{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T14:10:03Z","timestamp":1723903803834},"reference-count":65,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100007504","name":"Istanbul Teknik \u00dcniversitesi","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007504","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2021,2]]},"DOI":"10.1016\/j.media.2020.101902","type":"journal-article","created":{"date-parts":[[2020,11,16]],"date-time":"2020-11-16T16:41:09Z","timestamp":1605544869000},"page":"101902","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":17,"special_numbering":"C","title":["Brain graph synthesis by dual adversarial domain alignment and target graph prediction from a source graph"],"prefix":"10.1016","volume":"68","author":[{"given":"Alaa","family":"Bessadok","sequence":"first","affiliation":[]},{"given":"Mohamed Ali","family":"Mahjoub","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5595-6673","authenticated-orcid":false,"given":"Islem","family":"Rekik","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.media.2020.101902_bib0001","series-title":"Advances in Neural Information Processing Systems","first-page":"2270","article-title":"Learning the number of neurons in deep networks","author":"Alvarez","year":"2016"},{"key":"10.1016\/j.media.2020.101902_bib0002","first-page":"22","article-title":"Joint pairing and structured mapping of convolutional brain morphological multiplexes for early dementia diagnosis","volume":"9","author":"Alzheimer\u2019s Disease Neuroimaging Initiative","year":"2018","journal-title":"Brain Connect."},{"key":"10.1016\/j.media.2020.101902_bib0003","series-title":"International Conference on Machine Learning","first-page":"214","article-title":"Wasserstein generative adversarial networks","author":"Arjovsky","year":"2017"},{"key":"10.1016\/j.media.2020.101902_bib0004","unstructured":"Arslan, S., Ktena, S. I., Glocker, B., Rueckert, D., Graph saliency maps through spectral convolutional networks: application to sex classification with brain connectivity. arXiv preprint arXiv:1806.01764."},{"key":"10.1016\/j.media.2020.101902_bib0005","series-title":"International Workshop on Connectomics in Neuroimaging","first-page":"74","article-title":"Adversarial connectome embedding for mild cognitive impairment identification using cortical morphological networks","author":"Banka","year":"2019"},{"key":"10.1016\/j.media.2020.101902_bib0006","series-title":"International Workshop on PRedictive Intelligence In MEdicine","first-page":"129","article-title":"XmoNet: a fully convolutional network for cross-modality MR image inference","author":"Bano","year":"2018"},{"key":"10.1016\/j.media.2020.101902_bib0007","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.engappai.2018.11.013","article-title":"Cross-modality synthesis from CT to PET using FCN and GAN networks for improved automated lesion detection","volume":"78","author":"Ben-Cohen","year":"2019","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.media.2020.101902_bib0008","unstructured":"Bresson, X., Laurent, T., A two-step graph convolutional decoder for molecule generation. arXiv preprint arXiv:1906.03412."},{"issue":"4","key":"10.1016\/j.media.2020.101902_bib0009","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","article-title":"Geometric deep learning: going beyond euclidean data","volume":"34","author":"Bronstein","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.media.2020.101902_bib0010","series-title":"International Workshop on PRedictive Intelligence In MEdicine","first-page":"164","article-title":"Prediction to atrial fibrillation using deep convolutional neural networks","author":"Cho","year":"2018"},{"key":"10.1016\/j.media.2020.101902_bib0011","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"8789","article-title":"Stargan: unified generative adversarial networks for multi-domain image-to-image translation","author":"Choi","year":"2018"},{"key":"10.1016\/j.media.2020.101902_bib0012","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.jneumeth.2018.09.028","article-title":"Clustering-based multi-view network fusion for estimating brain network atlases of healthy and disordered populations","volume":"311","author":"Dhifallah","year":"2019","journal-title":"J. Neurosci. Methods"},{"issue":"5","key":"10.1016\/j.media.2020.101902_bib0013","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1002\/asi.21009","article-title":"The relation between pearson\u2019s correlation coefficient r and salton\u2019s cosine measure","volume":"60","author":"Egghe","year":"2009","journal-title":"J. Am. Soc. Inf. Sci. Technol."