{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T15:42:59Z","timestamp":1775576579112,"version":"3.50.1"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T00:00:00Z","timestamp":1612483200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,2,5]],"date-time":"2021-02-05T00:00:00Z","timestamp":1612483200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"crossref","award":["ANR-16-LCV2-0006"],"award-info":[{"award-number":["ANR-16-LCV2-0006"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"crossref","award":["ANR-10-COHO-05-01"],"award-info":[{"award-number":["ANR-10-COHO-05-01"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"crossref","award":["ANR-10-LABX-57"],"award-info":[{"award-number":["ANR-10-LABX-57"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Conseil R\u00e9gional de la Nouvelle Aquitaine","award":["4370420"],"award-info":[{"award-number":["4370420"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s12021-021-09514-x","type":"journal-article","created":{"date-parts":[[2021,2,4]],"date-time":"2021-02-04T20:16:03Z","timestamp":1612469763000},"page":"619-637","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Deep Learning\u2010based Classification of Resting\u2010state fMRI Independent\u2010component Analysis"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2529-1839","authenticated-orcid":false,"given":"Victor","family":"Nozais","sequence":"first","affiliation":[]},{"given":"Philippe","family":"Boutinaud","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2385-3079","authenticated-orcid":false,"given":"Violaine","family":"Verrecchia","sequence":"additional","affiliation":[]},{"given":"Marie-Fateye","family":"Gueye","sequence":"additional","affiliation":[]},{"given":"Pierre-Yves","family":"Herv\u00e9","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6517-2984","authenticated-orcid":false,"given":"Christophe","family":"Tzourio","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0970-2837","authenticated-orcid":false,"given":"Bernard","family":"Mazoyer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7792-308X","authenticated-orcid":false,"given":"Marc","family":"Joliot","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,5]]},"reference":[{"key":"9514_CR1","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., et al. TensorFlow: A system for large-scale machine learning. In Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI \u201916), Savannah, GA, USA, November 2\u20134, 2016 (pp. 265\u2013283)."},{"key":"9514_CR2","doi-asserted-by":"publisher","first-page":"37","DOI":"10.3389\/fnsys.2011.00037","volume":"5","author":"A Abou Elseoud","year":"2011","unstructured":"Abou Elseoud, A., Littow, H., Remes, J., Starck, T., Nikkinen, J., Nissila, J., et al. (2011). Group-ICA model order highlights patterns of functional brain connectivity. Frontiers in Systems Neuroscience, 5, 37. https:\/\/doi.org\/10.3389\/fnsys.2011.00037.","journal-title":"Frontiers in Systems Neuroscience"},{"issue":"4","key":"9514_CR3","doi-asserted-by":"publisher","first-page":"550","DOI":"10.1016\/j.neuron.2010.02.005","volume":"65","author":"JR Andrews-Hanna","year":"2010","unstructured":"Andrews-Hanna, J. R., Reidler, J. S., Sepulcre, J., Poulin, R., & Buckner, R. L. (2010). Functional-anatomic fractionation of the brain\u2019s default network. Neuron, 65(4), 550\u2013562. doi:https:\/\/doi.org\/10.1016\/j.neuron.2010.02.005.","journal-title":"Neuron"},{"issue":"1457","key":"9514_CR4","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1098\/rstb.2005.1634","volume":"360","author":"CF Beckmann","year":"2005","unstructured":"Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 360(1457), 1001\u20131013. https:\/\/doi.org\/10.1098\/rstb.2005.1634.","journal-title":"Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences"},{"key":"9514_CR5","unstructured":"Bergstra, J., Bardenet, R., Bengio, Y., & K\u00e9gl, B. (2011). Algorithms for Hyper-parameter Optimization (pp. 2546\u20132554). Red Hook: Curran Associates Inc."