{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T03:21:17Z","timestamp":1790652077400,"version":"4.1.0"},"reference-count":80,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T00:00:00Z","timestamp":1690156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01GM124061"],"award-info":[{"award-number":["R01GM124061"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01DA048993"],"award-info":[{"award-number":["R01DA048993"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01MH105561"],"award-info":[{"award-number":["R01MH105561"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["CIF-2102227"],"award-info":[{"award-number":["CIF-2102227"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01AG061303"],"award-info":[{"award-number":["R01AG061303"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01AG062542"],"award-info":[{"award-number":["R01AG062542"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["R01MH123458"],"award-info":[{"award-number":["R01MH123458"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["U01DA041106"],"award-info":[{"award-number":["U01DA041106"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Delineating associations between images and covariates is a central aim of imaging studies. To tackle this problem, we propose a novel non-parametric approach in the framework of spatially varying coefficient models, where the spatially varying functions are estimated through deep neural networks. Our method incorporates spatial smoothness, handles subject heterogeneity, and provides straightforward interpretations. It is also highly flexible and accurate, making it ideal for capturing complex association patterns. We establish estimation and selection consistency and derive asymptotic error bounds. We demonstrate the method\u2019s advantages through intensive simulations and analyses of two functional magnetic resonance imaging data sets.<\/jats:p>","DOI":"10.1093\/jrsssb\/qkad073","type":"journal-article","created":{"date-parts":[[2023,7,24]],"date-time":"2023-07-24T19:31:39Z","timestamp":1690227099000},"page":"1589-1614","source":"Crossref","is-referenced-by-count":10,"title":["Image response regression via deep neural networks"],"prefix":"10.1093","volume":"85","author":[{"given":"Daiwei","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania , Philadelphia, PA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2962-1989","authenticated-orcid":false,"given":"Lexin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Biostatistics and Epidemiology, University of California , Berkeley, CA , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chandra","family":"Sripada","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, University of Michigan , Ann Arbor, MI , USA"},{"name":"Department of Philosophy, University of Michigan , Ann Arbor, MI , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5643-2668","authenticated-orcid":false,"given":"Jian","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Michigan , Ann Arbor, MI , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,7,24]]},"reference":[{"key":"2024072400010945600_qkad073-B1"},{"key":"2024072400010945600_qkad073-B2","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.neuroimage.2013.05.033","article-title":"Function in the human connectome: Task-fMRI and individual differences in behavior","volume":"80","author":"Barch","year":"2013","journal-title":"Neuroimage"},{"key":"2024072400010945600_qkad073-B3","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/BF00993164","article-title":"Approximation and estimation bounds for artificial neural networks","volume":"14","author":"Barron","year":"1994","journal-title":"Machine Learning"},{"issue":"515","key":"2024072400010945600_qkad073-B4","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1080\/01621459.2015.1092974","article-title":"Conditional sure independence screening","volume":"111","author":"Barut","year":"2016","journal-title":"Journal of the American Statistical Association"},{"key":"2024072400010945600_qkad073-B5","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.intell.2015.04.009","article-title":"Where smart brains are different: A quantitative meta-analysis of functional and structural brain imaging studies on intelligence","volume":"51","author":"Basten","year":"2015","journal-title":"Intelligence"},{"issue":"4","key":"2024072400010945600_qkad073-B6","doi-asserted-by":"crossref","first-page":"2261","DOI":"10.1214\/18-AOS1747","article-title":"On deep learning as a remedy for the curse of dimensionality in nonparametric regression","volume":"47","author":"Bauer","year":"2019","journal-title":"The Annals of Statistics"},{"key":"2024072400010945600_qkad073-B7","author":"Biswas","year":"2021"},{"key":"2024072400010945600_qkad073-B8","author":"Bottou","year":"2010"},{"issue":"2","key":"2024072400010945600_qkad073-B9","doi-asserted-by":"crossref","first-page":"563","DOI":"10.3758\/BRM.42.2.563","article-title":"Validating