Identification of autism spectrum disorder using deep learning and the ABIDE dataset
The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database know...
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| Published in | NeuroImage clinical Vol. 17; pp. 16 - 23 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier Inc
01.01.2018
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model.
•We successfully applied Deep Learning to classify ASD and controls using ABIDE data•We extracted patterns of brain function in rs-fMRI and showed anterior-posterior underconnectivity in the autistic brain•Underconnected areas in ASD rs-fMRI data: Paracingulate Gyrus, Supramarginal Gyrus and Middle Temporal Gyrus•We surpass the state-of-the-art in deep learning classification of brain activation by achieving 70% accuracy |
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| AbstractList | The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model.
•We successfully applied Deep Learning to classify ASD and controls using ABIDE data•We extracted patterns of brain function in rs-fMRI and showed anterior-posterior underconnectivity in the autistic brain•Underconnected areas in ASD rs-fMRI data: Paracingulate Gyrus, Supramarginal Gyrus and Middle Temporal Gyrus•We surpass the state-of-the-art in deep learning classification of brain activation by achieving 70% accuracy The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model. The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model. Keywords: Autism, fMRI, ABIDE, Resting state, Deep learning The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model. • We successfully applied Deep Learning to classify ASD and controls using ABIDE data • We extracted patterns of brain function in rs-fMRI and showed anterior-posterior underconnectivity in the autistic brain • Underconnected areas in ASD rs-fMRI data: Paracingulate Gyrus, Supramarginal Gyrus and Middle Temporal Gyrus • We surpass the state-of-the-art in deep learning classification of brain activation by achieving 70% accuracy The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model.The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model. AbstractThe goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based solely on the patients brain activation patterns. We investigated ASD patients brain imaging data from a world-wide multi-site database known as ABIDE (Autism Brain Imaging Data Exchange). ASD is a brain-based disorder characterized by social deficits and repetitive behaviors. According to recent Centers for Disease Control data, ASD affects one in 68 children in the United States. We investigated patterns of functional connectivity that objectively identify ASD participants from functional brain imaging data, and attempted to unveil the neural patterns that emerged from the classification. The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset. The patterns that emerged from the classification show an anticorrelation of brain function between anterior and posterior areas of the brain; the anticorrelation corroborates current empirical evidence of anterior-posterior disruption in brain connectivity in ASD. We present the results and identify the areas of the brain that contributed most to differentiating ASD from typically developing controls as per our deep learning model. |
| Author | Craddock, R. Cameron Meneguzzi, Felipe Heinsfeld, Anibal Sólon Franco, Alexandre Rosa Buchweitz, Augusto |
| AuthorAffiliation | g Nathan Kline Institute for Psychiatric Research, Orangeburg, New York 10962, USA c PUCRS, School of Engineering, Porto Alegre 90619, Rio Grande do Sul, Brazil e PUCRS, School of Humanities, Porto Alegre 90619, Rio Grande do Sul, Brazil b PUCRS, Brain Institute of Rio Grande do Sul (BraIns), Porto Alegre 90619, Rio Grande do Sul, Brazil a PUCRS, School of Computer Science, Porto Alegre 90619, Rio Grande do Sul, Brazil d PUCRS, School of Medicine, Porto Alegre 90619, Rio Grande do Sul, Brazil f Center for the Developing Brain, Child Mind Institute, New York, New York 10022, USA |
