Skip to main content
SpringerLink
Log in

Recent Trends in Automatic Autism Spectrum Disorder Detection Using Brain MRI

  • Conference paper
  • First Online:
Proceedings of Third International Conference on Sustainable Expert Systems

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 587))

  • 552 Accesses

Abstract

Autism spectrum disorder (ASD) is a multifaceted developmental and psychological disability that consists of importunate challenges regarding non-verbal and speech communication, repetitive or restricted behavior and social interaction. Early detection of ASD can help to take proper curative and preventive measures to improve the health and lifestyle of the patients. Various machine learning-based and deep learning-based approaches have been presented in the past for the automatic detection of ASD. This paper presents the survey of a recent machine and deep learning approaches for ASD detection using brain Magnetic Resonance Images (MRI). It focuses on the methodology, feature extraction techniques, classifiers, database, and evaluation metrics of the various ASD detection approaches. The performance of several machine learning systems such as K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Classification Tree (CT) is validated for ASD detection on ABIDE-I dataset. Finally, it provides the challenges, constraints and gives the future direction to enhance the performance of the various machine and deep learning-based ASD detection approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 279.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Autism Spectrum Disorder. https://www.cdc.gov/ncbddd/autism/index.html. Accessed 10 Feb 2021

  2. Lord C, Cook EH, Leventhal BL, Amaral DG (2000) Autism spectrum disorders. Neuron 28(2):355–363

    Google Scholar 

  3. Faras H, Al Ateeqi N, Tidmarsh L (2010)Autism spectrum disorders. Annals Saudi Med 30(4):295–300

    Google Scholar 

  4. Lauritsen MB (2013) Autism spectrum disorders. Eur Child Adolesc Psychiatr 22(1):37–42

    Article  Google Scholar 

  5. Shaw KA, Maenner MJ, Baio J (2020) Early identification of autism spectrum disorder among children aged 4 years—Early Autism and Developmental Disabilities Monitoring Network, six sites, United States, 2016. MMWR Surveill Summ 69(3)

    Google Scholar 

  6. Wieckowski AT, White SW (2017) Eye-gaze analysis of facial emotion recognition and expression in adolescents with ASD. J Clin Child Adolesc Psychol 46(1):110–124

    Google Scholar 

  7. Leo M, Carcagnì P, Distante C, Spagnolo P, Mazzeo PL, Rosato AC, Petrocchi S, Pellegrino C, Levante A, De Lumè F, Lecciso F (2018) Computational assessment of facial expression production in ASD children. Sensors 18(11):3993

    Google Scholar 

  8. Loth E, Garrido L, Ahmad J, Watson E, Duff A, Duchaine B (2018) Facial expression recognition as a candidate marker for autism spectrum disorder: how frequent and severe are deficits? Mol Autism 9(1):1–11

    Article  Google Scholar 

  9. Rad NM, Kia SM, Zarbo C, van Laarhoven T, Jurman G, Venuti P, Marchiori E, Furlanello C (2018) Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorders. Signal Process 144:180–191

    Google Scholar 

  10. Rodrigues JL, Gonçalves N, Costa S, Soares F (2013) Stereotyped movement recognition in children with ASD. Sens Actuat A 202:162–169

    Article  Google Scholar 

  11. Sonawane A, Inamdar MU, Bhangale KB (2017) Sound based human emotion recognition using MFCC & multiple SVM. In: 2017 ınternational conference on ınformation, communication, ınstrumentation and control (ICICIC). IEEE, pp 1–4

    Google Scholar 

  12. Bonneh YS, Levanon Y, Dean-Pardo O, Lossos L, Adini Y (2011) Abnormal speech spectrum and increased pitch variability in young autistic children. Front Hum Neurosci 4:237

    Article  Google Scholar 

  13. Bhangale KB, Mohanaprasad K (2021) A review on speech processing using machine learning paradigm. Int J Speech Technol 1–22

    Google Scholar 

  14. Bosl WJ, Tager-Flusberg H, Nelson CA (2018) EEG analytics for early detection of autism spectrum disorder: a data-driven approach. Sci Rep 8(1):1–20

    Article  Google Scholar 

  15. Jones CRG, Pickles A, Falcaro M, Marsden AJS, Happé F, Scott SK, Sauter D et al (2011) A multimodal approach to emotion recognition ability in autism spectrum disorders. J Child Psychol Psychiatr 52(3):275–285

    Google Scholar 

  16. Peacock G, Amendah D, Ouyang L, Grosse SD (2012) Autism spectrum disorders and health care expenditures: the effects of co-occurring conditions. J Dev Behav Pediatr 33(1):2–8

    Article  Google Scholar 

  17. Cody H, Pelphrey K, Piven J (2002) Structural and functional magnetic resonance imaging of autism. Int J Dev Neurosci 20(3–5):421–438

