:: Volume 9, Issue 3 (Summer 2021) ::
Shefaye Khatam 2021, 9(3): 1-11 Back to browse issues page
Dimension Reduction in fMRI Images based on Metaheuristic Algorithm to Diagnose Autism
Farzaneh Sadeghiyan , Hadiseh Hasani * , Marzieh Jafari
Department of Geodesy and Surveying Engineering, Tafresh University, Tafresh, Iran , h.hasani@tafreshu.ac.ir
Abstract:   (2818 Views)
Introduction: Autism Spectrum Disorder (ASD) is a mental disorder and affects a person's linguistic skills and social interactions. With the production of Functional Magnetic Resonance Imaging (fMRI) and the development of their processing tools, the use of these images in identifying and evaluating the brain function of autistic people received a lot of attention. However, in this approach using the functional connectivity matrices leads to the creation of feature space with very high dimensions. Some of these features are dependent, unnecessary and additional, which reduces the quality of detection and increases the number of calculations. Therefore, regarding the large dimensions of the search space, the Particle Swarm Optimization (PSO) algorithm has been used as one of the powerful meta-heuristic search tools in selecting the optimal features. Materials and Methods: To evaluate the capability of the proposed method, the principal component analysis (PCA) algorithm is used as a standard dimension reduction method. In this study, the Support Vector Machines (SVM) classifier was used to detect autistic and healthy persons on the ABIDE database data. Feature space has been generated based on a functional connectivity matrix which has 6670 dimensions. Results: SVM accuracy in high-dimensional specialty space is 56%. The proposed method based on PSO eliminates 3442 redundant features and increases classification accuracy up to 62.19 % that performs better than PCA. The findings show that this meta-heuristic algorithm by removing almost half of the features results in a 6% increase in classification precision. Conclusion: The results indicate the ability of SVM in comparison with the Random Forest and K-Nearest Neighbor (KNN). PSO algorithm was used for dimension reduction of the input data space.
Keywords: Magnetic Resonance Imaging, Support Vector Machine, Autistic Disorder
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Type of Study: Research --- Open Access, CC-BY-NC | Subject: Basic research in Neuroscience
1. Ghaffari M.A, Mousavinejad. E, Riahi. F, Mousavinejad. M, Afsharmanesh M.R. Increased Serum Levels of Tumor Necrosis Factor-Alpha, Resistin, And Visfatin in the Children with Autism Spectrum Disorders: A Case-Control Study. Hindawi Publishing Corporation; Article ID 9060751 2016.; pp: 7. [DOI:10.1155/2016/9060751]
2. Savoy R.L. History and Future Directions of Human Brain Mapping and Functional Neuroimaging. Elsevier Science B.V 2001; pp: 9-42. [DOI:10.1016/S0001-6918(01)00018-X]
3. Ogawa. S. Magnetic Resonance Imaging of Blood Vessels at High Fields: in Vivo and in Vitro Measurements and Image Simulation. Magnetic Resonance Imaging 1990; vol.16, No.1. pp: 9-18. [DOI:10.1002/mrm.1910160103]
4. Fox M.D and Raichle M.E. Spontaneous Fluctuations in Brain Activity Observed with Functional Magnetic Resonance Imaging. Nature 2007; vol. 8, pp: 700-711. [DOI:10.1038/nrn2201]
5. Nielsen J.A, Zielinski B.A, Fletcher P.T, Alexander A.L, Lange. N, Bigler E.D, et. Multisite Functional Connectivity MRI Classification of Autism: ABIDE Results. Brain a Journal of Neurology 2013; pp: 134: 3742-3754. [DOI:10.3389/fnhum.2013.00599]
6. Dickstein D.P, Pescosolido M.F, Reidy B.L, Galvan. T, Kim K.L, Seymour K.E, et. Developmental Meta-Analysis of the Functional Neural Correlates of Autism Spectrum Disorders. Journal of the American Academy of Child and Adolescent Psychiatry 2013; vol.52, pp: 279-289. [DOI:10.1016/j.jaac.2012.12.012]
7. Plitt. M, Barnes K.A, Martin. A. Functional Connectivity Classification of Autism Identifies Highly Predictive Brain Features but Falls Short of Biomarker Standards. NeuroImage: Clinical 2015; vol.7, pp: 359-366. [DOI:10.1016/j.nicl.2014.12.013]
8. Friston K.J. Functional and Effective Connectivity in Neuroimaging: A Synthesis. Hum. Brain Mapp 1994; vol.2, pp: 56-78. [DOI:10.1002/hbm.460020107]
9. Iidaka. T. Resting State Functional Magnetic Resonance Imaging and Neural Network Classified Autism and Control. Cortex 2015; vol.63, pp: 55-67. [DOI:10.1016/j.cortex.2014.08.011]
10. Kassraian Fard. P, Matthis. C, Balsters J.H, Maathuis M.H, Wenderoth. N. Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example. Front. Psychiatry 2016; doi: 10.3389/fpsyt.2016.00177. [DOI:10.3389/fpsyt.2016.00177]
11. Heinsfeld A.S, Franco A.R, Craddock R.C, Buchweitz. A, Meneguzzi. F. Identification of Autism Spectrum Disorder Using Deep Learning and the ABIDE Dataset. NeuroImage: Clinical 17 2017; pp: 16-23. [DOI:10.1016/j.nicl.2017.08.017]
12. Fredo A.R.J, Jahedi. A, Reiter. M, Müller R.A. Diagnostic Classification of Autism Using Resting-State fMRI Data and Conditional Random Forest. IEEE; 978-1-5386-3646-6/18/$31.00, 2018.