},{"issue":"2","key":"10.1016\/j.media.2020.101902_bib0014","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","article-title":"Freesurfer","volume":"62","author":"Fischl","year":"2012","journal-title":"Neuroimage"},{"key":"10.1016\/j.media.2020.101902_bib0015","unstructured":"Flam-Shepherd, D., Wu, T., Aspuru-Guzik, A., Graph deconvolutional generation. arXiv preprint arXiv:2002.07087."},{"key":"10.1016\/j.media.2020.101902_bib0016","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.neuroimage.2013.04.087","article-title":"Graph analysis of the human connectome: promise, progress, and pitfalls","volume":"80","author":"Fornito","year":"2013","journal-title":"Neuroimage"},{"article-title":"Residual embedding similarity-based network selection for predicting brain network evolution trajectory from a single observation","year":"2020","series-title":"International Workshop on PRedictive Intelligence In MEdicine","author":"Goktas","key":"10.1016\/j.media.2020.101902_bib0017"},{"key":"10.1016\/j.media.2020.101902_bib0018","unstructured":"Goodfellow, I., NIPS 2016 tutorial: generative adversarial networks. arXiv preprint arXiv:1701.00160."},{"key":"10.1016\/j.media.2020.101902_bib0019","series-title":"Advances in neural information processing systems","first-page":"2672","article-title":"Generative adversarial nets","author":"Goodfellow","year":"2014"},{"issue":"12","key":"10.1016\/j.media.2020.101902_bib0020","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1162\/0899766042321814","article-title":"Canonical correlation analysis: an overview with application to learning methods","volume":"16","author":"Hardoon","year":"2004","journal-title":"Neural Comput."},{"key":"10.1016\/j.media.2020.101902_bib0021","series-title":"International Workshop on Simulation and Synthesis in Medical Imaging","first-page":"31","article-title":"Cross-modality image synthesis from unpaired data using CycleGAN","author":"Hiasa","year":"2018"},{"key":"10.1016\/j.media.2020.101902_bib0022","series-title":"International Conference on Machine Learning","first-page":"1989","article-title":"Cycada: cycle-consistent adversarial domain adaptation","author":"Hoffman","year":"2018"},{"key":"10.1016\/j.media.2020.101902_bib0023","unstructured":"Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A. A., Darrell, T., Cycada: cycle-consistent adversarial domain adaptation. arXiv preprint arXiv:1711.03213."},{"issue":"1","key":"10.1016\/j.media.2020.101902_bib0024","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/TMI.2015.2461533","article-title":"Estimating CT image from MRI data using structured random forest and auto-context model","volume":"35","author":"Huynh","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2020.101902_bib0025","series-title":"2013 IEEE 10th International Symposium on Biomedical Imaging","first-page":"350","article-title":"Magnetic resonance image synthesis through patch regression","author":"Jog","year":"2013"},{"issue":"2065","key":"10.1016\/j.media.2020.101902_bib0026","doi-asserted-by":"crossref","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: a review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. A"},{"key":"10.1016\/j.media.2020.101902_bib0027","unstructured":"Kipf, T. N., Welling, M., Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907."},{"key":"10.1016\/j.media.2020.101902_bib0028","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"469","article-title":"Distance metric learning using graph convolutional networks: application to functional brain networks","author":"Ktena","year":"2017"},{"key":"10.1016\/j.media.2020.101902_bib0029","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"305","article-title":"Deep learning based imaging data completion for improved brain disease diagnosis","author":"Li","year":"2014"},{"key":"10.1016\/j.media.2020.101902_bib0030","series-title":"Advances in Neural Information Processing Systems","first-page":"4257","article-title":"Efficient graph generation with graph recurrent attention networks","author":"Liao","year":"2019"},{"issue":"6","key":"10.1016\/j.media.2020.101902_bib0031","doi-asserted-by":"crossref","first-page":"938","DOI":"10.1109\/29.56055","article-title":"Adaptive stack filtering under the mean absolute error criterion","volume":"38","author":"Lin","year":"1990","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"10.1016\/j.media.2020.101902_bib0032","series-title":"International Workshop on Connectomics in Neuroimaging","first-page":"42","article-title":"Pairing-based ensemble classifier learning using convolutional brain multiplexes and multi-view brain networks for early dementia diagnosis","author":"Lisowska","year":"2017"},{"key":"10.1016\/j.media.2020.101902_bib0033","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"26","article-title":"Outcome prediction for patient with high-grade gliomas from brain functional and structural networks","author":"Liu","year":"2016"},{"key":"10.1016\/j.media.2020.101902_bib0034","unstructured":"Liu, W., Chen, P.