Skip to main content

Advertisement

SpringerLink
Log in

Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

A Correction to this article was published on 04 January 2024

This article has been updated

Abstract

Accurate diagnosis of autism spectrum disorder (ASD) is crucial for effective treatment and prognosis. Functional brain networks (FBNs) constructed from functional magnetic resonance imaging (fMRI) have become a popular tool for ASD diagnosis. However, existing model-driven approaches used to construct FBNs lack the ability to capture potential non-linear relationships between data and labels. Moreover, most existing studies treat the FBNs construction and disease classification as separate steps, leading to large inter-subject variability in the estimated FBNs and reducing the statistical power of subsequent group comparison. To address these limitations, we propose a new approach to FBNs construction called the deep unrolling-based spatial constraint representation (DUSCR) model and integrate it with a convolutional classifier to create an end-to-end framework for ASD recognition. Specifically, the model spatial constraint representation (SCR) is solved using a proximal gradient descent algorithm, and we unroll it into deep networks using the deep unrolling algorithm. Classification is then performed using a convolutional prototype learning model. We evaluated the effectiveness of the proposed method on the ABIDE I dataset and observed a significant improvement in model performance and classification accuracy.

Graphical abstract

The resting state fMRI images are preprocessed into time series data and 3D coordinates of each region of interest. The data are fed into the DUSCR model, a model for building functional brain networks using deep learning instead of traditional models, that we propose, and then the outputs are fed into the convolutional classifier with prototype learning to determine whether the patient has ASD disease.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Change history

References

  1. Abraham A, Milham MP, Di Martino A, Craddock RC, Samaras D, Thirion B, Varoquaux G (2017) Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example. NeuroImage 147:736–745

    Article  PubMed  Google Scholar 

  2. Alaerts K, Swinnen SP, Wenderoth N (2016) Sex differences in autism: a resting-state fMRI investigation of functional brain connectivity in males and females. Soc Cogn Affect Neurosci 11(6):1002–1016

    Article  PubMed  PubMed Central  Google Scholar 

  3. Bi X, Wang Y, Shu Q, Sun Q, Xu Q (2018) Classification of autism spectrum disorder using random support vector machine cluster. Front Genet 9:18

    Article  PubMed  PubMed Central  Google Scholar 

  4. Blumberg SJ, Zablotsky B, Avila RM, Colpe LJ, Pringle BA, Kogan MD (2016) Diagnosis lost: differences between children who had and who currently have an autism spectrum disorder diagnosis. Autism 20(7):783–795

    Article  PubMed  Google Scholar 

  5. Bougou V, Mporas I, Schirmer P, Ganchev T (2019) Evaluation of EEG connectivity network measures based features in schizophrenia classification. In: 2019 International Conference on Biomedical Innovations and Applications (BIA), IEEE, pp 1–4

  6. Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29(3):162–173

    Article  CAS  PubMed  Google Scholar 

  7. DeYoe EA, Bandettini P, Neitz J, Miller D, Winans P (1994) Functional magnetic resonance imaging (fMRI) of the human brain. J Neurosci Methods 54(2):171–187

    Article  CAS  PubMed  Google Scholar 

  8. Di Martino A, Yan CG, Li Q, Denio E, Castellanos FX, Alaerts K, Anderson JS, Assaf M, Bookheimer SY, Dapretto M et al (2014) The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol Psychiatry 19(6):659–667

    Article  PubMed  Google Scholar 

  9. 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

    Article  PubMed  Google Scholar 

  10. Horien C, Floris DL, Greene AS, Noble S, Rolison M, Tejavibulya L, O’Connor D, McPartland JC, Scheinost D, Chawarska K et al (2022) Functional connectome-based predictive modelling in autism. Biol Psychiatry

  11. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) FSL. Neuroimage 62(2):782–790

    Article  PubMed  Google Scholar 

  12. Ji J, Yao Y (2020) Convolutional neural network with graphical lasso to extract sparse topological features for brain disease classification. IEEE/ACM Trans Comput Biol Bioinforma 18(6):2327–2338

    Article  Google Scholar 

  13. Ju R, Hu C, Li Q et al (2017) Early diagnosis of Alzheimer’s disease based on resting-state brain networks and deep learning. IEEE/ACM Trans Comput Biol Bioinforma 16(1):244–257

    Article  Google Scholar 

  14. Kam TE, Suk HI, Lee SW (2017) Multiple functional networks modeling for autism spectrum disorder diagnosis. Hum Brain Mapp 38(11):5804–5821

