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Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity.
A. M. Dale, B. Fischl, and M. I. Sereno, “Cortical surface-based analysis: I. segmentation and surface reconstruction,” Neuroimage , vol. 9, no. 2, pp. 179–194, 1999
1999
Earlier work this paper cites.
C. J. Stam, “Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’network?” Neuroscience letters , vol. 355, no. 1-2, pp. 25–28, 2004
2004
Earlier work this paper cites.
R. S. Desikan, F. Ségonne, B. Fischl, B. T. Quinn, B. C. Dickerson, D. Blacker, R. L. Buckner, A. M. Dale, R. P. Maguire, B. T. Hyman et al. , “An automated labeling system for subdividing the human cerebral cortex on mri scans into gyral based regions of interest,” Neuroimage , vol. 31, no. 3, pp. 968–980, 2006
2006
Earlier work this paper cites.
D. S. Bassett, E. Bullmore, B. A. Verchinski, V. S. Mattay, D. R. Weinberger, and A. Meyer-Lindenberg, “Hierarchical organization of human cortical networks in health and schizophrenia,” Journal of Neuroscience , vol. 28, no. 37, pp. 9239–9248, 2008
2008
Earlier work this paper cites.
P. Hagmann, L. Cammoun, X. Gigandet, R. Meuli, C. J. Honey, V. J. Wedeen, and O. Sporns, “Mapping the structural core of human cerebral cortex,” PLoS biology , vol. 6, no. 7, p. e159, 2008
2008
Earlier work this paper cites.
N. K. Logothetis, “What we can do and what we cannot do with fmri,” Nature , vol. 453, no. 7197, pp. 869–878, 2008
2008
Earlier work this paper cites.
M. P. van den Heuvel, C. J. Stam, M. Boersma, and H. H. Pol, “Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain,” Neuroimage , vol. 43, no. 3, pp. 528–539, 2008
2008
Earlier work this paper cites.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Transactions on Neural Networks , vol. 20, no. 1, pp. 61–80, 2009
2009
Earlier work this paper cites.
A. Fornito, A. Zalesky, and E. T. Bullmore, “Network scaling effects in graph analytic studies of human resting-state fmri data,” Frontiers in systems neuroscience , vol. 4, p. 22, 2010
2010
Earlier work this paper cites.
S. Whitfield-Gabrieli and A. Nieto-Castanon, “Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks,” Brain connectivity , vol. 2, no. 3, pp. 125–141, 2012
2012
Earlier work this paper cites.
M. Jenkinson, C. F. Beckmann, T. E. Behrens, M. W. Woolrich, and S. M. Smith, “Fsl,” Neuroimage , vol. 62, no. 2, pp. 782–790, 2012
2012
Earlier work this paper cites.
B. Fischl, “Freesurfer,” Neuroimage , vol. 62, no. 2, pp. 774–781, 2012
2012
Earlier work this paper cites.
X. Shen, F. Tokoglu, X. Papademetris, and R. T. Constable, “Groupwise whole-brain parcellation from resting-state fmri data for network node identification,” Neuroimage , vol. 82, pp. 403–415, 2013
2013
Earlier work this paper cites.
W. Li, X. Wang, J. Bai, T. Ma, Z. Li, Y. Li, and P. Jiang, “Construction and immunogenicity of recombinant porcine circovirus-like particles displaying somatostatin,” Veterinary microbiology , vol. 163, no. 1-2, pp. 23–32, 2013
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
B. G. Booth and G. Hamarneh, “Diffusion mri for brain connectivity mapping and analysis,” MRI: Physics, Image Reconstruction, and Analysis , pp. 137–171, 2015
2015
Earlier work this paper cites.
S. Jbabdi, S. N. Sotiropoulos, S. N. Haber, D. C. Van Essen, and T. E. Behrens, “Measuring macroscopic brain connections in vivo,” Nature neuroscience , vol. 18, no. 11, pp. 1546–1555, 2015
2015
Earlier work this paper cites.
