Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental incurable disorder with a long diagnostic period encountered in the early years of life. If diagnosed early, the negative effects of this disease can be reduced by starting special education early. Machine learning (ML), an increasingly ubiquitous technology, can be applied for the early diagnosis of ASD. The aim of this study is to examine and provide a comprehensive state-of-the-art review of ML research for the diagnosis of ASD based on (a) structural magnetic resonance image (MRI), (b) functional MRI and (c) hybrid imaging techniques over the past decade. The accuracy of the studies with a large number of participants is in general lower than those with fewer participants leading to the conclusion that further large-scale studies are needed. An examination of the age of the participants shows that the accuracy of the automated diagnosis of ASD is higher at a younger age range. ML technology is expected to contribute significantly to the early and rapid diagnosis of ASD in the coming years and become available to clinicians in the near future. This review is aimed to facilitate that.
Funding source: The Scientific and Technological Research Council of Turkey
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: This study was supported by The Scientific and Technological Research Council of Turkey (TUBITAK).
Conflicts of interest: The authors declare that they have no conflict of interest.
References
Abraham, A., Milham, M.P., Di Martino, A., Craddock, R.C., Samaras, D., Thirion, B., and Varoquaux, G. (2017). Deriving reproducible biomarkers from multi-site resting-state data: an autism based example. NeuroImage 147: 736–745, https://doi.org/10.1016/j.neuroimage.2016.10.045.Suche in Google Scholar PubMed
Acharya, R.U., Oh, S.L., Hagiwara, Y., Tan, J.H., and Adeli, H. (2018). Deep convolutional neural network for the automated detection of seizure using EEG signals. Comput. Biol. Med. 100: 270–278, https://doi.org/10.1016/j.compbiomed.2017.09.017.Suche in Google Scholar PubMed
Aghdam, M.A., Sharifi, A., and Pedram, M.M. (2018). Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J. Digit. Imag. 31: 895–903, https://doi.org/10.1007/s10278-018-0093-8.Suche in Google Scholar PubMed PubMed Central
Ahmadlou, M., and Adeli, H. (2010). Enhanced probabilistic neural network with local decision circles: a robust classifier. Integr Comput. Aid E. 17: 197–210, https://doi.org/10.3233/ica-2010-0345.Suche in Google Scholar
Ahmadlou, M., Adeli, H., and Adeli, A. (2010). Fractality and a wavelet-chaos-neural network methodology for EEG-based diagnosis of autistic spectrum disorder. J. Clin. Neurophysiol. 27: 328–333, https://doi.org/10.1097/wnp.0b013e3181f40dc8.Suche in Google Scholar PubMed
Ahmadlou, M., Adeli, H., and Adeli, A. (2012a). Improved visibility graph fractality with application for diagnosis of autism spectrum disorder. Phys 391: 4720–4726, https://doi.org/10.1016/j.physa.2012.04.025.Suche in Google Scholar
Ahmadlou, M., Adeli, H., and Adeli. (2012b). A fuzzy synchronization likelihood-wavelet methodology for diagnosis of autism spectrum disorder. J. Neurosci. Methods 211: 203–209, https://doi.org/10.1016/j.jneumeth.2012.08.020.Suche in Google Scholar PubMed
American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders, 5th edn. Washington, DC: American Psychiatric Publishing.10.1176/appi.books.9780890425596Suche in Google Scholar
Anderson, J.S., Nielsen, J.A., Froehlich, A.L., DuBray, M.B., Druzgal, T.J., Cariello, A.N., Cooperrider, J.R., Zielinski, B.A., Ravichandran, C., Fletcher, P.T., et al. (2011). Functional connectivity magnetic resonance imaging classification of autism. Brain 134: 3742–3754, https://doi.org/10.1093/brain/awr263.Suche in Google Scholar PubMed PubMed Central
Antoniades, A., Spyrou, L., Martin-Lopez, D., Valentin, A., Alarcon, G., Sanei, S., and Took, C.C. (2018). Deep neural architectures for mapping scalp to intracranial EEG. Int. J. Neural Syst. 28: 8, https://doi.org/10.1142/s0129065718500090.Suche in Google Scholar
