{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T11:38:13Z","timestamp":1758281893669,"version":"3.44.0"},"publisher-location":"Cham","reference-count":28,"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_8","type":"book-chapter","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T23:27:14Z","timestamp":1758238034000},"page":"76-86","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A$$\\upbeta $$-PET Pattern Prediction via\u00a0Graph Reconstruction-Aware Fusion (GRAF) of\u00a0Functional and\u00a0Structural Networks"],"prefix":"10.1007","author":[{"given":"Haoyue","family":"Yuan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxiao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feihong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"8_CR1","doi-asserted-by":"crossref","unstructured":"Cui, H., Dai, W., Zhu, Y., Li, X., He, L., Yang, C.: Interpretable graph neural networks for connectome-based brain disorder analysis. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 375\u2013385. Springer (2022)","DOI":"10.1007\/978-3-031-16452-1_36"},{"key":"8_CR2","first-page":"28","volume":"4","author":"D Ding","year":"2016","unstructured":"Ding, D., Zhao, Q., Guo, Q., Liang, X., Luo, J., Yu, L., Zheng, L., Hong, Z.: Progression and predictors of mild cognitive impairment in Chinese elderly: a prospective follow-up in the shanghai aging study. Alzheimer\u2019s & Dementia: Diagnosis, Assessment & Disease Monitoring 4, 28\u201336 (2016)","journal-title":"Alzheimer\u2019s & Dementia: Diagnosis, Assessment & Disease Monitoring"},{"issue":"2","key":"8_CR3","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1016\/j.neuroimage.2011.09.015","volume":"62","author":"M Jenkinson","year":"2012","unstructured":"Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W., Smith, S.M.: FSL. NeuroImage 62(2), 782\u2013790 (2012)","journal-title":"FSL. NeuroImage"},{"issue":"12","key":"8_CR4","doi-asserted-by":"crossref","first-page":"1547","DOI":"10.1001\/jamaneurol.2014.1482","volume":"71","author":"K Kantarci","year":"2014","unstructured":"Kantarci, K., et al.: White matter integrity determined with diffusion tensor imaging in older adults without dementia: influence of amyloid load and neurodegeneration. JAMA Neurol. 71(12), 1547\u20131554 (2014)","journal-title":"JAMA Neurol."},{"issue":"4","key":"8_CR5","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.3233\/JAD-215497","volume":"86","author":"C Li","year":"2022","unstructured":"Li, C., et al.: Predicting brain amyloid-$$\\beta $$ PET grades with Graph Convolutional Networks based on functional MRI and multi-level functional connectivity. J. Alzheimers Dis. 86(4), 1679\u20131693 (2022)","journal-title":"J. Alzheimers Dis."},{"key":"8_CR6","volume":"74","author":"X Li","year":"2021","unstructured":"Li, X., et al.: BrainGNN: interpretable brain graph neural network for fMRI analysis. Med. Image Anal. 74, 102233 (2021)","journal-title":"Med. Image Anal."},{"key":"8_CR7","doi-asserted-by":"crossref","unstructured":"Liu, F., et al.: Identifying alzheimer\u2019s disease-induced topology alterations in structural networks using convolutional neural networks. In: International Workshop on Machine Learning in Medical Imaging, pp. 33\u201342. Springer (2023)","DOI":"10.1007\/978-3-031-45676-3_4"},{"key":"8_CR8","doi-asserted-by":"crossref","unstructured":"Liu, J., Ma, G., Jiang, F., Lu, C.T., Philip, S.Y., Ragin, A.B.: Community-preserving graph convolutions for structural and functional joint embedding of brain networks. In: 2019 IEEE International Conference on Big Data (Big Data), pp. 1163\u20131168. IEEE (2019)","DOI":"10.1109\/BigData47090.2019.9005586"},{"key":"8_CR9","unstructured":"Liu, J., Ma, G., Jiang, F., Lu, C.T., Philip, S.Y., Ragin, A.B.: M-GCN: a multimodal graph convolutional network to integrate functional and structural connectomics data to predict multidimensional phenotypic characterizations. In: Medical Imaging with Deep Learning, pp. 119\u2013130. PMLR (2021)"},{"key":"8_CR10","doi-asserted-by":"crossref","unstructured":"Liu, M., Zhang, H., Shi, F., Shen, D.: Hierarchical graph convolutional network built by multiscale atlases for brain disorder diagnosis using functional connectivity. IEEE Trans. Neural Networks Learn. Syst. (2023)","DOI":"10.1109\/TNNLS.2023.3282961"},{"issue":"7","key":"8_CR11","doi-asserted-by":"crossref","first-page":"1473","DOI":"10.1109\/TBME.2015.2496233","volume":"63","author":"M Liu","year":"2015","unstructured":"Liu, M., Zhang, D., Adeli, E., Shen, D.: Inherent structure-based multiview learning with multitemplate feature representation for Alzheimer\u2019s disease diagnosis. IEEE Trans. Biomed. Eng. 63(7), 1473\u20131482 (2015)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, M., Zhang, Y., Guan, Y., Guo, Q., Xie, F., Shen, D.: Amyloid-$$\\beta $$ deposition prediction with large language model driven and task oriented learning of brain functional networks. IEEE Trans. Med. Imaging (2025)","DOI":"10.1109\/TMI.2024.3525022"},{"issue":"2","key":"8_CR13","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.cell.2019.09.001","volume":"179","author":"JM Long","year":"2019","unstructured":"Long, J.M., Holtzman, D.M.: