Skip to main content
Key Specialties
Anesthesiology Cardiology Dermatology Diabetology Endocrinology Gastroenterology and Hepatology General Medicine Geriatrics Hematology Infectious Diseases Internal Medicine Nephrology Neurology Obstetrics and Gynecology Oncology Ophthalmology Pediatrics Psychiatry Radiology Respiratory Medicine Rheumatology Surgery Urology
Congress Coverage
ASH 2024 Annual Meeting 2024 ESMO Congress 2024 ERS Congress 2024 EASD Congress 2024 ESC Congress 2024 EAN Congress 2024 ASCO Annual Meeting 2024 ACC Congress
Clinical Collections
Advances in Alzheimer’s

Podcast | Sexual health in older age

Dr Holly Thomas helps us unpack the needlessly taboo subject of sex and sexual health in women of older age.

Listen now
ADVANCED SEARCH
Log in
Top
Journal of Imaging Informatics in Medicine
Published in:

13-02-2024 | Autism Spectrum Disorder

Identification of Autism Spectrum Disorder Using Topological Data Analysis

Authors: Xudong Zhang, Yaru Gao, Yunge Zhang, Fengling Li, Huanjie Li, Fengchun Lei

Published in: Journal of Imaging Informatics in Medicine | Issue 3/2024

Login to get access

Abstract

Autism spectrum disorder (ASD) is a pervasive brain development disease. Recently, the incidence rate of ASD has increased year by year and posed a great threat to the lives and families of individuals with ASD. Therefore, the study of ASD has become very important. A suitable feature representation that preserves the data intrinsic information and also reduces data complexity is very vital to the performance of established models. Topological data analysis (TDA) is an emerging and powerful mathematical tool for characterizing shapes and describing intrinsic information in complex data. In TDA, persistence barcodes or diagrams are usually regarded as visual representations of topological features of data. In this paper, the Regional Homogeneity (ReHo) data of subjects obtained from Autism Brain Imaging Data Exchange (ABIDE) database were used to extract features by using TDA. The average accuracy of cross validation on ABIDE I database was 95.6% that was higher than any other existing methods (the highest accuracy among existing methods was 93.59%). The average accuracy for sampling with the same resolutions with the ABIDE I on the ABIDE II database was 96.5% that was also higher than any other existing methods (the highest accuracy among existing methods was 75.17%).
Literature
1.
T. Wahlberg, A. F. Rotatori, J. Deisinger, S. Burkhardt, Students with autism spectrum disorders, Advances in Special Education 15 (03) (2003) 195–232.CrossRef
2.
M. A. Just, T. A. Keller, V. L. Malave, R. K. Kana, S. Varma, Autism as a neural systems disorder: A theory of frontal-posterior underconnectivity, Neurosci Biobehav Rev 36 (4) (2012) 1292–1313.CrossRefPubMedPubMedCentral
3.
A. S. Heinsfeld, A. R. Franco, R. C. Craddock, A. Buchweitz, F. Meneguzzi, Identification of autism spectrum disorder using deep learning and the abide dataset, NeuroImage: Clinical 17 (2018) 16–23.
4.
C. Wong, E. L. Meaburn, A. Ronald, T. S. Price, J. Mill, Methylomic analysis of monozygotic twins discordant for autism spectrum disorder and related behavioural traits, Molecular Psychiatry.
5.
K. Lyall, J. N. Constantino, M. G. Weisskopf, A. L. Roberts, A. Ascherio, S. L. Santangelo, Parental social responsiveness and risk of autism spectrum disorder in offspring, Jama Psychiatry 71 (8) (2014) 936–942.CrossRefPubMedPubMedCentral
6.
Elmose, Mette, Happe, Francesca, Being aware of own performance: How accurately do children with autism spectrum disorder judge own memory performance?, Autism Research Official Journal of the International Society for Autism Research (2014).
7.
R. J. Swatzyna, N. N. Boutros, A. C. Genovese, E. K. MacInerney, A. J. Roark, G. P. Kozlowski, Electroencephalogram (eeg) for children with autism spectrum disorder: Evidential considerations for routine screening, European Child & Adolescent Psychiatry 28 (2019) 615–624.CrossRef
