{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T18:35:20Z","timestamp":1758393320462,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031787607"},{"type":"electronic","value":"9783031787614"}],"license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-78761-4_9","type":"book-chapter","created":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T07:43:39Z","timestamp":1733557419000},"page":"91-101","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Lesion-Aware Edge-Based Graph Neural Network for\u00a0Predicting Language Ability in\u00a0Patients with\u00a0Post-stroke Aphasia"],"prefix":"10.1007","author":[{"given":"Zijian","family":"Chen","sequence":"first","affiliation":[]},{"given":"Maria","family":"Varkanitsa","sequence":"additional","affiliation":[]},{"given":"Prakash","family":"Ishwar","sequence":"additional","affiliation":[]},{"given":"Janusz","family":"Konrad","sequence":"additional","affiliation":[]},{"given":"Margrit","family":"Betke","sequence":"additional","affiliation":[]},{"given":"Swathi","family":"Kiran","sequence":"additional","affiliation":[]},{"given":"Archana","family":"Venkataraman","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","first-page":"163","DOI":"10.2165\/00002512-200522020-00006","volume":"22","author":"ML Berthier","year":"2005","unstructured":"Berthier, M.L.: Poststroke aphasia: epidemiology, pathophysiology and treatment. Drugs & aging 22, 163\u2013182 (2005)","journal-title":"Drugs & aging"},{"issue":"5","key":"9_CR2","doi-asserted-by":"publisher","first-page":"1606","DOI":"10.1161\/STROKEAHA.121.036749","volume":"53","author":"A Billot","year":"2022","unstructured":"Billot, A., et al.: Multimodal neural and behavioral data predict response to rehabilitation in chronic poststroke aphasia. Stroke 53(5), 1606\u20131614 (2022)","journal-title":"Stroke"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Chennuri, S., et al.: Fusion approaches to predict post-stroke aphasia severity from multimodal neuroimaging data. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2644\u20132653 (2023)","DOI":"10.1109\/ICCVW60793.2023.00279"},{"key":"9_CR4","unstructured":"Dsouza, N.S., Nebel, M.B., Crocetti, D., Robinson, J., Mostofsky, S., Venkataraman, A.: 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":"9_CR5","doi-asserted-by":"crossref","unstructured":"Falconer, I., Varkanitsa, M., Kiran, S.: Resting-state brain network connectivity is an independent predictor of responsiveness to language therapy in chronic post-stroke aphasia. Cortex (2024)","DOI":"10.1016\/j.cortex.2023.11.022"},{"issue":"8","key":"9_CR6","doi-asserted-by":"publisher","first-page":"3508","DOI":"10.1093\/cercor\/bhw157","volume":"26","author":"L Fan","year":"2016","unstructured":"Fan, L., et al.: The human brainnetome atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26(8), 3508\u20133526 (2016)","journal-title":"Cereb. Cortex"},{"issue":"1","key":"9_CR7","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1177\/17474930211065917","volume":"17","author":"VL Feigin","year":"2022","unstructured":"Feigin, V.L., et al.: World stroke organization (WSO): global stroke fact sheet 2022. Int. J. Stroke 17(1), 18\u201329 (2022)","journal-title":"Int. J. Stroke"},{"key":"9_CR8","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1007\/978-3-031-34048-2_22","volume-title":"IPMI 2023","author":"J Huang","year":"2023","unstructured":"Huang, J., Chung, M.K., Qiu, A.: Heterogeneous graph convolutional neural network via hodge-laplacian for brain functional data. In: Frangi, A., de Bruijne, M., Wassermann, D., Navab, N. (eds.) IPMI 2023. LNCS, vol. 13939, pp. 278\u2013290. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-34048-2_22"},{"key":"9_CR9","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":"9_CR10","doi-asserted-by":"crossref","unstructured":"Kertesz, A.: Western aphasia battery\u2013revised (2007). https:\/\/doi.org\/10.1037\/t15168-000","DOI":"10.1037\/t15168-000"},{"issue":"6","key":"9_CR11","doi-asserted-by":"publisher","first-page":"1682","DOI":"10.1002\/hbm.25321","volume":"42","author":"S Kristinsson","year":"2021","unstructured":"Kristinsson, S., et al.: Machine learning-based multimodal prediction of language outcomes in chronic aphasia. Hum. Brain Mapp. 42(6), 1682\u20131698 (2021)","journal-title":"Hum. Brain Mapp."},{"key":"9_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102233","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."},{"issue":"1","key":"9_CR13","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1038\/s41597-022-01923-0","volume":"10","author":"CF Liu","year":"2023","unstructured":"Liu, C.F., et al.: Digital 3D brain MRI arterial territories atlas. Sci. Data 10(1), 74 (2023)","journal-title":"Sci. Data"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Nandakumar, N., Hsu, D., Ahmed, R., Venkataraman, A.: A deep learning framework to localize the epileptogenic zone from dynamic functional connectivity using a combined graph convolutional and transformer network. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), pp.\u00a01\u20135. IEEE (2023)","DOI":"10.1109\/ISBI53787.2023.10230831"},{"key":"9_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102203","volume":"74","author":"N Nandakumar","year":"2021","unstructured":"Nandakumar, N., Manzoor, K., Agarwal, S., Pillai, J.J., Gujar, S.K., Sair, H.I., Venkataraman, A.: Automated eloquent cortex localization in brain tumor patients using multi-task graph neural networks. Med. Image Anal. 74, 102203 (2021)","journal-title":"Med. Image Anal."},{"key":"9_CR16","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/978-3-030-78191-0_19","volume-title":"Information Processing in Medical Imaging","author":"N Nandakumar","year":"2021","unstructured":"Nandakumar, N., et al.: A multi-scale spatial and temporal attention network on dynamic connectivity to localize the eloquent cortex in brain tumor patients. In: Feragen, A., Sommer, S., Schnabel, J., Nielsen, M. (eds.) IPMI 2021. LNCS, vol. 12729, pp. 241\u2013252. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-78191-0_19"},{"issue":"11","key":"9_CR17","doi-asserted-by":"publisher","first-page":"5603","DOI":"10.1002\/hbm.23752","volume":"38","author":"D Pustina","year":"2017","unstructured":"Pustina, D., et al.: Enhanced estimations of post-stroke aphasia severity using stacked multimodal predictions. Hum. Brain Mapp. 38(11), 5603\u20135615 (2017)","journal-title":"Hum. Brain Mapp."},{"key":"9_CR18","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.neuroimage.2013.05.039","volume":"80","author":"SM Smith","year":"2013","unstructured":"Smith, S.M., et al.: Resting-state fMRI in the human connectome project. Neuroimage 80, 144\u2013168 (2013)","journal-title":"Neuroimage"},{"key":"9_CR19","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","volume":"80","author":"DC Van Essen","year":"2013","unstructured":"Van Essen, D.C., et al.: The WU-MINN human connectome project: an overview. Neuroimage 80, 62\u201379 (2013)","journal-title":"Neuroimage"},{"issue":"20","key":"9_CR20","first-page":"10","volume":"1050","author":"P Velickovic","year":"2017","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y., et al.: Graph attention networks. Stat 1050(20), 10\u201348550 (2017)","journal-title":"Stat"},{"key":"9_CR21","unstructured":"Wang, Y., Yin, J., Desai, R.H.: Topological inference on brain networks across subtypes of post-stroke aphasia. ArXiv (2023)"},{"issue":"3","key":"9_CR22","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1089\/brain.2012.0073","volume":"2","author":"S Whitfield-Gabrieli","year":"2012","unstructured":"Whitfield-Gabrieli, S., Nieto-Castanon, A.: Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2(3), 125\u2013141 (2012)","journal-title":"Brain Connect."},{"key":"9_CR23","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S.: How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)"},{"issue":"8","key":"9_CR24","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1038\/nmeth.1635","volume":"8","author":"T Yarkoni","year":"2011","unstructured":"Yarkoni, T., Poldrack, R.A., Nichols, T.E., Van Essen, D.C., Wager, T.D.: Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8(8), 665\u2013670 (2011)","journal-title":"Nat. Methods"},{"key":"9_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103137","volume":"94","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Guo, Y., Zhou, L., Wang, L., Wu, W., Shen, D.: Constructing hierarchical attentive functional brain networks for early AD diagnosis. Med. Image Anal. 94, 103137 (2024)","journal-title":"Med. Image Anal."},{"key":"9_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2021.118774","volume":"246","author":"K Zhao","year":"2022","unstructured":"Zhao, K., Duka, B., Xie, H., Oathes, D.J., Calhoun, V., Zhang, Y.: A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. Neuroimage 246, 118774 (2022)","journal-title":"Neuroimage"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Clinical Neuroimaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78761-4_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,7]],"date-time":"2024-12-07T08:06:15Z","timestamp":1733558775000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78761-4_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,6]]},"ISBN":["9783031787607","9783031787614"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78761-4_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,6]]},"assertion":[{"value":"6 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLCN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Clinical Neuroimaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Marrakesh","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlcn2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mlcnworkshop.github.io\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}