},{"issue":"1","key":"9514_CR6","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1145\/1656274.1656280","volume":"11","author":"MR Berthold","year":"2009","unstructured":"Berthold, M. R., Cebron, N., Dill, F., Gabriel, T. R., K\u00f6tter, T., Meinl, T., et al. (2009). KNIME - the Konstanz information miner: Version 2.0 and beyond. SIGKDD Explorations Newsletter, 11(1), 26\u201331. https:\/\/doi.org\/10.1145\/1656274.1656280.","journal-title":"SIGKDD Explorations Newsletter"},{"key":"9514_CR7","doi-asserted-by":"publisher","unstructured":"Bijsterbosch, J. D., Beckmann, C. F., Woolrich, M. W., Smith, S. M., & Harrison, S. J. (2019). The relationship between spatial configuration and functional connectivity of brain regions revisited. Elife, 8. https:\/\/doi.org\/10.7554\/eLife.44890.","DOI":"10.7554\/eLife.44890"},{"key":"9514_CR8","doi-asserted-by":"publisher","unstructured":"Bijsterbosch, J. D., Woolrich, M. W., Glasser, M. F., Robinson, E. C., Beckmann, C. F., Van Essen, D. C., et al. (2018). The relationship between spatial configuration and functional connectivity of brain regions. Elife, 7. https:\/\/doi.org\/10.7554\/eLife.32992.","DOI":"10.7554\/eLife.32992"},{"issue":"4","key":"9514_CR9","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1002\/mrm.1910340409","volume":"34","author":"B Biswal","year":"1995","unstructured":"Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34(4), 537\u2013541. https:\/\/doi.org\/10.1002\/mrm.1910340409.","journal-title":"Magnetic Resonance in Medicine"},{"key":"9514_CR10","doi-asserted-by":"publisher","unstructured":"Braga, R. M., & Buckner, R. L. (2017). Parallel interdigitated distributed networks within the individual estimated by intrinsic functional connectivity. Neuron, 95(2), 457\u2013471 e455. https:\/\/doi.org\/10.1016\/j.neuron.2017.06.038.","DOI":"10.1016\/j.neuron.2017.06.038"},{"issue":"3","key":"9514_CR11","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1038\/nrn2575","volume":"10","author":"E Bullmore","year":"2009","unstructured":"Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews. Neuroscience, 10(3), 186\u2013198. https:\/\/doi.org\/10.1038\/nrn2575.","journal-title":"Nature Reviews. Neuroscience"},{"issue":"7","key":"9514_CR12","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1002\/hbm.20581","volume":"29","author":"VD Calhoun","year":"2008","unstructured":"Calhoun, V. D., Kiehl, K. A., & Pearlson, G. D. (2008). Modulation of temporally coherent brain networks estimated using ICA at rest and during cognitive tasks. Human Brain Mapping, 29(7), 828\u2013838. https:\/\/doi.org\/10.1002\/hbm.20581.","journal-title":"Human Brain Mapping"},{"key":"9514_CR13","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1016\/j.neuroimage.2013.05.118","volume":"82","author":"T Chen","year":"2013","unstructured":"Chen, T., Ryali, S., Qin, S., & Menon, V. (2013). Estimation of resting-state functional connectivity using random subspace based partial correlation: a novel method for reducing global artifacts. Neuroimage, 82, 87\u2013100. doi:https:\/\/doi.org\/10.1016\/j.neuroimage.2013.05.118.","journal-title":"Neuroimage"},{"key":"9514_CR14","doi-asserted-by":"crossref","unstructured":"Chou, Y., Roy, S., Chang, C., Butman, J. A., & Pham, L. Deep learning of resting state networks from independant component analysis. In 2018 IEEE 15th International Symposium on Biomedical Imaging, Washington, D.C., USA, 2018 (pp. 747\u2013751): New York: IEEE.","DOI":"10.1109\/ISBI.2018.8363681"},{"issue":"37","key":"9514_CR15","doi-asserted-by":"publisher","first-page":"13848","DOI":"10.1073\/pnas.0601417103","volume":"103","author":"JS Damoiseaux","year":"2006","unstructured":"Damoiseaux, J. S., Rombouts, S. A., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., et al. (2006). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences of the United States of America, 103(37), 13848\u201313853. https:\/\/doi.org\/10.1073\/pnas.0601417103.","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"issue":"5997","key":"9514_CR16","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.1126\/science.1194144","volume":"329","author":"NU Dosenbach","year":"2010","unstructured":"Dosenbach, N. U., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., et al. (2010). Prediction of individual brain maturity using fMRI. Science, 329(5997), 1358\u20131361. doi:https:\/\/doi.org\/10.1126\/science.1194144.","journal-title":"Science"},{"issue":"5","key":"9514_CR17","doi-asserted-by":"publisher","first-page":"799","DOI":"10.1016\/j.neuron.2006.04.031","volume":"50","author":"NU Dosenbach","year":"2006","unstructured":"Dosenbach, N. U., Visscher, K. M., Palmer, E. D., Miezin, F. M., Wenger, K. K., Kang, H. C., et al. (2006). A core system for the implementation of task sets. Neuron, 50(5), 799\u2013812. doi:https:\/\/doi.org\/10.1016\/j.neuron.2006.04.031.","journal-title":"Neuron"},{"issue":"6","key":"9514_CR18","doi-asserted-by":"publisher","first-page":"2753","DOI":"10.1152\/jn.00895.2010","volume":"105","author":"G Doucet","year":"2011","unstructured":"Doucet, G., Naveau, M., Petit, L., Delcroix, N., Zago, L., Crivello, F., et al. (2011). Brain activity at rest: a multiscale hierarchical functional organization. Journal of Neurophysiology, 105(6), 2753\u20132763. https:\/\/doi.org\/10.1152\/jn.00895.2010.","journal-title":"Journal of Neurophysiology"},{"issue":"4","key":"9514_CR19","doi-asserted-by":"publisher","first-page":"3194","DOI":"10.1016\/j.neuroimage.2011.11.059","volume":"59","author":"G Doucet","year":"2012","unstructured":"Doucet, G., Naveau, M., Petit, L., Zago, L., Crivello, F., Jobard, G., et al. (2012). Patterns of hemodynamic low-frequency oscillations in the brain are modulated by the nature of free thought during rest. Neuroimage, 59(4), 3194\u20133200. doi:https:\/\/doi.org\/10.1016\/j.neuroimage.2011.11.059.","journal-title":"Neuroimage"},{"issue":"15","key":"9514_CR20","doi-asserted-by":"publisher","first-page":"4577","DOI":"10.1002\/hbm.24722","volume":"40","author":"GE Doucet","year":"2019","unstructured":"Doucet, G. E., Lee, W. H., & Frangou, S. (2019). Evaluation of the spatial variability in the major resting-state networks across human brain functional atlases. Human Brain Mapping, 40(15), 4577\u20134587. https:\/\/doi.org\/10.1002\/hbm.24722.","journal-title":"Human Brain Mapping"},{"issue":"27","key":"9514_CR21","doi-asserted-by":"publisher","first-page":"9673","DOI":"10.1073\/pnas.0504136102","volume":"102","author":"MD Fox","year":"2005","unstructured":"Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673\u20139678. https:\/\/doi.org\/10.1073\/pnas.0504136102.","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"9514_CR22","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1002\/mrm.1910350312","volume":"35","author":"KJ Friston","year":"1996","unstructured":"Friston, K. J., Williams, S., Howard, R., Frackowiak, R. S. J., & Turner, R. (1996). Movement-related effects in fMRI times-series. Magnetic Resonance in Medicine, 35, 346\u2013356.","journal-title":"Magnetic Resonance in Medicine"},{"key":"9514_CR23","unstructured":"Glorot, X., & Bengio, Y. (2010). Understanding the difficulty of training deep forward neural networks. Paper presented at the 13th International conference on artificial intelligence and statistics, Sardinia, Italy."},{"issue":"1","key":"9514_CR24","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1093\/cercor\/bhv239","volume":"27","author":"EM Gordon","year":"2015","unstructured":"Gordon, E. M., Laumann, T. O., Adeyemo, B., & Petersen, S. E. (2015). Individual variability of the system-level organization of the human brain. Cerebral Cortex, 27(1), 386\u2013399. https:\/\/doi.org\/10.1093\/cercor\/bhv239.","journal-title":"Cerebral Cortex"},{"key":"9514_CR25","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.nicl.2017.08.017","volume":"17","author":"AS Heinsfeld","year":"2017","unstructured":"Heinsfeld, A. S., Franco, A. R., Craddock, R. C., Buchweitz, A., & Meneguzzi, F. (2017). Identification of autism spectrum disorder using deep learning and the ABIDE dataset. NeuroImage: Clinical, 17, 16\u201323. doi:https:\/\/doi.org\/10.1016\/j.nicl.2017.08.017.","journal-title":"NeuroImage: Clinical"},{"issue":"3","key":"9514_CR26","doi-asserted-by":"publisher","first-page":"1214","DOI":"10.1016\/j.neuroimage.2004.03.027","volume":"22","author":"J Himberg","year":"2004","unstructured":"Himberg, J., Hyvarinen, A., & Esposito, F. (2004). Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage, 22(3), 1214\u20131222. doi:https:\/\/doi.org\/10.1016\/j.neuroimage.2004.03.027.","journal-title":"Neuroimage"},{"key":"9514_CR27","unstructured":"Ioffe, S., & Szegedy, C. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. In 32nd International Conference on International Conference on Machine Learning, Lille, France, 2015 (Vol. 37, pp. 448\u2013456). https:\/\/arxiv.org\/pdf\/1502.03167.pdf:JMLR.org."},{"key":"9514_CR28","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.jneumeth.2015.07.013","volume":"254","author":"M Joliot","year":"2015","unstructured":"Joliot, M., Jobard, G., Naveau, M., Delcroix, N., Petit, L., Zago, L., et al. (2015). AICHA: An atlas of intrinsic connectivity of homotopic areas. Journal of Neuroscience Methods, 254, 46\u201359. https:\/\/doi.org\/10.1016\/j.jneumeth.2015.07.013.","journal-title":"Journal of Neuroscience Methods"},{"key":"9514_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/0165-1684(91)90079-X","volume":"24","author":"C Jutten","year":"1991","unstructured":"Jutten, C., & Herault, J. (1991). Blind separation of sources, part i: An adaptive algorithm based on neuromimetic architecture. Signal Processing, 24, 1\u201310. doi:https:\/\/doi.org\/10.1016\/0165-1684(91)90079-X.","journal-title":"Signal Processing"},{"key":"9514_CR30","doi-asserted-by":"publisher","first-page":"108451","DOI":"10.1016\/j.jneumeth.2019.108451","volume":"330","author":"HC Kim","year":"2020","unstructured":"Kim, H. C., Jang, H., & Lee, J. H. (2020). Test-retest reliability of spatial patterns from resting-state functional MRI using the restricted Boltzmann machine and hierarchically organized spatial patterns from the deep belief network. Journal of Neuroscience Methods, 330, 108451. https:\/\/doi.org\/10.1016\/j.jneumeth.2019.108451.","journal-title":"Journal of Neuroscience Methods"},{"key":"9514_CR31","unstructured":"Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv:1412.6980 [cs]."},{"key":"9514_CR32","doi-asserted-by":"publisher","DOI":"10.1162\/nol_a_00004","author":"XZ Kong","year":"2020","unstructured":"Kong, X. Z., Tzourio Mazoyer, N., Joliot, M., Fedorenko, E., Liu, J., Fisher, E. S., et al. (2020). Gene expression correlates of the cortical network underlying sentence processing. Neurobiology of Language. https:\/\/doi.org\/10.1162\/nol_a_00004.","journal-title":"Neurobiology of Language"},{"issue":"1","key":"9514_CR33","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1385\/ni:3:1:065","volume":"3","author":"AR Laird","year":"2005","unstructured":"Laird, A. R., Lancaster, J. L., & Fox, P. T. (2005). BrainMap: the social evolution of a human brain mapping database. Neuroinformatics, 3(1), 65\u201378. doi:https:\/\/doi.org\/10.1385\/ni:3:1:065.","journal-title":"Neuroinformatics"},{"issue":"1","key":"9514_CR34","doi-asserted-by":"publisher","first-page":"112","DOI":"10.1016\/j.media.2014.10.011","volume":"20","author":"J Lv","year":"2015","unstructured":"Lv, J., Jiang, X., Li, X., Zhu, D., Chen, H., Zhang, T., et al. (2015). Sparse representation of whole-brain fMRI signals for identification of functional networks. Medical Image Analysis, 20(1), 112\u2013134. https:\/\/doi.org\/10.1016\/j.media.2014.10.011.","journal-title":"Medical Image Analysis"},{"key":"9514_CR35","unstructured":"Maaten, L. v. d., & Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9(Nov), 2579\u20132605."},{"issue":"2","key":"9514_CR36","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.neuron.2017.07.006","volume":"95","author":"DS Margulies","year":"2017","unstructured":"Margulies, D. S. (2017). Unraveling the complex tapestry of association networks. Neuron, 95(2), 239\u2013241. https:\/\/doi.org\/10.1016\/j.neuron.2017.07.006.","journal-title":"Neuron"},{"issue":"2","key":"9514_CR37","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1016\/j.neuroimage.2007.05.019","volume":"37","author":"DS Margulies","year":"2007","unstructured":"Margulies, D. S., Kelly, A. M., Uddin, L. Q., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2007). Mapping the functional connectivity of