8.
R. A. Carper, P. Moses, Z. D. Tigue, E. Courchesne, Cerebral lobes in autism: Early hyperplasia and abnormal age effects, Neuroimage 16 (4) (2002) 1038–1051.CrossRefPubMed
9.
S. R. Chandana, M. E. Behen, C. Juhász, O. Muzik, R. D. Rothermel, T. J. Mangner, P. K. Chakraborty, H. T. Chugani, D. C. Chugani, Significance of abnormalities in developmental trajectory and asymmetry of cortical serotonin synthesis in autism, International Journal of Developmental Neuroscience 23 (2-3) (2005) 171–182.
10.
S. Ogawa, T. M. Lee, A. Tank, Brain magnetic resonance imaging with contrast dependent on blood oxygenation, Proceedings of the National Academy of Sciences of the United States of America 87 (24) (1990) 9868–9872.CrossRefPubMedPubMedCentral
11.
N. M. Kleinhans, R. Müller, D. N. Cohen, E. Courchesne, Atypical functional lateralization of language in autism spectrum disorders, Brain Research 1221 (2008) 115–125.
12.
G. J. Harris, C. F. Chabris, J. Clark, T. Urban, I. Aharon, S. Steele, L. Mcgrath, K. Condouris, H. Tager-Flusberg, Brain activation during semantic processing in autism spectrum disorders via functional magnetic resonance imaging, Brain & Cognition 61 (1) (2006) 54–68.CrossRef
13.
C. J. Brown, J. Kawahara, G. Hamarneh, Connectome priors in deep neural networks to predict autism, in: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), 2018.
14.
M. A. Reiter, A. Jahedi, A. J. Fredo, I. Fishman, B. Bailey, R.-A. Müller, Performance of machine learning classification models of autism using resting-state fmri is contingent on sample heterogeneity, Neural Computing and Applications 33 (2021) 3299–3310.CrossRefPubMed
15.
V. Subbaraju, M. B. Suresh, S. Sundaram, S. Narasimhan, Identifying differences in brain activities and an accurate detection of autism spectrum disorder using resting state functional-magnetic resonance imaging : A spatial filtering approach, Medical Image Analysis 35 (2017) 375–389.CrossRefPubMed
16.
H. Felouat, S. Oukid-Khouas, Graph convolutional networks and functional connectivity for identification of autism spectrum disorder, in: 2020 Second International Conference on Embedded & Distributed Systems (EDiS), 2020.
17.
F. Almuqhim, F. Saeed, Asd-saenet: A sparse autoencoder, and deep-neural network model for detecting autism spectrum disorder (asd) using fmri data, Frontiers in Computational Neuroscience 15 (2021).
18.
T. Eslami, V. Mirjalili, A. Fong, A. Laird, F. Saeed, Asd-diagnet: A hybrid learning approach for detection of autism spectrum disorder using fmri data, Frontiers in Neuroinformatics (2019).
19.
S. Mostafa, L. Tang, F.-X. Wu, Diagnosis of autism spectrum disorder based on eigenvalues of brain networks, Ieee Access 7 (2019) 128474–128486.CrossRef
20.
Y. Zhan, J. Wei, J. Liang, X. Xu, Z. Wang, Diagnostic classification for human autism and obsessive-compulsive disorder based on machine learning from a primate genetic model, American Journal of Psychiatry 178 (1) (2020) appi.ajp.2020.1.
21.
H. Guo, W. Yin, S. Mostafa, F. X. Wu, Diagnosis of asd from rs-fmri images based on brain dynamic networks, in: Springer, Cham, 2020.
22.
E. Canario, D. Chen, B. Biswal, A review of resting-state fmri and its use to examine psychiatric disorders, Psychoradiology (2021).
23.
C. Wang, Z. Xiao, B. Wang, J. Wu, Identification of autism based on svm-rfe and stacked sparse auto-encoder, IEEE Access PP (99) (2019) 1–1.
24.
R. Ghrist, Barcodes: The persistent topology of data, Bulletin of the American Mathematical Society 45 (1) (2008) 61–75.CrossRef
25.
G. Carlsson, Topology and data, Bulletin of the American Mathematical Society 46 (2) (2009) 255–308.CrossRef
26.
P. Bubenik, Statistical topological data analysis using persistence landscapes, Journal of Machine Learning Research 16 (1) (2015) 77–102.
27.