Antunes, G., Faria Da Silva, S.F., and Simoes De Souza, F.M. (2018). Mirror neurons modeled through spike-timing dependent plasticity are affected by channelopathies associated with autism spectrum disorder. Int. J. Neural Syst. 28: 1750058, https://doi.org/10.1142/s0129065717500587.Suche in Google Scholar
Ansari, A.H., Cherian, P.J., Caicedo, A., Naulaers, G., De Vos, M., and Van Huffel, S. (2019). Neonatal seizure detection using deep convolutional neural networks. Int. J. Neural Syst. 29: 1850011, https://doi.org/10.1142/s0129065718500119.Suche in Google Scholar
Ashburner, J., and Friston, K.J. (2000). Voxel-based morphometry—the methods. NeuroImage 11: 805–821, https://doi.org/10.1006/nimg.2000.0582.Suche in Google Scholar PubMed
Autism and Developmental Disabilities Monitoring Network (2016). Prevalence and characteristics of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States 2012. Morb 65: 1–23, https://doi.org/10.15585/mmwr.ss6503a1.Suche in Google Scholar PubMed PubMed Central
Bang, S., Park, S., Kim, H., and Kim, H. (2019). Encoder–decoder network for pixel-level road crack detection in black-box images. Comput. Aided Civ. Infrastruct. Eng. 34: 713–727, https://doi.org/10.1111/mice.12440.Suche in Google Scholar
Bernas, A., Aldenkamp, A.P., and Zinger, S. (2018). Wavelet coherence-based classifier: a resting-state functional MRI study on neurodynamics in adolescents with high-functioning autism. Comput. Meth Prog. Bio. 154: 143–151, https://doi.org/10.1016/j.cmpb.2017.11.017.Suche in Google Scholar PubMed
Bhat, S., Acharya, U.R., Adeli, H., Muralidhar Bairy, G., and Adeli, A. (2014). Automated diagnosis of autism: in search of a mathematical marker. Rev. Neurosci. 25: 851–861, https://doi.org/10.1515/revneuro-2014-0036.Suche in Google Scholar PubMed
Bhaumik, R., Pradhan, A., Das, S., and Bhaumik, D.K. (2018). Predicting autism spectrum disorder using domain-adaptive cross-site evaluation. Neuroinformatics 16: 197–205, https://doi.org/10.1007/s12021-018-9366-0.Suche in Google Scholar PubMed
Biswal, B., Yetkin, F.Z., Haughton, V.M., and Hyde, J.S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34: 537–541, https://doi.org/10.1002/mrm.1910340409.Suche in Google Scholar PubMed
Calderoni, S., Retico, A., Biagi, L., Tancredi, R., Muratori, F., and Tosetti, M. (2012). Female children with autism spectrum disorder: an insight from mass-univariate and pattern classification analyses. NeuroImage 59: 1013–1022, https://doi.org/10.1016/j.neuroimage.2011.08.070.Suche in Google Scholar PubMed
Carbonell, F., Zijdenbos, A.P., Charil, A., Grand’Maison, M., and Bedell, B.J. (2015). Optimal target region for subject classification on the basis of amyloid PET images. J. Nucl. Med. 56: 1351–1358, https://doi.org/10.2967/jnumed.115.158774.Suche in Google Scholar
Chen, C.P., Keown, C.L., Jahedi, A., Nair, A., Pflieger, M.E., Bailey, B.A., and Muller, A.R. (2015). Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. NeuroImage: Clinical 8: 238–245, https://doi.org/10.1016/j.nicl.2015.04.002.Suche in Google Scholar
Chen, Y-W., and Lin, C-J. (2006). Combining SVMs with various feature selection strategies, feature extraction: Springer, pp. 315–324.10.1007/978-3-540-35488-8_13Suche in Google Scholar
Cheng, L., Zhu, Y., Sun, J., Deng, L., He, N., Yang, Y., Ling, H., Ayaz, H., Fu, Y., and Tong, S. (2018). Principal states of dynamic functional connectivity reveal the link between resting-state and task-state brain: an fMRI Study. Int. J. Neural Syst. 28: 7, https://doi.org/10.1142/s0129065718500028.Suche in Google Scholar
Constantino, J.N., and Charman, T. (2016). Diagnosis of autism spectrum disorder: reconciling the syndrome, its diverse origins, and variation in expression. 15, www.thelancet.com/neurology.10.1016/S1474-4422(15)00151-9Suche in Google Scholar
Corsi, M.C., Chavez, M., Schwartz, D., Hugueville, L., Khambhati, A.K., Bassett, D.S., and De Vico Fallani, F. (2019). Integrating EEG and MEG signals to improve motor imagery classification in brain-computer interfaces. Int. J. Neural Syst. 29: 1850014, https://doi.org/10.1142/s0129065718500144.Suche in Google Scholar PubMed
