{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T11:29:45Z","timestamp":1758281385871,"version":"3.44.0"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032051615","type":"print"},{"value":"9783032051622","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"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\/978-3-032-05162-2_38","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T23:27:23Z","timestamp":1758238043000},"page":"394-404","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hierarchical Characterization of\u00a0Brain Dynamics via\u00a0State Space-Based Vector Quantization"],"prefix":"10.1007","author":[{"given":"Yanwu","family":"Yang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Wolfers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"38_CR1","unstructured":"Baevski, A., Zhou, Y., Mohamed, A., Auli, M.: wav2vec 2.0: a framework for self-supervised learning of speech representations. In: Advances in Neural Information Processing Systems, vol. 33, pp. 12449\u201312460 (2020)"},{"key":"38_CR2","unstructured":"Bao, H., Dong, L., Piao, S., Wei, F.: Beit: Bert pre-training of image transformers. arXiv preprint arXiv:2106.08254 (2021)"},{"key":"38_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102841","volume":"88","author":"HA Bedel","year":"2023","unstructured":"Bedel, H.A., Sivgin, I., Dalmaz, O., Dar, S.U., \u00c7ukur, T.: Bolt: fused window transformers for fMRI time series analysis. Med. Image Anal. 88, 102841 (2023)","journal-title":"Med. Image Anal."},{"key":"38_CR4","doi-asserted-by":"publisher","first-page":"62","DOI":"10.3389\/fncom.2019.00062","volume":"13","author":"P Beim Graben","year":"2019","unstructured":"Beim Graben, P., Jimenez-Marin, A., Diez, I., Cortes, J.M., Desroches, M., Rodrigues, S.: Metastable resting state brain dynamics. Front. Comput. Neurosci. 13, 62 (2019)","journal-title":"Front. Comput. Neurosci."},{"issue":"1","key":"38_CR5","doi-asserted-by":"publisher","first-page":"5135","DOI":"10.1038\/s41598-017-05425-7","volume":"7","author":"J Cabral","year":"2017","unstructured":"Cabral, J., et al.: Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Sci. Rep. 7(1), 5135 (2017)","journal-title":"Sci. Rep."},{"key":"38_CR6","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1016\/j.neuroimage.2017.09.065","volume":"180","author":"F Cavanna","year":"2018","unstructured":"Cavanna, F., Vilas, M.G., Palmucci, M., Tagliazucchi, E.: Dynamic functional connectivity and brain metastability during altered states of consciousness. Neuroimage 180, 383\u2013395 (2018)","journal-title":"Neuroimage"},{"key":"38_CR7","unstructured":"Devlin, J.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"38_CR8","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.jad.2021.04.005","volume":"289","author":"M Du","year":"2021","unstructured":"Du, M., et al.: Abnormal transitions of dynamic functional connectivity states in bipolar disorder: a whole-brain resting-state fMRI study. J. Affect. Disord. 289, 7\u201315 (2021)","journal-title":"J. Affect. Disord."},{"key":"38_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"528","DOI":"10.1007\/978-3-030-59728-3_52","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020","author":"S Gadgil","year":"2020","unstructured":"Gadgil, S., Zhao, Q., Pfefferbaum, A., Sullivan, E.V., Adeli, E., Pohl, K.M.: Spatio-temporal graph convolution for resting-state fMRI analysis. In: Martel, A.L., et al. (eds.) MICCAI 2020. LNCS, vol. 12267, pp. 528\u2013538. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59728-3_52"},{"key":"38_CR10","unstructured":"Gu, A., Dao, T.: Mamba: linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)"},{"key":"38_CR11","doi-asserted-by":"crossref","unstructured":"Jeong, A.Y., Heo, D.W., Kang, E., Suk, H.I.: Brainwavenet: wavelet-based transformer for autism spectrum disorder diagnosis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 56\u201366. Springer (2024)","DOI":"10.1007\/978-3-031-72069-7_6"},{"key":"38_CR12","unstructured":"Kan, X., Cui, H., Lukemire, J., Guo, Y., Yang, C.: Fbnetgen: task-aware GNN-based fMRI analysis via functional brain network generation. In: International Conference on Medical Imaging with Deep Learning, pp. 618\u2013637. PMLR (2022)"},{"key":"38_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2023.119945","volume":"271","author":"DL Kurtin","year":"2023","unstructured":"Kurtin, D.L., Scott, G., Hebron, H., Skeldon, A.C., Violante, I.R.: Task-based differences in brain state dynamics and their relation to cognitive ability. Neuroimage 271, 119945 (2023)","journal-title":"Neuroimage"},{"key":"38_CR14","doi-asserted-by":"crossref","unstructured":"Lee, D., Kim, C., Kim, S., Cho, M., Han, W.S.: Autoregressive image generation using residual quantization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11523\u201311532 (2022)","DOI":"10.1109\/CVPR52688.2022.01123"},{"issue":"1","key":"38_CR15","first-page":"9027803","volume":"2019","author":"C Liu","year":"2019","unstructured":"Liu, C., Xue, J., Cheng, X., Zhan, W., Xiong, X., Wang, B.: Tracking the brain state transition process of dynamic function connectivity based on resting state fMRI. Comput. Intell. Neurosci. 2019(1), 9027803 (2019)","journal-title":"Comput. Intell. Neurosci."