{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T21:49:35Z","timestamp":1793396975109,"version":"4.5.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032049469","type":"print"},{"value":"9783032049476","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,9,21]],"date-time":"2025-09-21T00:00:00Z","timestamp":1758412800000},"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\/978-3-032-04947-6_61","type":"book-chapter","created":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T17:32:05Z","timestamp":1758389525000},"page":"640-650","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["T2I-Diff: fMRI Signal Generation via\u00a0Time-Frequency Image Transform and\u00a0Classifier-Free Denoising Diffusion Models"],"prefix":"10.1007","author":[{"given":"Hwa Hui","family":"Tew","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junn Yong","family":"Loo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yee-Fan","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hernando","family":"Ombao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuad","family":"Noman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rapha\u00ebl C.-W.","family":"Phan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chee-Ming","family":"Ting","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,9,21]]},"reference":[{"key":"61_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/fnimg.2022.981642","volume":"1","author":"LK Avber\u0161ek","year":"2022","unstructured":"Avber\u0161ek, L.K., Repov\u0161, G.: Deep learning in neuroimaging data analysis: applications, challenges, and solutions. Front. Neuroimaging 1, 981642 (2022)","journal-title":"Front. Neuroimaging"},{"key":"61_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.pneurobio.2020.101936","volume":"207","author":"S Bollmann","year":"2021","unstructured":"Bollmann, S., Barth, M.: New acquisition techniques and their prospects for the achievable resolution of fmri. Prog. Neurobiol. 207, 101936 (2021)","journal-title":"Prog. Neurobiol."},{"issue":"11","key":"61_CR3","doi-asserted-by":"publisher","first-page":"7327","DOI":"10.1109\/TPAMI.2021.3116668","volume":"44","author":"S Bond-Taylor","year":"2021","unstructured":"Bond-Taylor, S., Leach, A., Long, Y., Willcocks, C.G.: Deep generative modelling: a comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 7327\u20137347 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"61_CR4","doi-asserted-by":"crossref","unstructured":"Coletta, A., Gopalakrishnan, S., Borrajo, D., Vyetrenko, S.: On the constrained time-series generation problem. Adv. Neural Inf. Process. Syst. 36, 61048\u201361059 (2023)","DOI":"10.52202\/075280-2668"},{"key":"61_CR5","unstructured":"Desai, A., Freeman, C., Wang, Z., Beaver, I.: Timevae: a variational auto-encoder for multivariate time series generation. arXiv preprint arXiv:2111.08095 (2021)"},{"key":"61_CR6","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of wasserstein gans. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"61_CR7","doi-asserted-by":"crossref","unstructured":"Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. Adv. Neural Inf. Process. Syst. 35, 26565\u201326577 (2022)","DOI":"10.52202\/068431-1926"},{"key":"61_CR8","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1016\/j.neuroimage.2016.09.046","volume":"146","author":"J Kawahara","year":"2017","unstructured":"Kawahara, J., et al.: Brainnetcnn: convolutional neural networks for brain networks; towards predicting neurodevelopment. Neuroimage 146, 1038\u20131049 (2017)","journal-title":"Neuroimage"},{"key":"61_CR9","unstructured":"Kong, Z., Ping, W., Huang, J., Zhao, K., Catanzaro, B.: Diffwave: a versatile diffusion model for audio synthesis. arXiv preprint arXiv:2009.09761 (2020)"},{"issue":"1","key":"61_CR10","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1631\/FITEE.2300310","volume":"25","author":"L Lin","year":"2024","unstructured":"Lin, L., Li, Z., Li, R., Li, X., Gao, J.: Diffusion models for time-series applications: a survey. Front. Inf. Technol. Electron. Eng. 25(1), 19\u201341 (2024)","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"61_CR11","unstructured":"Loo, J.Y., et al.: Learning energy-based generative models via potential flow: a variational principle approach to probability density homotopy matching. Trans. Mach. Learn. Res. (2025)"},{"key":"61_CR12","doi-asserted-by":"crossref","unstructured":"Naiman, I., Berman, N., Pemper, I., Arbiv, I., Fadlon, G., Azencot, O.: Utilizing image transforms and diffusion models for generative modeling of short and long time series. Adv. Neural Inf. Process. Syst. 37, 121699\u2013121730 (2024)","DOI":"10.52202\/079017-3868"},{"issue":"3","key":"61_CR13","doi-asserted-by":"publisher","first-page":"1644","DOI":"10.1109\/JBHI.2024.3351177","volume":"28","author":"F Noman","year":"2024","unstructured":"Noman, F., Ting, C.M., Kang, H., Phan, R.C.W., Ombao, H.: Graph autoencoders for embedding learning in brain networks and major depressive disorder identification. IEEE J. Biomed. Health Inform. 