AIMAFE: Autism spectrum disorder identification with multi-atlas deep feature representation and ensemble learning
•Multi-atlas functional connectivity is calculated as the original feature representation.•Multi-atlas deep feature representation is extracted by a deep learning method.•An ensemble learning strategy is proposed to perform the final ASD identification task.•Our proposed method achieves classificati...
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| Vydáno v: | Journal of neuroscience methods Ročník 343; s. 108840 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Netherlands
Elsevier B.V
01.09.2020
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| Témata: | |
| ISSN: | 0165-0270, 1872-678X, 1872-678X |
| On-line přístup: | Získat plný text |
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| Abstract | •Multi-atlas functional connectivity is calculated as the original feature representation.•Multi-atlas deep feature representation is extracted by a deep learning method.•An ensemble learning strategy is proposed to perform the final ASD identification task.•Our proposed method achieves classification accuracy of 74.52%.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on behavioral criteria while this approach is not objective enough and could cause delayed diagnosis. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. However, satisfactory accuracy for ASD identification has not been achieved.
To improve the performance of ASD identification, we propose an ASD identification method based on multi-atlas deep feature representation and ensemble learning. We first calculate multiple functional connectivity based on different brain atlases from fMRI data of each subject. Then, to get the more discriminative features for ASD identification, we propose a multi-atlas deep feature representation method based on stacked denoising autoencoder (SDA). Finally, we propose multilayer perceptron (MLP) and an ensemble learning method to perform the final ASD identification task.
Our proposed method is evaluated on 949 subjects (including 419 ASDs and 530 typical control (TCs)) from the Autism Brain Imaging Data Exchange (ABIDE) and achieves accuracy of 74.52% (sensitivity of 80.69%, specificity of 66.71%, AUC of 0.8026) for ASD identification.
Compared with some previously published methods, our proposed method obtains the better performance for ASD identification.
The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis. |
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| AbstractList | Autism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on behavioral criteria while this approach is not objective enough and could cause delayed diagnosis. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. However, satisfactory accuracy for ASD identification has not been achieved.BACKGROUNDAutism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on behavioral criteria while this approach is not objective enough and could cause delayed diagnosis. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. However, satisfactory accuracy for ASD identification has not been achieved.To improve the performance of ASD identification, we propose an ASD identification method based on multi-atlas deep feature representation and ensemble learning. We first calculate multiple functional connectivity based on different brain atlases from fMRI data of each subject. Then, to get the more discriminative features for ASD identification, we propose a multi-atlas deep feature representation method based on stacked denoising autoencoder (SDA). Finally, we propose multilayer perceptron (MLP) and an ensemble learning method to perform the final ASD identification task.NEW METHODTo improve the performance of ASD identification, we propose an ASD identification method based on multi-atlas deep feature representation and ensemble learning. We first calculate multiple functional connectivity based on different brain atlases from fMRI data of each subject. Then, to get the more discriminative features for ASD identification, we propose a multi-atlas deep feature representation method based on stacked denoising autoencoder (SDA). Finally, we propose multilayer perceptron (MLP) and an ensemble learning method to perform the final ASD identification task.Our proposed method is evaluated