{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:20:00Z","timestamp":1767320400292,"version":"3.48.0"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032095121","type":"print"},{"value":"9783032095138","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-09513-8_3","type":"book-chapter","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:16:14Z","timestamp":1767320174000},"page":"20-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint Motion Correction of Multi-Atlas Functional Connectivity During Infancy"],"prefix":"10.1007","author":[{"given":"Weiran","family":"Xia","sequence":"first","affiliation":[]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Dan","family":"Hu","sequence":"additional","affiliation":[]},{"given":"Jiale","family":"Cheng","sequence":"additional","affiliation":[]},{"given":"Kangfu","family":"Han","sequence":"additional","affiliation":[]},{"given":"Zhengwang","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Weili","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,1,2]]},"reference":[{"issue":"1","key":"3_CR1","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.neuroimage.2011.07.044","volume":"59","author":"DK Van","year":"2012","unstructured":"Van, D.K., Mert, R.S., Randy, L.B.: The influence of head motion on intrinsic functional connectivity MRI. Neuroimage 59(1), 431\u2013438 (2012)","journal-title":"Neuroimage"},{"issue":"3","key":"3_CR2","doi-asserted-by":"publisher","first-page":"2142","DOI":"10.1016\/j.neuroimage.2011.10.018","volume":"59","author":"JD Power","year":"2012","unstructured":"Power, J.D., Barnes, K.A., Snyder, A.Z., et al.: Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59(3), 2142\u20132154 (2012)","journal-title":"Neuroimage"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Pruim, R.H.R, Mennes, M., Rooij, D.V., et al.: ICA-AROMA: a robust ICA-based strategy for removing motion artifacts from fMRI data.\u00a0Neuroimage\u00a0112, 267\u2013277 (2015)","DOI":"10.1016\/j.neuroimage.2015.02.064"},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.neuroimage.2014.03.012","volume":"95","author":"AX Patel","year":"2014","unstructured":"Patel, A.X., Kundu, P., Rubinov, M., et al.: A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series. Neuroimage 95, 287\u2013304 (2014)","journal-title":"Neuroimage"},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1002\/mrm.24314","volume":"69","author":"J Maclaren","year":"2013","unstructured":"Maclaren, J., Herbst, M., Speck, O., et al.: Prospective motion correction in brain imaging: a review. Magn. Reson. Med. 69, 621\u2013636 (2013)","journal-title":"Magn. Reson. Med."},{"key":"3_CR6","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.neuroimage.2016.11.014","volume":"154","author":"M Zaitsev","year":"2017","unstructured":"Zaitsev, M., Akin, B., LeVan, P., et al.: Prospective motion correction in functional MRI. Neuroimage 154, 33\u201342 (2017)","journal-title":"Neuroimage"},{"key":"3_CR7","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1016\/j.neuroimage.2014.10.044","volume":"105","author":"JD Power","year":"2015","unstructured":"Power, J.D., Bradley, L.S., Steven, E.P.: Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage 105, 536\u2013551 (2015)","journal-title":"Neuroimage"},{"issue":"3","key":"3_CR8","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1016\/j.neuroimage.2006.01.021","volume":"31","author":"RS Desikan","year":"2006","unstructured":"Desikan, R.S., S\u00e9gonne, F., Fischl, B., et al.: An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31(3), 968\u2013980 (2006)","journal-title":"Neuroimage"},{"issue":"9","key":"3_CR9","doi-asserted-by":"publisher","first-page":"3095","DOI":"10.1093\/cercor\/bhx179","volume":"28","author":"A Schaefer","year":"2018","unstructured":"Schaefer, A., Kong, R., Gordon, E.M., et al.: Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28(9), 3095\u20133114 (2018)","journal-title":"Cereb. Cortex"},{"key":"3_CR10","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30, pp. 6000\u20136010 (2017)"},{"key":"3_CR11","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: International Conference on Learning Representation (2021)"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., et al.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 15979\u201315988 (2021)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Bonneel, N., Panne, M.V.D., Paris, S., et al.: Displacement interpolation using Lagrangian mass transport. In: Proceedings of the 2011 SIGGRAPH Asia conference, vol. 30, pp. 1\u201312 (2011)","DOI":"10.1145\/2024156.2024192"},{"key":"3_CR14","unstructured":"Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport.\u00a0In: Advances in Neural Information Processing Systems, vol. 26, pp. 2292\u20132300 (2013)"},{"key":"3_CR15","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1016\/j.neuroimage.2018.03.049","volume":"185","author":"BR Howell","year":"2019","unstructured":"Howell, B.R., Styner, M.A., Gao, W., et al.: The UNC\/UMN baby connectome project (BCP): an overview of the study design and protocol development. Neuroimage 185, 891\u2013905 (2019)","journal-title":"Neuroimage"},{"key":"3_CR16","doi-asserted-by":"publisher","first-page":"906","DOI":"10.1016\/j.neuroimage.2018.03.042","volume":"185","author":"G Li","year":"2019","unstructured":"Li, G., Wang, L., Yap, P.T., et al.: Computational neuroanatomy of baby brains: a review. Neuroimage 185, 906\u2013925 (2019)","journal-title":"Neuroimage"},{"key":"3_CR17","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.neuroimage.2013.12.038","volume":"90","author":"G Li","year":"2014","unstructured":"Li, G., Nie, J., Wang, L., et al.: Measuring the dynamic longitudinal cortex development in infants by reconstruction of temporally consistent cortical surfaces. Neuroimage 90, 266\u2013279 (2014)","journal-title":"Neuroimage"},{"key":"3_CR18","unstructured":"Cao, J., Mo, L., Zhang, Y., et al.: Multi-marginal wasserstein GAN. In: Advances in Neural Information Processing Systems, vol. 32, pp. 1776\u20131786 (2019)"},{"key":"3_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102463","volume":"79","author":"L Zhang","year":"2022","unstructured":"Zhang, L., Wang, L., Zhu, D.: Predicting brain structural network using functional connectivity. Med. Image Anal. 79, 102463 (2022)","journal-title":"Med. Image Anal."},{"key":"3_CR20","first-page":"2579","volume":"9","author":"L Van Der Maaten","year":"2008","unstructured":"Van Der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"Wang, L., Wu, Z., Chen, L., et al.: ibeat v2. 0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction. Nat. Protocols 18(5), 1488\u20131509 (2023)","DOI":"10.1038\/s41596-023-00806-x"},{"key":"3_CR22","first-page":"58","volume":"12","author":"V Sacc\u00e0","year":"2018","unstructured":"Sacc\u00e0, V., Sarica, A., Novellino, F., et al.: Evaluation of the MCFLIRT correction algorithm in head motion from resting state fMRI data. Int. J. Biomed. Biol. Engin 12, 58\u201361 (2018)","journal-title":"Int. J. Biomed. Biol. Engin"},{"issue":"3","key":"3_CR23","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1523\/JNEUROSCI.0480-21.2021","volume":"42","author":"D Hu","year":"2022","unstructured":"Hu, D., Wang, F., Zhang, H., et al.: Existence of functional connectome fingerprint during infancy and its stability over months. J. Neurosci. 42(3), 377\u2013389 (2022)","journal-title":"J. Neurosci."},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Wang, F., Zhang, H., Wu, Z., et al.: Fine-grained functional parcellation maps of the infant cerebral cortex. elife 12, e75401 (2023)","DOI":"10.7554\/eLife.75401"},{"key":"3_CR25","doi-asserted-by":"crossref","unstructured":"Yin, W., Li, T., Wu, Z., et al.: Charting brain functional development from birth to 6 years of age. Nat. Hum. Behav. 1\u201314 (2025)","DOI":"10.1038\/s41562-025-02160-2"},{"key":"3_CR26","doi-asserted-by":"crossref","unstructured":"Xia, W., Zhang, X., Hu, D., et al.: Individualized trajectory prediction of early developing functional connectivity. In: International Symposium on Biomedical Imaging, pp. 1\u20135 (2025)","DOI":"10.1109\/ISBI60581.2025.10980810"},{"key":"3_CR27","doi-asserted-by":"crossref","unstructured":"Hu, D., Yin, W., Wu, Z., et al.: Reference-relation guided autoencoder with deep CCA restriction for awake-to-sleep brain functional connectome prediction. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 231\u2013240 (2021)","DOI":"10.1007\/978-3-030-87199-4_22"},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Yu, X., Hu, D., Zhang, L., et al.: Longitudinal infant functional connectivity prediction via conditional intensive triplet network. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 255\u2013264 (2022)","DOI":"10.1007\/978-3-031-16452-1_25"},{"key":"3_CR29","doi-asserted-by":"crossref","unstructured":"Hu, D., Wang, F., Zhang, H., et al.: Disentangled intensive triplet autoencoder for infant functional connectome fingerprinting. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 72\u201382 (2020)","DOI":"10.1007\/978-3-030-59728-3_8"},{"issue":"10","key":"3_CR30","doi-asserted-by":"publisher","first-page":"2764","DOI":"10.1109\/TMI.2022.3171778","volume":"41","author":"Y Li","year":"2022","unstructured":"Li, Y., Zhang, X., Nie, J., et al.: Brain connectivity based graph convolutional networks and its application to infant age prediction. IEEE Trans. Med. Imaging 41(10), 2764\u20132776 (2022)","journal-title":"IEEE Trans. Med. Imaging"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-09513-8_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T02:16:16Z","timestamp":1767320176000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-09513-8_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032095121","9783032095138"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-09513-8_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"2 January 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Daejeon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2025\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}