running memory span: Measurement of working memory capacity and links with fluid intelligence","volume":"42","author":"Broadway","year":"2010","journal-title":"Behavior Research Methods"},{"issue":"9-10","key":"2024072400010945600_qkad073-B10","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1007\/s10994-017-5639-3","article-title":"Varying-coefficient models for geospatial transfer learning","volume":"106","author":"Bussas","year":"2017","journal-title":"Machine Learning"},{"key":"2024072400010945600_qkad073-B11","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.dcn.2018.03.001","article-title":"The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites","volume":"32","author":"Casey","year":"2018","journal-title":"Developmental Cognitive Neuroscience"},{"issue":"7","key":"2024072400010945600_qkad073-B12","doi-asserted-by":"crossref","first-page":"2113","DOI":"10.1148\/rg.2017170077","article-title":"Deep learning: A primer for radiologists","volume":"37","author":"Chartrand","year":"2017","journal-title":"Radiographics"},{"issue":"5","key":"2024072400010945600_qkad073-B13","first-page":"1","article-title":"Non-convex projected gradient descent for generalized low-rank tensor regression","volume":"20","author":"Chen","year":"2019","journal-title":"The Journal of Machine Learning Research"},{"issue":"4","key":"2024072400010945600_qkad073-B14","doi-asserted-by":"crossref","first-page":"1203","DOI":"10.1093\/imaiai\/iaac001","article-title":"Nonparametric regression on low-dimensional manifolds using deep ReLU networks: Function approximation and statistical recovery","volume":"11","author":"Chen","year":"2022","journal-title":"Information and Inference: A Journal of the IMA"},{"issue":"2","key":"2024072400010945600_qkad073-B15","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1080\/10618600.2020.1814305","article-title":"Nonlinear variable selection via deep neural networks","volume":"30","author":"Chen","year":"2021","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"2024072400010945600_qkad073-B16","author":"Chen","year":"2016"},{"issue":"1","key":"2024072400010945600_qkad073-B17","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neuroimage.2008.05.021","article-title":"False discovery rate revisited: FDR and topological inference using gaussian random fields","volume":"44","author":"Chumbley","year":"2009","journal-title":"Neuroimage"},{"key":"2024072400010945600_qkad073-B18","doi-asserted-by":"crossref","first-page":"404","DOI":"10.1016\/j.neunet.2021.06.004","article-title":"A deep network construction that adapts to intrinsic dimensionality beyond the domain","volume":"141","author":"Cloninger","year":"2021","journal-title":"Neural Networks"},{"key":"2024072400010945600_qkad073-B19","author":"Deng","year":"2009"},{"issue":"3","key":"2024072400010945600_qkad073-B20","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1006\/cogp.1996.0008","article-title":"Intelligence and the frontal lobe: The organization of goal-directed behavior","volume":"30","author":"Duncan","year":"1996","journal-title":"Cognitive Psychology"},{"issue":"11","key":"2024072400010945600_qkad073-B21","doi-asserted-by":"crossref","first-page":"3679","DOI":"10.1109\/TMI.2020.3002417","article-title":"Optimization for medical image segmentation: Theory and practice when evaluating with dice score or Jaccard index","volume":"39","author":"Eelbode","year":"2020","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"2024072400010945600_qkad073-B22","author":"Eldan","year":"2016"},{"issue":"55","key":"2024072400010945600_qkad073-B23","first-page":"1","article-title":"Neural architecture search: A survey","volume":"20","author":"Elsken","year":"2019","journal-title":"The Journal of Machine Learning Research"},{"key":"2024072400010945600_qkad073-B24","author":"Fan","year":"2019"},{"issue":"1","key":"2024072400010945600_qkad073-B25","doi-asserted-by":"crossref","first-page":"181","DOI":"10.3982\/ECTA16901","article-title":"Deep neural networks for estimation and inference","volume":"89","author":"Farrell","year":"2021","journal-title":"Econometrica"},{"key":"2024072400010945600_qkad073-B26","author":"Feng","year":"2017"},{"key":"2024072400010945600_qkad073-B27","volume-title":"Deep learning","author":"Goodfellow","year":"2016"},{"key":"2024072400010945600_qkad073-B28","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.jml.2016.06.004","article-title":"The structure of working memory in young children and its relation to intelligence","volume":"92","author":"Gray","year":"2017","journal-title":"Journal of Memory and Language"},{"key":"2024072400010945600_qkad073-B29","doi-asserted-by":"crossref","first-page":"116091","DOI":"10.1016\/j.neuroimage.2019.116091","article-title":"Image