on 949 subjects (including 419 ASDs and 530 typical control (TCs)) from the Autism Brain Imaging Data Exchange (ABIDE) and achieves accuracy of 74.52% (sensitivity of 80.69%, specificity of 66.71%, AUC of 0.8026) for ASD identification.RESULTSOur proposed method is evaluated on 949 subjects (including 419 ASDs and 530 typical control (TCs)) from the Autism Brain Imaging Data Exchange (ABIDE) and achieves accuracy of 74.52% (sensitivity of 80.69%, specificity of 66.71%, AUC of 0.8026) for ASD identification.Compared with some previously published methods, our proposed method obtains the better performance for ASD identification.COMPARISON WITH EXISTING METHODSCompared with some previously published methods, our proposed method obtains the better performance for ASD identification.The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis.CONCLUSIONThe results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis. •Multi-atlas functional connectivity is calculated as the original feature representation.•Multi-atlas deep feature representation is extracted by a deep learning method.•An ensemble learning strategy is proposed to perform the final ASD identification task.•Our proposed method achieves classification accuracy of 74.52%. Autism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on behavioral criteria while this approach is not objective enough and could cause delayed diagnosis. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. However, satisfactory accuracy for ASD identification has not been achieved. To improve the performance of ASD identification, we propose an ASD identification method based on multi-atlas deep feature representation and ensemble learning. We first calculate multiple functional connectivity based on different brain atlases from fMRI data of each subject. Then, to get the more discriminative features for ASD identification, we propose a multi-atlas deep feature representation method based on stacked denoising autoencoder (SDA). Finally, we propose multilayer perceptron (MLP) and an ensemble learning method to perform the final ASD identification task. Our proposed method is evaluated on 949 subjects (including 419 ASDs and 530 typical control (TCs)) from the Autism Brain Imaging Data Exchange (ABIDE) and achieves accuracy of 74.52% (sensitivity of 80.69%, specificity of 66.71%, AUC of 0.8026) for ASD identification. Compared with some previously published methods, our proposed method obtains the better performance for ASD identification. The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis. Autism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on behavioral criteria while this approach is not objective enough and could cause delayed diagnosis. Since functional magnetic resonance imaging (fMRI) can measure brain activity, it provides data for the study of brain dysfunction disorders and has been widely used in ASD identification. However, satisfactory accuracy for ASD identification has not been achieved. To improve the performance of ASD identification, we propose an ASD identification method based on multi-atlas deep feature representation and ensemble learning. We first calculate multiple functional connectivity based on different brain atlases from fMRI data of each subject. Then, to get the more discriminative features for ASD identification, we propose a multi-atlas deep feature representation method based on stacked denoising autoencoder (SDA). Finally, we propose multilayer perceptron (MLP) and an ensemble learning method to perform the final ASD identification task. Our proposed method is evaluated on 949 subjects (including 419 ASDs and 530 typical control (TCs)) from the Autism Brain Imaging Data Exchange (ABIDE) and achieves accuracy of 74.52% (sensitivity of 80.69%, specificity of 66.71%, AUC of 0.8026) for ASD identification. Compared with some previously published methods, our proposed method obtains the better performance for ASD identification. The results suggest that our proposed method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis. |
| ArticleNumber | 108840 |
| Author | Wang, Yufei Liu, Jin Wu, Fang-Xiang Wang, Jianxin Hayrat, Rahmatjan |
