AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Big Data Mining and Analytics Article
PDF (5.2 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder

Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China, and also with University of Chinese Academy of Sciences, Beijing 100049, China
Shenzhen Key Laboratory of Intelligent Bioinformatics, and with Center for High Performance Computing, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Show Author Information

Abstract

Autism Spectrum Disorder (ASD) is a highly disabling mental disease that brings significant impairments of social interaction ability to the patients, making early screening and intervention of ASD critical. With the development of the machine learning and neuroimaging technology, extensive research has been conducted on machine classification of ASD based on structural Magnetic Resonance Imaging (s-MRI). However, most studies involve with datasets where participants’ age are above 5 and lack interpretability. In this paper, we propose a machine learning method for ASD classification in children with age range from 0.92 to 4.83 years, based on s-MRI features extracted using Contrastive Variational AutoEncoder (CVAE). 78 s-MRIs, collected from Shenzhen Children’s Hospital, are used for training CVAE, which consists of both ASD-specific feature channel and common-shared feature channel. The ASD participants represented by ASD-specific features can be easily discriminated from Typical Control (TC) participants represented by the common-shared features. In case of degraded predictive accuracy when data size is extremely small, a transfer learning strategy is proposed here as a potential solution. Finally, we conduct neuroanatomical interpretation based on the correlation between s-MRI features extracted from CVAE and surface area of different cortical regions, which discloses potential biomarkers that could help target treatments of ASD in the future.

Keywords

Autism Spectrum Disorder (ASD) classificationContrastive Variational AutoEncoder (CVAE)transfer learningneuroanatomical interpretation

References

[1]

C. Lord, M. Elsabbagh, G. Baird, and J. Veenstra-Vanderweele, Autism spectrum disorder, Lancet, vol. 392, no. 10146, pp. 508–520, 2018.
Crossref Google Scholar
[2]

F. Thabtah and D. Peebles, A new machine learning model based on induction of rules for autism detection, Health Inf. J., vol. 26, no. 1, pp. 264–286, 2020.
Crossref Google Scholar
[3]
P. Moridian, N. Ghassemi, M. Jafari, S. Salloum-Asfar, D. Sadeghi, M. Khodatars, A. Shoeibi, A. Khosravi, S. H. Ling, A. Subasi, et al., Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review, arXiv preprint arXiv: 2206.11233, 2022.
Crossref
[4]

A. Di Martino, C. G. Yan, Q. Li, E. Denio, F. X. Castellanos, K. Alaerts, J. S. Anderson, M. Assaf, S. Y. Bookheimer, M. Dapretto, et al., The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism, Mol. Psychiatry, vol. 19, no. 6, pp. 659–667, 2014.
Crossref Google Scholar
[5]

A. Di Martino, D. O’Connor, B. Chen, K. Alaerts, J. S. Anderson, M. Assaf, J. H. Balsters, L. Baxter, A. Beggiato, S. Bernaerts, et al., Enhancing studies of the connectome in autism using the autism brain imaging data exchange II, Sci. Data, vol. 4, p. 170010, 2017.
Crossref Google Scholar
[6]

Y. Kong, J. Gao, Y. Xu, Y. Pan, J. Wang, and J. Liu, Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier, Neurocomputing, vol. 324, p. 63–68, 2019.
Crossref Google Scholar
[7]
Y. Hiremath, M. Ismail, R. Verma, J. Antunes, and P. Tiwari, Combining deep and hand-crafted MRI features for identifying sex-specific differences in autism spectrum disorder versus controls, in Proc. SPIE 11314, Medical Imaging 2020: Computer-Aided Diagnosis, Houston, TX, USA, 2020, p. 113141W.
Crossref
[8]

J. Gao, M. Chen, Y. Li, Y. Gao, Y. Li, S. Cai, and J. Wang, Multisite autism spectrum disorder classification using convolutional neural network classifier and individual morphological brain networks, Front. Neurosci., vol. 14, p. 629630, 2021.
Crossref Google Scholar
[9]

T. Rohlfing, N. M. Zahr, E. V. Sullivan, and A. Pfefferbaum, The SRI24 multichannel atlas of normal adult human brain structure, Hum. Brain Mapp., vol. 31, no. 5, pp. 798–819, 2010.
Crossref Google Scholar
[10]
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, Learning deep features for discriminative localization, in Proc. IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 2921–2929.
Crossref
[11]
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, Grad-CAM: Visual explanations from deep networks via gradient-based localization, in Proc. IEEE Int. Conf. Computer Vision, Venice, Italy, 2017, pp. 618–626.
Crossref
[12]

R. Yang, F. Ke, H. Liu, M. Zhou, and H. M. Cao, Exploring sMRI biomarkers for diagnosis of autism spectrum disorders based on multi class activation mapping models, IEEE Access, vol. 9, pp. 124122–124131, 2021.
Crossref Google Scholar
[13]