| AuthorAffiliation_xml | – name: a PUCRS, School of Computer Science, Porto Alegre 90619, Rio Grande do Sul, Brazil – name: g Nathan Kline Institute for Psychiatric Research, Orangeburg, New York 10962, USA – name: d PUCRS, School of Medicine, Porto Alegre 90619, Rio Grande do Sul, Brazil – name: b PUCRS, Brain Institute of Rio Grande do Sul (BraIns), Porto Alegre 90619, Rio Grande do Sul, Brazil – name: c PUCRS, School of Engineering, Porto Alegre 90619, Rio Grande do Sul, Brazil – name: e PUCRS, School of Humanities, Porto Alegre 90619, Rio Grande do Sul, Brazil – name: f Center for the Developing Brain, Child Mind Institute, New York, New York 10022, USA |
| Author_xml | – sequence: 1 givenname: Anibal Sólon orcidid: 0000-0002-2050-0614 surname: Heinsfeld fullname: Heinsfeld, Anibal Sólon organization: PUCRS, School of Computer Science, Porto Alegre 90619, Rio Grande do Sul, Brazil – sequence: 2 givenname: Alexandre Rosa surname: Franco fullname: Franco, Alexandre Rosa organization: PUCRS, Brain Institute of Rio Grande do Sul (BraIns), Porto Alegre 90619, Rio Grande do Sul, Brazil – sequence: 3 givenname: R. Cameron surname: Craddock fullname: Craddock, R. Cameron organization: Center for the Developing Brain, Child Mind Institute, New York, New York 10022, USA – sequence: 4 givenname: Augusto surname: Buchweitz fullname: Buchweitz, Augusto organization: PUCRS, Brain Institute of Rio Grande do Sul (BraIns), Porto Alegre 90619, Rio Grande do Sul, Brazil – sequence: 5 givenname: Felipe orcidid: 0000-0003-3549-6168 surname: Meneguzzi fullname: Meneguzzi, Felipe email: felipe.meneguzzi@pucrs.br organization: PUCRS, School of Computer Science, Porto Alegre 90619, Rio Grande do Sul, Brazil |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29034163$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.neuroimage.2008.11.007 10.1016/j.neuroimage.2007.04.042 10.1212/WNL.53.9.2145 10.1002/mrm.22159 10.1097/01.wnr.0000239956.45448.4c 10.1136/bmj.309.6947.102 10.1080/17470910802198510 10.1093/cercor/bhr099 10.1016/j.nicl.2014.12.013 10.1038/ng.608 10.1371/journal.pone.0066032 10.3389/fnhum.2013.00458 10.1093/cercor/bhr162 10.1016/j.neuroimage.2013.04.083 10.1016/j.neuroimage.2014.03.048 10.1162/0898929053467550 10.1002/mrm.1910340409 10.1093/brain/awr263 10.1007/BF03341111 10.1093/cercor/bhl006 10.1016/j.bandl.2011.09.003 10.1002/hbm.22842 10.1089/brain.2012.0134 10.1016/j.neuroimage.2016.10.045 10.1016/j.neuroimage.2010.10.042 10.1371/journal.pone.0113879 10.1093/brain/awh199 10.1073/pnas.0905267106 10.1126/science.1063736 10.1093/cercor/bhn256 10.1126/science.1152876 10.1186/2047-217X-3-28 |
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| References | Kassam, Markey, Cherkassky, Loewenstein, Just (bb0145) 2013; 8 Haxby (bb0095) 2001; 293 Ho (bb0110) 1995; 1 Kana, Keller, Cherkassky, Minshew, Just (bb0140) 2009; 4 Mitchell (bb0165) 2008; 320 Biswal, Yetkin, Haughton, Hyde (bb0045) 1995; 34 Craddock, James (bb0070) 2012; 33 Pereira, Mitchell, Botvinick (bb0180) 2009; 45 Shinkareva, Malave, Mason, Mitchell, Just (bb0205) 2011; 54 Behzadi, Restom, Liau, Liu (bb0040) 2007; 37 Maaten, Hinton, van der Maaten (bb0160) 2008; 9 Craddock, Holtzheimer, Hu, Mayberg (bb0065) 2009; 62 Anderson (bb0020) 2011; 134 Bauer, Just (bb0035) 2015; 36 O’Toole, Jiang, Abdi, Haxby (bb0175) 2005; 17 Koyamada, Shikauchi, Nakae, Koyama, Ishii (bb0150) 2015 Aylward (bb0030) 1999; 53 Cherkassky, Kana, Keller, Just (bb0060) 2006; 17 Heinsfeld, Franco, Buchweitz, Meneguzzi (bb0100) 2017 Hjelm (bb0105) 2014; 96 Varoquaux, Thirion (bb0235) 2014; 3 Shehzad (bb0200) 2009; 19 Buchweitz, Shinkareva, Mason, Mitchell, Just (bb0050) 2012; 120 Vincent, Larochelle, Bengio, Manzagol (bb0240) 2008 Nielsen (bb0170) 2013; 7 Altman, Bland (bb0015) 1994; 309 Arbabshirani, Plis, Sui, Calhoun (bb0025) 2016; 145 Zablotsky, Black, Maenner, Schieve, Blumberg (bb0255) 