processing and analysis methods for the Adolescent Brain Cognitive Development study","volume":"202","author":"Hagler","year":"2019","journal-title":"NeuroImage"},{"key":"2024072400010945600_qkad073-B30","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.neunet.2019.12.014","article-title":"On the minimax optimality and superiority of deep neural network learning over sparse parameter spaces","volume":"123","author":"Hayakawa","year":"2020","journal-title":"Neural Networks"},{"key":"2024072400010945600_qkad073-B31","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.intell.2016.11.001","article-title":"Efficient hubs in the intelligent brain: Nodal efficiency of hub regions in the salience network is associated with general intelligence","volume":"60","author":"Hilger","year":"2017","journal-title":"Intelligence"},{"issue":"2","key":"2024072400010945600_qkad073-B32","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1002\/hbm.24807","article-title":"Temporal stability of functional brain modules associated with human intelligence","volume":"41","author":"Hilger","year":"2020","journal-title":"Human Brain Mapping"},{"key":"2024072400010945600_qkad073-B33","author":"Huang","year":"2018"},{"key":"2024072400010945600_qkad073-B34","author":"Imaizumi","year":"2019"},{"key":"2024072400010945600_qkad073-B35","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.neuroimage.2015.02.002","article-title":"Gray matter correlates of creative potential: A latent variable voxel-based morphometry study","volume":"111","author":"Jauk","year":"2015","journal-title":"NeuroImage"},{"issue":"2","key":"2024072400010945600_qkad073-B36","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1017\/S0140525X07001185","article-title":"The Parieto\u2013Frontal integration theory (P-FIT) of intelligence: Converging neuroimaging evidence","volume":"30","author":"Jung","year":"2007","journal-title":"Behavioral and Brain Sciences"},{"key":"2024072400010945600_qkad073-B37","doi-asserted-by":"crossref","first-page":"119046","DOI":"10.1016\/j.neuroimage.2022.119046","article-title":"Reliability and stability challenges in ABCD task fMRI data","volume":"252","author":"Kennedy","year":"2022","journal-title":"NeuroImage"},{"key":"2024072400010945600_qkad073-B38","author":"Kirillov","year":"2023"},{"issue":"3","key":"2024072400010945600_qkad073-B39","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.1109\/TIT.2016.2634401","article-title":"Nonparametric regression based on hierarchical interaction models","volume":"63","author":"Kohler","year":"2017","journal-title":"IEEE Transactions on Information Theory"},{"issue":"4","key":"2024072400010945600_qkad073-B40","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1214\/20-AOS2034","article-title":"On the rate of convergence of fully connected deep neural network regression estimates","volume":"49","author":"Kohler","year":"2021","journal-title":"The Annals of Statistics"},{"key":"2024072400010945600_qkad073-B41","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.jspi.2022.05.008","article-title":"Estimation of a regression function on a manifold by fully connected deep neural networks","volume":"222","author":"Kohler","year":"2023","journal-title":"Journal of Statistical Planning and Inference"},{"issue":"7553","key":"2024072400010945600_qkad073-B42","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"issue":"519","key":"2024072400010945600_qkad073-B43","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1080\/01621459.2016.1195742","article-title":"A functional varying-coefficient single-index model for functional response data","volume":"112","author":"Li","year":"2017","journal-title":"Journal of the American Statistical Association"},{"issue":"519","key":"2024072400010945600_qkad073-B44","doi-asserted-by":"crossref","first-page":"1131","DOI":"10.1080\/01621459.2016.1193022","article-title":"Parsimonious tensor response regression","volume":"112","author":"Li","year":"2017","journal-title":"Journal of the American Statistical Association"},{"issue":"536","key":"2024072400010945600_qkad073-B45","doi-asserted-by":"crossref","first-page":"1994","DOI":"10.1080\/01621459.2020.1753523","article-title":"Sparse learning and structure identification for ultrahigh-dimensional image-on-scalar regression","volume":"116","author":"Li","year":"2021","journal-title":"Journal of the American Statistical Association"},{"issue":"1","key":"2024072400010945600_qkad073-B46","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1002\/cjs.11595","article-title":"Statistical disease mapping for heterogeneous neuroimaging studies","volume":"49","author":"Liu","year":"2021","journal-title":"Canadian Journal of Statistics"},{"key":"2024072400010945600_qkad073-B47","doi-asserted-by":"crossref","first-page":"102165","DOI":"10.1016\/j.media.2021.102165","article-title":"Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification","volume":"73","author":"Marini","year":"2021","journal-title":"Medical