Dekhil, O., Hajjdiab, H., Babajide, A., Shalaby, A., Switala, A., Sosnin, D., Elshamekh, A., Ghazal, M., Keynton, R., Barnes, G., et al. (2018). Using resting state functional MRI to build a personalized autism diagnosis system 2018 IEEE 15th international symposium on biomedical imaging. https://doi.org/10.1371/journal.pone.0206351.Suche in Google Scholar PubMed PubMed Central
Deshpande, G., Libero, L.E., Sreenivasan, K.R., Deshpande, H.D., and Kana, R.K. (2013). Identification of neural connectivity signatures of autism using machine learning. Front. Hum. Neurosci. 7: 670, https://doi.org/10.3389/fnhum.2013.00670.Suche in Google Scholar PubMed PubMed Central
Destrieux, C., Fischl, B., Dale, A., and Halgren, E. (2010). Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53: 1–15, https://doi.org/10.1016/j.neuroimage.2010.06.010.Suche in Google Scholar PubMed PubMed Central
Di Martino, A., Yan, C.G., Li, Q., Denio, E., Castellanos, F.X., Alaerts, K., Anderson, J.S, Assaf, M., Bookheimer, S.Y., Dapretto, M., et al. (2014). The autism brain imaging data exchange: towards large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatr. 19: 659–667, https://doi.org/10.1038/mp.2013.78.Suche in Google Scholar PubMed PubMed Central
Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern classification: A Wiley-Interscience Publication John Wiley & Sons Inc.Suche in Google Scholar
Ecker, C., Rocha-Rego, V., Johnston, P., Mourao-Miranda, J., Marquand, A., Daly, E.M., Brammer, M.J., Murphy, C., Murphy, G.D., and The MRC AIMS Consortium (2009). Investigating the predictive value of whole-brain structural MR scans in autism: a pattern classification approach. NeuroImage 49: 44–56, https://doi.org/10.1016/j.neuroimage.2009.08.024.Suche in Google Scholar PubMed
Eill, A., Jahedi, A., Gao, Y., Kohli, J.S., Fong, C.H., Solders, S., Carper, R.A., Valafar, F., Bailey, B.A., and Müller, R.A. (2019). Functional connectivities are more informative than anatomical variables in diagnostic classification of autism. Brain Connect. 9: 1–9. https://doi.org/10.1089/brain.2019.0689.Suche in Google Scholar PubMed PubMed Central
Feczko, E., Balba, N., Miranda-Dominguez, O., Cordova, M., Karalunas, S.L., Irwin, L., Demeter, D.V., Hill, A.P., Langhorst, B.H., Painter, G.J., et al. (2017). Subtyping cognitive profiles in autism spectrum disorder using a random forest algorithm: Neuroimage.10.1016/j.neuroimage.2017.12.044Suche in Google Scholar PubMed PubMed Central
Gao, Y., Kong, B., and Mosalam, K.M. (2019). Deep leaf-bootstrapping generative adversarial network for structural image data augmentation. Comput. Aided Civ. Infrastruct. Eng. 34: 755–773, https://doi.org/10.1111/mice.12458.Suche in Google Scholar
Gaur, P., McCreadie, K., Pachori, R.B., Wang, H., and Prasad, G. (2019). Tangent space feature-based transfer learning classification model for two-class motor imagery brain-computer interface. Int. J. Neural Syst. 29, 19500215, https://doi.org/10.1142/S0129065719500254.Suche in Google Scholar PubMed
Ghiassian, S., Greiner, R., Jin, P., and Brown, M.R. (2016). Using functional or structural magnetic resonance images and personal characteristic data to identify ADHD and autism. PloS One 11: e0166934, https://doi.org/10.1371/journal.pone.0166934.Suche in Google Scholar PubMed PubMed Central
Gori, I., Giuliano, A., Muratori, F., Saviozzi, I., Oliva, P., Tancredi, R., Cosenza, A., Tosetti, M., Calderoni, S., and Retico, A. (2015). Gray matter alterations in young children with autism spectrum disorders: comparing morphometry at the voxel and regional level. J. Neuroimaging 25: 866–874, https://doi.org/10.1111/jon.12280.Suche in Google Scholar PubMed
Gorriz, J.M., Ramırez, J., Segovia, F., Martınez, F.J., Lai, M.C., Lombardo, M.V, Baron-Cohen, S., and Suckling, J. (2019). A machine learning approach to reveal the NeuroPhenotypes of autisms. Int. J. Neural Syst. 29: 7, https://doi.org/10.1142/s0129065718500582.Suche in Google Scholar
Haar, S., Berman, S., Behrmann, M., and Dinstein, I. (2016). Anatomical abnormalities in autism? Cerebr. Cortex 26: 1440–1452, https://doi.org/10.1093/cercor/bhu242.Suche in Google Scholar PubMed