2015 Schipul, Williams, Keller, Minshew, Just (bb0195) 2012; 22 Abraham (bb0005) 2017; 147 Just, Cherkassky, Buchweitz, Keller, Mitchell (bb0125) 2014; 9 Plitt, Barnes, Martin (bb0190) 2015; 7 Fox (bb0085) 2010; 4 Just (bb0120) 2004; 127 Castellanos, Di Martino, Craddock, Mehta, Milham (bb0055) 2013; 80 Yang (bb0250) 2010; 42 Just, Keller, Kana (bb0135) 2013 Plis (bb0185) 2014; 8 Vapnik (bb0230) 1998 Franco, Mannell, Calhoun, Mayer (bb0090) 2013; 3 Just, Cherkassky, Keller, Kana, Minshew (bb0130) 2006; 17 Shirer, Ryali, Rykhlevskaia, Menon, Greicius (bb0210) 2012; 22 Smith (bb0215) 2009; 106 Akshoomoff, Corsello, Schmidt (bb0010) 2006; 11 Uddin, Supekar, Menon (bb0225) 2013; 7 Di Martino (bb0075) 2014; 19 Jordan, Mitchell (bb0115) 2015; 349 Vincent, Larochelle, Lajoie, Bengio, Manzagol (bb0245) 2010; 11 Hjelm (10.1016/j.nicl.2017.08.017_bb0105) 2014; 96 Just (10.1016/j.nicl.2017.08.017_bb0135) 2013 Vapnik (10.1016/j.nicl.2017.08.017_bb0230) 1998 Craddock (10.1016/j.nicl.2017.08.017_bb0070) 2012; 33 Nielsen (10.1016/j.nicl.2017.08.017_bb0170) 2013; 7 Behzadi (10.1016/j.nicl.2017.08.017_bb0040) 2007; 37 Anderson (10.1016/j.nicl.2017.08.017_bb0020) 2011; 134 O’Toole (10.1016/j.nicl.2017.08.017_bb0175) 2005; 17 Craddock (10.1016/j.nicl.2017.08.017_bb0065) 2009; 62 Shinkareva (10.1016/j.nicl.2017.08.017_bb0205) 2011; 54 Yang (10.1016/j.nicl.2017.08.017_bb0250) 2010; 42 Vincent (10.1016/j.nicl.2017.08.017_bb0245) 2010; 11 Varoquaux (10.1016/j.nicl.2017.08.017_bb0235) 2014; 3 Plis (10.1016/j.nicl.2017.08.017_bb0185) 2014; 8 Plitt (10.1016/j.nicl.2017.08.017_bb0190) 2015; 7 Bauer (10.1016/j.nicl.2017.08.017_bb0035) 2015; 36 Just (10.1016/j.nicl.2017.08.017_bb0125) 2014; 9 Just (10.1016/j.nicl.2017.08.017_bb0130) 2006; 17 Zablotsky (10.1016/j.nicl.2017.08.017_bb0255) 2015 Shehzad (10.1016/j.nicl.2017.08.017_bb0200) 2009; 19 Pereira (10.1016/j.nicl.2017.08.017_bb0180) 2009; 45 Abraham (10.1016/j.nicl.2017.08.017_bb0005) 2017; 147 Cherkassky (10.1016/j.nicl.2017.08.017_bb0060) 2006; 17 Kana (10.1016/j.nicl.2017.08.017_bb0140) 2009; 4 Uddin (10.1016/j.nicl.2017.08.017_bb0225) 2013; 7 Koyamada (10.1016/j.nicl.2017.08.017_bb0150) 2015 Schipul (10.1016/j.nicl.2017.08.017_bb0195) 2012; 22 Heinsfeld (10.1016/j.nicl.2017.08.017_bb0100) 2017 Castellanos (10.1016/j.nicl.2017.08.017_bb0055) 2013; 80 Ho (10.1016/j.nicl.2017.08.017_bb0110) 1995; 1 Shirer (10.1016/j.nicl.2017.08.017_bb0210) 2012; 22 Smith (10.1016/j.nicl.2017.08.017_bb0215) 2009; 106 Kassam (10.1016/j.nicl.2017.08.017_bb0145) 2013; 8 Di Martino (10.1016/j.nicl.2017.08.017_bb0075) 2014; 19 Franco (10.1016/j.nicl.2017.08.017_bb0090) 2013; 3 Aylward (10.1016/j.nicl.2017.08.017_bb0030) 1999; 53 Akshoomoff (10.1016/j.nicl.2017.08.017_bb0010) 2006; 11 Jordan (10.1016/j.nicl.2017.08.017_bb0115) 2015; 349 Altman (10.1016/j.nicl.2017.08.017_bb0015) 1994; 309 Maaten (10.1016/j.nicl.2017.08.017_bb0160) 2008; 9 Fox (10.1016/j.nicl.2017.08.017_bb0085) 2010; 4 Vincent (10.1016/j.nicl.2017.08.017_bb0240) 2008 Buchweitz (10.1016/j.nicl.2017.08.017_bb0050) 2012; 120 Arbabshirani (10.1016/j.nicl.2017.08.017_bb0025) 2016; 145 Biswal (10.1016/j.nicl.2017.08.017_bb0045) 1995; 34 Just (10.1016/j.nicl.2017.08.017_bb0120) 2004; 127 Haxby (10.1016/j.nicl.2017.08.017_bb0095) 2001; 293 Mitchell (10.1016/j.nicl.2017.08.017_bb0165) 2008; 320 |
| References_xml | – volume: 19 start-page: 659 year: 2014 end-page: 667 ident: bb0075 article-title: The autism brain imaging data exchange: towards large-scale evaluation of the intrinsic brain architecture in autism – volume: 293 start-page: 2425 year: 2001 end-page: 2430 ident: bb0095 article-title: Distributed and overlapping representations of faces and objects in ventral temporal cortex publication-title: Science – volume: 120 start-page: 282 year: 2012 end-page: 289 ident: bb0050 article-title: Identifying bilingual semantic neural representations across languages publication-title: Brain Lang. – volume: 349 year: 2015 ident: bb0115 article-title: Machine learning: trends, perspectives, and prospects – start-page: 35 year: 2013 end-page: 63 ident: bb0135 article-title: A theory of autism based on frontal-posterior underconnectivity publication-title: Development and Brain Systems in Autism – volume: 145 start-page: 137 year: 2016 end-page: 165 ident: bb0025 article-title: Single subject prediction of brain disorders in neuroimaging: promises and pitfalls publication-title: NeuroImage – volume: 62 start-page: 1619 year: 2009 end-page: 1628 ident: bb0065 article-title: Disease state prediction from resting state functional connectivity publication-title: Magn. Reson. Med. – volume: 17 start-page: 1687 year: 2006 end-page: 1690 ident: bb0060 article-title: Functional connectivity in a baseline resting-state network in autism publication-title: NeuroReport – volume: 80 start-page: 527 year: 2013 end-page: 540 ident: bb0055 article-title: Clinical applications of the functional connectome publication-title: NeuroImage – volume: 19 start-page: 2209 year: 2009 end-page: 2229 ident: bb0200 article-title: The resting brain: unconstrained yet reliable publication-title: Cereb. Cortex – start-page: 55 year: 1998 end-page: 85 ident: bb0230 article-title: The support vector method of function estimation publication-title: Nonlinear Modeling – volume: 147 start-page: 736 year: 2017 end-page: 745 ident: bb0005 article-title: Deriving reproducible biomarkers from multi-site resting-state data: an autism-based example publication-title: NeuroImage – volume: 3 start-page: 28 year: 2014 ident: bb0235 article-title: How machine learning is shaping cognitive neuroimaging publication-title: GigaScience – volume: 106 start-page: 13040 year: 2009 end-page: 13045 ident: bb0215 article-title: Correspondence of the brain's functional architecture during activation and rest publication-title: Proc. Natl. Acad. Sci. – volume: 22 start-page: 937 year: 2012 end-page: 950 ident: bb0195 article-title: Distinctive neural processes during learning in autism publication-title: Cereb. Cortex – volume: 11 start-page: 7 year: 2006 end-page: 19 ident: bb0010 article-title: The role of the autism diagnostic observation schedule in the assessment of autism spectrum disorders in school and community settings publication-title: Calif. Sch. Psychol. – volume: 4 start-page: 135 year: 2009 end-page: 152 ident: bb0140 article-title: Atypical fronto-posterior synchronization of theroy of mind regions in autism during mental state attribution publication-title: Soc. Neurosci. – year: 2015 ident: bb0150 article-title: Deep Learning of fMRI Big Data: A Novel Approach to Subject-Transfer Decoding – volume: 17 start-page: 951 year: 2006 end-page: 961 ident: bb0130 article-title: Functional and anatomical cortical underconnectivity in autism: evidence from an fMRI study of an executive function task and corpus callosum morphometry publication-title: Cereb. Cortex – volume: 320 start-page: 1191 year: 2008 end-page: 1195 ident: bb0165 article-title: Predicting human brain activity associated with the meanings of nouns publication-title: Science – volume: 1 start-page: 278 year: 1995 end-page: 282 ident: bb0110 article-title: Random decision forests publication-title: Document Analysis and Recognition, 1995., Proceedings of the Third International Conference on – volume: 9 start-page: 2579 year: 2008 end-page: 2605 ident: bb0160 article-title: Visualizing data using t-SNE publication-title: J. Mach. Learn. Res. – volume: 22 start-page: 158 year: 2012 end-page: 165 ident: bb0210 article-title: Decoding subject-driven cognitive states with whole-brain connectivity patterns publication-title: Cereb. Cortex – volume: 11 start-page: 3371 year: 2010 end-page: 3408 ident: bb0245 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 36 start-page: 3213 year: 2015 end-page: 3226 ident: bb0035 article-title: Monitoring the growth of the neural representations of new animal concepts publication-title: Hum. Brain Mapp. – volume: 