Image Analysis"},{"issue":"1","key":"2024072400010945600_qkad073-B48","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/0893-6080(94)90063-9","article-title":"Convergence rates for single hidden layer feedforward networks","volume":"7","author":"McCaffrey","year":"1994","journal-title":"Neural Networks"},{"issue":"10","key":"2024072400010945600_qkad073-B49","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.tics.2011.08.003","article-title":"Large-scale brain networks and psychopathology: A unifying triple network model","volume":"15","author":"Menon","year":"2011","journal-title":"Trends in Cognitive Sciences"},{"issue":"11","key":"2024072400010945600_qkad073-B50","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1038\/nn.4393","article-title":"Multimodal population brain imaging in the UK Biobank prospective epidemiological study","volume":"19","author":"Miller","year":"2016","journal-title":"Nature Neuroscience"},{"issue":"174","key":"2024072400010945600_qkad073-B51","first-page":"1","article-title":"Adaptive approximation and generalization of deep neural network with intrinsic dimensionality","volume":"21","author":"Nakada","year":"2020","journal-title":"The Journal of Machine Learning Research"},{"issue":"247","key":"2024072400010945600_qkad073-B52","first-page":"1","article-title":"Quantile regression with ReLU networks: Estimators and minimax rates","volume":"23","author":"Padilla","year":"2022","journal-title":"Journal of Machine Learning Research"},{"issue":"4","key":"2024072400010945600_qkad073-B53","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/j.neuron.2011.09.006","article-title":"Functional network organization of the human brain","volume":"72","author":"Power","year":"2011","journal-title":"Neuron"},{"issue":"478","key":"2024072400010945600_qkad073-B54","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1198\/016214507000000301","article-title":"Jump surface estimation, edge detection, and image restoration","volume":"102","author":"Qiu","year":"2007","journal-title":"Journal of the American Statistical Association"},{"key":"2024072400010945600_qkad073-B55","author":"Rabusseau","year":"2016"},{"issue":"3","key":"2024072400010945600_qkad073-B56","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1214\/18-AOS1725","article-title":"Convex regularization for high-dimensional multiresponse tensor regression","volume":"47","author":"Raskutti","year":"2019","journal-title":"The Annals of Statistics"},{"issue":"1","key":"2024072400010945600_qkad073-B57","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1093\/brain\/awp269","article-title":"Executive function and fluid intelligence after frontal lobe lesions","volume":"133","author":"Roca","year":"2010","journal-title":"Brain"},{"issue":"1","key":"2024072400010945600_qkad073-B58","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/S0893-6080(97)00097-X","article-title":"Universal approximation using feedforward neural networks: A survey of some existing methods, and some new results","volume":"11","author":"Scarselli","year":"1998","journal-title":"Neural Networks"},{"key":"2024072400010945600_qkad073-B59","author":"Schmidt-Hieber","year":"2019"},{"issue":"4","key":"2024072400010945600_qkad073-B60","doi-asserted-by":"publisher","first-page":"1875","DOI":"10.1214\/19-AOS1875","article-title":"Nonparametric regression using deep neural networks with ReLU activation function","volume":"48","author":"Schmidt-Hieber","year":"2020","journal-title":"Annals of Statistics"},{"issue":"1","key":"2024072400010945600_qkad073-B61","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annual Review of Biomedical Engineering"},{"key":"2024072400010945600_qkad073-B62","author":"Shi","year":"2015"},{"issue":"12","key":"2024072400010945600_qkad073-B63","doi-asserted-by":"publisher","first-page":"3413","DOI":"10.1038\/s41380-019-0481-6","article-title":"Prediction of neurocognition in youth from resting state fMRI","volume":"25","author":"Sripada","year":"2019","journal-title":"Molecular Psychiatry"},{"issue":"4","key":"2024072400010945600_qkad073-B64","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1214\/aos\/1176345969","article-title":"Optimal global rates of convergence for nonparametric regression","volume":"10","author":"Stone","year":"1982","journal-title":"The Annals of Statistics"},{"issue":"135","key":"2024072400010945600_qkad073-B65","first-page":"1","article-title":"Store: Sparse tensor response regression and neuroimaging analysis","volume":"18","author":"Sun","year":"2017","journal-title":"The Journal of Machine Learning Research"},{"key":"2024072400010945600_qkad073-B66","author":"Suzuki","year":"2019"},{"issue":"12","key":"2024072400010945600_qkad073-B67","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1109\/JPROC.2017.2761740","article-title":"Efficient