Heinsfeld, A.S., Franco, A.R., Craddock, C., Buchweitz, A., and Meneguzzi, F. (2018). Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clinical 17: 16–23, https://doi.org/10.1016/j.nicl.2017.08.017.Suche in Google Scholar PubMed PubMed Central
Horlin, C., Falkmer, M., Parsons, R., and AlbrechtFalkmer, M.A.T. (2014). The cost of autism spectrum disorders. PLoS One 9: e106552, https://doi.org/10.1371/journal.pone.0106552.Suche in Google Scholar PubMed PubMed Central
Hua, C., Wang, H., Wang, H., Lu, S., Liu, C., and Khalid, S.M. (2019). A novel method of building functional brain network using deep learning algorithm with application in proficiency detection. Int. J. Neural Syst. 29: 1850015, https://doi.org/10.1142/s0129065718500156.Suche in Google Scholar PubMed
Huang, H., Liu, X., Jin, Y., Lee, S., and WeeShen, C.D. (2019a). Enhancing the representation of functional connectivity networks by fusing multi-view information for autism spectrum disorder diagnosis. Hum. Brain Mapp. 40: 833–854, https://doi.org/10.1002/hbm.24415.Suche in Google Scholar PubMed PubMed Central
Huang, Y., Beck, J.L., and Li, H. (2019b). Multitask sparse Bayesian learning with applications in structural health monitoring. Comput. Aided Civ. Infrastruct. Eng. 34: 732–754, https://doi.org/10.1111/mice.12408.Suche in Google Scholar
Huettel, S.A., Song, A.W., and McCarthy, G. (2009). Functional magnetic resonance imaging, 2 ed. Massachusetts: Sinauer. 978-0-87893-286-3.Suche in Google Scholar
Iidaka, T. (2015). Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex 63: 55–67, https://doi.org/10.1016/j.cortex.2014.08.011.Suche in Google Scholar PubMed
Irimia, A., Lei, X., Torgerson, C.M., Jacokes, Z.J., Abe, S., and Van Horn, J.D. (2018). Support vector machines, multidimensional scaling and magnetic resonance imaging reveal structural brain abnormalities associated with the interaction between autism spectrum disorder and sex. Front. Comput. Neurosci. 12: 93, https://doi.org/10.3389/fncom.2018.00093.Suche in Google Scholar PubMed PubMed Central
Iturria-Medina, Y. (2013). Anatomical brain networks on the prediction of abnormal brain states. Brain Connect. 3: 1–21, https://doi.org/10.1089/brain.2012.0122.Suche in Google Scholar PubMed
Iturria-Medina, Y., Canales-Rodriguez, E.J., Melie-Garcia, L., Valdes-Hernandez, P.A., Martinez-Montes, E., Alemán-Gómez, Y., and Sánchez-Bornot, J.M. (2007). Characterizing brain anatomical connections using diffusion weighted MRI and graph theory. NeuroImage 36: 645–660, https://doi.org/10.1016/j.neuroimage.2007.02.012.Suche in Google Scholar PubMed
Jacob, S., Wolff, J.J., Steinbach, M.S., Doyle, B.C., Kumar, V., and Elison, J.T. (2019). Neurodevelopmental heterogeneity and computational approaches for understanding autism. Transl. Psychiatry 9: 63, https://doi.org/10.1038/s41398-019-0390-0.Suche in Google Scholar PubMed PubMed Central
Jiao, Y., Chen, R., Ke, X., Chu, K., Lu, Z., and Herskovits, E.H. (2010). Predictive models of autism spectrum disorder based on brain regional cortical thickness. NeuroImage 50: 589–599, https://doi.org/10.1016/j.neuroimage.2009.12.047.Suche in Google Scholar PubMed PubMed Central
Katuwal, G.J., Baum, S.A., Cahill, N.D., and Michael, A.M. (2016). Divide and conquer: sub-grouping of ASD improves ASD detection based on brain morphometry. PLoS One 11: e0153331, https://doi.org/10.1371/journal.pone.0153331.Suche in Google Scholar PubMed PubMed Central
Kazemi, M., Bordbar, M.R.F., and Shahri, N.M. (2017). Comparative dermatoglyphic study between autistic patients and normal people in Iran. Iran. J. Med. Sci. 42: 392–396.Suche in Google Scholar
Kazeminejad, A., and Sotero, R.C. (2018). Topological properties of resting-state fMRI functional networks improve machine learning-based autism classification. Front Neurol 12: 1018, https://doi.org/10.3389/fnins.2018.01018.Suche in Google Scholar
Klin, A., Klaiman, C., and Jones, W. (2015). Reducing age of autism diagnosis: developmental social neuroscience meets public health challenge. Rev. Neurologia. 60(Suppl. 1): S3–11, https://doi.org/10.33588/rn.60s01.2015019.Suche in Google Scholar