134 start-page: 3739 year: 2011 end-page: 3751 ident: bb0020 article-title: Functional connectivity magnetic resonance imaging classification of autism publication-title: Brain – volume: 127 start-page: 1811 year: 2004 end-page: 1821 ident: bb0120 article-title: Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity publication-title: Brain – start-page: 1 year: 2015 end-page: 20 ident: bb0255 article-title: Estimated prevalence of autism and other developmental disabilities following questionnaire changes in the 2014 National Health Interview Survey. publication-title: Natl. Health Stat. Rep. – volume: 3 start-page: 363 year: 2013 end-page: 374 ident: bb0090 article-title: Impact of analysis methods on the reproducibility and reliability of resting-state networks publication-title: Brain Connect. – volume: 34 start-page: 537 year: 1995 end-page: 541 ident: bb0045 article-title: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI publication-title: Magn. Reson. Med. – volume: 45 start-page: S199 year: 2009 end-page: S209 ident: bb0180 article-title: Machine learning classifiers and fMRI: a tutorial overview publication-title: NeuroImage – volume: 33 year: 2012 ident: bb0070 article-title: A whole brain fMRI atlas spatial generated via spatially constrained spectral clustering publication-title: Human brain … – year: 2017 ident: bb0100 article-title: lsa-pucrs/acerta-abide: Code companion to Neuroimage: Clinical Submission – start-page: 1096 year: 2008 end-page: 1103 ident: bb0240 article-title: Extracting and composing robust features with denoising autoencoders publication-title: Proceedings of the 25th International Conference on Machine Learning – volume: 37 start-page: 90 year: 2007 end-page: 101 ident: bb0040 article-title: A component based noise correction method (CompCor) for BOLD and perfusion based fMRI publication-title: NeuroImage – volume: 54 start-page: 2418 year: 2011 end-page: 2425 ident: bb0205 article-title: Commonality of neural representations of words and pictures publication-title: NeuroImage – volume: 8 start-page: e66032 year: 2013 ident: bb0145 article-title: Identifying emotions on the basis of neural activation publication-title: PLoS ONE – volume: 7 start-page: 1 year: 2013 end-page: 12 ident: bb0170 article-title: Multisite functional connectivity MRI classification of autism: ABIDE results publication-title: Front. Hum. Neurosci. – volume: 8 start-page: 229 year: 2014 ident: bb0185 article-title: Deep learning for neuroimaging: a validation study. publication-title: Front. Neurosci. – volume: 7 start-page: 359 year: 2015 end-page: 366 ident: bb0190 article-title: Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards publication-title: NeuroImage: Clinical – volume: 4 start-page: 19 year: 2010 ident: bb0085 article-title: Clinical applications of resting state functional connectivity publication-title: Front. Syst. Neurosci. – volume: 9 start-page: 1 year: 2014 end-page: 22 ident: bb0125 article-title: Identifying autism from neural representations of social interactions: neurocognitive markers of autism publication-title: PLoS ONE – volume: 17 start-page: 580 year: 2005 end-page: 590 ident: bb0175 article-title: Partially distributed representations of objects and faces in ventral temporal cortex publication-title: J. Cogn. Neurosci. – volume: 7 year: 2013 ident: bb0225 article-title: Reconceptualizing functional brain connectivity in autism from a developmental perspective publication-title: Front. Hum. Neurosci. – volume: 309 start-page: 102 year: 1994 ident: bb0015 article-title: Diagnostic tests 2: predictive values publication-title: BMJ – volume: 53 year: 1999 ident: bb0030 article-title: MRI volumes of amygdala and hippocampus in non-mentally retarded autistic adolescents and adults publication-title: Neurology – volume: 96 start-page: 245 year: 2014 end-page: 260 ident: bb0105 article-title: Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks publication-title: NeuroImage – volume: 42 start-page: 565 year: 2010 end-page: 569 ident: bb0250 article-title: Common SNPs explain a large proportion of the heritability for human height publication-title: Nat. Genet. – volume: 45 start-page: S199 issue: 1 year: 2009 ident: 10.1016/j.nicl.2017.08.017_bb0180 article-title: Machine learning classifiers and fMRI: a tutorial overview publication-title: NeuroImage doi: 10.1016/j.neuroimage.2008.11.007 – volume: 37 start-page: 90 issue: 1 year: 2007 ident: 10.1016/j.nicl.2017.08.017_bb0040 article-title: A component based noise correction method (CompCor) for BOLD and perfusion based fMRI publication-title: NeuroImage doi: 10.1016/j.neuroimage.2007.04.042 – start-page: 35 year: 2013 ident: 10.1016/j.nicl.2017.08.017_bb0135 article-title: A theory of autism based on frontal-posterior underconnectivity – volume: 53 issue: 9 year: 1999 ident: 10.1016/j.nicl.2017.08.017_bb0030 article-title: MRI volumes of amygdala and hippocampus in non-mentally retarded autistic adolescents and adults publication-title: Neurology doi: 10.1212/WNL.53.9.2145 – volume: 8 start-page: 229 issue: August year: 2014 ident: 10.1016/j.nicl.2017.08.017_bb0185 article-title: Deep learning for neuroimaging: a validation study. publication-title: Front. Neurosci. – volume: 62 start-page: 1619 issue: 6 year: 2009 ident: 10.1016/j.nicl.2017.08.017_bb0065 article-title: Disease state prediction from resting state functional connectivity publication-title: Magn. Reson. Med. doi: 10.1002/mrm.22159 – volume: 19 start-page: 659 issue: 6 year: 2014 ident: 10.1016/j.nicl.2017.08.017_bb0075 article-title: The autism brain imaging data exchange: towards large-scale evaluation of the intrinsic brain architecture in autism – volume: 17 start-page: 1687 issue: 16 year: 2006 ident: 10.1016/j.nicl.2017.08.017_bb0060 article-title: Functional connectivity in a baseline resting-state network in autism publication-title: NeuroReport doi: 10.1097/01.wnr.0000239956.45448.4c – volume: 33 issue: 8 year: 2012 ident: 10.1016/j.nicl.2017.08.017_bb0070 article-title: A whole brain fMRI atlas spatial generated via spatially constrained spectral clustering publication-title: Human brain … – year: 2017 ident: 10.1016/j.nicl.2017.08.017_bb0100 – volume: 309 start-page: 102 issue: 6947 year: 1994 ident: 10.1016/j.nicl.2017.08.017_bb0015 article-title: Diagnostic tests 2: predictive values publication-title: BMJ doi: 10.1136/bmj.309.6947.102 – volume: 4 start-page: 135 issue: 2 year: 2009 ident: 10.1016/j.nicl.2017.08.017_bb0140 article-title: Atypical fronto-posterior synchronization of theroy of mind regions in autism during mental state attribution publication-title: Soc. Neurosci. doi: 10.1080/17470910802198510 – volume: 9 start-page: 2579 year: 2008 ident: 10.1016/j.nicl.2017.08.017_bb0160 article-title: Visualizing data using t-SNE publication-title: J. Mach. Learn. Res. – volume: 349 issue: 6245 year: 2015 ident: 10.1016/j.nicl.2017.08.017_bb0115 article-title: Machine learning: trends, perspectives, and prospects – volume: 22 start-page: 158 issue: 1 year: 2012 ident: 10.1016/j.nicl.2017.08.017_bb0210 article-title: Decoding subject-driven cognitive states with whole-brain connectivity patterns publication-title: Cereb. Cortex doi: 10.1093/cercor/bhr099 – volume: 7 start-page: 359 year: 2015 ident: 10.1016/j.nicl.2017.08.017_bb0190 article-title: Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards publication-title: NeuroImage: Clinical doi: 10.1016/j.nicl.2014.12.013 – volume: 42 start-page: 565 issue: 7 year: 2010 ident: 10.1016/j.nicl.2017.08.017_bb0250 article-title: Common SNPs explain a large proportion of the heritability for human height publication-title: Nat. Genet. doi: 10.1038/ng.608 – volume: 8 start-page: e66032 issue: 6 year: 2013 ident: 10.1016/j.nicl.2017.08.017_bb0145 article-title: Identifying emotions on the basis of neural activation publication-title: PLoS ONE doi: 10.1371/journal.pone.0066032 – volume: 7 year: 2013 ident: 