Araştırma Makalesi
PDF Zotero Mendeley EndNote BibTex Kaynak Göster

Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images

Yıl 2021, Cilt 4, Sayı 1, 142 - 155, 30.04.2021
https://doi.org/10.35377/saucis.04.01.879735

Öz

Brain injuries are significant disorders affecting human life. Some of these damages can be completely eliminated by methods such as drug therapy. On the other hand, there is no known permanent treatment for damages caused by diseases such as Alzheimer, Autism spectrum disorder (ASD), Multiple sclerosis and Parkinson. Treatments aimed at slowing the progression of the disease are generally applied in these types of disorders. For this reason, essential to diagnose the disease at an early phase before behavioral disorders occur. In this study, a study is presented to detect ASD through resting-state functional magnetic resonance imaging rs-fMRI. However, fMRI data are highly complex data. Within the study's scope, ASD and healthy individuals were distinguished on 871 samples obtained from the ABIDE I data set. The long short-term memory network (LSTM), convolutional neural network (CNN) , and hybrid models are used together for the classification process. The results obtained are promising for the detection of ASD on fMRI.

Kaynakça

  • M. A. Aghdam, A. Sharifi, and 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, vol. 32, no. 6, pp. 899-918, 2019.
  • B. Crosson et al., "Functional Imaging and Related Techniques: An Introduction for Rehabilitation Researchers," Journal of Rehabilitation Research and Development, vol. 47, no. 2, pp. vii-xxxiv, 2010.
  • S. Sarraf and J. Sun, "Functional Brain Imaging: A Comprehensive Survey," ArXiv Preprint, arXiv:1602.02225, 2016.
  • 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, pp. 63-68, 2019.
  • D. G. Amaral, C. M. Schumann, and C. W. Nordahl, "Neuroanatomy of Autism," Trends in Neurosciences, vol. 31, no. 3, pp. 137-145, 2008.
  • K. C. Turner, L. Frost, D. Linsenbardt, J. R. Mcllroy and R. Müller, "Atypically Diffuse Functional Connectivity Between Caudate Nuclei and Cerebral Cortex in Autism," Behavioral and Brain Functions, vol. 2, no. 1, p. 34, 2006.
  • S. J. Blumberg, M. D. Bramlett, M. D. Kogan, L. A. Schieve, J. R. Jones and M. C. Lu, “Changes in prevalence of parent-reported autism spectrum disorder in school-aged US children: 2007 to 2011-2012,” National Center for Health Statistics, no. 65, pp. 1-11, 2013.
  • M. Langen, S. Durston, W. G. Staal, S. J.M.C.Palmen and H. V. Engeland, "Caudate Nucleus Is Enlarged in High-Functioning Medication-Naive Subjects with Autism," Biological psychiatry, vol. 62, no. 3, pp. 262-266, 2007.
  • M. Coleman and C. Gillberg, “The Autisms,” OUP USA, 2012.
  • L. Waterhouse, “Rethinking Autism: Variation and Complexity,” Academic Press, 2013.
  • E. Fernell, M. A. Eriksson, and C. Gillberg, "Early Diagnosis of Autism and Impact on Prognosis: a Narrative Review," Clinical Epidemiology, vol. 5, pp. 33-43, 2013.
  • B. E. Yerys and B. F. Pennington, "How do we establish a biological marker for a behaviorally defined disorder? Autism as a test case," Autism Research, vol. 4, no. 4, pp. 239-241, 2011.
  • M. Plitt, K. A. Barnes, and A. Martin. "Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards," NeuroImage: Clinical, vol. 7, pp. 359-366, 2015.
  • C. M. Bishop, “Pattern Recognition and Machine Learning,” Springer-Verlag New York Inc., Secaucus, NJ, USA, 2006.
  • A. L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers,” IBM J. Res. Dev., vol. 3, no. 3, pp. 210–229, Jul. 1959.
  • T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” Springer Series in Statistic, 2009.
  • M. Uçar and E. Uçar, "Computer-aided detection of lung nodules in chest X-rays using deep convolutional neural networks," Sakarya University Journal of Computer and Information Sciences, vol. 2, no. 1, pp. 1-8, 2019.
  • Y. Alakoç, V. Akdoğan, M. Korkmaz and O. Er, "Pre-Diagnosis of Osteoporosis Using Probabilistic Neural Networks," Sakarya University Journal of Computer and Information Sciences, vol. 1, no. 3, pp. 1-6, 2018.
  • E. Erdem and T. Aydin, "Detection of Pneumonia with a Novel CNN-based Approach," Sakarya University Journal of Computer and Information Sciences, vol. 4, no. 1, pp. 26-34, 2021.
  • D. B. Aydın and O. Er, "A new proposal for early stage diagnosis of urinary tract infection using computers aid systems," Sakarya University Journal of Computer and Information Sciences, vol. 1, no. 1, pp. 1-9, 2018.
