{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T01:11:43Z","timestamp":1728177103328},"reference-count":62,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T00:00:00Z","timestamp":1648771200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T00:00:00Z","timestamp":1643328000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1016\/j.media.2022.102370","type":"journal-article","created":{"date-parts":[[2022,1,30]],"date-time":"2022-01-30T01:00:59Z","timestamp":1643504459000},"page":"102370","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":17,"special_numbering":"C","title":["Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity"],"prefix":"10.1016","volume":"77","author":[{"given":"Shih-Gu","family":"Huang","sequence":"first","affiliation":[]},{"given":"Jing","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Liyuan","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Anqi","family":"Qiu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.media.2022.102370_bib0001","doi-asserted-by":"crossref","first-page":"e17","DOI":"10.1371\/journal.pcbi.0030017","article-title":"Efficiency and cost of economical brain functional networks","volume":"3","author":"Achard","year":"2007","journal-title":"PLoS Comput. Biol."},{"issue":"4","key":"10.1016\/j.media.2022.102370_bib0002","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1111\/mono.12038","article-title":"VIII. NIH toolbox cognition battery (CB): composite scores of crystallized, fluid, and overall cognition","volume":"78","author":"Akshoomoff","year":"2013","journal-title":"Monogr. Soc. Res. Child Dev."},{"key":"10.1016\/j.media.2022.102370_bib0003","series-title":"2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)","first-page":"1120","article-title":"A deep spatiotemporal graph learning architecture for brain connectivity analysis","author":"Azevedo","year":"2020"},{"key":"10.1016\/j.media.2022.102370_bib0004","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.dcn.2017.10.010","article-title":"Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: rationale and description","volume":"32","author":"Barch","year":"2018","journal-title":"Dev. Cogn. Neurosci."},{"key":"10.1016\/j.media.2022.102370_bib0005","unstructured":"Bruna, J., Zaremba, W., Szlam, A., LeCun, Y., Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203"},{"key":"10.1016\/j.media.2022.102370_bib0006","doi-asserted-by":"crossref","first-page":"280","DOI":"10.3389\/fnagi.2014.00280","article-title":"Resting-state functional connectivity in anterior cingulate cortex in normal aging","volume":"6","author":"Cao","year":"2014","journal-title":"Front. Aging Neurosci."},{"key":"10.1016\/j.media.2022.102370_bib0007","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":"Dev. Cogn. Neurosci."},{"key":"10.1016\/j.media.2022.102370_bib0008","first-page":"3844","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"Defferrard","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2022.102370_bib0009","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/s11065-014-9249-6","article-title":"Functional brain connectivity using fMRI in aging and Alzheimer\u2019s disease","volume":"24","author":"Dennis","year":"2014","journal-title":"Neuropsychol. Rev."},{"key":"10.1016\/j.media.2022.102370_bib0010","series-title":"Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. (MICCAI)","first-page":"709","article-title":"Integrating neural networks and dictionary learning for multidimensional clinical characterizations from functional connectomics data","author":"D\u2019Souza","year":"2019"},{"issue":"1","key":"10.1016\/j.media.2022.102370_bib0011","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.neuroimage.2011.01.067","article-title":"Whole brain diffeomorphic metric mapping via integration of sulcal and gyral curves, cortical surfaces, and images","volume":"56","author":"Du","year":"2011","journal-title":"NeuroImage"},{"key":"10.1016\/j.media.2022.102370_bib0012","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.tics.2010.01.004","article-title":"The multiple-demand (MD) system of the primate brain: mental programs for intelligent behaviour","volume":"14","author":"Duncan","year":"2010","journal-title":"Trends Cogn. Sci."},{"key":"10.1016\/j.media.2022.102370_bib0013","series-title":"International Workshop on Machine Learning in Medical Imaging","first-page":"362","article-title":"Identifying autism from resting-state fMRI using long short-term memory networks","author":"Dvornek","year":"2017"},{"issue":"11","key":"10.1016\/j.media.2022.102370_bib0014","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1038\/nn.4135","article-title":"Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity","volume":"18","author":"Finn","year":"2015","journal-title":"Nat. Neurosci."