    Article  PubMed  PubMed Central  Google Scholar 

  15. Kawahara J, Brown CJ, Miller SP, Booth BG, Chau V, Grunau RE, Zwicker JG, Hamarneh G (2017) BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146:1038–1049

    Article  PubMed  Google Scholar 

  16. Ktena SI, Parisot S, Ferrante E, Rajchl M, Lee M, Glocker B, Rueckert D (2018) Metric learning with spectral graph convolutions on brain connectivity networks. NeuroImage 169:431–442

    Article  PubMed  Google Scholar 

  17. Lee H, Lee DS, Kang H, Kim BN, Chung MK (2011) Sparse brain network recovery under compressed sensing. IEEE Trans Med Imaging 30(5):1154–1165

    Article  PubMed  Google Scholar 

  18. Li Y, Yang H, Lei B, Liu J, Wee CY (2018) Novel effective connectivity inference using ultra-group constrained orthogonal forward regression and elastic multilayer perceptron classifier for MCI identification. IEEE Trans Med Imaging 38(5):1227–1239

    Article  PubMed  Google Scholar 

  19. Li Y, Liu J, Tang Z, Lei B (2020) Deep spatial-temporal feature fusion from adaptive dynamic functional connectivity for MCI identification. IEEE Trans Med Imaging 39(9):2818–2830

    Article  PubMed  Google Scholar 

  20. Li X, Zhou Y, Dvornek N, Zhang M, Gao S, Zhuang J, Scheinost D, Staib LH, Ventola P, Duncan JS (2021) BrainGNN: interpretable brain graph neural network for fMRI analysis. Med Image Anal 74(102):233

    Google Scholar 

  21. Li Y, Liu J, Jiang Y, Liu Y, Lei B (2021) Virtual adversarial training-based deep feature aggregation network from dynamic effective connectivity for MCI identification. IEEE Trans Med Imaging 41(1):237–251

    Article  PubMed  Google Scholar 

  22. Li X, Dvornek NC, Zhou Y, Zhuang J, Ventola P, Duncan JS (2019) Graph neural network for interpreting task-fMRI biomarkers. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, pp 485–493

  23. Liu M, Zhang D, Adeli E, Shen D (2015) Inherent structure-based multiview learning with multitemplate feature representation for Alzheimer’s disease diagnosis. IEEE Trans Biomed Eng 63(7):1473–1482

    Article  PubMed  PubMed Central  Google Scholar 

  24. Liu J, Sheng Y, Lan W, Guo R, Wang Y, Wang J (2020) Improved ASD classification using dynamic functional connectivity and multi-task feature selection. Pattern Recogn Lett 138:82–87

    Article  Google Scholar 

  25. Lord C, Elsabbagh M, Baird G, Veenstra-Vanderweele J (2018) Autism spectrum disorder. Lancet 392(10146):508–520

    Article  PubMed  PubMed Central  Google Scholar 

  26. Marrelec G, Krainik A, Duffau H, Pélégrini-Issac M, Lehéricy S, Doyon J, Benali H (2006) Partial correlation for functional brain interactivity investigation in functional MRI. Neuroimage 32(1):228–237

    Article  PubMed  Google Scholar 

  27. Meszlényi RJ, Buza K, Vidnyánszky Z (2017) Resting state fMRI functional connectivity-based classification using a convolutional neural network architecture. Front Neuroinformatics 11:61

    Article  Google Scholar 

  28. Monga V, Li Y, Eldar YC (2021) Algorithm unrolling: interpretable, efficient deep learning for signal and image processing. IEEE Signal Proc Mag 38(2):18–44

    Article  Google Scholar 

  29. Niu YW, Zhang CY, Qiu Y, Lin QH, Sui J, Calhoun VD (2021) Fusion of multiple spatial networks derived from complex-valued fMRI data via CNN classification. In: 2021 International Joint Conference on Neural Networks (IJCNN), IEEE, pp 1–6

  30. Parisot S, Ktena SI, Ferrante E, Lee M, Guerrero R, Glocker B, Rueckert D (2018) Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease. Med Image Anal 48:117–130

    Article  PubMed  Google Scholar 

  31. Qiao L, Zhang H, Kim M, Teng S, Zhang L, Shen D (2016) Estimating functional brain networks by incorporating a modularity prior. Neuroimage 141:399–407

    Article  PubMed  Google Scholar 

  32. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3):1059–1069

    Article  PubMed  Google Scholar 

  33. Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE international conference on computer vision, pp 618–626