A. Fornito, A. Zalesky, and M. Breakspear, “The connectomics of brain disorders,” Nature Reviews Neuroscience , vol. 16, no. 3, pp. 159–172, 2015
2015
Earlier work this paper cites.
A. Q. Ye, O. A. Ajilore, G. Conte, J. GadElkarim, G. Thomas-Ramos, L. Zhan, S. Yang, A. Kumar, R. L. Magin, A. G Forbes et al. , “The intrinsic geometry of the human brain connectome,” Brain informatics , vol. 2, no. 4, pp. 197–210, 2015
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Earlier work this paper cites.
A. Fornito, A. Zalesky, and E. Bullmore, Fundamentals of brain network analysis . Academic Press, 2016
2016
Earlier work this paper cites.
M. F. Glasser, S. M. Smith, D. S. Marcus, J. L. Andersson, E. J. Auerbach, T. E. Behrens, T. S. Coalson, M. P. Harms, M. Jenkinson, S. Moeller et al. , “The human connectome project’s neuroimaging approach,” Nature neuroscience , vol. 19, no. 9, pp. 1175–1187, 2016
2016
Earlier work this paper cites.
D. C. Van Essen and M. F. Glasser, “The human connectome project: Progress and prospects,” in Cerebrum: the Dana forum on brain science , vol. 2016. Dana Foundation, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
D. S. Bassett and O. Sporns, “Network neuroscience,” Nature neuroscience , vol. 20, no. 3, pp. 353–364, 2017
2017
Earlier work this paper cites.
R. F. Betzel and D. S. Bassett, “Multi-scale brain networks,” Neuroimage , vol. 160, pp. 73–83, 2017
2017
Earlier work this paper cites.
I. Rekik, G. Li, W. Lin, and D. Shen, “Estimation of brain network atlases using diffusive-shrinking graphs: application to developing brains,” in International conference on information processing in medical imaging . Springer, 2017, pp. 385–397
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, and P. Vandergheynst, “Geometric deep learning: Going beyond euclidean data,” IEEE Signal Processing Magazine , vol. 34, no. 4, pp. 18–42, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, “Neural message passing for quantum chemistry,” in Proceedings of the 34th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, D. Precup and Y. W. Teh, Eds., vol. 70. PMLR, 06–11 Aug 2017, pp. 1263–1272. [Online]. Available: https://proceedings.mlr.press/v70/gilmer17a.html
2017
Earlier work this paper cites.
J. P. Lerch, A. J. Van Der Kouwe, A. Raznahan, T. Paus, H. Johansen-Berg, K. L. Miller, S. M. Smith, B. Fischl, and S. N. Sotiropoulos, “Studying neuroanatomy using mri,” Nature neuroscience , vol. 20, no. 3, pp. 314–326, 2017
2017
Earlier work this paper cites.
M. Simonovsky and N. Komodakis, “Dynamic edge-conditioned filters in convolutional neural networks on graphs,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3693–3702
2017
Earlier work this paper cites.
S. Parisot, S. I. Ktena, E. Ferrante, M. Lee, R. G. Moreno, B. Glocker, and D. Rueckert, “Spectral graph convolutions for population-based disease prediction,” in International conference on medical image computing and computer-assisted intervention . Springer, 2017, pp. 177–185
2017
Earlier work this paper cites.
F. Vecchio, F. Miraglia, and P. M. Rossini, “Connectome: Graph theory application in functional brain network architecture,” Clinical neurophysiology practice , vol. 2, pp. 206–213, 2017
2017
Earlier work this paper cites.
D. M. Lydon-Staley and D. S. Bassett, “Network neuroscience: a framework for developing biomarkers in psychiatry,” Biomarkers in Psychiatry , pp. 79–109, 2018
2018
Earlier work this paper cites.