anterior cingulate cortex. Neuroimage, 37(2), 579\u2013588. doi:https:\/\/doi.org\/10.1016\/j.neuroimage.2007.05.019.","journal-title":"Neuroimage"},{"issue":"47","key":"9514_CR38","doi-asserted-by":"publisher","first-page":"20069","DOI":"10.1073\/pnas.0905314106","volume":"106","author":"DS Margulies","year":"2009","unstructured":"Margulies, D. S., Vincent, J. L., Kelly, C., Lohmann, G., Uddin, L. Q., Biswal, B. B., et al. (2009). Precuneus shares intrinsic functional architecture in humans and monkeys. Proceedings of the National Academy of Sciences of the United States of America, 106(47), 20069\u201320074. https:\/\/doi.org\/10.1073\/pnas.0905314106.","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"key":"9514_CR39","doi-asserted-by":"publisher","unstructured":"Mazoyer, B., Mellet, E., Perchey, G., Zago, L., Crivello, F., Jobard, G., et al. (2016). BIL&GIN: A neuroimaging, cognitive, behavioral, and genetic database for the study of human brain lateralization. Neuroimage, 124(Pt B), 1225\u20131231. https:\/\/doi.org\/10.1016\/j.neuroimage.2015.02.071.","DOI":"10.1016\/j.neuroimage.2015.02.071"},{"issue":"3","key":"9514_CR40","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/S0361-9230(00)00437-8","volume":"54","author":"B Mazoyer","year":"2001","unstructured":"Mazoyer, B., Zago, L., Mellet, E., Bricogne, S., Etard, O., Houde, O., et al. (2001). Cortical networks for working memory and executive functions sustain the conscious resting state in man. Brain Research Bulletin, 54(3), 287\u2013298.","journal-title":"Brain Research Bulletin"},{"issue":"11","key":"9514_CR41","doi-asserted-by":"publisher","first-page":"1523","DOI":"10.1038\/nn.4393","volume":"19","author":"KL Miller","year":"2016","unstructured":"Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., et al. (2016). Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nature Neuroscience, 19(11), 1523\u20131536. https:\/\/doi.org\/10.1038\/nn.4393.","journal-title":"Nature Neuroscience"},{"key":"9514_CR42","unstructured":"Minka, T. (2000). Automatic choice of dimensionality for PCA. M.I.T. Media Laboratory Perceptual Computing Section Technical Report No. 514."},{"key":"9514_CR43","unstructured":"Naveau, M., Delcroix, N., Herv\u00e9, P. Y., Petit, L., Crivello, F., Jobard, G., et al. (2012a). MICCA: Multi-scale independent component clustering algorithm. In 18th Annual Meeting of the Organization for Human Brain Mapping, Beijing, China."},{"issue":"3","key":"9514_CR44","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1007\/s12021-012-9145-2","volume":"10","author":"M Naveau","year":"2012","unstructured":"Naveau, M., Doucet, G., Delcroix, N., Petit, L., Zago, L., Crivello, F., et al. (2012b). A novel group ICA approach based on multi-scale individual component clustering. Application to a large sample of fMRI data. Neuroinformatics, 10(3), 269\u2013285. doi:https:\/\/doi.org\/10.1007\/s12021-012-9145-2.","journal-title":"Neuroinformatics"},{"issue":"Oct","key":"9514_CR45","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011). Scikit-learn: Machine learning in python. Journal of Machine Learning Research, 12(Oct), 2825\u20132830.","journal-title":"Journal of Machine Learning Research"},{"key":"9514_CR46","doi-asserted-by":"publisher","first-page":"116604","DOI":"10.1016\/j.neuroimage.2020.116604","volume":"211","author":"U Pervaiz","year":"2020","unstructured":"Pervaiz, U., Vidaurre, D., Woolrich, M. W., & Smith, S. M. (2020). Optimising network modelling methods for fMRI. Neuroimage, 211, 116604. doi:https:\/\/doi.org\/10.1016\/j.neuroimage.2020.116604.","journal-title":"Neuroimage"},{"key":"9514_CR47","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3389\/fnins.2014.00229","volume":"8","author":"SM Plis","year":"2014","unstructured":"Plis, S. M., Hjelm, D. R., Salakhutdinov, R., Allen, E. A., Bockholt, H. J., Long, J. D., et al. (2014). Deep learning for neuroimaging: a validation study. Frontiers in Neuroscience, 8, 229. https:\/\/doi.org\/10.3389\/fnins.2014.00229.","journal-title":"Frontiers in Neuroscience"},{"issue":"2","key":"9514_CR48","doi-asserted-by":"publisher","first-page":"816","DOI":"10.1016\/j.neuroimage.2012.04.062","volume":"62","author":"CJ Price","year":"2012","unstructured":"Price, C. J. (2012). A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading. Neuroimage, 62(2), 816\u2013847. doi:https:\/\/doi.org\/10.1016\/j.neuroimage.2012.04.062.","journal-title":"Neuroimage"},{"issue":"2","key":"9514_CR49","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1073\/pnas.98.2.676","volume":"98","author":"ME Raichle","year":"2001","unstructured":"Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 676\u2013682.","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"issue":"6240","key":"9514_CR50","doi-asserted-by":"publisher","first-page":"1241","DOI":"10.1126\/science.1255905","volume":"348","author":"J Richiardi","year":"2015","unstructured":"Richiardi, J., Altmann, A., Milazzo, A. C., Chang, C., Chakravarty, M. M., Banaschewski, T., et al. (2015). Brain networks. Correlated gene expression supports synchronous activity in brain networks. Science, 348(6240), 1241\u20131244. https:\/\/doi.org\/10.1126\/science.1255905.","journal-title":"Science"},{"key":"9514_CR51","doi-asserted-by":"publisher","unstructured":"Shahriari, B., Swersky, K., Wang, Z., Adams, R. P., & de Freitas, N. (2016). Taking the human out of the loop: a review of Bayesian optimization. Proceedings of the IEEE. https:\/\/doi.org\/10.1109\/JPROC.2015.2494218.","DOI":"10.1109\/JPROC.2015.2494218"},{"issue":"1","key":"9514_CR52","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1093\/cercor\/bhr099","volume":"22","author":"WR Shirer","year":"2012","unstructured":"Shirer, W. R., Ryali, S., Rykhlevskaia, E., Menon, V., & Greicius, M. D. (2012). Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cerebral Cortex, 22(1), 158\u2013165. https:\/\/doi.org\/10.1093\/cercor\/bhr099.","journal-title":"Cerebral Cortex"},{"key":"9514_CR53","unstructured":"Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image reconstruction. In ICLR 21014. USA: San Diego. https:\/\/arxiv.org\/pdf\/1409.1556.pdf."},{"issue":"31","key":"9514_CR54","doi-asserted-by":"publisher","first-page":"13040","DOI":"10.1073\/pnas.0905267106","volume":"106","author":"SM Smith","year":"2009","unstructured":"Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., et al. (2009). Correspondence of the brain\u2019s functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106(31), 13040\u201313045. https:\/\/doi.org\/10.1073\/pnas.0905267106.","journal-title":"Proceedings of the National Academy of Sciences of the United States of America"},{"issue":"Suppl 1","key":"9514_CR55","doi-asserted-by":"publisher","first-page":"S208","DOI":"10.1016\/j.neuroimage.2004.07.051","volume":"23","author":"SM Smith","year":"2004","unstructured":"Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage, 23(Suppl 1), S208\u2013S219.","journal-title":"Neuroimage"},{"key":"9514_CR56","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929\u20131958.","journal-title":"Journal of Machine Learning Research"},{"key":"9514_CR57","doi-asserted-by":"publisher","DOI":"10.1101\/2020.06.17.154666","author":"A Tsuchida","year":"2020","unstructured":"Tsuchida, A., Laurent, A., Crivello, F., Petit, L., Joliot, M., Pepe, A., et al. (2020). The MRi-Share database: brain imaging in a cross-sectional cohort of 1,870 university students. bioRxiv. doi:https:\/\/doi.org\/10.1101\/2020.06.17.154666.","journal-title":"bioRxiv"},{"issue":"1","key":"9514_CR58","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1006\/nimg.2001.0978","volume":"15","author":"N Tzourio-Mazoyer","year":"2002","unstructured":"Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15(1), 273\u2013289. doi:https:\/\/doi.org\/10.1006\/nimg.2001.0978.","journal-title":"Neuroimage"},{"issue":"3","key":"9514_CR59","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1523\/JNEUROSCI.4227-13.2014","volume":"34","author":"AV Utevsky","year":"2014","unstructured":"Utevsky, A. V., Smith, D. V., & Huettel, S. A. (2014). Precuneus is a functional core of the default-mode network. The Journal of Neuroscience, 34(3), 932\u2013940. https:\/\/doi.org\/10.1523\/JNEUROSCI.4227-13.2014.","journal-title":"The Journal of