    Article  Google Scholar 

  18. Rakić M, Cabezas M, Kushibar K, Oliver A, Lladó X (2020) Improving the detection of autism spectrum disorder by combining structural and functional MRI information. NeuroImage: Clin 25:102–181

    Google Scholar 

  19. Conti E, Retico A, Palumbo L, Spera G, Bosco P, Biagi L, Fiori S et al (2020) Autism spectrum disorder and childhood apraxia of speech: early language-related hallmarks across structural MRI study. J Personal Med 10(4):275

    Google Scholar 

  20. de Belen RA, Bednarz T, Sowmya A, Del Favero D (2020) Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019. Transl Psychiatr 10(1):1–20

    Google Scholar 

  21. Chen R, Jiao Y, Herskovits EH (2011) Structural MRI in autism spectrum disorder. Pediatr Res 69(8):63–68

    Article  Google Scholar 

  22. Eliez S, Reiss AL (2000) MRI neuroimaging of childhood psychiatric disorders: a selective review. J Child Psychol Psychiatry 41:679–694

    Article  Google Scholar 

  23. Sparks BF, Friedman SD, Shaw DW, Aylward EH, Echelard D, Artru AA, Maravilla KR, Giedd JN, Munson J, Dawson G, Dager SR (2002) Brain structural abnormalities in young children with autism spectrum disorder. Neurology 59:184–192

    Article  Google Scholar 

  24. Courchesne E, Karns CM, Davis HR, Ziccardi R, Carper RA, Tigue ZD, Chisum HJ, Moses P, Pierce K, Lord C, Lincoln AJ, Pizzo S, Schreibman L, Haas RH, Akshoomoff NA, Courchesne RY (2001) Unusual brain growth patterns in early life in patients with autistic disorder: an MRI study. Neurology 57:245–254

    Article  Google Scholar 

  25. Boddaert N, Chabane N, Gervais H, Good CD, Bourgeois M, Plumet M-H, Barthélémy C, Mouren M-C, Artiges E, Samson Y, Brunelle F, Frackowiak RS, Zilbovicius M (2004) Superior temporal sulcus anatomical abnormalities in childhood autism: a voxel-based morphometry MRI study. Neuroimage 23:364–369

    Article  Google Scholar 

  26. McAlonan GM, Daly E, Kumari V, Critchley HD, van Amelsvoort T, Suckling J, Simmons A, Sigmundsson T, Greenwood K, Russell A, Schmitz N, Happe F, Howlin P, Murphy DG (2002) Brain anatomy and sensorimotor gating in Asperger’s syndrome. Brain 125:1594–1606

    Article  Google Scholar 

  27. Schumann CM, Bloss CS, Barnes CC, Wideman GM, Carper RA, Akshoomoff N, Pierce K, Hagler D, Schork N, Lord C, Courchesne E (2010) Longitudinal magnetic resonance imaging study of cortical development through early childhood in autism. J Neurosci 30:4419–4427

    Article  Google Scholar 

  28. Jiao Y, Chen R, Ke X, Chu K, Lu Z, Herskovits EH (2010) Predictive models of autism spectrum disorder based on brain regional cortical thickness. Neuroimage 50:589–599

    Article  Google Scholar 

  29. Müller R-A, Shih P, Keehn B, Deyoe JR, Leyden KM, Shukla DK (2011) Underconnected, but how? A survey of functional connectivity MRI studies in autism spectrum disorders. Cereb Cortex 21(10):2233–2243

    Article  Google Scholar 

  30. Ismail MM, Keynton RS, Mostapha MM, ElTanboly AH, Casanova MF, Gimel’Farb GL, El-Baz A (2016) Studying autism spectrum disorder with structural and diffusion magnetic resonance imaging: a survey. Front Hum Neurosci 10: 211

    Google Scholar 

  31. Thabtah F, Peebles D (2020) A new machine learning model based on induction of rules for autism detection. Health Inform J 26(1):264–286

    Article  Google Scholar 

  32. Katuwal GJ, Baum SA, Michael AM Early brain imaging can predict autism: application of machine learning to a clinical imaging archive. 471169

    Google Scholar 

  33. Sen B, Borle NC, Greiner R, Brown MR (2018) A general prediction model for the detection of ADHD and Autism using structural and functional MRI. PloS one 13(4):e0194856

    Google Scholar 

  34. Thomas RM, Gallo S, Cerliani L, Zhutovsky P, El-Gazzar A, Van Wingen G (2020) Classifying autism spectrum disorder using the temporal statistics of resting-state functional MRI data with 3D convolutional neural networks. Front Psychiatr 11:440

    Google Scholar 

  35. Dekhil O, Ali M, El-Nakieb Y, Shalaby A, Soliman A, Switala A, Mahmoud A et al (2019) A personalized autism diagnosis CAD system using a fusion of structural MRI and resting-state functional MRI data. Front Psychiatr 10:392

