{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T11:53:24Z","timestamp":1753358004766,"version":"3.28.0"},"reference-count":40,"publisher":"SPIE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,4]]},"DOI":"10.1117\/12.2613172","type":"proceedings-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T23:44:04Z","timestamp":1648683844000},"page":"43","source":"Crossref","is-referenced-by-count":3,"title":["Phenotype guided interpretable graph convolutional network analysis of fMRI data reveals changing brain connectivity during adolescence"],"prefix":"10.1117","author":[{"given":"Anton","family":"Orlichenko","sequence":"first","affiliation":[]},{"given":"Gang","family":"Qu","sequence":"additional","affiliation":[]},{"given":"Yu-Ping","family":"Wang","sequence":"additional","affiliation":[]}],"member":"189","reference":[{"key":"c1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1948051"},{"key":"c2","first-page":"34","article-title":"Functional connectivity in the motor cortex of resting human brain using echo-planar mri","author":"Biswal","year":"1995","journal-title":"Magnetic Resonance in Medicine"},{"key":"c3","doi-asserted-by":"publisher","DOI":"10.1038\/nn.4135"},{"key":"c4","first-page":"12","article-title":"Classification and prediction of brain disorders using functional connectivity: Promising but challenging","author":"Du","year":"2018","journal-title":"Frontiers in Neuroscience"},{"key":"c5","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.10"},{"key":"c6","first-page":"824","article-title":"Sex classification by resting state brain connectivity","volume":"30","author":"Weis","year":"2019"},{"key":"c7","first-page":"229","article-title":"Development of the brain functional connectome follows puberty-dependent nonlinear trajectories","author":"Gracia-Tabuenca","year":"2021","journal-title":"NeuroImage"},{"key":"c8","first-page":"13","article-title":"Support vector machine for analyzing contributions of brain regions during task-state fmri","author":"Wang","year":"2019","journal-title":"Frontiers in Neuroinformatics"},{"key":"c9","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3057635"},{"key":"c10","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5514839"},{"key":"c11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101871"},{"key":"c12","first-page":"14","article-title":"A deep network model on dynamic functional connectivity with applications to gender classification and intelligence prediction","author":"Fan","year":"2020","journal-title":"Frontiers in Neuroscience"},{"key":"c13","first-page":"12","article-title":"Deep learning encodes robust discriminative neuroimaging representations to outperform standard machine learning","author":"Abrol","year":"2021","journal-title":"Nature Communications"},{"key":"c14","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.79"},{"key":"c15","first-page":"17","article-title":"Semi-Supervised Classification with Graph Convolutional Networks","volume-title":"Proceedings of the 5th International Conference on Learning Representations","author":"Kipf","year":"2017"},{"key":"c16","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1109\/IJCNN.2005.1555942","article-title":"A new model for learning in graph domains","volume-title":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks","volume":"2","author":"Gori","year":"2005"},{"key":"c17","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2010.04.005"},{"key":"c18","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proceedings of the 34th International Conference on Machine Learning","volume":"70","author":"Gilmer","year":"2017"},{"key":"c19","first-page":"13","article-title":"Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models","author":"Jiang","year":"2021","journal-title":"Journal of Cheminformatics"},{"key":"c20","article-title":"Graph attention networks","volume-title":"6th International Conference on Learning Representations","author":"Veli\u010dkovi\u0107","year":"2017"},{"article-title":"Inductive representation learning on large graphs","year":"2017","author":"Hamilton","key":"c21"},{"key":"c22","first-page":"36","article-title":"The mythos of model interpretability","volume-title":"Commun","volume":"61","author":"Lipton","year":"2018"},{"key":"c23","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"Simonyan","year":"2014","journal-title":"CoRR abs\/1312.6034"},{"key":"c24","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46493-0"},{"key":"c25","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01228-7"},{"key":"c26","doi-asserted-by":"crossref","first-page":"10764","DOI":"10.1109\/CVPR.2019.01103","article-title":"Explainability methods for graph convolutional neural networks","volume-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Pope","year":"2019"},{"key":"c27","first-page":"9240","article-title":"Gnnexplainer: Generating explanations for graph neural networks","volume":"32","author":"Ying","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"c28","first-page":"101","article-title":"Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks","volume-title":"Proc. IEEE Int Symp Biomed Imaging","author":"Li","year":"2018"},{"key":"c29","article-title":"Computing personalized brain functional networks from fmri using self-supervised deep learning","author":"Li","year":"2021","journal-title":"bioRxiv"},{"key":"c30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-21735-7"},{"key":"c31","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4"},{"article-title":"Distance correlation based brain functional connectivity estimation and non-convex multi-task learning for developmental fmri studies","year":"2020","author":"Xiao","key":"c32"},{"key":"c33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.07.064"},{"key":"c34","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.1995.1019"},{"key":"c35","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuron.2011.09.006"},{"key":"c36","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2015","journal-title":"CoRR abs\/1412.6980"},{"key":"c37","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"c38","doi-asserted-by":"publisher","DOI":"10.1145\/3065386"},{"key":"c39","first-page":"1","article-title":"Brain functional connectivity analysis via graphical deep learning","author":"Qu","year":"2021","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"c40","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2021.3077875"}],"event":{"name":"Biomedical Applications in Molecular, Structural, and Functional Imaging","start":{"date-parts":[[2022,2,20]]},"location":"San Diego, United States","end":{"date-parts":[[2022,3,28]]}},"container-title":["Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging"],"original-title":[],"deposited":{"date-parts":[[2022,7,3]],"date-time":"2022-07-03T01:35:33Z","timestamp":1656812133000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.spiedigitallibrary.org\/conference-proceedings-of-spie\/12036\/2613172\/Phenotype-guided-interpretable-graph-convolutional-network-analysis-of-fMRI-data\/10.1117\/12.2613172.full"}},"subtitle":[],"editor":[{"given":"Barjor S.","family":"Gimi","sequence":"additional","affiliation":[]},{"given":"Andrzej","family":"Krol","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,4,4]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1117\/12.2613172","relation":{},"subject":[],"published":{"date-parts":[[2022,4,4]]}}}