S et al (2019) The role of iron and myelin in orientation dependent R2 * of white matter. NMR Biomed 32(7):e4092

    Article  Google Scholar 

  32. Thamburaj K, Soni A, Frasier LD, Tun KN, Weber SR, Dias MS (2019) Susceptibility-weighted imaging of retinal hemorrhages in abusive head trauma. Pediatr Radiol 49(2):210–216

  33. Johnson CP, Wang L, Tóth F et al (2019) Quantitative susceptibility mapping detects neovascularization of the epiphyseal cartilage after ischemic injury in a piglet model of Legg-Calvé-Perthes disease. J Magn Reson Imaging 50(1):106–113

    Article  Google Scholar 

  34. Mattern H, Sciarra A, Lüsebrink F, Acosta-Cabronero J, Speck O (2019) Prospective motion correction improves high-resolution quantitative susceptibility mapping at 7T. Magn Reson Med 81(3):1605–1619

  35. Vallée L (2017) Iron and neurodevelopment. Arch Pediatr 24(5S):5S18–5S22

    Article  Google Scholar 

  36. Anteraper SA, Guell X, Taylor HP, D'Mello A, Whitfield-Gabrieli S, Joshi G (2019) Intrinsic functional connectivity of dentate nuclei in autism spectrum disorder. Brain Connect 9(9):692–702

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Acknowledgments

We would like to thank Bin Qin, Ph.D., for constructive criticism of this manuscript. Shilong Tang and Ling He: experimental design, project management. Ye Xu: experimental design, data analysis. Xianfan Liu: statistical analysis, image analysis. Zhuo Chen and Yu Zhou: data acquisition; data analysis. Lisha Nie: software support.

Funding

The authors state that this work has not received any funding.

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Correspondence to Ling He.

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Guarantor

The scientific guarantor of this publication is Ling He.

Conflict of interest

One of the authors of this manuscript (Lisha Nie) is an employee of GE Healthcare. The remaining authors declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

One of the authors has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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•prospective

• diagnostic or prognostic study

• performed at one institution

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Tang, S., Xu, Y., Liu, X. et al. Quantitative susceptibility mapping shows lower brain iron content in children with autism. Eur Radiol 31, 2073–2083 (2021). https://doi.org/10.1007/s00330-020-07267-w

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  • DOI: https://doi.org/10.1007/s00330-020-07267-w

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