  • G. Ozen, R. Sultanov, Y. Özen and Z. Y. Güneş, "A Convolutional Neural Network Based on Raw Single Channel EEG for Automatic Sleep Staging," Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 2, pp. 149-158, 2020.
  • A. Di Martino et al., "The Autism Brain Imaging Data Exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism," Molecular Psychiatry, vol. 19, no. 6, pp. 659-667, 2014.
  • E. Wong, J. S. Anderson, B. A. Zielinski and P. T. Fletcher, "Riemannian regression and classification models of brain networks applied to autism," International Workshop on Connectomics in Neuroimaging, Springer, Cham, 2018.
  • A. Abraham, M. Milham, A. D. Martino, R. C. Craddock, D. Samaras, B. Thirion and G. Varoquaux, "Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example," NeuroImage, vol. 147, pp. 736-745, 2017.
  • S. Parisot, S. I. Ktena, E. Ferrante, M. Lee, R. G. Moreno, B. Glocker and D. Rueckert, "Spectral graph convolutions for population-based disease prediction,” International conference on medical image computing and computer-assisted intervention, Springer, Cham, 2017.
  • J. Wang et al., "Parcellation‐dependent small‐world brain functional networks: A resting‐state fMRI study," Human brain mapping, vol. 30, no. 5, pp. 1511-1523, 2009.
  • A. Zalesky, A. Fornito, I. H. Harding, L. Cocchi, M. Yücel, C. Pantelis and E. T. Bullmore, "Whole-brain anatomical networks: does the choice of nodes matter?," Neuroimage, vol. 50, no. 3, pp. 970-983, 2010.
  • C. T. Butts, "Revisiting the foundations of network analysis, " Science, vol. 325, no. 5939, pp. 414-416, 2009.
  • M. Jenkinson, P. Bannister, M. Brady and S. Smith, "Improved optimization for the robust and accurate linear registration and motion correction of brain images," Neuroimage, vol. 17, no. 2, pp. 825-841, 2002.
  • Z. Long, X. Duan, D. Mantini, and H. Chen, "Alteration of functional connectivity in autism spectrum disorder: effect of age and anatomical distance," Scientific Reports, vol. 6, no. 1, pp. 1-8, 2016.
  • P. Fransson, U. Aden, M. Blennow and H. Lagercrantz, "The functional architecture of the infant brain as revealed by resting-state fMRI," Cerebral Cortex, vol. 21, no. 1, pp. 145-154, 2011.
  • P. Bellec, P. Rosa-Neto, O. C. Lyttelton, H. Benali, A. C. Evans, "Multi-level bootstrap analysis of stable clusters in resting-state fMRI," Neuroimage, vol. 51, no. 3, pp. 1126-1139, 2010.
  • P. Bellec, "Mining the hierarchy of resting-state brain networks: selection of representative clusters in a multiscale structure," International Workshop on Pattern Recognition in Neuroimaging, IEEE, 2013.
  • C. Craddock et al., "The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives," Frontiers in Neuroinformatics, 2013.
  • A. T. Kabakuş, "A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study," Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 3, pp. 169-182, 2020.
  • Z. Özer, “The Effect of Normalization on the Classification of Traffic Comments,” Ph. D. Thesis, Karabük Unv. Grad. Sch. of Nat. and App. Sci, Dept. of Computer Engineering, Karabük, Turkey, 2019.
  • A. A. Müngen, İ. Aygün, and M. Kaya, "News and Social Media Users Emotions in the COVID-19 Process," Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 3, pp. 250-263, 2020.
  • Y. LeCun, L. Bottou, Y. Bengio and P. Haffner "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
  • S. Mostafa, L. Tang, and F-X. Wu, "Diagnosis of Autism Spectrum Disorder Based on Eigenvalues of Brain Networks," IEEE Access, vol. 7, pp. 128474-128486, 2019.
  • N. C. Dvornek, P. Ventola, and J. S. Duncan, "Combining phenotypic and resting-state fMRI data for autism classification with recurrent neural networks," IEEE 2018 15th International Symposium on Biomedical Imaging, IEEE, 2018.
  • A. S. Heinsfeld, A. R. Franco, R. C. Craddock, A. Buchweitz and F. Meneguzzi “Identification of autism spectrum disorder using deep learning and the ABIDE dataset,” NeuroImage: Clinical, vol. 17, pp. 16-23, 2017.
  • H. Sharif and R. A. Khan, "A novel framework for automatic detection of autism: A study on corpus callosum and intracranial brain volume," arXiv preprint, arXiv:1903.11323, 2019.
  • M. Khosla, K. Jamison, A. Kuceyeski, and M. R. Sabuncu, “3D convolutional neural networks for classification of functional connectomes,” Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, Cham, pp. 137-145, 2018.