Kohavi, R., and John, G.H. (1997). Wrappers for feature subset selection. Artif. Intell. 97: 273–324, https://doi.org/10.1016/s0004-3702(97)00043-x.Suche in Google Scholar
Kong, Y., Gao, J., Xu, Y., Pan, Y., Wang, J., and Liu, J. (2019). Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier. Neurocomputing 324: 63–68, https://doi.org/10.1016/j.neucom.2018.04.080.Suche in Google Scholar
Li, G., Liu, M., Sun, Q., Shen, D., and Wang, L. (2018a). Early diagnosis of autism disease by multi-channel CNNs, conference: 9th international workshop on machine learning in medical imaging (MLMI). Granada, Spain 11046: 303–309, https://doi.org/10.1007/978-3-030-00919-9_35.Suche in Google Scholar PubMed PubMed Central
Li, H., Parikh, N.A., and He, L. (2018b). A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes. Front Neurol 12: 491, https://doi.org/10.3389/fnins.2018.00491.Suche in Google Scholar PubMed PubMed Central
Li, S., Zhao, X., and Zhou, G. (2019). Automatic pixel-level multiple damage types detection of concrete structure using fully convolutional networks. Comput. Aided Civ. Infrastruct. Eng. 34: 616–634, https://doi.org/10.1111/mice.12433.Suche in Google Scholar
Liang, X. (2019). Image-based post-disaster inspection of reinforced concrete bridge systems using deep learning with bayesian optimization. Comput. Aided Civ. Infrastruct. Eng. 34: 415–430, https://doi.org/10.1111/mice.12425.Suche in Google Scholar
Libero, L.E., DeRamus, T.P., Lahti, A.C., Deshpande, G., and Kana, R.K. (2015). Multimodal neuroimaging based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates. Cortex 66: 46–59, https://doi.org/10.1016/j.cortex.2015.02.008.Suche in Google Scholar PubMed PubMed Central
Logothetis, N.K., Pauls, J., Auguth, M., Trinath, T., and Oeltermann, A. (2001). A neurophysiological investigation of the basis of the BOLD signal in fMRI. Nature 412: 150–157, https://doi.org/10.1038/35084005.Suche in Google Scholar PubMed
Lopez-Rubio, E., Molina-Cabello, M.A., Luque-Baena, R.M., and Dominguez, E. (2018). Foreground detection by competitive learning for varying input distributions. Int. J. Neural Syst. 28: 1750056, https://doi.org/10.1142/s0129065717500563.Suche in Google Scholar
Lord, C., Petkova, E., Hus, V., Gan, W., Lu, F., Martin, D.M, Ousley, O., Guy, L., Bernier, R., Gerdts, J., et al. (2012). A multisite study of the clinical diagnosis of different autism spectrum disorders. Arch. Gen. Psychiatry. 69: 306–313, https://doi.org/10.1001/archgenpsychiatry.2011.148.Suche in Google Scholar PubMed PubMed Central
Lord, C., Rutter, M., DiLavore, P.C., and Risi, S. (2000a). The autism diagnostic observation schedule (ADOS). Los Angeles, CA: Western Psychological Services.10.1037/t17256-000Suche in Google Scholar
Maeda, K., Ogawa, T., Haseyama, M., and Takahashi, S. (2019). Convolutional sparse coding-based deep random vector functional link network for distress classification of road structures. Comput. Aided Civ. Infrastruct. Eng. 34: 654–676, https://doi.org/10.1111/mice.12451.Suche in Google Scholar
Mandl, R.C., Schnack, H.G., Zwiers, M.P., Van Der Schaaf, A., Kahn, R.S., and Pol, H.E.H. (2008). Functional diffusion tensor imaging: measuring task-related fractional anisotropy changes in the human brain along white matter tracts. PLoS One 3: e3631, https://doi.org/10.1371/journal.pone.0003631.Suche in Google Scholar PubMed PubMed Central
Manzanera, M.O., Meles, S.K., Leenders, K.L., Renken, R.J., Pagani, M., Arnaldi, D., Nobili, F., Obeso, J., Oroz, M.R., Morbelli, S., et al. (2019). Scaled subprofile modeling and convolutional neural networks for the identification of Parkinson’s disease in 3D nuclear imaging data. Int. J. Neural Syst. 29: 1950010, https://doi.org/10.1142/s0129065719500102.Suche in Google Scholar PubMed
Matson, J.L., Rieske, R.D., and Williams, L.W. (2013). The relationship between autism spectrum disorders and attention-deficit/hyperactivity disorder: an overview. Res. Dev. Disabil. 34: 2475–2484, https://doi.org/10.1016/j.ridd.2013.05.021.Suche in Google Scholar PubMed