10.1016/j.nicl.2017.08.017_bb0225 article-title: Reconceptualizing functional brain connectivity in autism from a developmental perspective publication-title: Front. Hum. Neurosci. doi: 10.3389/fnhum.2013.00458 – volume: 4 start-page: 19 year: 2010 ident: 10.1016/j.nicl.2017.08.017_bb0085 article-title: Clinical applications of resting state functional connectivity publication-title: Front. Syst. Neurosci. – volume: 22 start-page: 937 issue: 4 year: 2012 ident: 10.1016/j.nicl.2017.08.017_bb0195 article-title: Distinctive neural processes during learning in autism publication-title: Cereb. Cortex doi: 10.1093/cercor/bhr162 – volume: 145 start-page: 137 issue: Pt B year: 2016 ident: 10.1016/j.nicl.2017.08.017_bb0025 article-title: Single subject prediction of brain disorders in neuroimaging: promises and pitfalls publication-title: NeuroImage – volume: 80 start-page: 527 year: 2013 ident: 10.1016/j.nicl.2017.08.017_bb0055 article-title: Clinical applications of the functional connectome publication-title: NeuroImage doi: 10.1016/j.neuroimage.2013.04.083 – volume: 7 start-page: 1 issue: September year: 2013 ident: 10.1016/j.nicl.2017.08.017_bb0170 article-title: Multisite functional connectivity MRI classification of autism: ABIDE results publication-title: Front. Hum. Neurosci. – volume: 11 start-page: 3371 issue: 3 year: 2010 ident: 10.1016/j.nicl.2017.08.017_bb0245 article-title: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion publication-title: J. Mach. Learn. Res. – volume: 96 start-page: 245 year: 2014 ident: 10.1016/j.nicl.2017.08.017_bb0105 article-title: Restricted Boltzmann machines for neuroimaging: an application in identifying intrinsic networks publication-title: NeuroImage doi: 10.1016/j.neuroimage.2014.03.048 – volume: 17 start-page: 580 issue: 4 year: 2005 ident: 10.1016/j.nicl.2017.08.017_bb0175 article-title: Partially distributed representations of objects and faces in ventral temporal cortex publication-title: J. Cogn. Neurosci. doi: 10.1162/0898929053467550 – start-page: 55 year: 1998 ident: 10.1016/j.nicl.2017.08.017_bb0230 article-title: The support vector method of function estimation – volume: 1 start-page: 278 year: 1995 ident: 10.1016/j.nicl.2017.08.017_bb0110 article-title: Random decision forests – volume: 34 start-page: 537 issue: 4 year: 1995 ident: 10.1016/j.nicl.2017.08.017_bb0045 article-title: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI publication-title: Magn. Reson. Med. doi: 10.1002/mrm.1910340409 – volume: 134 start-page: 3739 issue: 12 year: 2011 ident: 10.1016/j.nicl.2017.08.017_bb0020 article-title: Functional connectivity magnetic resonance imaging classification of autism publication-title: Brain doi: 10.1093/brain/awr263 – start-page: 1096 year: 2008 ident: 10.1016/j.nicl.2017.08.017_bb0240 article-title: Extracting and composing robust features with denoising autoencoders – volume: 11 start-page: 7 issue: 1 year: 2006 ident: 10.1016/j.nicl.2017.08.017_bb0010 article-title: The role of the autism diagnostic observation schedule in the assessment of autism spectrum disorders in school and community settings publication-title: Calif. Sch. Psychol. doi: 10.1007/BF03341111 – start-page: 1 issue: 87 year: 2015 ident: 10.1016/j.nicl.2017.08.017_bb0255 article-title: Estimated prevalence of autism and other developmental disabilities following questionnaire changes in the 2014 National Health Interview Survey. publication-title: Natl. Health Stat. Rep. – volume: 17 start-page: 951 issue: 4 year: 2006 ident: 10.1016/j.nicl.2017.08.017_bb0130 article-title: Functional and anatomical cortical underconnectivity in autism: evidence from an fMRI study of an executive function task and corpus callosum morphometry publication-title: Cereb. Cortex doi: 10.1093/cercor/bhl006 – volume: 120 start-page: 282 issue: 3 year: 2012 ident: 10.1016/j.nicl.2017.08.017_bb0050 article-title: Identifying bilingual semantic neural representations across languages publication-title: Brain