Y. Zang, T. Jiang, Y. Lu, Y. He, L. Tian, Regional homogeneity approach to fmri data analysis, Neuroimage 22 (1) (2004) 394–400.CrossRefPubMed
28.
Y. Behzadi, K. Restom, J. Liau, T. T. Liu, A component based noise correction method (compcor) for bold and perfusion based fmri., Neuroimage 37 (1) (2007) 90–101.
29.
C. G. Yan, X. D. Wang, X. N. Zuo, Y. F. Zang, Dpabi: Data processing & analysis for (resting-state) brain imaging, Neuroinformatics 14 (3) (2016) 339–351.CrossRefPubMed
30.
A. Zomorodian, G. Carlsson, Computing persistent homology, in: Twentieth Symposium on Computational Geometry, 2019.
31.
Edelsbrunner, Letscher, Zomorodian, Topological persistence and simplification, Discrete & Computational Geometry 28 (4) (2002) 511–533.CrossRef
32.
C. S. Pun, S. X. Lee, K. Xia, Persistent-homology-based machine learning: a survey and a comparative study, Artificial Intelligence Review 55 (7) (2022) 5169–5213.CrossRef
33.
T. K. Dey, K. Li, S. Jian, D. Cohen-Steiner, Computing geometry-aware handle and tunnel loops in 3d models, ACM Transactions on Graphics 27 (3) (2008).
34.
T. K. Dey, Y. Wang, Reeb graphs: Approximation and persistence, ACM (2011).
35.
K. Mischaikow, V. Nanda, Morse theory for filtrations and efficient computation of persistent homology, Discrete & Computational Geometry 50 (2) (2013) 330–353.CrossRef
36.
P. Niyogi, S. Smale, S. Weinberger, A topological view of unsupervised learning from noisy data, Siam Journal on Computing (2011).
37.
T. Bonis, M. Ovsjanikov, S. Oudot, Persistence-based pooling for shape pose recognition, Springer International Publishing (2016).
38.
Z. Cang, L. Mu, K. Wu, K. Opron, K. Xia, G. W. Wei, A topological approach for protein classification, International Society for Optics and Photonics (2015).
39.
T. Qaiser, Y. W. Tsang, D. Taniyama, N. Sakamoto, K. Nakane, D. Epstein, N. Rajpoot, Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features (2018).
40.
Z. Cang, G.-W. Wei, Analysis and prediction of protein folding energy changes upon mutation by element specific persistent homology, Bioinformatics 33 (22) (2017) 3549–3557.PubMed
41.
J. D. Boissonnat, M. Glisse, C. Maria, M. Yvinec, Gudhi library.
42.
43.
44.
45.
U. Bauer, M. Kerber, J. Reininghaus, H. Wagner, Phat–persistent homology algorithms toolbox, Journal of symbolic computation 78 (2017) 76–90.CrossRef
46.
S. Kaji, T. Sudo, K. Ahara, Cubical ripser: Software for computing persistent homology of image and volume data, arXiv preprint arXiv:​2005.​12692 (2020).
47.
V. Vapnik, The support vector method of function estimation, NATO ASI SERIES F COMPUTER AND SYSTEMS SCIENCES (1998).
48.
K. Hornik, M. B. Stinchcombe, H. White, Multilayer feedforward networks are universal approximators, Neural Networks (1989).
49.
L. Breiman, Random forests, machine learning 45, Journal of Clinical Microbiology 2 (2001) 199–228.
50.
J. Friedman, Greedy function approximation : A gradient boosting machine, Annals of Statistics 29 (2001).
51.
J. A. Nielsen, B. A. Zielinski, F. P. Thomas, A. L. Alexander, L. Nicholas, E. D. Bigler, J. E. Lainhart, J. S. Anderson, Multisite functional connectivity mri classification of autism: Abide results, Frontiers in Human Neuroscience 7 (1) (2013) 599.PubMedPubMedCentral
52.
S. Vigneshwaran, B. Mahanand, S. Suresh, N. Sundararajan, Using regional homogeneity from functional mri for diagnosis of asd among males, in: 2015 International Joint Conference on Neural Networks (IJCNN), IEEE, 2015, pp. 1–8.
53.
A. Abraham, M. P. Milham, A. Di Martino, R. C. Craddock, D. Samaras, B. Thirion, G. Varoquaux, Deriving reproducible biomarkers from multi-site resting-state data: An autism-based example, NeuroImage 147 (2017) 736–745.CrossRefPubMed
54.
S. Parisot, S. I. Ktena, E. Ferrante, M. Lee, R. G. Moreno, B. Glocker, D. Rueckert, Spectral graph convolutions for population-based disease prediction, in: Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20, Springer, 2017, pp. 177–185.
55.