},{"issue":"3","key":"10.1016\/j.media.2022.102370_bib0015","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1038\/nrn3901","article-title":"The connectomics of brain disorders","volume":"16","author":"Fornito","year":"2015","journal-title":"Nat. Rev. Neurosci."},{"key":"10.1016\/j.media.2022.102370_bib0016","series-title":"Medical Image Computing and Computer-Assisted Intervention: MICCAI... International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"528","article-title":"Spatio-temporal graph convolution for resting-state fMRI analysis","volume":"12267","author":"Gadgil","year":"2020"},{"issue":"2","key":"10.1016\/j.media.2022.102370_bib0017","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.nec.2010.11.001","article-title":"Overview of functional magnetic resonance imaging","volume":"22","author":"Glover","year":"2011","journal-title":"Neurosurg. Clin."},{"key":"10.1016\/j.media.2022.102370_bib0018","doi-asserted-by":"crossref","DOI":"10.3389\/fnagi.2016.00306","article-title":"The functional integration in the sensory-motor system predicts aging in healthy older adults","volume":"8","author":"He","year":"2017","journal-title":"Front. Aging Neurosci."},{"key":"10.1016\/j.media.2022.102370_bib0019","doi-asserted-by":"crossref","first-page":"116276","DOI":"10.1016\/j.neuroimage.2019.116276","article-title":"Deep neural networks and kernel regression achieve comparable accuracies for functional connectivity prediction of behavior and demographics","volume":"206","author":"He","year":"2020","journal-title":"NeuroImage"},{"key":"10.1016\/j.media.2022.102370_bib0020","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7132","article-title":"Squeeze-and-excitation networks","author":"Hu","year":"2018"},{"issue":"7","key":"10.1016\/j.media.2022.102370_bib0021","doi-asserted-by":"crossref","first-page":"2541","DOI":"10.1109\/TMI.2020.2973650","article-title":"Attention-diffusion-bilinear neural network for brain network analysis","volume":"39","author":"Huang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"20","key":"10.1016\/j.media.2022.102370_bib0022","doi-asserted-by":"crossref","first-page":"13693","DOI":"10.1007\/s00521-021-06006-6","article-title":"Revisiting convolutional neural network on graphs with polynomial approximations of laplace-beltrami spectral filtering","volume":"33","author":"Huang","year":"2021","journal-title":"Neural Comput. Appl."},{"issue":"6","key":"10.1016\/j.media.2022.102370_bib0023","doi-asserted-by":"crossref","first-page":"2201","DOI":"10.1109\/TMI.2020.2967451","article-title":"Fast polynomial approximation of heat kernel convolution on manifolds and its application to brain sulcal and gyral graph pattern analysis","volume":"39","author":"Huang","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2022.102370_bib0024","series-title":"Functional Magnetic Resonance Imaging","volume":"vol.\u00a01","author":"Huettel","year":"2004"},{"key":"10.1016\/j.media.2022.102370_bib0025","doi-asserted-by":"crossref","first-page":"104096","DOI":"10.1016\/j.compbiomed.2020.104096","article-title":"Hi-GCN: a hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction","volume":"127","author":"Jiang","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.media.2022.102370_bib0026","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1017\/S0140525X07001185","article-title":"The parieto-frontal integration theory (p-FIT) of intelligence: converging neuroimaging evidence","volume":"30","author":"Jung","year":"2007","journal-title":"Behav. Brain Sci."},{"key":"10.1016\/j.media.2022.102370_bib0027","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1016\/j.neuroimage.2016.09.046","article-title":"BrainNetCNN: convolutional neural networks for brain networks; towards predicting neurodevelopment","volume":"146","author":"Kawahara","year":"2017","journal-title":"NeuroImage"},{"key":"10.1016\/j.media.2022.102370_bib0028","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.mri.2019.05.031","article-title":"Machine learning in resting-state fMRI analysis","volume":"64","author":"Khosla","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.media.2022.102370_bib0029","unstructured":"Kipf, T. N., Welling, M., Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"key":"10.1016\/j.media.2022.102370_bib0030","doi-asserted-by":"crossref","DOI":"10.1002\/hbm.22143","article-title":"Functional connectivity between parietal and frontal brain regions and intelligence in young children: the generation r study","volume":"34","author":"Langeslag","year":"2013","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.media.2022.102370_bib0031","doi-asserted-by":"crossref","first-page":"118048","DOI":"10.1016\/j.neuroimage.2021.118048","article-title":"A unified framework for personalized regions selection and functional relation modeling for early MCI identification","volume":"236","author":"Lee","year":"2021","journal-title":"NeuroImage"},{"key":"10.1016\/j.media.2022.102370_bib0032","doi-asserted-by":"crossref","first-page":"1150","DOI":"10.3174\/ajnr.A3850","article-title":"Association between resting-state coactivation in the parieto-frontal network and intelligence during late childhood and adolescence. AJNR","volume":"35","author":"Li","year":"2014","journal-title":"Am. J. Neuroradiol."