I. Mahjoub, M. A. Mahjoub, and I. Rekik, “Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states,” Scientific reports , vol. 8, no. 1, p. 4103, 2018
2018
Earlier work this paper cites.
——, “Unsupervised manifold learning using high-order morphological brain networks derived from t1-w mri for autism diagnosis,” Frontiers in neuroinformatics , vol. 12, p. 70, 2018
2018
Cited alongside, same era.
M. Zhu and I. Rekik, “Multi-view brain network prediction from a source view using sample selection via cca-based multi-kernel connectomic manifold learning,” in International Workshop on PRedictive Intelligence In MEdicine . Springer, 2018, pp. 94–102
2018
Cited alongside, same era.
C. Seguin, M. P. Van Den Heuvel, and A. Zalesky, “Navigation of brain networks,” Proceedings of the National Academy of Sciences , vol. 115, no. 24, pp. 6297–6302, 2018
2018
Cited alongside, same era.
L. He, H. Li, S. K. Holland, W. Yuan, M. Altaye, and N. A. Parikh, “Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework,” NeuroImage: Clinical , vol. 18, pp. 290–297, 2018
2018
Cited alongside, same era.
A. Allard and M. Á. Serrano, “Navigable maps of structural brain networks across species,” PLoS computational biology , vol. 16, no. 2, p. e1007584, 2020
2020
Later among the works it cites.
M. Zheng, A. Allard, P. Hagmann, Y. Alemán-Gómez, and M. Á. Serrano, “Geometric renormalization unravels self-similarity of the multiscale human connectome,” Proceedings of the National Academy of Sciences , vol. 117, no. 33, pp. 20 244–20 253, 2020
2020
Later among the works it cites.
J. You, J. Leskovec, K. He, and S. Xie, “Graph structure of neural networks,” in International Conference on Machine Learning . PMLR, 2020, pp. 10 881–10 891
2020
Later among the works it cites.
J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun, “Graph neural networks: A review of methods and applications,” AI Open , vol. 1, pp. 57–81, 2020
2020
Later among the works it cites.
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R. Raeper, A. Lisowska, and I. Rekik, “Cooperative correlational and discriminative ensemble classifier learning for early dementia diagnosis using morphological brain multiplexes,” IEEE Access , vol. 6, pp. 43 830–43 839, 2018
2018
Cited alongside, same era.
A. Lisowska and I. Rekik, “Predicting emotional intelligence scores from multi-session functional brain connectomes,” in International Workshop on PRedictive Intelligence In MEdicine . Springer, 2018, pp. 103–111
2018
Cited alongside, same era.
S. I. Ktena, S. Parisot, E. Ferrante, M. Rajchl, M. Lee, B. Glocker, and D. Rueckert, “Metric learning with spectral graph convolutions on brain connectivity networks,” NeuroImage , vol. 169, pp. 431–442, 2018
2018
Cited alongside, same era.
S.-B. Seong, C. Pae, and H.-J. Park, “Geometric convolutional neural network for analyzing surface-based neuroimaging data,” Frontiers in neuroinformatics , vol. 12, p. 42, 2018
2018
Cited alongside, same era.
V. Fleischer, A. Radetz, D. Ciolac, M. Muthuraman, G. Gonzalez-Escamilla, F. Zipp, and S. Groppa, “Graph theoretical framework of brain networks in multiple sclerosis: a review of concepts,” Neuroscience , vol. 403, pp. 35–53, 2019
2019
Cited alongside, same era.
M. P. van den Heuvel and O. Sporns, “A cross-disorder connectome landscape of brain dysconnectivity,” Nature reviews neuroscience , vol. 20, no. 7, pp. 435–446, 2019
2019
Cited alongside, same era.
M. Soussia and I. Rekik, “7 years of developing seed techniques for alzheimer’s disease diagnosis using brain image and connectivity data largely bypassed prediction for prognosis,” in International Workshop on PRedictive Intelligence In MEdicine . Springer, 2019, pp. 81–93
2019
Cited alongside, same era.