Neuroscience"},{"key":"9514_CR60","doi-asserted-by":"publisher","unstructured":"van den Heuvel, M., Mandl, R., & Hulshoff Pol, H. (2008). Normalized cut group clustering of resting-state FMRI data. PLoS One, 3(4), e2001. https:\/\/doi.org\/10.1371\/journal.pone.0002001.","DOI":"10.1371\/journal.pone.0002001"},{"issue":"8","key":"9514_CR61","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1016\/j.euroneuro.2010.03.008","volume":"20","author":"MP van den Heuvel","year":"2010","unstructured":"van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010). Exploring the brain network: a review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20(8), 519\u2013534. https:\/\/doi.org\/10.1016\/j.euroneuro.2010.03.008.","journal-title":"European Neuropsychopharmacology"},{"issue":"36","key":"9514_CR62","doi-asserted-by":"publisher","first-page":"14489","DOI":"10.1523\/JNEUROSCI.2128-13.2013","volume":"33","author":"MP van den Heuvel","year":"2013","unstructured":"van den Heuvel, M. P., & Sporns, O. (2013). An anatomical substrate for integration among functional networks in human cortex. The Journal of Neuroscience, 33(36), 14489\u201314500. https:\/\/doi.org\/10.1523\/JNEUROSCI.2128-13.2013.","journal-title":"The Journal of Neuroscience"},{"issue":"5","key":"9514_CR63","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1136\/jamia.2001.0080443","volume":"8","author":"DC Van Essen","year":"2001","unstructured":"Van Essen, D. C., Drury, H. A., Dickson, J., Harwell, J., Hanlon, D., & Anderson, C. H. (2001). An integrated software suite for surface-based analyses of cerebral cortex. Journal of the American Medical Informatics Association, 8(5), 443\u2013459. https:\/\/doi.org\/10.1136\/jamia.2001.0080443.","journal-title":"Journal of the American Medical Informatics Association"},{"key":"9514_CR64","doi-asserted-by":"publisher","first-page":"562","DOI":"10.1007\/978-3-642-22092-0_46","volume":"22","author":"G Varoquaux","year":"2011","unstructured":"Varoquaux, G., Gramfort, A., Pedregosa, F., Michel, V., & Thirion, B. (2011). Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. Information Processing in Medical Imaging, 22, 562\u2013573. https:\/\/doi.org\/10.1007\/978-3-642-22092-0_46.","journal-title":"Information Processing in Medical Imaging"},{"key":"9514_CR65","doi-asserted-by":"publisher","unstructured":"Vergun, S., Gaggl, W., Nair, V. A., Suhonen, J. I., Birn, R. M., Ahmed, A. S., et al. (2016). Classification and extraction of resting state networks using healthy and epilepsy fMRI data. Frontiers in Neuroscience, 10. https:\/\/doi.org\/10.3389\/fnins.2016.00440.","DOI":"10.3389\/fnins.2016.00440"},{"issue":"4","key":"9514_CR66","doi-asserted-by":"publisher","first-page":"1414","DOI":"10.1016\/j.neuroimage.2005.11.002","volume":"30","author":"M Vigneau","year":"2006","unstructured":"Vigneau, M., Beaucousin, V., Herve, P. Y., Duffau, H., Crivello, F., Houde, O., et al. (2006). Meta-analyzing left hemisphere language areas: phonology, semantics, and sentence processing. Neuroimage, 30(4), 1414\u20131432. doi:https:\/\/doi.org\/10.1016\/j.neuroimage.2005.11.002.","journal-title":"Neuroimage"},{"issue":"8","key":"9514_CR67","doi-asserted-by":"publisher","first-page":"2036","DOI":"10.1093\/cercor\/bht056","volume":"24","author":"GS Wig","year":"2014","unstructured":"Wig, G. S., Laumann, T. O., Cohen, A. L., Power, J. D., Nelson, S. M., Glasser, M. F., et al. (2014). Parcellating an individual subject\u2019s cortical and subcortical brain structures using snowball sampling of resting-state correlations. Cerebral Cortex, 24(8), 2036\u20132054. https:\/\/doi.org\/10.1093\/cercor\/bht056.","journal-title":"Cerebral Cortex"},{"key":"9514_CR68","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Dollar, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. In IEEE conference on computer vision and pattern recognition, Honolulu, USA. https:\/\/arxiv.org\/pdf\/1611.05431.pdf.","DOI":"10.1109\/CVPR.2017.634"},{"key":"9514_CR69","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1016\/j.neuroimage.2013.10.046","volume":"88","author":"BT Yeo","year":"2014","unstructured":"Yeo, B. T., Krienen, F. M., Chee, M. W., & Buckner, R. L. (2014). Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex. Neuroimage, 88, 212\u2013227. doi:https:\/\/doi.org\/10.1016\/j.neuroimage.2013.10.046.","journal-title":"Neuroimage"},{"issue":"3","key":"9514_CR70","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1152\/jn.00338.2011","volume":"106","author":"BT Yeo","year":"2011","unstructured":"Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., et al. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125\u20131165. https:\/\/doi.org\/10.1152\/jn.00338.2011.","journal-title":"Journal of Neurophysiology"},{"issue":"7","key":"9514_CR71","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s10916-016-0525-2","volume":"40","author":"Y Zhang","year":"2016","unstructured":"Zhang, Y., Sun, Y., Phillips, P., Liu, G., Zhou, X., & Wang, S. (2016). A multilayer perceptron based smart pathological brain detection system by fractional fourier entropy. Journal of Medical Systems, 40(7), 173. https:\/\/doi.org\/10.1007\/s10916-016-0525-2.","journal-title":"Journal of Medical Systems"},{"issue":"9","key":"9514_CR72","doi-asserted-by":"publisher","first-page":"1975","DOI":"10.1109\/TBME.2017.2715281","volume":"65","author":"Y Zhao","year":"2018","unstructured":"Zhao, Y., Dong, Q., Zhang, S., Zhang, W., Chen, H., Jiang, X., et al. (2018). Automatic recognition of fMRI-derived functional networks using 3-D convolutional neural networks. IEEE Transactions on Biomedical Engineering, 65(9), 1975\u20131984. https:\/\/doi.org\/10.1109\/TBME.2017.2715281.","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"9514_CR73","doi-asserted-by":"publisher","unstructured":"Zhennan, Y., Zhan, Y., Peng, Z., Liao, S., Shinagawa, Y., Zhang, S., et al. (2016). Multi-instance deep learning: discover discriminative local anatomies for bodypart recognition. IEEE Transactions on Medical Imaging, 35(5), 1332\u20131343. https:\/\/doi.org\/10.1109\/TMI.2016.2524985.","DOI":"10.1109\/TMI.2016.2524985"},{"issue":"45","key":"9514_CR74","doi-asserted-by":"publisher","first-page":"15034","DOI":"10.1523\/JNEUROSCI.2612-10.2010","volume":"30","author":"XN Zuo","year":"2010","unstructured":"Zuo, X. N., Kelly, C., Di Martino, A., Mennes, M., Margulies, D. S., Bangaru, S., et al. (2010). Growing together and growing apart: regional and sex differences in the lifespan developmental trajectories of functional homotopy. The Journal of Neuroscience, 30(45), 15034\u201315043. https:\/\/doi.org\/10.1523\/JNEUROSCI.2612-10.2010.","journal-title":"The Journal of Neuroscience"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-021-09514-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12021-021-09514-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-021-09514-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T23:09:31Z","timestamp":1635980971000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12021-021-09514-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,5]]},"references-count":74,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["9514"],"URL":"https:\/\/doi.org\/10.1007\/s12021-021-09514-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.07.02.183772","asserted-by":"object"}]},"ISSN":["1539-2791","1559-0089"],"issn-type":[{"value":"1539-2791","type":"print"},{"value":"1559-0089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,5]]},"assertion":[{"value":"17 January 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest\/Competing Interests"}},{"value":"The BIL&GIN study was approved by the ethics committee of Basse-Normandie (France). The MRi-Share study was approved by the ethics committee of Bordeaux (France).\u00a0","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Participants in the BIL&GIN and MRi-Share provided their written consent for participation in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"All authors have agreed to the publication of the results presented in this work.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The classifier and documentation are provided for free upon request to the author of the publication.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Code Availability"}}]}}