    Google Scholar 

  36. Pereira AM, Campos BM, Coan AC, Pegoraro LF, De Rezende TJ, Obeso I, Dalgalarrondo P, Da Costa JC, Dreher JC, Cendes F (2018) Differences in cortical structure and functional MRI connectivity in high functioning autism. Front Neurol 9:539

    Google Scholar 

  37. Manciu FS, Lee KH, Durrer WG, Bennet KE (2013) Detection and monitoring of neurotransmitters—A spectroscopic analysis. Neuromodulation: Technol Neural Interface 16(3):192–199

    Google Scholar 

  38. Badgaiyan RD (2014) Imaging dopamine neurotransmission in live human brain. Prog Brain Res 211:165–182

    Article  Google Scholar 

  39. Hugdahl K, Beyer MonaK, Brix M, Ersland L (2012) Autism spectrum disorder, functional MRI and MR spectroscopy: possibilities and challenges. Microb Ecol Health Dis 23(1):18960

    Google Scholar 

  40. Liu X, Wu Q, Zhao W, Luo X (2017) Technology-facilitated diagnosis and treatment of individuals with autism spectrum disorder: an engineering perspective. Appl Sci 7(10):1051

    Article  Google Scholar 

  41. Peng X, Lin P, Zhang T, Wang J (2013) Extreme learning machine-based classification of ADHD using brain structural MRI data. PLoS ONE 8(11):e79476

    Article  Google Scholar 

  42. Cheng W, Ji X, Zhang J, Feng J (2012) Individual classification of ADHD patients by integrating multiscale neuroimaging markers and advanced pattern recognition techniques. Front Syst Neurosci 6:58

    Article  Google Scholar 

  43. Calderoni S, Retico A, Biagi L, Tancredi R, Muratori F, Tosetti M (2012) Female children with autism spectrum disorder: an insight from mass-univariate and pattern classification analyses. Neuroimage 59(2):1013–1022

    Article  Google Scholar 

  44. Ecker C, Rocha-Rego V, Johnston P, Mourao-Miranda J, Marquand A, Daly EM, Brammer MJ, Murphy C, Murphy DG (2010) MRC AIMS Consortium. Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. Neuroimage 49(1):44–56

    Google Scholar 

  45. Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F (2018) Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clin 17:16–23

    Google Scholar 

  46. Kong Y, Gao J, Yunpei X, Pan Y, Wang J, Liu J (2019) Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier. Neurocomputing 324:63–68

    Article  Google Scholar 

  47. Sherkatghanad Z, Akhondzadeh M, Salari S, Zomorodi-Moghadam M, Abdar M, Acharya UR, Khosrowabadi R, Salari V (2020) Automated detection of autism spectrum disorder using a convolutional neural network. Front Neurosci 13:1325

    Google Scholar 

  48. Wang Y, Wang J, Wu FX, Hayrat R, Liu J (2020) AIMAFE: autism spectrum disorder identification with multi-atlas deep feature representation and ensemble learning. J Neurosci Methods 108840

    Google Scholar 

  49. Dvornek NC, Ventola P, Pelphrey KA, Duncan JS (2017) Identifying autism from resting-state fMRI using long short-term memory networks. In: International workshop on machine learning in medical ımaging. Springer, Cham, pp 362–370

    Google Scholar 

  50. Soussia M, Rekik I (2018) Unsupervised manifold learning using high-order morphological brain networks derived from T1-w MRI for autism diagnosis. Front Neuroinform 12:70

    Article  Google Scholar 

  51. Karunakaran P, Hamdan YB (2020) Early prediction of autism spectrum disorder by computational approaches to fMRI analysis with early learning technique. J Artif Intell 2(04):207–216

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Scholar, Pimpri Chinchwad College of Engineering, SPPU, Pune, India

    Triveni D. Dhamale

  2. Assistant Professor, Department of E&TC, Pimpri Chinchwad College of Engineering and Research, Ravet, SPPU, Pune, India

    Triveni D. Dhamale

  3. Department of E&TC, Pimpri Chinchwad College of Engineering, Nigdi, SPPU, Pune, India

    Sheetal U. Bhandari

Authors
  1. Triveni D. Dhamale
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sheetal U. Bhandari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Triveni D. Dhamale .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Lalitpur, Nepal

    Subarna Shakya

  2. Automation and Applied Informatics, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Go Perception Laboratory, Cornell University, Ithaca, NY, USA

    Wang Haoxiang

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dhamale, T.D., Bhandari, S.U. (2023). Recent Trends in Automatic Autism Spectrum Disorder Detection Using Brain MRI. In: Shakya, S., Balas, V.E., Haoxiang, W. (eds) Proceedings of Third International Conference on Sustainable Expert Systems . Lecture Notes in Networks and Systems, vol 587. Springer, Singapore. https://doi.org/10.1007/978-981-19-7874-6_27

Download citation

Publish with us

Policies and ethics

Search

Navigation