B. R. Howell, M. A. Styner, W. Gao, P.-T. Yap, L. Wang, K. Baluyot, E. Yacoub, G. Chen, T. Potts, A. Salzwedel et al. , “The unc/umn baby connectome project (bcp): An overview of the study design and protocol development,” NeuroImage , vol. 185, pp. 891–905, 2019
2019
Cited alongside, same era.
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip, “A comprehensive survey on graph neural networks,” IEEE transactions on neural networks and learning systems , vol. 32, no. 1, pp. 4–24, 2020
2020
Later among the works it cites.
Z. Zhang, P. Cui, and W. Zhu, “Deep learning on graphs: A survey,” IEEE Transactions on Knowledge and Data Engineering , pp. 1–1, 2020
2020
Later among the works it cites.
Z. Liu and J. Zhou, “Introduction to graph neural networks,” Synthesis Lectures on Artificial Intelligence and Machine Learning , vol. 14, no. 2, pp. 1–127, 2020
2020
Later among the works it cites.
H. Ashoor, X. Chen, W. Rosikiewicz, J. Wang, A. Cheng, P. Wang, Y. Ruan, and S. Li, “Graph embedding and unsupervised learning predict genomic sub-compartments from hic chromatin interaction data,” Nature communications , vol. 11, no. 1, pp. 1–11, 2020
2020
Later among the works it cites.
W. Zhang, L. Zhan, P. Thompson, and Y. Wang, “Deep representation learning for multimodal brain networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 613–624
2020
Later among the works it cites.
——, “Topology-aware generative adversarial network for joint prediction of multiple brain graphs from a single brain graph,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 551–561
2020
Later among the works it cites.
M. Isallari and I. Rekik, “Gsr-net: Graph super-resolution network for predicting high-resolution from low-resolution functional brain connectomes,” in International Workshop on Machine Learning in Medical Imaging . Springer, 2020, pp. 139–149
2020
Later among the works it cites.
A. S. Göktaş, A. Bessadok, and I. Rekik, “Residual embedding similarity-based network selection for predicting brain network evolution trajectory from a single observation,” in International Workshop on PRedictive Intelligence In MEdicine . Springer, 2020, pp. 12–23
2020
Later among the works it cites.
Z. Gürler, A. Nebli, and I. Rekik, “Foreseeing brain graph evolution over time using deep adversarial network normalizer,” in International Workshop on PRedictive Intelligence In MEdicine . Springer, 2020, pp. 111–122
2020
Later among the works it cites.
A. Nebli, U. A. Kaplan, and I. Rekik, “Deep evographnet architecture for time-dependent brain graph data synthesis from a single timepoint,” in International Workshop on PRedictive Intelligence In MEdicine . Springer, 2020, pp. 144–155
2020
Later among the works it cites.
U. Demir, M. A. Gharsallaoui, and I. Rekik, “Clustering-based deep brain multigraph integrator network for learning connectional brain templates,” in Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis . Springer, 2020, pp. 109–120
2020
Later among the works it cites.
M. B. Gurbuz and I. Rekik, “Deep graph normalizer: A geometric deep learning approach for estimating connectional brain templates,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 155–165
2020
Later among the works it cites.
2020
Later among the works it cites.
A. Banka, I. Buzi, and I. Rekik, “Multi-view brain hyperconnectome autoencoder for brain state classification,” in International Workshop on PRedictive Intelligence In MEdicine . Springer, 2020, pp. 101–110
2020
Later among the works it cites.
2020
Later among the works it cites.
J. Huang, L. Zhou, L. Wang, and D. Zhang, “Attention-diffusion-bilinear neural network for brain network analysis,” IEEE transactions on medical imaging , vol. 39, no. 7, pp. 2541–2552, 2020
2020
Later among the works it cites.
D. Wu, X. Li, and J. Feng, “Multi-hops functional connectivity improves individual prediction of fusiform face activation via a graph neural network,” Frontiers in neuroscience , vol. 14, 2020
2020
Later among the works it cites.
X. Song, A. Frangi, X. Xiao, J. Cao, T. Wang, and B. Lei, “Integrating similarity awareness and adaptive calibration in graph convolution network to predict disease,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 124–133
2020
Later among the works it cites.
X. Li, Y. Zhou, N. C. Dvornek, M. Zhang, J. Zhuang, P. Ventola, and J. S. Duncan, “Pooling regularized graph neural network for fmri biomarker analysis,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 625–635
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
Y. Tian, G. Maicas, L. Z. C. T. Pu, R. Singh, J. W. Verjans, and G. Carneiro, “Few-shot anomaly detection for polyp frames from colonoscopy,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 274–284
2020
Later among the works it cites.
W. Ding, L. Li, X. Zhuang, and L. Huang, “Cross-modality multi-atlas segmentation using deep neural networks,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 233–242
2020
Later among the works it cites.
Y. Lu, W. Li, K. Zheng, Y. Wang, A. P. Harrison, C. Lin, S. Wang, J. Xiao, L. Lu, C.-F. Kuo et al. , “Learning to segment anatomical structures accurately from one exemplar,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2020, pp. 678–688
2020
Later among the works it cites.
Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni, “Generalizing from a few examples: A survey on few-shot learning,” ACM Computing Surveys (CSUR) , vol. 53, no. 3, pp. 1–34, 2020
2020
Later among the works it cites.
J. Wang, A. Ma, Y. Chang, J. Gong, Y. Jiang, R. Qi, C. Wang, H. Fu, Q. Ma, and D. Xu, “scgnn is a novel graph neural network framework for single-cell rna-seq analyses,” Nature communications , vol. 12, no. 1, pp. 1–11, 2021
2021
Closest in time.
A. Sserwadda and I. Rekik, “Topology-guided cyclic brain connectivity generation using geometric deep learning,” Journal of Neuroscience Methods , vol. 353, p. 108988, 2021
2021
Closest in time.
A. Bessadok, M. A. Mahjoub, and I. Rekik, “Brain graph synthesis by dual adversarial domain alignment and target graph prediction from a source graph,” Medical Image Analysis , vol. 68, p. 101902, 2021
2021
Closest in time.
X. Xing, Q. Li, M. Yuan, H. Wei, Z. Xue, T. Wang, F. Shi, and D. Shen, “Ds-gcns: Connectome classification using dynamic spectral graph convolution networks with assistant task training,” Cerebral Cortex , vol. 31, no. 2, pp. 1259–1269, 2021
2021
Closest in time.
X. Li, Y. Zhou, N. Dvornek, M. Zhang, S. Gao, J. Zhuang, D. Scheinost, L. H. Staib, P. Ventola, and J. S. Duncan, “Braingnn: Interpretable brain graph neural network for fmri analysis,” Medical Image Analysis , vol. 74, p. 102233, 2021
2021
Closest in time.
U. Guvercin, M. A. Gharsallaoui, and I. Rekik, “One representative-shot learning using a population-driven template with application to brain connectivity classification and evolution prediction,” International Workshop on PRedictive Intelligence In MEdicine , pp. 25–36, 2021
2021
Closest in time.
G. Özen, A. Nebli, and I. Rekik, “Flat-net: Longitudinal brain graph evolution prediction from a few training representative templates,” International Workshop on PRedictive Intelligence In MEdicine , pp. 266–278, 2021
2021
Closest in time.
F. Pala, I. Mhiri, and I. Rekik, “Template-based inter-modality super-resolution of brain connectivity,” International Workshop on PRedictive Intelligence In MEdicine , pp. 70–82, 2021
2021
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