},{"key":"10.1016\/j.media.2022.102370_bib0033","series-title":"Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. (MICCAI)","first-page":"320","article-title":"Brain decoding from functional MRI using long short-term memory recurrent neural networks","author":"Li","year":"2018"},{"key":"10.1016\/j.media.2022.102370_bib0034","series-title":"IEEE 15th International Symposium on Biomedical Imaging","first-page":"101","article-title":"Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks","author":"Li","year":"2018"},{"key":"10.1016\/j.media.2022.102370_bib0035","doi-asserted-by":"crossref","unstructured":"Li, X., Duncan, J., 2020. BrainGNN: interpretable brain graph neural network for fMRI analysis. bioRxiv.","DOI":"10.1101\/2020.05.16.100057"},{"key":"10.1016\/j.media.2022.102370_bib0036","series-title":"Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. (MICCAI)","first-page":"485","article-title":"Graph neural network for interpreting task-fMRI biomarkers","author":"Li","year":"2019"},{"issue":"3","key":"10.1016\/j.media.2022.102370_bib0037","doi-asserted-by":"crossref","first-page":"75","DOI":"10.3390\/a14030075","article-title":"A deep learning model for data-driven discovery of functional connectivity","volume":"14","author":"Mahmood","year":"2021","journal-title":"Algorithms"},{"key":"10.1016\/j.media.2022.102370_bib0038","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ins.2019.05.043","article-title":"Spatio-temporal deep learning method for ADHD fMRI classification","volume":"499","author":"Mao","year":"2019","journal-title":"Inf. Sci."},{"key":"10.1016\/j.media.2022.102370_bib0039","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.media.2018.06.001","article-title":"Disease prediction using graph convolutional networks: application to autism spectrum disorder and Alzheimer\u2019s disease","volume":"48","author":"Parisot","year":"2018","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2022.102370_bib0040","series-title":"Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent. (MICCAI)","first-page":"177","article-title":"Spectral graph convolutions for population-based disease prediction","author":"Parisot","year":"2017"},{"issue":"5","key":"10.1016\/j.media.2022.102370_bib0041","doi-asserted-by":"crossref","first-page":"056001","DOI":"10.1117\/1.JMI.7.5.056001","article-title":"Spatiotemporal feature extraction and classification of Alzheimer\u2019s disease using deep learning 3D-CNN for fMRI data","volume":"7","author":"Parmar","year":"2020","journal-title":"J. Med. Imaging"},{"key":"10.1016\/j.media.2022.102370_bib0042","doi-asserted-by":"crossref","first-page":"116604","DOI":"10.1016\/j.neuroimage.2020.116604","article-title":"Optimising network modelling methods for fMRI","volume":"211","author":"Pervaiz","year":"2020","journal-title":"NeuroImage"},{"key":"10.1016\/j.media.2022.102370_bib0043","unstructured":"Pornpattananangkul, N., Wang, Y., Stringaris, A., 2021a. Multimodal-neural predictive models of children\u2019s general intelligence that are stable across two years of development. bioRxiv. 10.1101\/2021.02.21.432130"},{"key":"10.1016\/j.media.2022.102370_bib0044","unstructured":"Pornpattananangkul, N., Wang, Y., Stringaris, A., 2021b. Multimodal-neural predictive models of children\u2019s general intelligence that are stable across two years of development. bioRxiv."},{"issue":"3","key":"10.1016\/j.media.2022.102370_bib0045","doi-asserted-by":"crossref","first-page":"2142","DOI":"10.1016\/j.neuroimage.2011.10.018","article-title":"Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion","volume":"59","author":"Power","year":"2012","journal-title":"NeuroImage"},{"key":"10.1016\/j.media.2022.102370_bib0046","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.neucom.2020.04.118","article-title":"Log-sum enhanced sparse deep neural network","volume":"407","author":"Qiao","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.media.2022.102370_bib0047","series-title":"Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging","first-page":"113170A","article-title":"A graph deep learning model for the classification of groups with different IQ using resting state fMRI","volume":"vol. 11317","author":"Qu","year":"2020"},{"issue":"3","key":"10.1016\/j.media.2022.102370_bib0048","doi-asserted-by":"crossref","first-page":"506","DOI":"10.1038\/nprot.2016.178","article-title":"Using connectome-based predictive modeling to predict individual behavior from brain connectivity","volume":"12","author":"Shen","year":"2017","journal-title":"Nat. Protoc."