Lang. doi: 10.1016/j.bandl.2011.09.003 – volume: 36 start-page: 3213 issue: 8 year: 2015 ident: 10.1016/j.nicl.2017.08.017_bb0035 article-title: Monitoring the growth of the neural representations of new animal concepts publication-title: Hum. Brain Mapp. doi: 10.1002/hbm.22842 – volume: 3 start-page: 363 issue: 4 year: 2013 ident: 10.1016/j.nicl.2017.08.017_bb0090 article-title: Impact of analysis methods on the reproducibility and reliability of resting-state networks publication-title: Brain Connect. doi: 10.1089/brain.2012.0134 – volume: 147 start-page: 736 year: 2017 ident: 10.1016/j.nicl.2017.08.017_bb0005 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 – volume: 54 start-page: 2418 issue: 3 year: 2011 ident: 10.1016/j.nicl.2017.08.017_bb0205 article-title: Commonality of neural representations of words and pictures publication-title: NeuroImage doi: 10.1016/j.neuroimage.2010.10.042 – volume: 9 start-page: 1 issue: 12 year: 2014 ident: 10.1016/j.nicl.2017.08.017_bb0125 article-title: Identifying autism from neural representations of social interactions: neurocognitive markers of autism publication-title: PLoS ONE doi: 10.1371/journal.pone.0113879 – volume: 127 start-page: 1811 issue: 8 year: 2004 ident: 10.1016/j.nicl.2017.08.017_bb0120 article-title: Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity publication-title: Brain doi: 10.1093/brain/awh199 – year: 2015 ident: 10.1016/j.nicl.2017.08.017_bb0150 – volume: 106 start-page: 13040 issue: 31 year: 2009 ident: 10.1016/j.nicl.2017.08.017_bb0215 article-title: Correspondence of the brain's functional architecture during activation and rest publication-title: Proc. Natl. Acad. Sci. doi: 10.1073/pnas.0905267106 – volume: 293 start-page: 2425 issue: 5539 year: 2001 ident: 10.1016/j.nicl.2017.08.017_bb0095 article-title: Distributed and overlapping representations of faces and objects in ventral temporal cortex publication-title: Science doi: 10.1126/science.1063736 – volume: 19 start-page: 2209 issue: 10 year: 2009 ident: 10.1016/j.nicl.2017.08.017_bb0200 article-title: The resting brain: unconstrained yet reliable publication-title: Cereb. Cortex doi: 10.1093/cercor/bhn256 – volume: 320 start-page: 1191 issue: 5880 year: 2008 ident: 10.1016/j.nicl.2017.08.017_bb0165 article-title: Predicting human brain activity associated with the meanings of nouns publication-title: Science doi: 10.1126/science.1152876 – volume: 3 start-page: 28 issue: 1 year: 2014 ident: 10.1016/j.nicl.2017.08.017_bb0235 article-title: How machine learning is shaping cognitive neuroimaging publication-title: GigaScience doi: 10.1186/2047-217X-3-28 |
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| Snippet | The goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging dataset, based... AbstractThe goal of the present study was to apply deep learning algorithms to identify autism spectrum disorder (ASD) patients from large brain imaging... |
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| SubjectTerms | ABIDE Adolescent Adult Autism Autism Spectrum Disorder - diagnostic imaging Brain - diagnostic imaging Brain Mapping Case-Control Studies Child Datasets as Topic Deep learning Female fMRI Functional Neuroimaging Humans Image Processing, Computer-Assisted Machine Learning - classification Male Neural Networks, Computer Neural Pathways - diagnostic imaging Radiology Regular Rest Resting state Young Adult |
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| Title | Identification of autism spectrum disorder using deep learning and the ABIDE dataset |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S2213158217302073 https://www.clinicalkey.es/playcontent/1-s2.0-S2213158217302073 https://dx.doi.org/10.1016/j.nicl.2017.08.017 https://www.ncbi.nlm.nih.gov/pubmed/29034163 https://www.proquest.com/docview/1951565048 https://pubmed.ncbi.nlm.nih.gov/PMC5635344 https://doaj.org/article/ffda85221f50479296e51f3653912a52 |
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