Diagnostic accuracy of MRI in the diagnosis of preschool children with autism spectrum disorder: a meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

GOU Xinyun ¹ , LI Juan ¹ , ZHONG Dongling ¹ , LI Yuxi ¹ , ZHAO Jing ¹ , LIU Xiaobo ¹ , XIA Haisha ¹ , AI Shuangchun ² ,  JIN Rongjiang ¹

1. School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine, Chengdu 610075, P. R. China;
2. Department of Rehabilitation, Mianyang Hospital of Traditional Chinese Medicine, Mianyang 621000, P. R. China;

Corresponding author：

JIN Rongjiang, Email: cdzyydxjrj@126.com

Keywords：

Magnetic resonance imaging; Autism spectrum disorder; Early diagnosis; Meta-analysis; Preschool children

DOI：

10.7507/1672-2531.202111011

Video：

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Objective To systematically review the diagnostic accuracy of MRI in preschool children with autism spectrum disorder. Methods Databases including Web of Science, PubMed, The Cochrane Library, EMbase, CBM, CNKI, WanFang Data, and VIP were electronically searched for studies on MRI in diagnosis of preschool children with autism spectrum disorder from inception to January 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias the included studies. Then, meta-analysis was performed using Meta-Disc, RevMan 5.4 and Stata 16.0 software. Results A total of 17 studies were finally included. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnosis odds ratio and area under the curve of SROC were 0.78 (95%CI 0.76 to 0.81), 0.77 (95%CI 0.75 to 0.79), 3.92 (95%CI 2.83 to 5.41), 0.28 (95%CI 0.23 to 0.35), 14.8 (95%CI 9.31 to 23.52) and 0.86, respectively. Subgroup analysis revealed that the source of patients, age, prediction methods and analysis features were potential sources of heterogeneity. Heterogeneity in the subgroup of 1-3 years old was small, and the pooled sensitivity, specificity and area under the curve of SROC were 0.81 (95%CI 0.76 to 0.85), 0.82 (95%CI 0.78 to 0.86) and 0.87, respectively. The pooled sensitivity, specificity and area under the curve of SROC for non-site studies were 0.80 (95%CI 0.75 to 0.84), 0.80 (95%CI 0.76 to 0.85) and 0.86, respectively. Conclusion MRI has a high accuracy in the diagnosis of preschool children with autism spectrum disorder; however, the possibility of misdiagnosis and missed diagnosis should be considered. Due to the limited quantity and quality of the included studies, more high-quality studies are needed to verify the above conclusion.

Citation： GOU Xinyun, LI Juan, ZHONG Dongling, LI Yuxi, ZHAO Jing, LIU Xiaobo, XIA Haisha, AI Shuangchun, JIN Rongjiang. Diagnostic accuracy of MRI in the diagnosis of preschool children with autism spectrum disorder: a meta-analysis. Chinese Journal of Evidence-Based Medicine, 2022, 22(4): 422-429. doi: 10.7507/1672-2531.202111011 Copy