Milièiæ, J., Petkoviæ, B.Z., and Boikov, J. (2003). Dermatoglyphs of digito-palmar complex in autistic disorder: family analysis. Croatian Med. J. 44: 469–476.Suche in Google Scholar
Mirzaei, G., and Adeli, H. (2019). Segmentation and clustering in brain MRI imaging. Rev. Neurosci. 30: 31–44, https://doi.org/10.1515/revneuro-2018-0050.Suche in Google Scholar PubMed
Molina-Cabello, M.A., Luque-Baena, R.M., López-Rubio, E., and Thurnhofer-Hemsi, K. (2018). Vehicle type detection by ensembles of convolutional neural networks operating on super-resolved images. Integr Comput. Aid E. 25: 321–333, https://doi.org/10.3233/ica-180577.Suche in Google Scholar
Moradi, E., Khundrakpam, B., Lewis, J.D., Evans, A.C., and Tohka, J. (2017). Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data. NeuroImage 144: 128–141, https://doi.org/10.1016/j.neuroimage.2016.09.049.Suche in Google Scholar PubMed
Nielsen, J.A., Zielinski, B.A., Fletcher, P.T., Alexander, A.L., Lange, N., Bigler, E.D., Lainhart, J.E., and Anderson, J.S. (2013). Multisite functional connectivity MRI classification of autism: ABIDE results. Front. Hum. Neurosci. 7: 599, https://doi.org/10.3389/fnhum.2013.00599.Suche in Google Scholar PubMed PubMed Central
Park, S.E., Laxpati, N.G., Gutekunst, C.A., Connolly, M.J., Tung, J., Berglund, K., Mahmoudi, B., and Gross, R.E. (2019). A machine learning approach to characterize the modulation of the hippocampal rhythms via optogenetic stimulation of the medial septum. Int. J. Neural Syst. 29: 1950020, https://doi.org/10.1142/s0129065719500205.Suche in Google Scholar
Pereira, F., Mitchell, T., and Botvinick, M. (2009). Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 45: 199–209, https://doi.org/10.1016/j.neuroimage.2008.11.007.Suche in Google Scholar PubMed PubMed Central
Plitt, M., Barnes, K.A., and Martin, A. (2015). Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. NeuroImage: Clinical 7: 359–366, https://doi.org/10.1016/j.nicl.2014.12.013.Suche in Google Scholar PubMed PubMed Central
Preeti, K., Shoba, S., Shekhar, P.S., Satish, C.G., and John, V.S.K. (2017). Lost time: need for more awareness in early intervention of autism spectrum disorder. Asian J Psychiatr 25: 13–15, https://doi.org/10.1016/j.ajp.2016.07.021.Suche in Google Scholar PubMed
Rad, N.M., Kia, M.S., Zarbo1, C., Laarhoven, T.V., Jurman, G., Venuti, P., Marchiori, E., and Furlanello, C. (2018). Deep learning for automatic stereotypical motor movement detection using wearable sensors in autism spectrum disorder. Signal Process. 144: 180–191, https://doi.org/10.1016/j.sigpro.2017.10.011.Suche in Google Scholar
Rafiei, M.H., and Adeli, H. (2017). A new neural dynamic classification algorithm. IEEE T Neur Net Lear 28: 3074–3083, https://doi.org/10.1109/tnnls.2017.2682102.Suche in Google Scholar
Rane, S., Jolly, E., Park, A., Jang, H., and Craddock, C. (2017). Developing predictive imaging biomarkers using whole-brain classifiers: application to the ABIDE I dataset. Res. Ideas Outcomes 3: 1–5, https://doi.org/10.3897/rio.3.e12733.Suche in Google Scholar
Reyes, O., and Ventura, S. (2019). Performing multi-target regression via a parameter sharing-based deep network. Int. J. Neural Syst. 29: 1950014, https://doi.org/10.1142/s012906571950014x.Suche in Google Scholar PubMed
Rinck, P.A. (2017). Magnetic Resonance in Medicine, A Peer-Reviewed, A Critical Introduction, 11–04 Functional Imaging, web version, 11th Edition 2017.Suche in Google Scholar
Rodriguez Lera, F.J., Rico, F.M., and Olivera, V.M. (2019). Neural networks for recognizing human activities in home-like environments. Integr Comput. Aid E. 26: 37–47, https://doi.org/10.3233/ica-180587.Suche in Google Scholar
Rubinov, M., and Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52: 1059–1069, https://doi.org/10.1016/j.neuroimage.2009.10.003.Suche in Google Scholar PubMed
Schetinin, V., Jakaite, L., and andKrzanowski, W. (2018). Bayesian learning of models for estimating uncertainty in alert systems: application to aircraft collision avoidance. Integr Comput. Aid E. 25: 229–245, https://doi.org/10.3233/ica-180567.Suche in Google Scholar