| Author_xml | – sequence: 1 givenname: Yufei surname: Wang fullname: Wang, Yufei email: willem@csu.edu.cn organization: Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China – sequence: 2 givenname: Jianxin surname: Wang fullname: Wang, Jianxin email: jxwang@mail.csu.edu.cn organization: Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China – sequence: 3 givenname: Fang-Xiang surname: Wu fullname: Wu, Fang-Xiang email: faw341@mail.usask.ca organization: Division of Biomedical Engineering and Department of Mechanical Engineering, University of Saskatchewan, Saskatoon S7N 5A9, Canada – sequence: 4 givenname: Rahmatjan surname: Hayrat fullname: Hayrat, Rahmatjan email: rahmatjan@csu.edu.cn organization: Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China – sequence: 5 givenname: Jin orcidid: 0000-0002-4961-7074 surname: Liu fullname: Liu, Jin email: liujin06@csu.edu.cn organization: Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha 410083, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32653384$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1007/s11042-017-5470-7 10.1016/j.media.2018.06.001 10.1002/hbm.23575 10.1016/j.neuron.2011.09.006 10.1016/j.tins.2007.12.005 10.1038/nrn2201 10.1016/j.neuroimage.2009.09.037 10.3389/fnins.2018.00525 10.1016/j.pnpbp.2015.06.014 10.1109/TCBB.2016.2635144 10.1016/j.media.2017.07.005 10.1016/j.cortex.2014.08.011 10.1016/j.nicl.2014.12.013 10.1097/WCO.0b013e32835ee548 10.1089/brain.2017.0496 10.3389/fnhum.2014.00349 10.1007/BF02172145 10.1016/j.nicl.2017.08.017 10.1176/appi.ajp.2011.10101532 10.1016/j.neucom.2018.04.080 10.1109/TNB.2017.2751074 10.1016/j.neuroimage.2016.10.045 10.26599/BDMA.2018.9020001 10.3389/fbioe.2019.00479 10.1023/A:1005592401947 10.1186/s12888-015-0382-4 10.1002/aur.204 10.1093/brain/awr263 10.1126/science.1194144 10.1038/nature14539 10.1109/TCBB.2017.2731849 10.1006/nimg.2001.0978 10.1002/hbm.21333 10.1007/s12264-017-0102-9 |
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| Keywords | Functional connectivity Functional magnetic resonance imaging Ensemble learning Stacked denoising autoencoder Autism spectrum disorder identification |
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| References | Kong, Gao, Xu, Pan, Wang, Liu (bib0090) 2019; 324 Chen, Duan, Liu, Lu, Ma, Zhang, Uddin, Chen (bib0025) 2016; 64 Jahedi, Nasamran, Faires, Fan, Müller (bib0075) 2017; 7 Lord, Rutter, Le Couteur (bib0135) 1994; 24 Fox, Raichle (bib0055) 2007; 8 Lord, Risi, Lambrecht, Cook, Leventhal, DiLavore, Pickles, Rutter (bib0140) 2000; 30 Huettel, Song, McCarthy (bib0065) 2004 Anderson, Nielsen, Froehlich, DuBray, Druzgal, Cariello, Cooperrider, Zielinski, Ravichandran, Fletcher (bib0015) 2011; 134 Liu, Pan, Wu, Wang (bib0130) 2020 LeCun, Bengio, Hinton (bib0095) 2015; 521 Abraham, Milham, Di Martino, Craddock, Samaras, Thirion, Varoquaux (bib0005) 2017; 147 Liu, Li, Pan, Wu, Chen, Wang (bib0105) 2017; 16 Heinsfeld, Franco, Craddock, Buchweitz, Meneguzzi (bib0060) 2018; 17 Liu, Li, Lan, Wu, Pan, Wang (bib0115) 2018; 15 Tzourio-Mazoyer, Landeau, Papathanassiou, Crivello, Etard, Delcroix, Mazoyer, Joliot (bib0175) 2002; 15 Wang, Deng, You, Chen, Li, Tang, Ceng, Zou, Zou (bib0195) 2017; 33 Dvornek, Ventola, Pelphrey, Duncan (bib0050) 2017 Plitt, Barnes, Martin (bib0160) 2015; 7 Timimi, Milton, Bovell, Kapp, Russell (bib0170) 2019; 1 Litjens, Kooi, Bejnordi, Setio, Ciompi, Ghafoorian, Van Der Laak, Van Ginneken, Sánchez (bib0100) 2017; 42 Craddock, Sikka, Cheung, Khanuja, Ghosh, Yan, Li, Lurie, Vogelstein, Burns (bib0035) 2013; 42 Liu, Wang, Tang, Hu, Wu, Pan (bib0120) 2018; 15 Iidaka (bib0070) 2015; 63 Kim, Leventhal, Koh, Fombonne, Laska, Lim, Cheon, Kim, Kim, Lee (bib0085) 2011; 168 Parisot, Ktena, Ferrante, Lee, Guerrero, Glocker, Rueckert (bib0150) 2018; 48 Xiang, Wang, Tan, Wu, Liu (bib0205) 2020; 7 Liu, Wang, Zhang, Pan, Wang, Wang (bib0125) 2018; 77 Vincent, Larochelle, Bengio, Manzagol (bib0180) 2008 Buxton (bib0020) 2009 Vincent, Larochelle, Lajoie, Bengio, Manzagol (bib0185) 2010; 11 Dosenbach, Nardos, Cohen, Fair, Power, Church, Nelson, Wig, Vogel, Lessov-Schlaggar (bib0040) 2010; 329 Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel, Blondel, Prettenhofer, Weiss, Dubourg, Vanderplas, Passos, Cournapeau, Brucher, Perrot, Duchesnay, Scikit-learn (bib0155) 2011; 12 Ou, Shi, Xun, Chen, Wu, Luo, Zhang, Zhao (bib0145) 2015; 15 Kana, Uddin, Kenet, Chugani, Müller (bib0080) 2014; 8 Power, Cohen, Nelson, Wig, Barnes, Church, Vogel, Laumann, Miezin, Schlaggar (bib0165) 2011; 72 Wang, Wang, Peng, Nie, Zhao, Kim, Zhang, Wee, Wang, Shen (bib0190) 2017; 38 Zuo, Di Martino, Kelly, Shehzad, Gee, Klein, Castellanos, Biswal, Milham (bib0215) 2010; 49 Craddock, James, Holtzheimer, Hu, Mayberg (bib0030) 2012; 33 Werling, Geschwind (bib0200) 2013; 26 Yerys, Pennington (bib0210) 2011; 4 Amaral, Schumann, Nordahl (bib0010) 2008; 31 Du, Fu, Calhoun (bib0045) 2018; 12 Liu, Pan, Li, Chen, Tang, Lu, Wang (bib0110) 2018; 1 Craddock (10.1016/j.jneumeth.2020.108840_bib0030) 2012; 33 Xiang (10.1016/j.jneumeth.2020.108840_bib0205) 2020; 7 Amaral (10.1016/j.jneumeth.2020.108840_bib0010) 2008; 31 Yerys (10.1016/j.jneumeth.2020.108840_bib0210) 2011; 4 Anderson (10.1016/j.jneumeth.2020.108840_bib0015) 2011; 134 Abraham (10.1016/j.jneumeth.2020.108840_bib0005) 2017; 147 Buxton (10.1016/j.jneumeth.2020.108840_bib0020) 2009 Kong (10.1016/j.jneumeth.2020.108840_bib0090) 2019; 324 Tzourio-Mazoyer (10.1016/j.jneumeth.2020.108840_bib0175) 2002; 15 Liu (10.1016/j.jneumeth.2020.108840_bib0115) 2018; 15 Vincent (10.1016/j.jneumeth.2020.108840_bib0180) 2008 Pedregosa (10.1016/j.jneumeth.2020.108840_bib0155) 2011; 12 Lord (10.1016/j.jneumeth.2020.108840_bib0140) 2000; 30 Liu (10.1016/j.jneumeth.2020.108840_bib0120) 2018; 15 Liu (10.1016/j.jneumeth.2020.108840_bib0130) 2020 Fox (10.1016/j.jneumeth.2020.108840_bib0055) 2007; 8 Liu (10.1016/j.jneumeth.2020.108840_bib0125) 2018; 77 Liu (10.1016/j.jneumeth.2020.108840_bib0110) 2018; 1 Timimi (10.1016/j.jneumeth.2020.108840_bib0170) 2019; 1 Craddock (10.1016/j.jneumeth.2020.108840_bib0035) 2013; 42 Zuo (10.1016/j.jneumeth.2020.108840_bib0215) 2010; 49 Ou (10.1016/j.jneumeth.2020.108840_bib0145) 2015; 15 Dosenbach (10.1016/j.jneumeth.2020.108840_bib0040) 2010; 329 Liu (10.1016/j.jneumeth.2020.108840_bib0105) 2017; 16 Iidaka (10.1016/j.jneumeth.2020.108840_bib0070) 2015; 63 LeCun (10.1016/j.jneumeth.2020.108840_bib0095) 2015; 521 Dvornek (10.1016/j.jneumeth.2020.108840_bib0050) 2017 Heinsfeld (10.1016/j.jneumeth.2020.108840_bib0060) 2018; 17 Wang (10.1016/j.jneumeth.2020.108840_bib0195) 2017; 33 Litjens (10.1016/j.jneumeth.2020.108840_bib0100) 2017; 42 Kana (10.1016/j.jneumeth.2020.108840_bib0080) 2014; 8 Du (10.1016/j.jneumeth.2020.108840_bib0045) 2018; 12 Kim (10.1016/j.jneumeth.2020.108840_bib0085) 2011; 168 Parisot (10.1016/j.jneumeth.2020.108840_bib0150) 2018; 48 Werling (10.1016/j.jneumeth.2020.108840_bib0200) 2013; 26 Wang (10.1016/j.jneumeth.2020.108840_bib0190) 2017; 38 Lord (10.1016/j.jneumeth.2020.108840_bib0135) 1994; 24 Chen (10.1016/j.jneumeth.2020.108840_bib0025) 2016; 64 Huettel (10.1016/j.jneumeth.2020.108840_bib0065) 2004 Plitt (10.1016/j.jneumeth.2020.108840_bib0160) 2015; 7 Power (10.1016/j.jneumeth.2020.108840_bib0165) 2011; 72 Jahedi (10.1016/j.jneumeth.2020.108840_bib0075) 2017; 7 Vincent (10.1016/j.jneumeth.2020.108840_bib0185) 2010; 11 |
| References_xml | – volume: 1 start-page: 1 year: 2019 end-page: 39 ident: bib0170 article-title: Deconstructing diagnosis: four commentaries on a diagnostic tool to assess individuals for autism spectrum disorders publication-title: Autonomy (Birm) – volume: 64 start-page: 1 year: 2016 end-page: 9 ident: bib0025 article-title: Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity – a multi-center study publication-title: Prog. Neuro-Psychopharmacol. Biol. Psychiatry – volume: 33 start-page: 1914 year: 2012 end-page: 1928 ident: bib0030 article-title: A whole brain fMRI atlas generated via spatially constrained spectral clustering publication-title: Human Brain Mapp. – volume: 7 start-page: 359 year: 2015 end-page: 366 ident: bib0160 article-title: Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards publication-title: NeuroImage: Clin. – volume: 33 start-page: 153 year: 2017 end-page: 160 ident: bib0195 article-title: Sex differences in diagnosis and clinical phenotypes of Chinese children with autism spectrum disorder publication-title: Neurosci. Bull. – volume: 42 start-page: 60 year: 2017 end-page: 88 ident: bib0100 article-title: A survey on deep learning in medical image analysis publication-title: Med. Image Anal. – volume: 42 year: 2013 ident: bib0035 article-title: Towards automated analysis of connectomes: the configurable pipeline for the analysis of connectomes (c-pac) publication-title: Front. Neuroinform. – volume: 134 start-page: 