-Y., Cooper, H., Oh, M. H., Yeung, S., Suzumura, T., Can GAN learn topological features of a graph? arXiv preprint arXiv:1707.06197."},{"issue":"Nov","key":"10.1016\/j.media.2020.101902_bib0035","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"10.1016\/j.media.2020.101902_bib0036","doi-asserted-by":"crossref","first-page":"4103","DOI":"10.1038\/s41598-018-21568-7","article-title":"Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states","volume":"8","author":"Mahjoub","year":"2018","journal-title":"Sci. Rep."},{"key":"10.1016\/j.media.2020.101902_bib0037","doi-asserted-by":"crossref","first-page":"101768","DOI":"10.1016\/j.media.2020.101768","article-title":"Brain graph super-resolution for boosting neurological disorder diagnosis using unsupervised multi-topology connectional brain template learning","volume":"65","author":"Mhiri","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2020.101902_bib0038","doi-asserted-by":"crossref","first-page":"101596","DOI":"10.1016\/j.media.2019.101596","article-title":"Joint functional brain network atlas estimation and feature selection for neurological disorder diagnosis with application to Autism","volume":"60","author":"Mhiri","year":"2020","journal-title":"Med. Image Anal."},{"issue":"5","key":"10.1016\/j.media.2020.101902_bib0039","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.1007\/s11682-019-00123-6","article-title":"Gender differences in cortical morphological networks","volume":"14","author":"Nebli","year":"2020","journal-title":"Brain Imaging Behav."},{"key":"10.1016\/j.media.2020.101902_bib0040","unstructured":"Olut, S., Sahin, Y. H., Demir, U., Unal, G., Generative adversarial training for MRA image synthesis using multi-contrast MRI. arXiv preprint arXiv:1804.04366."},{"key":"10.1016\/j.media.2020.101902_bib0041","unstructured":"Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., Zhang, C., Adversarially regularized graph autoencoder. arXiv preprint arXiv:1802.04407."},{"key":"10.1016\/j.media.2020.101902_bib0042","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"455","article-title":"Synthesizing missing PET from MRI with cycle-consistent generative adversarial networks for Alzheimer\u2019s disease diagnosis","author":"Pan","year":"2018"},{"key":"10.1016\/j.media.2020.101902_bib0043","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"177","article-title":"Spectral graph convolutions for population-based disease prediction","author":"Parisot","year":"2017"},{"article-title":"Multi-adversarial domain adaptation","year":"2018","series-title":"Thirty-Second AAAI Conference on Artificial Intelligence","author":"Pei","key":"10.1016\/j.media.2020.101902_bib0044"},{"key":"10.1016\/j.media.2020.101902_bib0045","series-title":"Advances in neural information processing systems","first-page":"2352","article-title":"Variational autoencoder for deep learning of images, labels and captions","author":"Pu","year":"2016"},{"key":"10.1016\/j.media.2020.101902_bib0046","unstructured":"Redko, I., Morvant, E., Habrard, A., Sebban, M., Bennani, Y., A survey on domain adaptation theory: learning bounds and theoretical guarantees. arXiv preprint arXiv:2004.11829."},{"key":"10.1016\/j.media.2020.101902_bib0047","series-title":"International Conference on Information Processing in Medical Imaging","first-page":"417","article-title":"Brain tumor segmentation on MRI with missing modalities","author":"Shen","year":"2019"},{"key":"10.1016\/j.media.2020.101902_bib0048","unstructured":"Soussia, M., Rekik, I., A review on image-and network-based brain data analysis techniques for Alzheimer\u2019s disease diagnosis reveals a gap in developing predictive methods for prognosis. arXiv preprint arXiv:1808.01951."},{"key":"10.1016\/j.media.2020.101902_bib0049","doi-asserted-by":"crossref","first-page":"70","DOI":"10.3389\/fninf.2018.00070","article-title":"Unsupervised manifold learning using high-order morphological brain networks derived from T1-w MRI for autism diagnosis","volume":"12","author":"Soussia","year":"2018","journal-title":"Front. Neuroinformatics"},{"key":"10.1016\/j.media.2020.101902_bib0050","series-title":"International Workshop on PRedictive Intelligence In MEdicine","first-page":"81","article-title":"7 years of developing seed techniques for Alzheimer\u2019s Disease diagnosis using brain image and connectivity data largely bypassed prediction for prognosis","author":"Soussia","year":"2019"},{"key":"10.1016\/j.media.2020.101902_bib0051","unstructured":"Su, S.