Segovia, F., Holt, R., Spencer, M., Górriz, J.M., Ramírez, J., Puntonet, C.G., Phillips, C., Chura, L., Baron-Cohen, S., and Suckling, J. (2014). Identifying endophenotypes of autism: a multivariate approach. Front. Comput. Neurosci. 8: 60, https://doi.org/10.3389/fncom.2014.00060.Suche in Google Scholar PubMed PubMed Central
Soussia, M., and Rekik, I. (2018). Unsupervised manifold learning using high-order morphological brain networks derived from T1-w MRI for autism diagnosis. Front. Neuroinform. 12: 70, https://doi.org/10.3389/fninf.2018.00070.Suche in Google Scholar PubMed PubMed Central
Stošljeviü, M., and Adamoviü, M. (2013). Dermatoglyphic characteristics of digito-palmar complex in autistic boys in Serbia. Vojnosanit. Pregl. 70: 386–390, https://doi.org/10.2298/VSP1304386S.Suche in Google Scholar
Subbaraju, V., Sundaram, S., Narasimham, S., and Suresh, M.B. (2015). Accurate detection of autism spectrum disorder from structural MRI using extended metacognitive radial basis function network. Expert Syst. Appl. 42: 8775–8790, https://doi.org/10.1016/j.eswa.2015.07.031.Suche in Google Scholar
Torres, J.F., Galicia, A., Troncoso, A., and Martínez-Álvarez, F. (2018). A scalable approach based on deep learning for big data time series forecasting. Integr Comput. Aid E. 25: 335–348, https://doi.org/10.3233/ica-180580.Suche in Google Scholar
Uddin, L.Q., Supekar, K., Lynch, C.J., Khouzam, A., Phillips, J., Feinstein, C., Ryali, S., and Menon, V. (2013). Salience network-based classification and prediction of symptom severity in children with autism. JAMA Psychiatry 70: 869, https://doi.org/10.1001/jamapsychiatry.2013.104.Suche in Google Scholar PubMed PubMed Central
Uddin, L.Q., Supekar, K., and Menon, V. (2010). Typical and atypical development of functional human brain networks: insights from resting-state FMRI. Front. Syst. Neurosci. 4: 21, https://doi.org/10.3389/fnsys.2010.00021.Suche in Google Scholar PubMed PubMed Central
Vera-Olmos, F.J., Pardo, E., Melero, H., and Malpica, N. (2019). DeepEye: deep convolutional network for pupil detection in real environments. Integr Comput. Aid E. 26: 85–95, https://doi.org/10.3233/ICA-180584.Suche in Google Scholar
Van, D.K.R., Hedden, T., Venkataraman, A., Evans, K.C., Lazar, S.W., and Buckner, R.L. (2009). Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. J. Neurophysiol. 103: 297, https://doi.org/10.1152/jn.00783.2009.Suche in Google Scholar PubMed PubMed Central
Wang, P., and Bai, X. (2018). Regional parallel structure based CNN for thermal infrared face identification. Integr Comput. Aid E. 25: 247–260, https://doi.org/10.3233/ica-180560.Suche in Google Scholar
Wang, S., Hu, Y., and Shen, Y. (2018). Classification of diffusion tensor metrics for the diagnosis of a myelopathic cord using machine learning. Int. J. Neural Syst. 28: 1750036, https://doi.org/10.1142/s0129065717500368.Suche in Google Scholar
Wang, S., Jiang, M., Duchesne, X.M.M., Laugeson, E.A.A., Kennedy, D.P.P., Adolphs, R., and Zhao, Q. (2015). Atypical visual saliency in autism spectrum disorder quantified through model-based eye tracking. Neuron 88: 604–616, https://doi.org/10.1016/j.neuron.2015.09.042.Suche in Google Scholar PubMed PubMed Central
Wang, C., Xiao, Z., Wang, B., and Wu, J. (2019). Identification of autism based on SVM-RFE and stacked sparse auto-encoder. IEEE Access 7, https://doi.org/10.1109/ACCESS.2019.2936639.Suche in Google Scholar
Wang, L., Wee, C.Y., Tang, X., Yap, P.T., and Shen, D. (2016). Multi-task feature selection via supervised canonical graph matching for diagnosis of autism spectrum disorder. Brain Imaging Behav 10: 33–40, https://doi.org/10.1007/s11682-015-9360-1.Suche in Google Scholar PubMed PubMed Central
Wee, C.Y., Wang, L., Shi, F., Yap, P.T., and Shen, D. (2014). Diagnosis of autism spectrum disorders using regional and interregional morphological features. Hum. Brain Mapp. 35: 3414–3430, https://doi.org/10.1002/hbm.22411.Suche in Google Scholar PubMed PubMed Central
Yahata, N., Kasai, K., and Kawato, M. (2017). Computational neuroscience approach to biomarkers and treatments for mental disorders. Psychiatr. Clin. Neurosci. 71: 215–237, https://doi.org/10.1111/pcn.12502.Suche in Google Scholar PubMed