1.	武丽杰. 我国孤独症谱系障碍流行病学现状及趋势. 中国实用儿科杂志, 2013, 28(8): 571-576.
2.	Baxter AJ, Brugha TS, Erskine HE, et al. The epidemiology and global burden of autism spectrum disorders. Psychol Med, 2015, 45(3): 601-613.
3.	黎文倩, 刘晓, 代英, 等. 孤独症谱系障碍儿童的诊断年龄及其影响因素. 中国当代儿科杂志, 2018, 20(10): 799-803.
4.	刘贤, 林穗方, 陈文雄, 等. 中国儿童孤独症谱系障碍患病率Meta分析. 中国儿童保健杂志, 2018, 26(4): 402-406, 429.
5.	李爱文, 曾婷, 邓梁琼, 等. 儿童孤独症谱系障碍178例临床分析. 右江医学, 2020, 48(8): 587-591.
6.	Sheldrick RC, Maye MP, Carter AS. Age at first identification of autism spectrum disorder: an analysis of two US surveys. J Am Acad Child Adolesc Psychiatry, 2017, 56(4): 313-320.
7.	Zablotsky B, Colpe LJ, Pringle BA, et al. Age of parental concern, diagnosis, and service initiation among children with autism spectrum disorder. Am J Intellect Dev Disabil, 2017, 122(1): 49-61.
8.	Hosozawa M, Sacker A, Mandy W, et al. Determinants of an autism spectrum disorder diagnosis in childhood and adolescence: evidence from the UK Millennium Cohort Study. Autism, 2020, 24(6): 1557-1565.
9.	MacDuffie KE, Estes AM, Peay HL, et al. The ethics of predicting autism spectrum disorder in infancy. J Am Acad Child Adolesc Psychiatry, 2021, 60(8): 942-945.
10.	Pierce K, Courchesne E, Bacon E. To screen or not to screen universally for autism is not the question: why the Task Force Got It Wrong. J Pediatr, 2016, 176: 182-194.
11.	Anderson CM, Smith T, Wilczynski SM. Advances in school-based interventions for students with autism spectrum disorder: introduction to the special issue. Behav Modif, 2018, 42(1): 3-8.
12.	Kim SH, Bal VH, Benrey N, et al. Variability in autism symptom trajectories using repeated observations from 14 to 36 months of age. J Am Acad Child Adolesc Psychiatry, 2018, 57(11): 837-848.
13.	Shen MD, Nordahl CW, Young GS, et al. Early brain enlargement and elevated extra-axial fluid in infants who develop autism spectrum disorder. Brain, 2013, 136(9): 2825-2835.
14.	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.
15.	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): eaag2882.
16.	Shen MD, Kim SH, McKinstry RC, et al. Increased extra-axial cerebrospinal fluid in high-risk infants who later develop autism. Biol Psychiatry, 2017, 82(3): 186-193.
17.	Jin Y, Wee CY, Shi F, et al. Identification of infants at high-risk for autism spectrum disorder using multiparameter multiscale white matter connectivity networks. Hum Brain Mapp, 2015, 36(12): 4880-4896.
18.	Xiao X, Fang H, Wu J, et al. Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder. Autism Res, 2017, 10(4): 620-630.
19.	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.
20.	Akhavan AM, Sharifi A, Pedram MM. Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J Digit Imaging, 2018, 31(6): 895-903.
21.	Aghdam MA, Sharifi A, Pedram MM. Diagnosis of autism spectrum disorders in young children based on resting-state functional magnetic resonance imaging data using convolutional neural networks. J Digit Imaging, 2019, 32(6): 899-918.
22.	Haweel R, Shalaby A, Mahmoud A, et al. A robust DWT-CNN-based CAD system for early diagnosis of autism using task-based fMRI. Med Phys, 2021, 48(5): 2315-2326.
23.	Dinstein I, Pierce K, Eyler L, et al. Disrupted neural synchronization in toddlers with autism. Neuron, 2011, 70(6): 1218-1225.
24.	Lombardo MV, Pierce K, Eyler LT, et al. Different functional neural substrates for good and poor language outcome in autism. Neuron, 2015, 86(2): 567-577.
25.	张鹏华. 临床参数和扩散张量成像在孤独症谱系障碍中的研究. 郑州: 郑州大学, 2020.
