{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:49:46Z","timestamp":1784821786481,"version":"3.55.0"},"reference-count":60,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:00:00Z","timestamp":1722470400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["E2ET1105X2"],"award-info":[{"award-number":["E2ET1105X2"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01 AG024904"],"award-info":[{"award-number":["U01 AG024904"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007333","name":"Alzheimer's Disease Neuroimaging Initiative","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007333","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["NeuroImage"],"published-print":{"date-parts":[[2024,8]]},"DOI":"10.1016\/j.neuroimage.2024.120755","type":"journal-article","created":{"date-parts":[[2024,7,27]],"date-time":"2024-07-27T16:11:42Z","timestamp":1722096702000},"page":"120755","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Contrastive voxel clustering for multiscale modeling of brain network"],"prefix":"10.1016","volume":"297","author":[{"given":"Zhiyuan","family":"Ding","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yulang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangzhu","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyin","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuyang","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenduo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7645-4834","authenticated-orcid":false,"given":"Yingying","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0110-9297","authenticated-orcid":false,"given":"Yan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.neuroimage.2024.120755_b1","series-title":"2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society","first-page":"3965","article-title":"Deep learning in resting-state fMRI","author":"Abrol","year":"2021"},{"key":"10.1016\/j.neuroimage.2024.120755_b2","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1126\/science.abq2599","article-title":"Scale matters: The nested human connectome","volume":"378","author":"Axer","year":"2022","journal-title":"Science"},{"key":"10.1016\/j.neuroimage.2024.120755_b3","doi-asserted-by":"crossref","unstructured":"Baldasano, C., Iordan, M.C., Beck, D.M., Fei-Fei, L., 2012. Discovering voxel-level functional connectivity between cortical regions. In: Machine Learning and Interpretation in NeuroImaging Workshop, NIPS.","DOI":"10.1016\/j.neuroimage.2012.07.046"},{"key":"10.1016\/j.neuroimage.2024.120755_b4","series-title":"A cookbook of self-supervised learning","author":"Balestriero","year":"2023"},{"issue":"2","key":"10.1016\/j.neuroimage.2024.120755_b5","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1002\/mrm.1910250220","article-title":"Time course EPI of human brain function during task activation","volume":"25","author":"Bandettini","year":"1992","journal-title":"Magn. Reson. Med."},{"issue":"Suppl 1","key":"10.1016\/j.neuroimage.2024.120755_b6","doi-asserted-by":"crossref","first-page":"S148","DOI":"10.1016\/S1053-8119(09)71511-3","article-title":"Group comparison of resting-state FMRI data using multi-subject ICA and dual regression","volume":"47","author":"Beckmann","year":"2009","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120755_b7","series-title":"International Conference on Machine Learning","first-page":"1597","article-title":"A simple framework for contrastive learning of visual representations","author":"Chen","year":"2020"},{"key":"10.1016\/j.neuroimage.2024.120755_b8","doi-asserted-by":"crossref","unstructured":"Chen, X., et al., 2021. An empirical study of training self-supervised vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 9640\u20139649.","DOI":"10.1109\/ICCV48922.2021.00950"},{"issue":"1","key":"10.1016\/j.neuroimage.2024.120755_b9","doi-asserted-by":"crossref","first-page":"16402","DOI":"10.1038\/s41598-020-73328-1","article-title":"Functional parcellation of the hippocampus by semi-supervised clustering of resting state fMRI data","volume":"10","author":"Cheng","year":"2020","journal-title":"Sci. Rep."},{"issue":"8","key":"10.1016\/j.neuroimage.2024.120755_b10","doi-asserted-by":"crossref","first-page":"1914","DOI":"10.1002\/hbm.21333","article-title":"A whole brain fMRI atlas generated via spatially constrained spectral clustering","volume":"33","author":"Craddock","year":"2012","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.neuroimage.2024.120755_b11","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.neuroimage.2020.117126","article-title":"Fine-grain atlases of functional modes for fMRI analysis","volume":"221","author":"Dadi","year":"2020","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2024.120755_b12","doi-asserted-by":"crossref","DOI":"10.1177\/2398212817701448","article-title":"Segmenting subregions of the human hippocampus on structural magnetic resonance image scans: An illustrated tutorial","volume":"1","author":"Dalton","year":"2017","journal-title":"Brain Neurosci. Adv."