28(3), 1644\u20131655 (2024)","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"61_CR14","doi-asserted-by":"crossref","unstructured":"Noman, F., Yap, S.Y., Phan, R.C.W., Ombao, H., Ting, C.M.: Graph autoencoder-based embedded learning in dynamic brain networks for autism spectrum disorder identification. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 2891\u20132895 (2022)","DOI":"10.1109\/ICIP46576.2022.9898034"},{"key":"61_CR15","doi-asserted-by":"crossref","unstructured":"Power, J.D., Mitra, A., Laumann, T.O., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E.: Methods to detect, characterize, and remove motion artifact in resting state fmri. Neuroimage 84, 320\u2013341 (2014)","DOI":"10.1016\/j.neuroimage.2013.08.048"},{"key":"61_CR16","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)"},{"key":"61_CR17","doi-asserted-by":"crossref","unstructured":"Tan, Y.F., Loo, J.Y., Ting, C.M., Noman, F., Phan, R.C.W., Ombao, H.: BrainFC-CGAN: a conditional generative adversarial network for brain functional connectivity augmentation and aging synthesis. In: ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1511\u20131515. IEEE (2024)","DOI":"10.1109\/ICASSP48485.2024.10446319"},{"key":"61_CR18","unstructured":"Tan, Y.F., Ting, C.M., Noman, F., Phan, R.C.W., Ombao, H.: Graph-regularized manifold-aware conditional wasserstein gan for brain functional connectivity generation. arXiv preprint arXiv:2212.05316 (2022)"},{"key":"61_CR19","doi-asserted-by":"crossref","unstructured":"Tan, Y.F., Ting, C.M., Noman, F., Phan, R.C.W., Ombao, H.: A unified framework for static and dynamic functional connectivity augmentation for multi-domain brain disorder classification. In: Proceedings of the 2023 IEEE International Conference on Image Processing (ICIP), pp. 635\u2013639 (2023)","DOI":"10.1109\/ICIP49359.2023.10222266"},{"key":"61_CR20","doi-asserted-by":"crossref","unstructured":"Tew, H.H., et al.: ST-HCSS: deep spatio-temporal hypergraph convolutional neural network for soft sensing. arXiv preprint arXiv:2501.02016 (2025)","DOI":"10.1109\/ICASSP49660.2025.10890078"},{"key":"61_CR21","doi-asserted-by":"crossref","unstructured":"Tew, H.H., et al.: Kans: knowledge discovery graph attention network for soft sensing in multivariate industrial processes. In: 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 4377\u20134383. IEEE (2024)","DOI":"10.1109\/SMC54092.2024.10831311"},{"issue":"6","key":"61_CR22","doi-asserted-by":"publisher","first-page":"1431","DOI":"10.1109\/TMI.2021.3139428","volume":"41","author":"CM Ting","year":"2022","unstructured":"Ting, C.M., Skipper, J.I., Noman, F., Small, S.L., Ombao, H.: Separating stimulus-induced and background components of dynamic functional connectivity in naturalistic fmri. IEEE Trans. Med. Imaging 41(6), 1431\u20131442 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"61_CR23","unstructured":"Xu, T., Wenliang, L.K., Munn, M., Acciaio, B.: COT-GAN: generating sequential data via causal optimal transport. Adv. Neural Inf. Process. Syst. 33, 8798\u20138809 (2020)"},{"issue":"18","key":"61_CR24","doi-asserted-by":"publisher","first-page":"9078","DOI":"10.1073\/pnas.1900390116","volume":"116","author":"CG Yan","year":"2019","unstructured":"Yan, C.G., et al.: Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc. Natl. Acad. Sci. 116(18), 9078\u20139083 (2019)","journal-title":"Proc. Natl. Acad. Sci."},{"key":"61_CR25","first-page":"1377","volume":"4","author":"C Yan","year":"2010","unstructured":"Yan, C., Zang, Y.: DPARSF: a matlab toolbox for \u201cpipeline\u2019\u2019 data analysis of resting-state fmri. Front. Syst. Neurosci. 4, 1377 (2010)","journal-title":"Front. Syst. Neurosci."},{"key":"61_CR26","doi-asserted-by":"crossref","unstructured":"Yap, S.Y., et al.: A deep probabilistic spatiotemporal framework for dynamic graph representation learning with application to brain disorder identification. In: Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24, pp. 5353\u20135361 (2024)","DOI":"10.24963\/ijcai.2024\/592"},{"key":"61_CR27","unstructured":"Yoon, J., Jarrett, D., Van\u00a0der Schaar, M.: Time-series generative adversarial networks. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"61_CR28","unstructured":"Yuan, X., Qiao, Y.: Diffusion-ts: interpretable diffusion for general time series generation. arXiv preprint arXiv:2403.01742 (2024)"},{"key":"61_CR29","unstructured":"Zhao, H., et al.: Diffusion models for computational neuroimaging: a survey. arXiv preprint arXiv:2502.06552 (2025)"}],"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\/978-3-032-04947-6_61","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T07:11:15Z","timestamp":1790147475000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-04947-6_61"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,21]]},"ISBN":["9783032049469","9783032049476"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-04947-6_61","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,21]]},"assertion":[{"value":"21 September 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","label":"Disclosure of Interests","group":{"name":"EthicsHeading","label":"Ethics"}},{"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"}}]}}