26.	Shen MD, Nordahl CW, Li DD, et al. Extra-axial cerebrospinal fluid in high-risk and normal-risk children with autism aged 2-4 years: a case-control study. Lancet Psychiatry, 2018, 5(11): 895-904.
27.	Akshoomoff N, Lord C, Lincoln AJ, et al. Outcome classification of preschool children with autism spectrum disorders using MRI brain measures. J Am Acad Child Adolesc Psychiatry, 2004, 43(3): 349-357.
28.	Kim JI, Bang S, Yang JJ, et al. Classification of preschoolers with low-functioning autism spectrum disorder using multimodal MRI data. J Autism Dev Disord, 2022 Jan 4.
29.	Guo X, Wang J, Wang X, et al. Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms. Eur Radiol, 2022, 32(2): 761-770.
30.	Gao K, Sun Y, Niu S, et al. Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging. Autism Res, 2021, 14(12): 2512-2523.
31.	Power JD, Schlaggar BL. Neural plasticity across the lifespan. Wiley Interdiscip Rev Dev Biol, 2017, 6(1): 10.1002/wdev. 216.
32.	Werker JF, Hensch TK. Critical periods in speech perception: new directions. Annu Rev Psychol, 2015, 66: 173-196.
33.	Ismail FY, Fatemi A, Johnston MV. Cerebral plasticity: windows of opportunity in the developing brain. Eur J Paediatr Neurol, 2017, 21(1): 23-48.
34.	Pickles A, Anderson DK, Lord C. Heterogeneity and plasticity in the development of language: a 17-year follow-up of children referred early for possible autism. J Child Psychol Psychiatry, 2014, 55(12): 1354-1362.
35.	胡雁. 用于诊断性试验的指标. 中国护理管理, 2019, 19(12): 1832.
36.	Song DY, Topriceanu CC, Ilie-Ablachim DC, et al. Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis. Neuroradiology, 2021, 63(12): 2057-2072.
37.	Li X, Gu Y, Dvornek N, et al. Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results. Med Image Anal, 2020, 65: 101765.
38.	Xu M, Calhoun V, Jiang R, et al. Brain imaging-based machine learning in autism spectrum disorder: methods and applications. J Neurosci Methods, 2021, 361: 109271.
39.	Moon SJ, Hwang J, Kana R, et al. Accuracy of machine learning algorithms for the diagnosis of autism spectrum disorder: systematic review and meta-analysis of brain magnetic resonance imaging studies. JMIR Ment Health, 2019, 6(12): e14108.
40.	Dubois J, Alison M, Counsell SJ, et al. MRI of the neonatal brain: a review of methodological challenges and neuroscientific advances. J Magn Reson Imaging, 2021, 53(5): 1318-1343.
41.	Wang L, Li G, Adeli E, et al. Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism. Hum Brain Mapp, 2018, 39(6): 2609-2623.
42.	Walsh MJM, Wallace GL, Gallegos SM, et al. Brain-based sex differences in autism spectrum disorder across the lifespan: a systematic review of structural MRI, fMRI, and DTI findings. Neuroimage Clin, 2021, 31: 102719.
43.	Nunes AS, Vakorin VA, Kozhemiako N, et al. Atypical age-related changes in cortical thickness in autism spectrum disorder. Sci Rep, 2020, 10(1): 11067.
44.	Li L, Zuo Y, Chen Y. Relationship between local gyrification index and age, intelligence quotient, symptom severity with autism spectrum disorder: a large-scale MRI study. J Clin Neurosci, 2021, 91: 193-199.
45.	Constantino JN, Charman T. Diagnosis of autism spectrum disorder: reconciling the syndrome, its diverse origins, and variation in expression. Lancet Neurol, 2016, 15(3): 279-291.
46.	Feczko E, Balba NM, Miranda-Dominguez O, et al. Subtyping cognitive profiles in autism spectrum disorder using a functional random forest algorithm. Neuroimage, 2018, 172: 674-688.
47.	Girault JB, Piven J. The neurodevelopment of autism from infancy through toddlerhood. Neuroimaging Clin N Am, 2020, 30(1): 97-114.
48.	张俊, 徐志伟, 李克. 诊断性试验Meta分析的效应指标评价. 中国循证医学杂志, 2013, 13(7): 890-5.