},{"issue":"1","key":"10.1016\/j.neuroimage.2024.120755_b13","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.jneumeth.2010.11.029","article-title":"Functional connectivity analysis of fMRI data based on regularized multiset canonical correlation analysis","volume":"197","author":"Deleus","year":"2011","journal-title":"J. Neurosci. Methods"},{"issue":"4","key":"10.1016\/j.neuroimage.2024.120755_b14","doi-asserted-by":"crossref","first-page":"5404","DOI":"10.1111\/ejn.15386","article-title":"Modelling state-transition dynamics in resting-state brain signals by the hidden Markov and Gaussian mixture models","volume":"54","author":"Ezaki","year":"2021","journal-title":"Eur. J. Neurosci."},{"key":"10.1016\/j.neuroimage.2024.120755_b15","doi-asserted-by":"crossref","first-page":"585","DOI":"10.3389\/fnins.2019.00585","article-title":"Application of graph theory for identifying connectivity patterns in human brain networks: a systematic review","volume":"13","author":"Farahani","year":"2019","journal-title":"Front. Neurosci."},{"issue":"8","key":"10.1016\/j.neuroimage.2024.120755_b16","doi-asserted-by":"crossref","first-page":"2444","DOI":"10.1002\/hbm.25795","article-title":"An integrated cluster-wise significance measure for fMRI analysis","volume":"43","author":"Ge","year":"2022","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.neuroimage.2024.120755_b17","series-title":"2020 IEEE International Conference on Data Mining","first-page":"1052","article-title":"STGCN: a spatial-temporal aware graph learning method for POI recommendation","author":"Han","year":"2020"},{"issue":"1","key":"10.1016\/j.neuroimage.2024.120755_b18","doi-asserted-by":"crossref","first-page":"100","DOI":"10.2307\/2346830","article-title":"Algorithm as 136: A K-means clustering algorithm","volume":"28","author":"Hartigan","year":"1979","journal-title":"Appl. Stat."},{"key":"10.1016\/j.neuroimage.2024.120755_b19","series-title":"2022 IEEE 19th International Symposium on Biomedical Imaging","first-page":"1","article-title":"A contrastive learning-based approach to measure spatial coupling among brain networks: A schizophrenia study","author":"Hassanzadeh","year":"2022"},{"issue":"2","key":"10.1016\/j.neuroimage.2024.120755_b20","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/j.neuroimage.2006.04.233","article-title":"Cluster-based analysis of FMRI data","volume":"33","author":"Heller","year":"2006","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2024.120755_b21","doi-asserted-by":"crossref","unstructured":"Hong, S., et al., 2020. Holmes: health online model ensemble serving for deep learning models in intensive care units. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 1614\u20131624.","DOI":"10.1145\/3394486.3403212"},{"issue":"9","key":"10.1016\/j.neuroimage.2024.120755_b22","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1002\/hbm.25369","article-title":"Diagnostic power of resting-state fMRI for detection of network connectivity in Alzheimer\u2019s disease and mild cognitive impairment: A systematic review","volume":"42","author":"Ibrahim","year":"2021","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.neuroimage.2024.120755_b23","series-title":"Understanding cognitive fatigue from fmri scans with self-supervised learning","author":"Jaiswal","year":"2021"},{"issue":"1","key":"10.1016\/j.neuroimage.2024.120755_b24","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0227021","article-title":"Percent amplitude of fluctuation: a simple measure for resting-state fMRI signal at single voxel level","volume":"15","author":"Jia","year":"2020","journal-title":"PLoS One"},{"issue":"2","key":"10.1016\/j.neuroimage.2024.120755_b25","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1109\/TMI.2019.2928790","article-title":"Deep learning of static and dynamic brain functional networks for early MCI detection","volume":"39","author":"Kam","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2024.120755_b26","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.mri.2019.05.031","article-title":"Machine learning in resting-state fMRI analysis","volume":"64","author":"Khosla","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.neuroimage.2024.120755_b27","first-page":"4314","article-title":"Learning dynamic graph representation of brain connectome with spatio-temporal attention","volume":"34","author":"Kim","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neuroimage.2024.120755_b28","series-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016"},{"issue":"3","key":"10.1016\/j.neuroimage.2024.120755_b29","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1162\/NETN_a_00013","article-title":"Consistency of regions of interest as nodes of fMRI functional brain networks","volume":"1","author":"Korhonen","year":"2017","journal-title":"Netw. Neurosci."