Yahata, N., Morimoto, J., Hashimoto, R., Lisi, G., Shibata, K., Kawakubo, Y., Kuwabara, H., Kuroda, M., Yamada, T., Megumi, F., et al. (2016). A small number of abnormal brain connections predicts adult autism spectrum disorder. Nat. Commun. 7: 11254, https://doi.org/10.1038/ncomms11254.Suche in Google Scholar PubMed PubMed Central
Yamagata, B., Itahashi, T., Fujino, J., Ohta, H., Nakamura, M., Kato, N., Mimura, M., Hashimoto, R., and Aoki, Y. (2018). Machine learning approach to identify resting-state functional connectivity pattern serving as an endophenotype of autism spectrum disorder. Brain Imaging and Behav. 13: 1689–1698. https://doi.org/10.1007/s11682-018-9973-2.Suche in Google Scholar PubMed
Yang, T., Cappelle, C., Ruichek, Y., and El Bagdouri, M. (2019). Multi-object tracking with discriminant correlation filter based deep learning tracker. Integr Comput. Aid E. 26: 273–284, https://doi.org/10.3233/ica-180596.Suche in Google Scholar
Yerys, B.E., Jankowski, K.F., Shook, D., Rosenberger, L,R., Barnes, K.A., Berl, M.M., Ritzl, E.K., VanMeter, J., Vaidya, C.J., and Gaillard, W.D. (2009). The fMRI success rate of children and adolescents: typical development, epilepsy, attention deficit/hyperactivity disorder, and autism spectrum disorders. Hum. Brain Mapp. 30: 3426–3435, https://doi.org/10.1002/hbm.20767.Suche in Google Scholar PubMed PubMed Central
Yu, B., Wang, H., Shan, W., and Yao, B. (2018). Prediction of bus travel time using random forests based on near neighbors. Comput. Aided Civ. Infrastruct. Eng. 33: 333–350, https://doi.org/10.1111/mice.12315.Suche in Google Scholar
Zeighami, Y., Ulla, M., Iturria-Medina, Y., Dadar, M., Zhang, Y., Larcher, K.M-H., Fonov, V., Evans, A.C., Collins, D.L., and Dagher, A. (2015). Network structure of brain atrophy in de novo Parkinson’s disease. eLife 4: e08440, https://doi.org/10.7554/elife.08440.Suche in Google Scholar
Zhang, A., Wang, K.C.P., Fei, Y., Liu, Y., Chen, C., Yang, G., Li, J.Q., Yang, E., and Qiu, S. (2019). Automated pixel-level pavement crack detection on 3D asphalt surfaces with a recurrent neural network. Comput. Aided Civ. Infrastruct. Eng. 34: 213–229, https://doi.org/10.1111/mice.12409.Suche in Google Scholar
Zhang, F., Savadjiev, P., Cai, W., Song, Y., Rathi, Y., Tunç, B., Parker, D., Kapur, T., Schultz, R.T., Makris, N., et al. (2018). Whole brain white matter connectivity analysis using machine learning: an application to autism. NeuroImage 172: 826–837, https://doi.org/10.1016/j.neuroimage.2017.10.029.Suche in Google Scholar PubMed PubMed Central
Zhou, Y., Yu, F., and Duong, T. (2014). Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning. PLoS One 9: 1–10: e90405, https://doi.org/10.1371/journal.pone.0090405.Suche in Google Scholar PubMed PubMed Central
© 2020 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- A bigger brain for a more complex environment
- Lifestyle intervention to prevent Alzheimer’s disease
- Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging
- Nanomaterial integration into the scaffolding materials for nerve tissue engineering: a review
- Resveratrol in the treatment of neuroblastoma: a review
- Retinal involvement in Alzheimer's disease (AD): evidence and current progress on the non-invasive diagnosis and monitoring of AD-related pathology using the eye
- Noninvasive brain stimulation for patients with a disorder of consciousness: a systematic review and meta-analysis
Artikel in diesem Heft
- Frontmatter
- A bigger brain for a more complex environment
- Lifestyle intervention to prevent Alzheimer’s disease
- Machine learning (ML) for the diagnosis of autism spectrum disorder (ASD) using brain imaging
- Nanomaterial integration into the scaffolding materials for nerve tissue engineering: a review
- Resveratrol in the treatment of neuroblastoma: a review
- Retinal involvement in Alzheimer's disease (AD): evidence and current progress on the non-invasive diagnosis and monitoring of AD-related pathology using the eye
- Noninvasive brain stimulation for patients with a disorder of consciousness: a systematic review and meta-analysis