1. 武丽杰. 我国孤独症谱系障碍流行病学现状及趋势. 中国实用儿科杂志, 2013, 28(8): 571-576.
2. Baxter AJ, Brugha TS, Erskine HE, et al. The epidemiology and global burden of autism spectrum disorders. Psychol Med, 2015, 45(3): 601-613.
3. 黎文倩, 刘晓, 代英, 等. 孤独症谱系障碍儿童的诊断年龄及其影响因素. 中国当代儿科杂志, 2018, 20(10): 799-803.
4. 刘贤, 林穗方, 陈文雄, 等. 中国儿童孤独症谱系障碍患病率Meta分析. 中国儿童保健杂志, 2018, 26(4): 402-406, 429.
5. 李爱文, 曾婷, 邓梁琼, 等. 儿童孤独症谱系障碍178例临床分析. 右江医学, 2020, 48(8): 587-591.
6. Sheldrick RC, Maye MP, Carter AS. Age at first identification of autism spectrum disorder: an analysis of two US surveys. J Am Acad Child Adolesc Psychiatry, 2017, 56(4): 313-320.
7. Zablotsky B, Colpe LJ, Pringle BA, et al. Age of parental concern, diagnosis, and service initiation among children with autism spectrum disorder. Am J Intellect Dev Disabil, 2017, 122(1): 49-61.
8. Hosozawa M, Sacker A, Mandy W, et al. Determinants of an autism spectrum disorder diagnosis in childhood and adolescence: evidence from the UK Millennium Cohort Study. Autism, 2020, 24(6): 1557-1565.
9. MacDuffie KE, Estes AM, Peay HL, et al. The ethics of predicting autism spectrum disorder in infancy. J Am Acad Child Adolesc Psychiatry, 2021, 60(8): 942-945.
10. Pierce K, Courchesne E, Bacon E. To screen or not to screen universally for autism is not the question: why the Task Force Got It Wrong. J Pediatr, 2016, 176: 182-194.
11. Anderson CM, Smith T, Wilczynski SM. Advances in school-based interventions for students with autism spectrum disorder: introduction to the special issue. Behav Modif, 2018, 42(1): 3-8.
12. Kim SH, Bal VH, Benrey N, et al. Variability in autism symptom trajectories using repeated observations from 14 to 36 months of age. J Am Acad Child Adolesc Psychiatry, 2018, 57(11): 837-848.
13. Shen MD, Nordahl CW, Young GS, et al. Early brain enlargement and elevated extra-axial fluid in infants who develop autism spectrum disorder. Brain, 2013, 136(9): 2825-2835.
14. 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.
15. 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): eaag2882.
16. Shen MD, Kim SH, McKinstry RC, et al. Increased extra-axial cerebrospinal fluid in high-risk infants who later develop autism. Biol Psychiatry, 2017, 82(3): 186-193.
17. Jin Y, Wee CY, Shi F, et al. Identification of infants at high-risk for autism spectrum disorder using multiparameter multiscale white matter connectivity networks. Hum Brain Mapp, 2015, 36(12): 4880-4896.
18. Xiao X, Fang H, Wu J, et al. Diagnostic model generated by MRI-derived brain features in toddlers with autism spectrum disorder. Autism Res, 2017, 10(4): 620-630.
19. 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.
20. Akhavan AM, Sharifi A, Pedram MM. Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J Digit Imaging, 2018, 31(6): 895-903.
21. Aghdam MA, Sharifi A, Pedram MM. Diagnosis of autism spectrum disorders in young children based on resting-state functional magnetic resonance imaging data using convolutional neural networks. J Digit Imaging, 2019, 32(6): 899-918.
22. Haweel R, Shalaby A, Mahmoud A, et al. A robust DWT-CNN-based CAD system for early diagnosis of autism using task-based fMRI. Med Phys, 2021, 48(5): 2315-2326.
23. Dinstein I, Pierce K, Eyler L, et al. Disrupted neural synchronization in toddlers with autism. Neuron, 2011, 70(6): 1218-1225.
24. Lombardo MV, Pierce K, Eyler LT, et al. Different functional neural substrates for good and poor language outcome in autism. Neuron, 2015, 86(2): 567-577.
25. 张鹏华. 临床参数和扩散张量成像在孤独症谱系障碍中的研究. 郑州: 郑州大学, 2020.
26. Shen MD, Nordahl CW, Li DD, et al. Extra-axial cerebrospinal fluid in high-risk and normal-risk children with autism aged 2-4 years: a case-control study. Lancet Psychiatry, 2018, 5(11): 895-904.