Yıl 2021, Cilt 4, Sayı 1, 142 - 155, 30.04.2021
https://doi.org/10.35377/saucis.04.01.879735

Öz

Kaynakça

  • M. A. Aghdam, A. Sharifi, and 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, vol. 32, no. 6, pp. 899-918, 2019.
  • B. Crosson et al., "Functional Imaging and Related Techniques: An Introduction for Rehabilitation Researchers," Journal of Rehabilitation Research and Development, vol. 47, no. 2, pp. vii-xxxiv, 2010.
  • S. Sarraf and J. Sun, "Functional Brain Imaging: A Comprehensive Survey," ArXiv Preprint, arXiv:1602.02225, 2016.
  • 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, pp. 63-68, 2019.
  • D. G. Amaral, C. M. Schumann, and C. W. Nordahl, "Neuroanatomy of Autism," Trends in Neurosciences, vol. 31, no. 3, pp. 137-145, 2008.
  • K. C. Turner, L. Frost, D. Linsenbardt, J. R. Mcllroy and R. Müller, "Atypically Diffuse Functional Connectivity Between Caudate Nuclei and Cerebral Cortex in Autism," Behavioral and Brain Functions, vol. 2, no. 1, p. 34, 2006.
  • S. J. Blumberg, M. D. Bramlett, M. D. Kogan, L. A. Schieve, J. R. Jones and M. C. Lu, “Changes in prevalence of parent-reported autism spectrum disorder in school-aged US children: 2007 to 2011-2012,” National Center for Health Statistics, no. 65, pp. 1-11, 2013.
  • M. Langen, S. Durston, W. G. Staal, S. J.M.C.Palmen and H. V. Engeland, "Caudate Nucleus Is Enlarged in High-Functioning Medication-Naive Subjects with Autism," Biological psychiatry, vol. 62, no. 3, pp. 262-266, 2007.
  • M. Coleman and C. Gillberg, “The Autisms,” OUP USA, 2012.
  • L. Waterhouse, “Rethinking Autism: Variation and Complexity,” Academic Press, 2013.
  • E. Fernell, M. A. Eriksson, and C. Gillberg, "Early Diagnosis of Autism and Impact on Prognosis: a Narrative Review," Clinical Epidemiology, vol. 5, pp. 33-43, 2013.
  • B. E. Yerys and B. F. Pennington, "How do we establish a biological marker for a behaviorally defined disorder? Autism as a test case," Autism Research, vol. 4, no. 4, pp. 239-241, 2011.
  • M. Plitt, K. A. Barnes, and A. Martin. "Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards," NeuroImage: Clinical, vol. 7, pp. 359-366, 2015.
  • C. M. Bishop, “Pattern Recognition and Machine Learning,” Springer-Verlag New York Inc., Secaucus, NJ, USA, 2006.
  • A. L. Samuel, “Some Studies in Machine Learning Using the Game of Checkers,” IBM J. Res. Dev., vol. 3, no. 3, pp. 210–229, Jul. 1959.
  • T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Prediction,” Springer Series in Statistic, 2009.
  • M. Uçar and E. Uçar, "Computer-aided detection of lung nodules in chest X-rays using deep convolutional neural networks," Sakarya University Journal of Computer and Information Sciences, vol. 2, no. 1, pp. 1-8, 2019.
  • Y. Alakoç, V. Akdoğan, M. Korkmaz and O. Er, "Pre-Diagnosis of Osteoporosis Using Probabilistic Neural Networks," Sakarya University Journal of Computer and Information Sciences, vol. 1, no. 3, pp. 1-6, 2018.