},{"issue":"3","key":"10.1016\/j.neuroimage.2024.120755_b30","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1093\/cercor\/bhab223","article-title":"Differential role for hippocampal subfields in Alzheimer\u2019s disease progression revealed with deep learning","volume":"32","author":"Kwak","year":"2022","journal-title":"Cereb. Cortex"},{"key":"10.1016\/j.neuroimage.2024.120755_b31","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1126\/science.abq2594","article-title":"Atlas-based data integration for mapping the connections and architecture of the brain","volume":"378","author":"Leergaard","year":"2022","journal-title":"Science"},{"issue":"3","key":"10.1016\/j.neuroimage.2024.120755_b32","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1002\/hbm.22689","article-title":"Toward systems neuroscience in mild cognitive impairment and Alzheimer\u2019s disease: A meta-analysis of 75 fMRI studies","volume":"36","author":"Li","year":"2015","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.neuroimage.2024.120755_b33","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102233","article-title":"Braingnn: Interpretable brain graph neural network for fmri analysis","author":"Li","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2024.120755_b34","series-title":"AAAI2021","first-page":"8547","article-title":"Contrastive clustering","volume":"Vol. 35","author":"Li","year":"2021"},{"key":"10.1016\/j.neuroimage.2024.120755_b35","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.neuroimage.2017.09.012","article-title":"Interpreting temporal fluctuations in resting-state functional connectivity MRI","volume":"163","author":"Liegeois","year":"2017","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120755_b36","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.neuroimage.2016.09.008","article-title":"Noise contributions to the fMRI signal: An overview","volume":"143","author":"Liu","year":"2016","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2024.120755_b37","first-page":"263","article-title":"Intrinsic brain hub connectivity underlies individual differences in spatial working memory","volume":"29","author":"Liu","year":"2019","journal-title":"Cereb. Cortex"},{"issue":"11","key":"10.1016\/j.neuroimage.2024.120755_b38","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.neuroimage.2024.120755_b39","series-title":"Consistency of Network Modules in Resting-State FMRI Connectome Data","author":"Moussa","year":"2012"},{"issue":"2","key":"10.1016\/j.neuroimage.2024.120755_b40","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.neuroimage.2010.07.073","article-title":"Encoding and decoding in fMRI","volume":"56","author":"Naselaris","year":"2011","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120755_b41","doi-asserted-by":"crossref","unstructured":"Parisot, S., othersl, 2017. Spectral graph convolutions for population-based disease prediction. In: Medical Image Computing and Computer Assisted Intervention- MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part III 20. pp. 177\u2013185.","DOI":"10.1007\/978-3-319-66179-7_21"},{"issue":"2","key":"10.1016\/j.neuroimage.2024.120755_b42","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1109\/TMI.2022.3201974","article-title":"GATE: graph CCA for temporal SElf-supervised learning for label-efficient fMRI analysis","volume":"42","author":"Peng","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.neuroimage.2024.120755_b43","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1109\/89.365379","article-title":"Robust text-independent speaker identification using Gaussian mixture speaker models","volume":"3","author":"Reynolds","year":"1995","journal-title":"IEEE Trans. Speech Audio Process."},{"issue":"6619","key":"10.1016\/j.neuroimage.2024.120755_b44","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1126\/science.adf4275","article-title":"No neuron is an island","volume":"378","author":"Stern","year":"2022","journal-title":"Science"},{"issue":"1","key":"10.1016\/j.neuroimage.2024.120755_b45","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1016\/j.neuroimage.2005.06.054","article-title":"Detection of signal synchronizations in resting-state fMRI datasets","volume":"29","author":"Thirion","year":"2006","journal-title":"Neuroimage"},{"issue":"1","key":"10.1016\/j.neuroimage.2024.120755_b46","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1006\/nimg.2001.0978","article-title":"Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain","volume":"15","author":"Tzourio-Mazoyer","year":"2002","journal-title":"Neuroimage"},{"issue":"4","key":"10.1016\/j.neuroimage.2024.120755_b47","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0002001","article-title":"Normalized