-Y., Hajimirsadeghi, H., Mori, G., Graph generation with variational recurrent neural network. arXiv preprint arXiv:1910.01743."},{"key":"10.1016\/j.media.2020.101902_bib0052","unstructured":"Tiao, L., Elinas, P., Nguyen, H., Bonilla, E. V.,. Variational graph convolutional networks."},{"key":"10.1016\/j.media.2020.101902_bib0053","unstructured":"Toldo, M., Maracani, A., Michieli, U., Zanuttigh, P., Unsupervised domain adaptation in semantic segmentation: a review. arXiv preprint arXiv:2005.10876."},{"key":"10.1016\/j.media.2020.101902_bib0054","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7167","article-title":"Adversarial discriminative domain adaptation","author":"Tzeng","year":"2017"},{"key":"10.1016\/j.media.2020.101902_bib0055","doi-asserted-by":"crossref","unstructured":"Wang, B., Ramazzotti, D., De Sano, L., Zhu, J., Pierson, E., Batzoglou, S., 2017. SIMLR: a tool for large-scale single-cell analysis by multi-kernel learning. bioRxiv, 118901.","DOI":"10.1101\/118901"},{"article-title":"GraphGAN: graph representation learning with generative adversarial nets","year":"2018","series-title":"Thirty-Second AAAI Conference on Artificial Intelligence","author":"Wang","key":"10.1016\/j.media.2020.101902_bib0056"},{"key":"10.1016\/j.media.2020.101902_bib0057","doi-asserted-by":"crossref","DOI":"10.1109\/TMI.2020.2987817","article-title":"Multi-class ASD classification based on functional connectivity and functional correlation tensor via multi-source domain adaptation and multi-view sparse representation","author":"Wang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"10.1016\/j.media.2020.101902_bib0058","doi-asserted-by":"crossref","first-page":"3988","DOI":"10.1002\/hbm.23643","article-title":"Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in tourette syndrome children","volume":"38","author":"Wen","year":"2017","journal-title":"Hum. Brain Mapp."},{"issue":"5","key":"10.1016\/j.media.2020.101902_bib0059","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3400066","article-title":"A survey of unsupervised deep domain adaptation","volume":"11","author":"Wilson","year":"2020","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"issue":"2","key":"10.1016\/j.media.2020.101902_bib0060","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","article-title":"Robust face recognition via sparse representation","volume":"31","author":"Wright","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2020.101902_bib0061","series-title":"Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support","first-page":"174","article-title":"Unpaired brain MR-to-CT synthesis using a structure-constrained CycleGAN","author":"Yang","year":"2018"},{"key":"10.1016\/j.media.2020.101902_bib0062","unstructured":"Yi, X., Walia, E., Babyn, P., Generative adversarial network in medical imaging: areview. arXiv preprint arXiv:1809.07294."},{"key":"10.1016\/j.media.2020.101902_bib0063","series-title":"International Conference on Information Processing in Medical Imaging","first-page":"141","article-title":"Limited angle tomography reconstruction: synthetic reconstruction via unsupervised sinogram adaptation","author":"Zhou","year":"2019"},{"key":"10.1016\/j.media.2020.101902_bib0064","doi-asserted-by":"crossref","unstructured":"Zhu, J.-Y., Park, T., Isola, P., Efros, A. A., 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint.","DOI":"10.1109\/ICCV.2017.244"},{"key":"10.1016\/j.media.2020.101902_bib0065","series-title":"International Workshop on PRedictive Intelligence In MEdicine","first-page":"94","article-title":"Multi-view brain network prediction from a source view using sample selection via CCA-based multi-kernel connectomic manifold learning","author":"Zhu","year":"2018"}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841520302668?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841520302668?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T13:42:37Z","timestamp":1723902157000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841520302668"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2]]},"references-count":65,"alternative-id":["S1361841520302668"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2020.101902","relation":{},"ISSN":["1361-8415"],"issn-type":[{"type":"print","value":"1361-8415"}],"subject":[],"published":{"date-parts":[[2021,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Brain graph synthesis by dual adversarial domain alignment and target graph prediction from a source graph","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2020.101902","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"101902"}}