N. C. Dvornek, P. Ventola, K. A. Pelphrey, J. S. Duncan, Identifying autism from resting-state fmri using long short-term memory networks, in: Machine Learning in Medical Imaging: 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings 8, Springer, 2017, pp. 362–370.
56.
P. Sarah, K. S. Ira, F. Enzo, L. Matthew, G. Ricardo, G. Ben, R. Daniel, Disease prediction using graph convolutional networks: Application to autism spectrum disorder and alzheimer’s disease, Medical Image Analysis (2018) S1361841518303554–.
57.
M. Khosla, K. Jamison, A. Kuceyeski, M. R. Sabuncu, Ensemble learning with 3d convolutional neural networks for connectome-based prediction, NeuroImage (2018).
58.
E. Wong, J. S. Anderson, B. A. Zielinski, P. T. Fletcher, Riemannian regression and classification models of brain networks applied to autism, in: Connectomics in NeuroImaging: Second International Workshop, CNI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 2, Springer, 2018, pp. 78–87.
59.
M. Khosla, K. Jamison, A. Kuceyeski, M. R. Sabuncu, 3d convolutional neural networks for classification of functional connectomes, in: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings 4, Springer, 2018, pp. 137–145.
60.
S. Itani, D. Thanou, Combining anatomical and functional networks for neuropathology identification: A case study on autism spectrum disorder, Medical image analysis 69 (2021) 101986.CrossRefPubMed
61.
T. M. Epalle, Y. Song, Z. Liu, H. Lu, Multi-atlas classification of autism spectrum disorder with hinge loss trained deep architectures: Abide i results, Applied soft computing 107 (2021) 107375.CrossRef
62.
S. Mostafa, W. Yin, F.-X. Wu, Autoencoder based methods for diagnosis of autism spectrum disorder, in: International Conference on Computational Advances in Bio and Medical Sciences, Springer, 2019, pp. 39–51.
63.
R. Kashef, Ecnn: Enhanced convolutional neural network for efficient diagnosis of autism spectrum disorder, Cognitive Systems Research 71 (2022) 41–49.CrossRef
64.
W. Yin, L. Li, F.-X. Wu, A semi-supervised autoencoder for autism disease diagnosis, Neurocomputing 483 (2022) 140–147.CrossRef
65.
M. A. Aghdam, A. Sharifi, M. M. Pedram, Diagnosis of autism spectrum disorders in young children based on resting-state functional magnetic resonance imaging data using convolutional neural networks, Journal of Digital Imaging 32 (6) (2019) 899–918.CrossRefPubMedPubMedCentral
66.
M. Pominova, E. Kondrateva, M. Sharaev, A. Bernstein, E. Burnaev, Fader networks for domain adaptation on fmri: Abide-ii study, in: International Conference on Machine Vision, 2021.
67.
S. Bressan, J. Li, S. Ren, J. Wu, The embedded homology of hypergraphs and applications, Asian Journal of Mathematics (2016).
68.
J. Grbić, J. Wu, K. Xia, G.-W. Wei, Aspects of topological approaches for data science, Foundations of data science (Springfield, Mo.) 4 (2) (2022) 165.
Metadata
Title
Identification of Autism Spectrum Disorder Using Topological Data Analysis
Authors
Xudong Zhang
Yaru Gao
Yunge Zhang
Fengling Li
Huanjie Li
Fengchun Lei
Publication date
13-02-2024
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 3/2024
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-024-01002-3

Other articles of this Issue 3/2024

Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification

A Motion-Aware DNN Model with Edge Focus Loss and Quality Control for Short-Axis Left Ventricle Segmentation of Cine MR Sequences

Synthesis of Hybrid Data Consisting of Chest Radiographs and Tabular Clinical Records Using Dual Generative Models for COVID-19 Positive Cases

MOTC: Abdominal Multi-objective Segmentation Model with Parallel Fusion of Global and Local Information

Special Challenges of Sensitive Images: A HIMSS-SIIM Enterprise Imaging Community Whitepaper

  • Open Access

Identifying Pathological Subtypes of Brain Metastasis from Lung Cancer Using MRI-Based Deep Learning Approach: A Multicenter Study