},{"issue":"5","key":"38_CR16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1000072","volume":"4","author":"MI Rabinovich","year":"2008","unstructured":"Rabinovich, M.I., Huerta, R., Varona, P., Afraimovich, V.S.: Transient cognitive dynamics, metastability, and decision making. PLoS Comput. Biol. 4(5), e1000072 (2008)","journal-title":"PLoS Comput. Biol."},{"key":"38_CR17","unstructured":"Razavi, A., Van\u00a0den Oord, A., Vinyals, O.: Generating diverse high-fidelity images with VQ-VAE-2. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"issue":"1","key":"38_CR18","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.pscychresns.2010.04.005","volume":"183","author":"MF Schneider","year":"2010","unstructured":"Schneider, M.F., et al.: Impairment of fronto-striatal and parietal cerebral networks correlates with attention deficit hyperactivity disorder (ADHD) psychopathology in adults\u2014a functional magnetic resonance imaging (fMRI) study. Psychiatry Res. Neuroimaging 183(1), 75\u201384 (2010)","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"38_CR19","unstructured":"Song, C., et al.: Lvpnet: a latent-variable-based prediction-driven end-to-end framework for lossless compression of medical images (2025). https:\/\/arxiv.org\/abs\/2506.17983"},{"key":"38_CR20","unstructured":"Song, C., et al.: Bpclip: a bottom-up image quality assessment from distortion to semantics based on clip (2025). https:\/\/arxiv.org\/abs\/2506.17969"},{"key":"38_CR21","doi-asserted-by":"crossref","unstructured":"ADHD-200 Consortium: The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front. Syst. Neurosci. 6, 62 (2012)","DOI":"10.3389\/fnsys.2012.00062"},{"key":"38_CR22","unstructured":"Van Den\u00a0Oord, A., Vinyals, O., et\u00a0al.: Neural discrete representation learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"38_CR23","doi-asserted-by":"publisher","first-page":"1155","DOI":"10.1016\/j.neuroimage.2015.06.065","volume":"124","author":"L Wang","year":"2016","unstructured":"Wang, L., et al.: Schizconnect: mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration. Neuroimage 124, 1155\u20131167 (2016)","journal-title":"Neuroimage"},{"issue":"4","key":"38_CR24","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1080\/87565641.2013.783833","volume":"38","author":"L Weyandt","year":"2013","unstructured":"Weyandt, L., Swentosky, A., Gudmundsdottir, B.G.: Neuroimaging and ADHD: fMRI, PET, DTI findings, and methodological limitations. Dev. Neuropsychol. 38(4), 211\u2013225 (2013)","journal-title":"Dev. Neuropsychol."},{"key":"38_CR25","doi-asserted-by":"crossref","unstructured":"Yang, Y., Chen, H., Hu, J., Guo, X., Ma, T.: Advancing brain imaging analysis step-by-step via progressive self-paced learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 58\u201368. Springer (2024)","DOI":"10.1007\/978-3-031-72120-5_6"},{"key":"38_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102916","volume":"89","author":"Y Yang","year":"2023","unstructured":"Yang, Y., Guo, X., Ye, C., Xiang, Y., Ma, T.: CREG-KD: model refinement via confidence regularized knowledge distillation for brain imaging. Med. Image Anal. 89, 102916 (2023)","journal-title":"Med. Image Anal."},{"key":"38_CR27","doi-asserted-by":"crossref","unstructured":"Yang, Y., et al.: Hypercomplex graph neural network: towards deep intersection of multi-modal brain networks. IEEE J. Biomed. Health Inform. (2024)","DOI":"10.1109\/JBHI.2024.3490664"},{"key":"38_CR28","unstructured":"Yang, Y., Ye, C., Guo, X., Wu, T., Xiang, Y., Ma, T.: Mapping multi-modal brain connectome for brain disorder diagnosis via cross-modal mutual learning. IEEE. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10182318\/"},{"key":"38_CR29","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.neunet.2023.04.025","volume":"164","author":"Y Yang","year":"2023","unstructured":"Yang, Y., Ye, C., Ma, T.: A deep connectome learning network using graph convolution for connectome-disease association study. Neural Netw. 164, 91\u2013104 (2023)","journal-title":"Neural Netw."},{"key":"38_CR30","doi-asserted-by":"crossref","unstructured":"Yang, Y., et al.: Brainmass: advancing brain network analysis for diagnosis with large-scale self-supervised learning. IEEE Trans. Med. Imaging (2024)","DOI":"10.1109\/TMI.2024.3414476"},{"key":"38_CR31","unstructured":"Zhao, W., Zou, Q., Shah, R., Liu, D.: Representation collapsing problems in vector quantization. arXiv preprint arXiv:2411.16550 (2024)"},{"key":"38_CR32","doi-asserted-by":"crossref","unstructured":"Zheng, C., Vedaldi, A.: Online clustered codebook. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 22798\u201322807 (2023)","DOI":"10.1109\/ICCV51070.2023.02084"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2025"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-05162-2_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T23:27:34Z","timestamp":1758238054000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05162-2_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032051615","9783032051622"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05162-2_38","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,19]]},"assertion":[{"value":"19 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","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":"27 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}