3742 year: 2011 end-page: 3754 ident: bib0015 article-title: Functional connectivity magnetic resonance imaging classification of autism publication-title: Brain – volume: 8 start-page: 349 year: 2014 ident: bib0080 article-title: Brain connectivity in autism publication-title: Front. Human Neurosci. – start-page: 1096 year: 2008 end-page: 1103 ident: bib0180 article-title: Extracting and composing robust features with denoising autoencoders publication-title: Proceedings of the 25th International Conference on Machine Learning – volume: 168 start-page: 904 year: 2011 end-page: 912 ident: bib0085 article-title: Prevalence of autism spectrum disorders in a total population sample publication-title: Am. J. Psychiatry – volume: 329 start-page: 1358 year: 2010 end-page: 1361 ident: bib0040 article-title: Prediction of individual brain maturity using fMRI publication-title: Science – volume: 17 start-page: 16 year: 2018 end-page: 23 ident: bib0060 article-title: Identification of autism spectrum disorder using deep learning and the abide dataset publication-title: NeuroImage: Clin. – volume: 147 start-page: 736 year: 2017 end-page: 745 ident: bib0005 article-title: Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example publication-title: NeuroImage – volume: 15 start-page: 624 year: 2018 end-page: 632 ident: bib0115 article-title: Classification of Alzheimer’s disease using whole brain hierarchical network publication-title: IEEE/ACM Trans. Comput. Bio. Bioinformatics – volume: 15 start-page: 3 year: 2015 ident: bib0145 article-title: Employment and financial burden of families with preschool children diagnosed with autism spectrum disorders in urban china: results from a descriptive study publication-title: BMC Psychiatry – volume: 48 start-page: 117 year: 2018 end-page: 130 ident: bib0150 article-title: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease publication-title: Med. Image Anal. – volume: 38 start-page: 3081 year: 2017 end-page: 3097 ident: bib0190 article-title: Multi-task diagnosis for autism spectrum disorders using multi-modality features: a multi-center study publication-title: Human Brain Mapp. – year: 2004 ident: bib0065 article-title: Functional Magnetic Resonance Imaging, vol. 1 – volume: 24 start-page: 659 year: 1994 end-page: 685 ident: bib0135 article-title: Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders publication-title: J. Autism Dev. Disord. – volume: 15 start-page: 273 year: 2002 end-page: 289 ident: bib0175 article-title: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain publication-title: Neuroimage – volume: 4 start-page: 239 year: 2011 end-page: 241 ident: bib0210 article-title: How do we establish a biological marker for a behaviorally defined disorder?. autism as a test case publication-title: Autism Res. – volume: 1 start-page: 1 year: 2018 end-page: 18 ident: bib0110 article-title: Applications of deep learning to MRI images: a survey publication-title: Big Data Mining Anal. – volume: 30 start-page: 205 year: 2000 end-page: 223 ident: bib0140 article-title: The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism publication-title: J. Autism Dev. Disord. – volume: 49 start-page: 1432 year: 2010 end-page: 1445 ident: bib0215 article-title: The oscillating brain: complex and reliable publication-title: Neuroimage – volume: 31 start-page: 137 year: 2008 end-page: 145 ident: bib0010 article-title: Neuroanatomy of autism publication-title: Trends Neurosci. – volume: 521 start-page: 436 year: 2015 end-page: 444 ident: bib0095 article-title: Deep learning publication-title: Nature – volume: 12 start-page: 525 year: 2018 ident: bib0045 article-title: Classification and prediction of brain disorders using functional connectivity: promising but challenging publication-title: Front. Neurosci. – volume: 15 start-page: 1649 year: 2018 end-page: 1659 ident: bib0120 article-title: Improving Alzheimer’s disease classification by combining multiple measures publication-title: IEEE/ACM Trans. Comput. Biol. Bioinformatics – volume: 77 start-page: 29651 year: 2018 end-page: 29667 ident: bib0125 article-title: MMM: classification of schizophrenia using multi-modality multi-atlas feature representation and multi-kernel learning publication-title: Multimedia Tools Appl. – volume: 16 start-page: 600 year: 2017 end-page: 608 ident: bib0105 article-title: Classification of schizophrenia based on individual hierarchical brain networks constructed from structural MRI images publication-title: IEEE Trans. Nanobiosci. – volume: 72 start-page: 665 year: 2011 end-page: 678 ident: bib0165 article-title: Functional network organization of the human brain publication-title: Neuron – volume: 324 start-page: 63 year: 2019 end-page: 68 ident: bib0090 article-title: Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier publication-title: Neurocomputing – year: 2009 ident: bib0020 article-title: Introduction to Functional Magnetic Resonance Imaging: Principles and Techniques – start-page: 362 year: 2017 end-page: 370 ident: bib0050 article-title: Identifying autism from resting-state fMRI using long short-term memory networks publication-title: International Workshop on Machine Learning in Medical Imaging – volume: 8 start-page: 700 year: 2007 end-page: 711 ident: bib0055 article-title: Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging publication-title: Nat. Rev. Neurosci. – volume: 26 start-page: 146 year: 2013 ident: bib0200 article-title: Sex differences in autism spectrum disorders publication-title: Curr. Opin. Neurol. – volume: 11 start-page: 3371 year: 2010 end-page: 3408 ident: bib0185 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: bib0155 article-title: Machine learning in Python publication-title: J. Mach. Learn. Res. – volume: 7 start-page: 479 year: 2020 ident: bib0205 article-title: Schizophrenia identification using multi-view graph measures of functional brain networks publication-title: Front. Bioeng. Biotechnol. – volume: 7 start-page: 515 year: 2017 end-page: 525 ident: bib0075 article-title: Distributed intrinsic functional connectivity patterns predict diagnostic status in large autism cohort publication-title: Brain Connect. – volume: 63 start-page: 55 year: 2015 end-page: 67 ident: bib0070 article-title: Resting state functional magnetic resonance imaging and neural network classified autism and control publication-title: Cortex – year: 2020 ident: bib0130 article-title: Enhancing the feature representation of multi-modal MRI data by combining multi-view information for mci classification publication-title: Neurocomputing – year: 2009 ident: 10.1016/j.jneumeth.2020.108840_bib0020 – volume: 77 start-page: 29651 issue: 22 year: 2018 ident: 10.1016/j.jneumeth.2020.108840_bib0125 article-title: MMM: classification of schizophrenia using multi-modality multi-atlas feature representation and multi-kernel learning publication-title: Multimedia Tools Appl. doi: 10.1007/s11042-017-5470-7 – volume: 48 start-page: 117 year: 2018 ident: 10.1016/j.jneumeth.2020.108840_bib0150 article-title: Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer’s disease publication-title: Med. Image Anal. doi: 10.1016/j.media.2018.06.001 – volume: 38 start-page: 3081 issue: 6 year: 2017 ident: 10.1016/j.jneumeth.2020.108840_bib0190 article-title: Multi-task diagnosis for autism spectrum disorders using multi-modality features: a multi-center study publication-title: Human Brain Mapp. doi: 10.1002/hbm.23575 – start-page: 1096 year: 2008 ident: 10.1016/j.jneumeth.2020.108840_bib0180 article-title: Extracting and composing robust features with denoising autoencoders publication-title: Proceedings of the 25th International Conference on Machine Learning – volume: 1 start-page: 1 issue: 6 year: 2019 ident: 10.1016/j.jneumeth.2020.108840_bib0170 article-title: Deconstructing diagnosis: four commentaries on a diagnostic tool to assess individuals for autism spectrum disorders publication-title: Autonomy (Birm) – volume: 11 start-page: 3371 issue: Dec year: 2010 ident: 10.1016/j.jneumeth.2020.108840_bib0185 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 72 start-page: 665 issue: 4 year: 2011 ident: 10.1016/j.jneumeth.2020.108840_bib0165 article-title: Functional network organization of the human brain publication-title: Neuron doi: 10.1016/j.neuron.2011.09.006 – volume: 31 start-page: 137 issue: 3 year: 2008 ident: 10.1016/j.jneumeth.2020.108840_bib0010 article-title: Neuroanatomy of autism publication-title: Trends Neurosci. doi: 10.1016/j.tins.2007.12.005 – volume: 8 start-page: 700 issue: 9 year: 2007 ident: 10.1016/j.jneumeth.2020.108840_bib0055 article-title: Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging publication-title: Nat. Rev. Neurosci. doi: 10.1038/nrn2201 – year: 2020 ident: 10.1016/j.jneumeth.2020.108840_bib0130 article-title: Enhancing the feature representation of multi-modal MRI data by combining multi-view information for mci classification publication-title: Neurocomputing – volume: 49 start-page: 1432 issue: 2 year: 2010 ident: 10.1016/j.jneumeth.2020.108840_bib0215 article-title: The oscillating brain: complex and reliable publication-title: Neuroimage doi: 10.1016/j.neuroimage.2009.09.037 – volume: 12 start-page: 525 year: 2018 ident: 10.1016/j.jneumeth.2020.108840_bib0045 article-title: Classification and prediction of brain disorders using functional connectivity: promising but challenging publication-title: Front. Neurosci. doi: 10.3389/fnins.2018.00525 – volume: 64 start-page: 1 year: 2016 ident: 10.1016/j.jneumeth.2020.108840_bib0025 article-title: Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity – a multi-center study publication-title: Prog. Neuro-Psychopharmacol. Biol. Psychiatry doi: 10.1016/j.pnpbp.2015.06.014 – volume: 15 start-page: 624 issue: 2 year: 2018 ident: 10.1016/j.jneumeth.2020.108840_bib0115 article-title: Classification of Alzheimer’s disease using whole brain hierarchical network publication-title: IEEE/ACM Trans. Comput. Bio. Bioinformatics doi: 10.1109/TCBB.2016.2635144 – volume: 42 start-page: 60 year: 2017 ident: 10.1016/j.jneumeth.2020.108840_bib0100 article-title: A survey on deep learning in medical image analysis publication-title: Med. Image Anal. doi: 10.1016/j.media.2017.07.005 – volume: 63 start-page: 55 year: 2015 ident: 10.1016/j.jneumeth.2020.108840_bib0070 article-title: Resting state functional magnetic resonance imaging and neural network classified autism and control publication-title: Cortex doi: 10.1016/j.cortex.2014.08.011 – volume: 7 start-page: 359 year: 2015 ident: 10.1016/j.jneumeth.2020.108840_bib0160 article-title: Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards publication-title: NeuroImage: Clin. doi: 10.1016/j.nicl.2014.12.013 – volume: 26 start-page: 146 issue: 2 year: 2013 ident: 10.1016/j.jneumeth.2020.108840_bib0200 article-title: Sex differences in autism spectrum disorders publication-title: Curr. Opin. Neurol. doi: 10.1097/WCO.0b013e32835ee548 – volume: 7 start-page: 515 issue: 8 year: 2017 ident: 10.1016/j.jneumeth.2020.108840_bib0075 article-title: Distributed intrinsic functional connectivity patterns predict diagnostic status in large autism cohort publication-title: Brain Connect. doi: 10.1089/brain.2017.0496 – volume: 8 start-page: 349 year: 2014 ident: 10.1016/j.jneumeth.2020.108840_bib0080 article-title: Brain connectivity in autism publication-title: Front. Human Neurosci. doi: 10.3389/fnhum.2014.00349 – volume: 24 start-page: 659 issue: 5 year: 1994 ident: 10.1016/j.jneumeth.2020.108840_bib0135 article-title: Autism diagnostic interview-revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders publication-title: J. Autism Dev. Disord. doi: 10.1007/BF02172145 – volume: 17 start-page: 16 year: 2018 ident: 10.1016/j.jneumeth.2020.108840_bib0060 article-title: Identification of autism spectrum disorder using deep learning and the abide dataset publication-title: NeuroImage: Clin. doi: 10.1016/j.nicl.2017.08.017 – volume: 42 year: 2013 ident: 10.1016/j.jneumeth.2020.108840_bib0035 article-title: Towards automated analysis of connectomes: the configurable pipeline for the analysis of connectomes (c-pac) publication-title: Front. Neuroinform. – volume: 168 start-page: 904 issue: 9 year: 2011 ident: 10.1016/j.jneumeth.2020.108840_bib0085 article-title: Prevalence of autism spectrum disorders in a total population sample publication-title: Am. J. Psychiatry doi: 10.1176/appi.ajp.2011.10101532 – volume: 324 start-page: 63 year: 2019 ident: 10.1016/j.jneumeth.2020.108840_bib0090 article-title: Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.04.080 – volume: 16 start-page: 600 issue: 7 year: 2017 ident: 10.1016/j.jneumeth.2020.108840_bib0105 article-title: Classification of schizophrenia based on individual hierarchical brain networks constructed from structural MRI images publication-title: IEEE Trans. Nanobiosci. doi: 10.1109/TNB.2017.2751074 – volume: 147 start-page: 736 year: 2017 ident: 10.1016/j.jneumeth.2020.108840_bib0005 