  • E. Erdem and T. Aydin, "Detection of Pneumonia with a Novel CNN-based Approach," Sakarya University Journal of Computer and Information Sciences, vol. 4, no. 1, pp. 26-34, 2021.
  • D. B. Aydın and O. Er, "A new proposal for early stage diagnosis of urinary tract infection using computers aid systems," Sakarya University Journal of Computer and Information Sciences, vol. 1, no. 1, pp. 1-9, 2018.
  • G. Ozen, R. Sultanov, Y. Özen and Z. Y. Güneş, "A Convolutional Neural Network Based on Raw Single Channel EEG for Automatic Sleep Staging," Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 2, pp. 149-158, 2020.
  • A. Di Martino et al., "The Autism Brain Imaging Data Exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism," Molecular Psychiatry, vol. 19, no. 6, pp. 659-667, 2014.
  • E. Wong, J. S. Anderson, B. A. Zielinski and P. T. Fletcher, "Riemannian regression and classification models of brain networks applied to autism," International Workshop on Connectomics in Neuroimaging, Springer, Cham, 2018.
  • A. Abraham, M. Milham, A. D. Martino, R. C. Craddock, D. Samaras, B. Thirion and G. Varoquaux, "Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example," NeuroImage, vol. 147, pp. 736-745, 2017.
  • S. Parisot, S. I. Ktena, E. Ferrante, M. Lee, R. G. Moreno, B. Glocker and D. Rueckert, "Spectral graph convolutions for population-based disease prediction,” International conference on medical image computing and computer-assisted intervention, Springer, Cham, 2017.
  • J. Wang et al., "Parcellation‐dependent small‐world brain functional networks: A resting‐state fMRI study," Human brain mapping, vol. 30, no. 5, pp. 1511-1523, 2009.
  • A. Zalesky, A. Fornito, I. H. Harding, L. Cocchi, M. Yücel, C. Pantelis and E. T. Bullmore, "Whole-brain anatomical networks: does the choice of nodes matter?," Neuroimage, vol. 50, no. 3, pp. 970-983, 2010.
  • C. T. Butts, "Revisiting the foundations of network analysis, " Science, vol. 325, no. 5939, pp. 414-416, 2009.
  • M. Jenkinson, P. Bannister, M. Brady and S. Smith, "Improved optimization for the robust and accurate linear registration and motion correction of brain images," Neuroimage, vol. 17, no. 2, pp. 825-841, 2002.
  • Z. Long, X. Duan, D. Mantini, and H. Chen, "Alteration of functional connectivity in autism spectrum disorder: effect of age and anatomical distance," Scientific Reports, vol. 6, no. 1, pp. 1-8, 2016.
  • P. Fransson, U. Aden, M. Blennow and H. Lagercrantz, "The functional architecture of the infant brain as revealed by resting-state fMRI," Cerebral Cortex, vol. 21, no. 1, pp. 145-154, 2011.
  • P. Bellec, P. Rosa-Neto, O. C. Lyttelton, H. Benali, A. C. Evans, "Multi-level bootstrap analysis of stable clusters in resting-state fMRI," Neuroimage, vol. 51, no. 3, pp. 1126-1139, 2010.
  • P. Bellec, "Mining the hierarchy of resting-state brain networks: selection of representative clusters in a multiscale structure," International Workshop on Pattern Recognition in Neuroimaging, IEEE, 2013.