cut group clustering of resting-state FMRI data","volume":"3","author":"Van Den Heuvel","year":"2008","journal-title":"PLoS One"},{"key":"10.1016\/j.neuroimage.2024.120755_b48","series-title":"2009 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"441","article-title":"Exploring functional connectivity in fMRI via clustering","author":"Venkataraman","year":"2009"},{"issue":"17","key":"10.1016\/j.neuroimage.2024.120755_b49","doi-asserted-by":"crossref","first-page":"5672","DOI":"10.1002\/hbm.26469","article-title":"Unsupervised contrastive graph learning for resting-state functional MRI analysis and brain disorder detection","volume":"44","author":"Wang","year":"2023","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.neuroimage.2024.120755_b50","first-page":"1419","article-title":"Graph-based network analysis of resting-state functional MRI","volume":"4","author":"Wang","year":"2010","journal-title":"Front. Syst. Neurosci."},{"key":"10.1016\/j.neuroimage.2024.120755_b51","series-title":"MICCAI","first-page":"221","article-title":"Contrastive functional connectivity graph learning for population-based fMRI classification","author":"Wang","year":"2022"},{"key":"10.1016\/j.neuroimage.2024.120755_b52","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.media.2021.102279","article-title":"Multi-site clustering and nested feature extraction for identifying autism spectrum disorder with resting-state fMRI","volume":"75","author":"Wang","year":"2022","journal-title":"Med. Image Anal."},{"issue":"3","key":"10.1016\/j.neuroimage.2024.120755_b53","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1007\/s12021-016-9299-4","article-title":"DPABI: data processing & analysis for (resting-state) brain imaging","volume":"14","author":"Yan","year":"2016","journal-title":"Neuroinformatics"},{"key":"10.1016\/j.neuroimage.2024.120755_b54","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.neuroimage.2019.116227","article-title":"Individualized psychiatric imaging based on inter-subject neural synchronization in movie watching","volume":"216","author":"Yang","year":"2020","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2024.120755_b55","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.neucom.2020.05.113","article-title":"Deep learning for brain disorder diagnosis based on fMRI images","volume":"469","author":"Yin","year":"2022","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.neuroimage.2024.120755_b56","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.neuroimage.2003.12.030","article-title":"Regional homogeneity approach to fMRI data analysis","volume":"22","author":"Zang","year":"2004","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2024.120755_b57","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.cortex.2015.09.002","article-title":"Investigating the functions of subregions within anterior hippocampus","volume":"73","author":"Zeidman","year":"2015","journal-title":"Cortex"},{"key":"10.1016\/j.neuroimage.2024.120755_b58","article-title":"Contrastive multi-view composite graph convolutional networks based on contribution learning for autism spectrum disorder classification","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.neuroimage.2024.120755_b59","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.neuroimage.2016.12.081","article-title":"A family of locally constrained CCA models for detecting activation patterns in fMRI","volume":"149","author":"Zhuang","year":"2017","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2024.120755_b60","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.jneumeth.2008.04.012","article-title":"An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF","volume":"172","author":"Zou","year":"2008","journal-title":"J. Neurosci. Methods"}],"container-title":["NeuroImage"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811924002520?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811924002520?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T12:09:04Z","timestamp":1737720544000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1053811924002520"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8]]},"references-count":60,"alternative-id":["S1053811924002520"],"URL":"https:\/\/doi.org\/10.1016\/j.neuroimage.2024.120755","relation":{},"ISSN":["1053-8119"],"issn-type":[{"value":"1053-8119","type":"print"}],"subject":[],"published":{"date-parts":[[2024,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Contrastive voxel clustering for multiscale modeling of brain network","name":"articletitle","label":"Article Title"},{"value":"NeuroImage","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neuroimage.2024.120755","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Author(s). Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"120755"}}