M. Mishra and U. C. Pati, A classification framework for autism spectrum disorder detection using sMRI: Optimizer based ensemble of deep convolution neural network with on-the-fly data augmentation, Biomed. Signal Process. Control, vol. 84, p. 104686, 2023.
Crossref Google Scholar
[14]

H. Sharif and R. A. Khan, A novel machine learning based framework for detection of autism spectrum disorder (ASD), Appl. Artif. Intell., vol. 36, no. 1, p. 2004655, 2022.
Crossref Google Scholar
[15]

Z. Wang, D. Peng, Y. Shang, and J. Gao, Autistic spectrum disorder detection and structural biomarker identification using self-attention model and individual-level morphological covariance brain networks, Front. Neurosci., vol. 15, p. 756868, 2021.
Crossref Google Scholar
[16]
R. Ma, Y. Wang, Y. Wei, and Y. Pan, Meta-data study in autism spectrum disorder classification based on structural MRI, arXiv preprint arXiv: 2206.05052, 2022.
[17]

D. Sarovic, N. Hadjikhani, J. Schneiderman, S. Lundström, and C. Gillberg, Autism classified by magnetic resonance imaging: A pilot study of a potential diagnostic tool, Int. J. Methods Psychiatr. Res., vol. 29, no. 4, p. e1846, 2020.
Crossref Google Scholar
[18]

E. Conti, A. Retico, L. Palumbo, G. Spera, P. Bosco, L. Biagi, S. Fiori, M. Tosetti, P. Cipriani, G. Cioni, et al., Autism spectrum disorder and childhood apraxia of speech: Early language-related hallmarks across structural MRI study, J. Pers. Med., vol. 10, no. 4, p. 275, 2020.
Crossref Google Scholar
[19]
X. Zhang, X. Ding, Z. Wu, J. Xia, H. Ni, X. Xu, L. Liao, L. Wang, and G. Li, Siamese verification framework for autism identification during infancy using cortical path signature features, in Proc. 17 th Int. Symp. Biomedical Imaging (ISBI ), Iowa City, IA, USA, 2020, pp. 1–4.
Crossref
[20]
National Database for Autism Research (NDAR), https://ndar.nih.gov, 2023.
[21]

L. Zöllei, J. E. Iglesias, Y. Ou, P. E. Grant, and B. Fischl, Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years, NeuroImage, vol. 218, p. 116946, 2020.
Crossref Google Scholar
[22]

N. J. Tustison, P. A. Cook, A. Klein, G. Song, S. R. Das, J. T. Duda, B. M. Kandel, N. van Strien, J. R. Stone, J. C. Gee, et al., Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements, NeuroImage, vol. 99, pp. 166–179, 2014.
Crossref Google Scholar
[23]

B. Fischl, FreeSurfer, NeuroImage, vol. 62, no. 2, pp. 774–781, 2012.
Crossref Google Scholar
[24]

R. S. Desikan, F. Ségonne, B. Fischl, B. T. Quinn, B. C. Dickerson, D. Blacker, R. L. Buckner, A. M. Dale, R. P. Maguire, B. T. Hyman, et al., An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest, NeuroImage, vol. 31, no. 3, pp. 968–980, 2006.
Crossref Google Scholar
[25]
K. A. Severson, S. Ghosh, and K. Ng, Unsupervised learning with contrastive latent variable models, in Proc. 33 rd AAAI Conf. Artificial Intelligence, Honolulu, HI, USA, 2019, pp. 4862–4869.
Crossref
[26]
A. Abid and J. Zou, Contrastive variational autoencoder enhances salient features, arXiv preprint arXiv: 1902.04601, 2019.
[27]
D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, Computer Science.
[28]

L. Breiman, Random forests, Mach. Learn., vol. 45, no. 1, pp. 5–32, 2001.
Crossref Google Scholar
[29]

L. van der Maaten and G. Hinton, Visualizing data using t-SNE, J. Mach. Learn. Res., vol. 9, no. 86, pp. 2579–2605, 2008.
Google Scholar
[30]

J. Jönemo, D. Abramian, and A. Eklund, Evaluation of augmentation methods in classifying autism spectrum disorders from fMRI data with 3D convolutional neural networks, Diagnostics, vol. 13, no. 17, p. 2773, 2023.
Crossref Google Scholar
[31]

H. S. Nogay and H. Adeli, Diagnostic of autism spectrum disorder based on structural brain MRI images using. grid search optimization. and convolutional neural networks, Biomed. Signal Process. Control, vol. 79, p. 104234, 2023.
Crossref Google Scholar
[32]