13. Kong. Y, Gao. J, Xu. Y, Pan. Y, Wang. J, Liu. J. Classification of Autism Spectrum Disorder by Combining Brain Connectivity and Deep Neural Network Classifier. Neurocomputing 2018; pp: 63-68. [DOI:10.1016/j.neucom.2018.04.080]
14. Eslami. T, Mirjalili. V, Fong. A, Laird. A, Saeed. F. ASD-DiagNet: A Hybrid Learning Aapproach for Detection of Autism Spectrum Disorder Using fMRI Data. arXiv:1904.07577v1, 2019. [DOI:10.3389/fninf.2019.00070]
15. Shihab A.I, Dawood F.A, Kashmar. AH. Data Analysis and Classification of Autism Spectrum Disorder Using Principal Component Analysis. Hindawi 2020; Article ID 3407907, pp: 8. [DOI:10.1155/2020/3407907]
16. Eslami. T, S. Raiker. J, Saeed. F. Explainable and Scalable Machine-Learnig Algorithms for Detection of Autism Spectrum Disorder Using fMRI Data. Neurons and Cognition 2020; arXiv:2003.01541. [DOI:10.1016/B978-0-12-822822-7.00004-1]
17. Thomas R.M, Gallo. S, Cerliani. L, Zhutovsky. P, El-Gazzar. A, Wingen GV. Classifying Autism Spectrum Disorder Using the Temporal Statistics of Resting-State Functional MRI Data with 3D Convolutional Neural Networks. Front. Psychiatry 2020 [DOI:10.3389/fpsyt.2020.00440. doi: 10.3389/fpsyt. 2020. 00440.]
18. ABIDE, http: //fcon1000.projects.nitrc.org/indi/abide.
19. Di Martino. A, Yan C.G, Li. Q, Denio. E, Castellanos F.X, Alaerts. K, et al. The Autism Brain Imaging Data Exchange: Towards a Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism. Molecular psychiatry 2013; vol.19, no.6, pp: 1-9. [DOI:10.1038/mp.2013.78]
20. Poldrack R.A, Mumford J.A, Nichols T.E. Handbook of Functional MRI Data Analysis. Columbia University Libraries 2011; vol.10.1017/CBO9780511895029(1), pp: 1-1291.
21. Frackowiak. R, Ashburner. J, Penny. W, Zeki. S. Human Brain Function. The Wellcome Dept. of Imaging Neuroscience 2004; 2 Edition, ISBN: 0122648412,9780122648410.
22. Lang E.W, Tomé A.M, Keck I.R, Górriz-Sáez J.M, Puntonet C.G. Brain Connectivity Analysis: A Short Survey. Published online 2012; 1148(7), pp: 781-7. [DOI:10.1155/2012/412512]
23. Just M.A, Cherkassky V.L, Keller T.A.K, Kana R.K, Minshew N.J. Functional and Anatomical Cortical Underconnectivity in Autism: Evidence from an fMRI Study of an Executive Function Task and Corpus Callosum Morphometry. Cerebral Cortex 2007; pp: 951-61. [DOI:10.1093/cercor/bhl006]
24. Hull J.V, Dokovna L.B, Jacokes Z.J, Torgerson C.M, Irimia. A, Van Horn J.D. Resting-State Functional Connectivity in Autism Spectrum Disorders: A Review. Published NCBI 2017; pp: 205. [DOI:10.3389/fpsyt.2016.00205]
25. Tzourio-Mazoyer. N, Landeau. B, Papathanassiou. D, Crivello. F, Etard. O, et al. Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain. Neuroimage 2002; vol.15, No.1, pp: 273-289. [DOI:10.1006/nimg.2001.0978]
26. Poli. R, Kennedy. J, Blackwell. T. Particle swarm optimization. Springer Science and Business Media 2007; pp: 1: 33-57. [DOI:10.1007/s11721-007-0002-0]
27. Cortes. C, Vapnik. V. Support Vector Network. Mach Learn 1995; vol.20, pp: 273-297. [DOI:10.1007/BF00994018]
28. Ulfarsson M.O, Solo. V. A Semiparametric PCA Approach to fMRI Data Analysis. International Conference on Acoustics Speech and Signal Processing (ICASSP) 2010; pp: 634-637. [DOI:10.1109/ICASSP.2010.5495164]

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