27. Akshoomoff N, Lord C, Lincoln AJ, et al. Outcome classification of preschool children with autism spectrum disorders using MRI brain measures. J Am Acad Child Adolesc Psychiatry, 2004, 43(3): 349-357.
28. Kim JI, Bang S, Yang JJ, et al. Classification of preschoolers with low-functioning autism spectrum disorder using multimodal MRI data. J Autism Dev Disord, 2022 Jan 4.
29. Guo X, Wang J, Wang X, et al. Diagnosing autism spectrum disorder in children using conventional MRI and apparent diffusion coefficient based deep learning algorithms. Eur Radiol, 2022, 32(2): 761-770.
30. Gao K, Sun Y, Niu S, et al. Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging. Autism Res, 2021, 14(12): 2512-2523.
31. Power JD, Schlaggar BL. Neural plasticity across the lifespan. Wiley Interdiscip Rev Dev Biol, 2017, 6(1): 10.1002/wdev. 216.
32. Werker JF, Hensch TK. Critical periods in speech perception: new directions. Annu Rev Psychol, 2015, 66: 173-196.
33. Ismail FY, Fatemi A, Johnston MV. Cerebral plasticity: windows of opportunity in the developing brain. Eur J Paediatr Neurol, 2017, 21(1): 23-48.
34. Pickles A, Anderson DK, Lord C. Heterogeneity and plasticity in the development of language: a 17-year follow-up of children referred early for possible autism. J Child Psychol Psychiatry, 2014, 55(12): 1354-1362.
35. 胡雁. 用于诊断性试验的指标. 中国护理管理, 2019, 19(12): 1832.
36. Song DY, Topriceanu CC, Ilie-Ablachim DC, et al. Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis. Neuroradiology, 2021, 63(12): 2057-2072.
37. Li X, Gu Y, Dvornek N, et al. Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results. Med Image Anal, 2020, 65: 101765.
38. Xu M, Calhoun V, Jiang R, et al. Brain imaging-based machine learning in autism spectrum disorder: methods and applications. J Neurosci Methods, 2021, 361: 109271.
39. Moon SJ, Hwang J, Kana R, et al. Accuracy of machine learning algorithms for the diagnosis of autism spectrum disorder: systematic review and meta-analysis of brain magnetic resonance imaging studies. JMIR Ment Health, 2019, 6(12): e14108.
40. Dubois J, Alison M, Counsell SJ, et al. MRI of the neonatal brain: a review of methodological challenges and neuroscientific advances. J Magn Reson Imaging, 2021, 53(5): 1318-1343.
41. Wang L, Li G, Adeli E, et al. Anatomy-guided joint tissue segmentation and topological correction for 6-month infant brain MRI with risk of autism. Hum Brain Mapp, 2018, 39(6): 2609-2623.
42. Walsh MJM, Wallace GL, Gallegos SM, et al. Brain-based sex differences in autism spectrum disorder across the lifespan: a systematic review of structural MRI, fMRI, and DTI findings. Neuroimage Clin, 2021, 31: 102719.
43. Nunes AS, Vakorin VA, Kozhemiako N, et al. Atypical age-related changes in cortical thickness in autism spectrum disorder. Sci Rep, 2020, 10(1): 11067.
44. Li L, Zuo Y, Chen Y. Relationship between local gyrification index and age, intelligence quotient, symptom severity with autism spectrum disorder: a large-scale MRI study. J Clin Neurosci, 2021, 91: 193-199.
45. Constantino JN, Charman T. Diagnosis of autism spectrum disorder: reconciling the syndrome, its diverse origins, and variation in expression. Lancet Neurol, 2016, 15(3): 279-291.
46. Feczko E, Balba NM, Miranda-Dominguez O, et al. Subtyping cognitive profiles in autism spectrum disorder using a functional random forest algorithm. Neuroimage, 2018, 172: 674-688.
47. Girault JB, Piven J. The neurodevelopment of autism from infancy through toddlerhood. Neuroimaging Clin N Am, 2020, 30(1): 97-114.
48. 张俊, 徐志伟, 李克. 诊断性试验Meta分析的效应指标评价. 中国循证医学杂志, 2013, 13(7): 890-5.

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