  • C. Craddock et al., "The neuro bureau preprocessing initiative: open sharing of preprocessed neuroimaging data and derivatives," Frontiers in Neuroinformatics, 2013.
  • A. T. Kabakuş, "A Comparison of the State-of-the-Art Deep Learning Platforms: An Experimental Study," Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 3, pp. 169-182, 2020.
  • Z. Özer, “The Effect of Normalization on the Classification of Traffic Comments,” Ph. D. Thesis, Karabük Unv. Grad. Sch. of Nat. and App. Sci, Dept. of Computer Engineering, Karabük, Turkey, 2019.
  • A. A. Müngen, İ. Aygün, and M. Kaya, "News and Social Media Users Emotions in the COVID-19 Process," Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 3, pp. 250-263, 2020.
  • Y. LeCun, L. Bottou, Y. Bengio and P. Haffner "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
  • S. Mostafa, L. Tang, and F-X. Wu, "Diagnosis of Autism Spectrum Disorder Based on Eigenvalues of Brain Networks," IEEE Access, vol. 7, pp. 128474-128486, 2019.
  • N. C. Dvornek, P. Ventola, and J. S. Duncan, "Combining phenotypic and resting-state fMRI data for autism classification with recurrent neural networks," IEEE 2018 15th International Symposium on Biomedical Imaging, IEEE, 2018.
  • A. S. Heinsfeld, A. R. Franco, R. C. Craddock, A. Buchweitz and F. Meneguzzi “Identification of autism spectrum disorder using deep learning and the ABIDE dataset,” NeuroImage: Clinical, vol. 17, pp. 16-23, 2017.
  • H. Sharif and R. A. Khan, "A novel framework for automatic detection of autism: A study on corpus callosum and intracranial brain volume," arXiv preprint, arXiv:1903.11323, 2019.
  • M. Khosla, K. Jamison, A. Kuceyeski, and M. R. Sabuncu, “3D convolutional neural networks for classification of functional connectomes,” Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer, Cham, pp. 137-145, 2018.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Bilimleri, Disiplinler Arası Uygulamalar, Bilgisayar Bilimleri, Yapay Zeka
Bölüm Makaleler
Yazarlar

Muhammed Ali BAYRAM (Sorumlu Yazar)
BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ
0000-0001-7784-2992
Türkiye


İlyas ÖZER
BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ
0000-0003-2112-5497
Türkiye


Feyzullah TEMURTAŞ
BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ
0000-0002-3158-4032
Türkiye

Destekleyen Kurum Bandirma Onyedi Eylül University
Proje Numarası BAP-21-1003-003
Teşekkür This study has supported by the Scientific Research Project (BAP) Coordinatorship of Bandirma Onyedi Eylül University under grant number BAP-21-1003-003.
Yayımlanma Tarihi 30 Nisan 2021
Başvuru Tarihi 13 Şubat 2021
Kabul Tarihi 23 Mart 2021
Yayınlandığı Sayı Yıl 2021, Cilt 4, Sayı 1

Kaynak Göster

IEEE M. A. Bayram , İ. Özer ve F. Temurtaş , "Deep Learning Methods for Autism Spectrum Disorder Diagnosis Based on fMRI Images", Sakarya University Journal of Computer and Information Sciences, c. 4, sayı. 1, ss. 142-155, Nis. 2021, doi:10.35377/saucis.04.01.879735