article-title: Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example publication-title: NeuroImage doi: 10.1016/j.neuroimage.2016.10.045 – start-page: 362 year: 2017 ident: 10.1016/j.jneumeth.2020.108840_bib0050 article-title: Identifying autism from resting-state fMRI using long short-term memory networks publication-title: International Workshop on Machine Learning in Medical Imaging – volume: 1 start-page: 1 issue: 1 year: 2018 ident: 10.1016/j.jneumeth.2020.108840_bib0110 article-title: Applications of deep learning to MRI images: a survey publication-title: Big Data Mining Anal. doi: 10.26599/BDMA.2018.9020001 – volume: 7 start-page: 479 year: 2020 ident: 10.1016/j.jneumeth.2020.108840_bib0205 article-title: Schizophrenia identification using multi-view graph measures of functional brain networks publication-title: Front. Bioeng. Biotechnol. doi: 10.3389/fbioe.2019.00479 – year: 2004 ident: 10.1016/j.jneumeth.2020.108840_bib0065 – volume: 30 start-page: 205 issue: 3 year: 2000 ident: 10.1016/j.jneumeth.2020.108840_bib0140 article-title: The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism publication-title: J. Autism Dev. Disord. doi: 10.1023/A:1005592401947 – volume: 15 start-page: 3 issue: 1 year: 2015 ident: 10.1016/j.jneumeth.2020.108840_bib0145 article-title: Employment and financial burden of families with preschool children diagnosed with autism spectrum disorders in urban china: results from a descriptive study publication-title: BMC Psychiatry doi: 10.1186/s12888-015-0382-4 – volume: 4 start-page: 239 issue: 4 year: 2011 ident: 10.1016/j.jneumeth.2020.108840_bib0210 article-title: How do we establish a biological marker for a behaviorally defined disorder?. autism as a test case publication-title: Autism Res. doi: 10.1002/aur.204 – volume: 12 start-page: 2825 year: 2011 ident: 10.1016/j.jneumeth.2020.108840_bib0155 article-title: Machine learning in Python publication-title: J. Mach. Learn. Res. – volume: 134 start-page: 3742 issue: 12 year: 2011 ident: 10.1016/j.jneumeth.2020.108840_bib0015 article-title: Functional connectivity magnetic resonance imaging classification of autism publication-title: Brain doi: 10.1093/brain/awr263 – volume: 329 start-page: 1358 issue: 5997 year: 2010 ident: 10.1016/j.jneumeth.2020.108840_bib0040 article-title: Prediction of individual brain maturity using fMRI publication-title: Science doi: 10.1126/science.1194144 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.jneumeth.2020.108840_bib0095 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – volume: 15 start-page: 1649 issue: 5 year: 2018 ident: 10.1016/j.jneumeth.2020.108840_bib0120 article-title: Improving Alzheimer’s disease classification by combining multiple measures publication-title: IEEE/ACM Trans. Comput. Biol. Bioinformatics doi: 10.1109/TCBB.2017.2731849 – volume: 15 start-page: 273 issue: 1 year: 2002 ident: 10.1016/j.jneumeth.2020.108840_bib0175 article-title: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain publication-title: Neuroimage doi: 10.1006/nimg.2001.0978 – volume: 33 start-page: 1914 issue: 8 year: 2012 ident: 10.1016/j.jneumeth.2020.108840_bib0030 article-title: A whole brain fMRI atlas generated via spatially constrained spectral clustering publication-title: Human Brain Mapp. doi: 10.1002/hbm.21333 – volume: 33 start-page: 153 issue: 2 year: 2017 ident: 10.1016/j.jneumeth.2020.108840_bib0195 article-title: Sex differences in diagnosis and clinical phenotypes of Chinese children with autism spectrum disorder publication-title: Neurosci. Bull. doi: 10.1007/s12264-017-0102-9 |
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| Snippet | •Multi-atlas functional connectivity is calculated as the original feature representation.•Multi-atlas deep feature representation is extracted by a deep... Autism spectrum disorder (ASD) is a neurodevelopmental disorder that could cause problems in social communications. Clinically, diagnosing ASD mainly relies on... |
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| SubjectTerms | Autism spectrum disorder identification Ensemble learning Functional connectivity Functional magnetic resonance imaging Stacked denoising autoencoder |
| Title | AIMAFE: Autism spectrum disorder identification with multi-atlas deep feature representation and ensemble learning |
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