  34. Smith SM, Vidaurre D, Beckmann CF, Glasser MF, Jenkinson M, Miller KL, Nichols TE, Robinson EC, Salimi-Khorshidi G, Woolrich MW et al (2013) Functional connectomics from resting-state fMRI. Trends Cogn Sci 17(12):666–682

    Article  PubMed  PubMed Central  Google Scholar 

  35. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in spm using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1):273–289

    Article  CAS  PubMed  Google Scholar 

  36. Wang J, Wang Q, Peng J, Nie D, Zhao F, Kim M, Zhang H, Wee CY, Wang S, Shen D (2017) Multi-task diagnosis for autism spectrum disorders using multi-modality features: a multi-center study. Hum Brain Mapp 38(6):3081–3097

    Article  PubMed  PubMed Central  Google Scholar 

  37. Wiggins JL, Bedoyan JK, Peltier SJ, Ashinoff S, Carrasco M, Weng SJ, Welsh RC, Martin DM, Monk CS (2012) The impact of serotonin transporter (5-HTTLPR) genotype on the development of resting-state functional connectivity in children and adolescents: a preliminary report. Neuroimage 59(3):2760–2770

    Article  CAS  PubMed  Google Scholar 

  38. Wing L, Gould J, Gillberg C (2011) Autism spectrum disorders in the DSM-V: better or worse than the DSM-IV? Res Dev Disabil 32(2):768–773

    Article  PubMed  Google Scholar 

  39. Xue Y, Zhang L, Qiao L, Shen D (2021) Correction: Estimating sparse functional brain networks with spatial constraints for MCI identification. PLoS ONE 16(6):e0253995

    Article  PubMed  PubMed Central  Google Scholar 

  40. Yang HM, Zhang XY, Yin F, Liu CL (2018) Robust classification with convolutional prototype learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 3474–3482

  41. Yang C, Wang P, Tan J, Liu Q, Li X (2021) Autism spectrum disorder diagnosis using graph attention network based on spatial-constrained sparse functional brain networks. Comput Biol Med 139:104963

    Article  PubMed  Google Scholar 

  42. Yin W, Li L, Wu FX (2021) A graph attention neural network for diagnosing ASD with fMRI data. In: 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), IEEE, pp 1131–1136

  43. Yu R, Zhang H, An L, Chen X, Wei Z, Shen D (2017) Connectivity strength-weighted sparse group representation-based brain network construction for MCI classification. Hum Brain Mapp 38(5):2370–2383

    Article  PubMed  PubMed Central  Google Scholar 

  44. Zhou Z, Chen X, Zhang Y, Hu D, Qiao L, Yu R, Yap PT, Pan G, Zhang H, Shen D (2020) A toolbox for brain network construction and classification (BrainNetClass). Hum Brain Mapp 41(10):2808–2826

Download references

Funding

This work was supported in part by the National Key Research and Development Program of China under Grant 2021YFF0704100; in part by the National Natural Science Foundation of China under Grant 62106032 and 62027827; in part by the China Postdoctoral Science Foundation under Grant 2022MD713691; in part by the Chongqing Postdoctoral Science Special Foundation under Grant 2021XM3028; in part by the Key Cooperation Projects of Chongqing Municipal Education Commission under Grant HZ2021008; and in part by the Natural Science Foundation of Chongqing under Grant cstc2020jcyjzdxmX0025 and Grant cstc2019jcyj-cxttX0002; in part by the Doctoral Research Fund of Chongqing University of Posts and Telecommunications under Grant A2023-01.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China

    Dajiang Lei, Tao Zhang, Yue Wu & Weisheng Li

  2. School of Bioinformatics, Chongqing University of Posts and Telecommunications, Chongqing, China

    Xinwei Li

Authors
  1. Dajiang Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yue Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weisheng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xinwei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Dajiang Lei: conceptualization, methodology, validation. Tao Zhang: data curation, software, writing-original draft preparation. Yue Wu: visualization, investigation. Weisheng Li: writing-reviewing and editing. Xinwei Li: writing-reviewing and editing, supervision.

Corresponding author

Correspondence to Xinwei Li.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The original online version of this article was revised: Graphical abstract image is missing.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lei, D., Zhang, T., Wu, Y. et al. Autism spectrum disorder diagnosis based on deep unrolling-based spatial constraint representation. Med Biol Eng Comput 61, 2829–2842 (2023). https://doi.org/10.1007/s11517-023-02859-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-023-02859-2

Keywords

Search

Navigation