},{"key":"10.1016\/j.media.2022.102370_bib0049","doi-asserted-by":"crossref","first-page":"1168","DOI":"10.1016\/j.neuroimage.2008.02.036","article-title":"Brain spontaneous functional connectivity and intelligence","volume":"41","author":"Song","year":"2008","journal-title":"NeuroImage"},{"issue":"12","key":"10.1016\/j.media.2022.102370_bib0050","doi-asserted-by":"crossref","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":"2020","journal-title":"Mol. Psychiatry"},{"issue":"11","key":"10.1016\/j.media.2022.102370_bib0051","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1016\/j.biopsych.2020.02.016","article-title":"Neuroimaging-based individualized prediction of cognition and behavior for mental disorders and health: methods and promises","volume":"88","author":"Sui","year":"2020","journal-title":"Biol. Psychiatry"},{"issue":"9","key":"10.1016\/j.media.2022.102370_bib0052","doi-asserted-by":"crossref","first-page":"4061","DOI":"10.1109\/TIP.2016.2574982","article-title":"Large deformation multiresolution diffeomorphic metric mapping for multiresolution cortical surfaces: a coarse-to-fine approach","volume":"25","author":"Tan","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.media.2022.102370_bib0053","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., Polosukhin, I., Attention is all you need. arXiv preprint:1706.03762"},{"key":"10.1016\/j.media.2022.102370_bib0054","doi-asserted-by":"crossref","first-page":"434","DOI":"10.3389\/fnins.2019.00434","article-title":"Application of convolutional recurrent neural network for individual recognition based on resting state fMRI data","volume":"13","author":"Wang","year":"2019","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.media.2022.102370_bib0055","doi-asserted-by":"crossref","first-page":"1435","DOI":"10.3389\/fnins.2019.01435","article-title":"Detecting the information of functional connectivity networks in normal aging using deep learning from a big data perspective","volume":"13","author":"Wen","year":"2020","journal-title":"Front. Neurosci."},{"issue":"7","key":"10.1016\/j.media.2022.102370_bib0056","doi-asserted-by":"crossref","first-page":"e01652","DOI":"10.1002\/brb3.1652","article-title":"Alterations of local functional connectivity in lifespan: a resting-state fMRI study","volume":"10","author":"Wen","year":"2020","journal-title":"Brain Behav."},{"key":"10.1016\/j.media.2022.102370_bib0057","series-title":"Proceedings of the European Conference on Computer Vision (ECCV)","first-page":"3","article-title":"CBAM: convolutional block attention module","author":"Woo","year":"2018"},{"issue":"8","key":"10.1016\/j.media.2022.102370_bib0058","doi-asserted-by":"crossref","first-page":"2140","DOI":"10.1109\/TBME.2018.2884129","article-title":"Alternating diffusion map based fusion of multimodal brain connectivity networks for IQ prediction","volume":"66","author":"Xiao","year":"2019","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.media.2022.102370_bib0059","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.ebiom.2019.08.023","article-title":"Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data","volume":"47","author":"Yan","year":"2019","journal-title":"EBioMedicine"},{"key":"10.1016\/j.media.2022.102370_bib0060","series-title":"Proceedings of the IEEE International Conference on Computer Vision","first-page":"1215","article-title":"Multi-scale recognition with DAG-CNNs","author":"Yang","year":"2015"},{"key":"10.1016\/j.media.2022.102370_bib0061","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2282","article-title":"Syncspeccnn: synchronized spectral CNN for 3D shape segmentation","author":"Yi","year":"2017"},{"key":"10.1016\/j.media.2022.102370_bib0062","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-02304-z","article-title":"Brain structural networks associated with intelligence and visuomotor ability","volume":"7","author":"Yoon","year":"2017","journal-title":"Sci. Rep."}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841522000238?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841522000238?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T11:48:18Z","timestamp":1715860098000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841522000238"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4]]},"references-count":62,"alternative-id":["S1361841522000238"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2022.102370","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2022,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Spatio-temporal directed acyclic graph learning with attention mechanisms on brain functional time series and connectivity","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2022.102370","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"102370"}}