A. Aglinskas, J. K. Hartshorne, and S. Anzellotti, Contrastive machine learning reveals the structure of neuroanatomical variation within autism, Science, vol. 376, no. 6597, pp. 1070–1074, 2022.
Crossref Google Scholar
[33]

Z. K. Khadem-Reza and H. Zare, Evaluation of brain structure abnormalities in children with autism spectrum disorder (ASD) using structural magnetic resonance imaging, Egypt. J. Neurol. Psychiatry Neurosurg., vol. 58, no. 1, p. 135, 2022.
Crossref Google Scholar
[34]

K. Weiss, T. M. Khoshgoftaar, and D. Wang, A survey of transfer learning, J. Big Data, vol. 3, no. 1, p. 9, 2016.
Crossref Google Scholar
[35]

R. Ma, Y. J. Colón, and T. Luo, A transfer learning study of gas adsorption in metal-organic frameworks, ACS Appl. Mater. Interfaces, vol. 12, no. 30, pp. 34041–34048, 2020.
Crossref Google Scholar
[36]

E. Courchesne, T. Pramparo, V. H. Gazestani, M. V. Lombardo, K. Pierce, and N. E. Lewis, The ASD living biology: From cell proliferation to clinical phenotype, Mol. Psychiatry, vol. 24, no. 1, pp. 88–107, 2019.
Crossref Google Scholar
[37]

A. A. S. Goldani, S. R. Downs, F. Widjaja, B. Lawton, and R. L. Hendren, Biomarkers in autism, Front. Psychiatry, vol. 5, p. 100, 2014.
Crossref Google Scholar
[38]
A. A. Mercadante and P. Tadi, Neuroanatomy, Gray Matter. Treasure Island, FL, USA: StatPearls Publishing, 2020.
[39]

N. Kriegeskorte, M. Mur, and P. Bandettini, Representational similarity analysis-connecting the branches of systems neuroscience, Front. Syst. Neurosci., vol. 2, p. 4, 2008.
Crossref Google Scholar
[40]
H. Abdi, The Kendall rank correlation coefficient, in Encyclopedia of Measurement and Statistics, N. J. Salkind, ed. Thousand Oaks, CA, USA: Sage, 2007, pp. 508–510.
[41]

G. Hartwigsen, A. Baumgaertner, C. J. Price, M. Koehnke, S. Ulmer, and H. R. Siebner, Phonological decisions require both the left and right supramarginal gyri, Proc. Natl. Acad. Sci. USA, vol. 107, no. 38, pp. 16494–16499, 2010.
Crossref Google Scholar
[42]

C. Stoeckel, P. M. Gough, K. E. Watkins, and J. T. Devlin, Supramarginal gyrus involvement in visual word recognition, Cortex, vol. 45, no. 9, pp. 1091–1096, 2009.
Crossref Google Scholar
[43]

Y. Miyashita, Inferior temporal cortex: Where visual perception meets memory, Annu. Rev. Neurosci., vol. 16, pp. 245–263, 1993.
Crossref Google Scholar
[44]
American Psychiatric Association and DSM-5 Task Force, Diagnostic and Statistical Manual of Mental Disorders: DSM-5™, 5th ed., Arlington, VA, USA: American Psychiatric Publishing, 2013.
Crossref
[45]

S. L. Hyman, S. E. Levy, S. M. Myers, D. Z. Kuo, S. Apkon, L. F. Davidson, K. A. Ellerbeck, J. E. A. Foster, G. H. Noritz, M. O. C. Leppert, et al., Management of children with autism spectrum disorder, Pediatrics, vol. 145, no. 1, p. e20193447, 2020.
Crossref Google Scholar
[46]
O. Lang, Y. Gandelsman, M. Yarom, Y. Wald, G. Elidan, A. Hassidim, W. T. Freeman, P. Isola, A. Globerson, M. Irani, et al., Explaining in style: Training a GAN to explain a classifier in StyleSpace, in Proc. IEEE/CVF Int. Conf. Computer Vision, Montreal, Canada, 2021, pp. 673–682.
Crossref
[47]
M. Atad, V. Dmytrenko, Y. Li, X. Zhang, M. Keicher, J. Kirschke, B. Wiestler, A. Khakzar, and N. Navab, Chexplaining in style: Counterfactual explanations for chest X-rays using StyleGAN, arXiv preprint arXiv: 2207.07553, 2022.
Big Data Mining and Analytics
Volume 7 Issue 3,
September 2024
Pages 781-793
DOI: 10.26599/BDMA.2024.9020004
Cite this article:
Ma R, Xie R, Wang Y, et al. Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder. Big Data Mining and Analytics, 2024, 7(3): 781-793. https://doi.org/10.26599/BDMA.2024.9020004

189

Views

14

Downloads

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics
Received: 14 September 2023
Revised: 04 December 2023
Accepted: 11 January 2024
Published: 28 August 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Return