processing of deep neural networks: A tutorial and survey","volume":"105","author":"Sze","year":"2017","journal-title":"Proceedings of the IEEE"},{"key":"2024072400010945600_qkad073-B68","author":"Telgarsky","year":"2016"},{"issue":"1","key":"2024072400010945600_qkad073-B69","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1109\/TNN.2005.860885","article-title":"Tuning the structure and parameters of a neural network by using hybrid Taguchi-genetic algorithm","volume":"17","author":"Tsai","year":"2006","journal-title":"IEEE Transactions on Neural Networks"},{"issue":"1","key":"2024072400010945600_qkad073-B70","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1006\/nimg.2001.0978","article-title":"Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain","volume":"15","author":"Tzourio-Mazoyer","year":"2002","journal-title":"Neuroimage"},{"issue":"6","key":"2024072400010945600_qkad073-B71","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1007\/s10548-019-00744-6","article-title":"Towards a universal taxonomy of macro-scale functional human brain networks","volume":"32","author":"Uddin","year":"2019","journal-title":"Brain Topography"},{"issue":"33","key":"2024072400010945600_qkad073-B72","doi-asserted-by":"crossref","first-page":"14899","DOI":"10.1073\/pnas.1007928107","article-title":"Fluid intelligence loss linked to restricted regions of damage within frontal and parietal cortex","volume":"107","author":"Woolgar","year":"2010","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"2024072400010945600_qkad073-B73","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.neucom.2021.12.093","article-title":"GAN-based anomaly detection: A review","volume":"493","author":"Xia","year":"2022","journal-title":"Neurocomputing"},{"issue":"3","key":"2024072400010945600_qkad073-B74","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1152\/jn.00338.2011","article-title":"The organization of the human cerebral cortex estimated by intrinsic functional connectivity","volume":"106","author":"Yeo","year":"2011","journal-title":"Journal of Neurophysiology"},{"issue":"3","key":"2024072400010945600_qkad073-B75","first-page":"1463","article-title":"Multivariate spline estimation and inference for image-on-scalar regression","volume":"31","author":"Yu","year":"2020","journal-title":"Statistica Sinica"},{"issue":"1","key":"2024072400010945600_qkad073-B76","doi-asserted-by":"crossref","first-page":"3","DOI":"10.4310\/SII.2010.v3.n1.a1","article-title":"Adaptive spatial smoothing of fMRI images","volume":"3","author":"Yue","year":"2010","journal-title":"Statistics and its Interface"},{"issue":"12","key":"2024072400010945600_qkad073-B77","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.tics.2013.10.001","article-title":"Fronto-parietal network: Flexible hub of cognitive control","volume":"17","author":"Zanto","year":"2013","journal-title":"Trends in Cognitive Sciences"},{"issue":"90","key":"2024072400010945600_qkad073-B78","first-page":"2541","article-title":"On model selection consistency of lasso","volume":"7","author":"Zhao","year":"2006","journal-title":"The Journal of Machine Learning Research"},{"issue":"507","key":"2024072400010945600_qkad073-B79","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1080\/01621459.2014.881742","article-title":"Spatially varying coefficient model for neuroimaging data with jump discontinuities","volume":"109","author":"Zhu","year":"2014","journal-title":"Journal of the American Statistical Association"},{"issue":"1","key":"2024072400010945600_qkad073-B80","doi-asserted-by":"crossref","DOI":"10.1146\/annurev-biodatasci-020722-100353","article-title":"Statistical learning methods for neuroimaging data analysis with applications","volume":"6","author":"Zhu","year":"2023","journal-title":"Annual Review of Biomedical Data Science"}],"container-title":["Journal of the Royal Statistical Society Series B: Statistical Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jrsssb\/article-pdf\/85\/5\/1589\/57520506\/qkad073.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jrsssb\/article-pdf\/85\/5\/1589\/57520506\/qkad073.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T00:07:51Z","timestamp":1721866071000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jrsssb\/article\/85\/5\/1589\/7230181"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,24]]},"references-count":80,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,7,24]]},"published-print":{"date-parts":[[2024,2,2]]}},"URL":"https:\/\/doi.org\/10.1093\/jrsssb\/qkad073","relation":{},"ISSN":["1369-7412","1467-9868"],"issn-type":[{"value":"1369-7412","type":"print"},{"value":"1467-9868","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,11]]},"published":{"date-parts":[[2023,7,24]]}}}