Alzheimer disease: an update on pathobiology and treatment strategies. Cell 179(2), 312\u2013339 (2019)","journal-title":"Cell"},{"key":"8_CR14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/alzrt155","volume":"5","author":"KL Moulder","year":"2013","unstructured":"Moulder, K.L., Snider, B.J., Mills, S.L., Buckles, V.D., Santacruz, A.M., Bateman, R.J., Morris, J.C.: Dominantly inherited Alzheimer network: facilitating research and clinical trials. Alzheimer\u2019s Res. Therapy 5, 1\u20137 (2013)","journal-title":"Alzheimer\u2019s Res. Therapy"},{"issue":"3","key":"8_CR15","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1016\/j.neuroimage.2009.10.003","volume":"52","author":"M Rubinov","year":"2010","unstructured":"Rubinov, M., Sporns, O.: Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52(3), 1059\u20131069 (2010)","journal-title":"Neuroimage"},{"issue":"9","key":"8_CR16","doi-asserted-by":"crossref","first-page":"3095","DOI":"10.1093\/cercor\/bhx179","volume":"28","author":"A Schaefer","year":"2018","unstructured":"Schaefer, A., 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":"8_CR17","unstructured":"Thakoor, S., Tallec, C., Azar, M.G., Munos, R., Veli\u010dkovi\u0107, P., Valko, M.: Bootstrapped representation learning on graphs. In: ICLR 2021 Workshop on Geometrical and Topological Representation Learning (2021)"},{"key":"8_CR18","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2019.116137","volume":"202","author":"JD Tournier","year":"2019","unstructured":"Tournier, J.D., Smith, R., Raffelt, D., Tabbara, R., Dhollander, T., Pietsch, M., Christiaens, D., Jeurissen, B., Yeh, C.H., Connelly, A.: MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation. Neuroimage 202, 116137 (2019)","journal-title":"Neuroimage"},{"issue":"6","key":"8_CR19","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","volume":"29","author":"NJ Tustison","year":"2010","unstructured":"Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A., Gee, J.C.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310\u20131320 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR20","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph Attention Networks. In: International Conference on Learning Representations (ICLR) (2018)"},{"issue":"2","key":"8_CR21","doi-asserted-by":"crossref","first-page":"1259","DOI":"10.1093\/cercor\/bhaa292","volume":"31","author":"X Xing","year":"2021","unstructured":"Xing, X., et al.: DS-GCNs: connectome classification using dynamic spectral graph convolution networks with assistant task training. Cereb. Cortex 31(2), 1259\u20131269 (2021)","journal-title":"Cereb. Cortex"},{"key":"8_CR22","first-page":"1377","volume":"4","author":"C Yan","year":"2010","unstructured":"Yan, C., Zang, Y.: DPARSF: a matlab toolbox for\" pipeline\" data analysis of resting-state fMRI. Front. Syst. Neurosci. 4, 1377 (2010)","journal-title":"Front. Syst. Neurosci."},{"issue":"1","key":"8_CR23","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1109\/TMI.2023.3294967","volume":"43","author":"Y Yang","year":"2023","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 Trans. Med. Imaging 43(1), 108\u2013121 (2023)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"8_CR24","doi-asserted-by":"crossref","unstructured":"Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 7370\u20137377 (2019)","DOI":"10.1609\/aaai.v33i01.33017370"},{"key":"8_CR25","first-page":"5812","volume":"33","author":"Y You","year":"2020","unstructured":"You, Y., Chen, T., Sui, Y., Chen, T., Wang, Z., Shen, Y.: Graph contrastive learning with augmentations. Adv. Neural. Inf. Process. Syst. 33, 5812\u20135823 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"9","key":"8_CR26","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1038\/s41582-021-00529-1","volume":"17","author":"M Yu","year":"2021","unstructured":"Yu, M., Sporns, O., Saykin, A.J.: The human connectome in alzheimer disease-relationship to biomarkers and genetics. Nat. Rev. Neurol. 17(9), 545\u2013563 (2021)","journal-title":"Nat. Rev. Neurol."},{"key":"8_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Huang, H.: New graph-blind convolutional network for brain connectome data analysis. In: International Conference on Information Processing in Medical Imaging, pp. 669\u2013681. Springer (2019)","DOI":"10.1007\/978-3-030-20351-1_52"},{"key":"8_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Sun, K., Liu, Y., Xie, F., Guo, Q., Shen, D.: A modality-flexible framework for Alzheimer\u2019s disease diagnosis following clinical routine. IEEE J. Biomed. Health Inform. (2024)","DOI":"10.1109\/JBHI.2024.3472011"}],"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_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T23:27:24Z","timestamp":1758238044000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-05162-2_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,19]]},"ISBN":["9783032051615","9783032051622"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-05162-2_8","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 have no competing interests to declare that are relevant to the content of this article.","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"}}]}}