@misc{att,
  doi = {10.48550/ARXIV.1706.03762},
  author = {Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia},
  keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {Attention Is All You Need},
  publisher = {arXiv},
  year = {2017},
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{lstm,
author = {Hochreiter, Sepp and Schmidhuber, Jürgen},
year = {1997},
month = {12},
pages = {1735-80},
title = {Long Short-term Memory},
volume = {9},
journal = {Neural computation},
doi = {10.1162/neco.1997.9.8.1735}
}

@article{preprocess, title={Brain responses to biological motion predict treatment outcome in young children with autism}, volume={6}, DOI={10.1038/tp.2016.213}, number={11}, journal={Translational Psychiatry}, author={Yang, D and Pelphrey, K A and Sukhodolsky, D G and Crowley, M J and Dayan, E and Dvornek, N C and Venkataraman, A and Duncan, J and Staib, L and Ventola, P and et al.}, year={2016}}

@article{biopoint,
author = {Martha D. Kaiser  and Caitlin M. Hudac  and Sarah Shultz  and Su Mei Lee  and Celeste Cheung  and Allison M. Berken  and Ben Deen  and Naomi B. Pitskel  and Daniel R. Sugrue  and Avery C. Voos  and Celine A. Saulnier  and Pamela Ventola  and Julie M. Wolf  and Ami Klin  and Brent C. Vander Wyk  and Kevin A. Pelphrey },
title = {Neural signatures of autism
        },
journal = {Proceedings of the National Academy of Sciences},
volume = {107},
number = {49},
pages = {21223-21228},
year = {2010},
doi = {10.1073/pnas.1010412107}
}

@article{bert,
  author    = {Jacob Devlin and
               Ming{-}Wei Chang and
               Kenton Lee and
               Kristina Toutanova},
  title     = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
               Understanding},
  journal   = {CoRR},
  volume    = {abs/1810.04805},
  year      = {2018},
  eprinttype = {arXiv},
  eprint    = {1810.04805},
  timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{nicha-lstm,
author = {Dvornek, Nicha and Ventola, Pamela and Pelphrey, Kevin and Duncan, James},
year = {2017},
month = {09},
pages = {362-370},
title = {Identifying Autism from Resting-State fMRI Using Long Short-Term Memory Networks},
volume = {10541},
isbn = {978-3-319-67388-2},
booktitle = {Machine learning in medical imaging. MLMI (Workshop)},
doi = {10.1007/978-3-319-67389-9_42}
}

@article{dakka-lstm,
  author       = {Jumana Dakka and
                  Pouya Bashivan and
                  Mina Gheiratmand and
                  Irina Rish and
                  Shantenu Jha and
                  Russell Greiner},
  title        = {Learning Neural Markers of Schizophrenia Disorder Using Recurrent
                  Neural Networks},
  journal      = {CoRR},
  volume       = {abs/1712.00512},
  year         = {2017},
  eprinttype    = {arXiv},
  eprint       = {1712.00512},
  timestamp    = {Mon, 13 Aug 2018 16:49:04 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1712-00512.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{BrainGNN,
title = {BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis},
journal = {Medical Image Analysis},
volume = {74},
pages = {102233},
year = {2021},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2021.102233},
author = {Xiaoxiao Li and Yuan Zhou and Nicha Dvornek and Muhan Zhang and Siyuan Gao and Juntang Zhuang and Dustin Scheinost and Lawrence H. Staib and Pamela Ventola and James S. Duncan},
keywords = {GNN, ASD, fMRI, Biomarker}
}

@article{pytorch,
  title={Automatic differentiation in PyTorch},
  author={Paszke, Adam and Gross, Sam and Chintala, Soumith and Chanan, Gregory and Yang, Edward and DeVito, Zachary and Lin, Zeming and Desmaison, Alban and Antiga, Luca and Lerer, Adam},
  year={2017}
}

@article{ViT,
  author       = {Alexey Dosovitskiy and
                  Lucas Beyer and
                  Alexander Kolesnikov and
                  Dirk Weissenborn and
                  Xiaohua Zhai and
                  Thomas Unterthiner and
                  Mostafa Dehghani and
                  Matthias Minderer and
                  Georg Heigold and
                  Sylvain Gelly and
                  Jakob Uszkoreit and
                  Neil Houlsby},
  title        = {An Image is Worth 16x16 Words: Transformers for Image Recognition
                  at Scale},
  journal      = {CoRR},
  volume       = {abs/2010.11929},
  year         = {2020},
  eprinttype    = {arXiv},
  eprint       = {2010.11929},
  timestamp    = {Fri, 20 Nov 2020 14:04:05 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2010-11929.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{desikan,
title = {An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest},
journal = {NeuroImage},
volume = {31},
number = {3},
pages = {968-980},
year = {2006},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2006.01.021},
author = {Rahul S. Desikan and Florent Ségonne and Bruce Fischl and Brian T. Quinn and Bradford C. Dickerson and Deborah Blacker and Randy L. Buckner and Anders M. Dale and R. Paul Maguire and Bradley T. Hyman and Marilyn S. Albert and Ronald J. Killiany}
}

@article{lg-gnn,
  author={Zhang, Hao and Song, Ran and Wang, Liping and Zhang, Lin and Wang, Dawei and Wang, Cong and Zhang, Wei},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Classification of Brain Disorders in rs-fMRI via Local-to-Global Graph Neural Networks}, 
  year={2023},
  volume={42},
  number={2},
  pages={444-455},
  keywords={Diseases;Feature extraction;Biomarkers;Functional magnetic resonance imaging;Deep learning;Medical diagnostic imaging;Visualization;Brain disorders;classification;non-imaing information;GNN;attention},
  doi={10.1109/TMI.2022.3219260}}

@article{task-rest,
title = {Task fMRI paradigms may capture more behaviorally relevant information than resting-state functional connectivity},
journal = {NeuroImage},
volume = {270},
pages = {119946},
year = {2023},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2023.119946},
author = {Weiqi Zhao and Carolina Makowski and Donald J. Hagler and Hugh P. Garavan and Wesley K. Thompson and Deanna J. Greene and Terry L. Jernigan and Anders M. Dale},
keywords = {Behavioral differences, Predictive modeling, Functional connectivity, Cognitive development, Behavioral inhibition},
abstract = {Characterizing the optimal fMRI paradigms for detecting behaviorally relevant functional connectivity (FC) patterns is a critical step to furthering our knowledge of the neural basis of behavior. Previous studies suggested that FC patterns derived from task fMRI paradigms, which we refer to as task-based FC, are better correlated with individual differences in behavior than resting-state FC, but the consistency and generalizability of this advantage across task conditions was not fully explored. Using data from resting-state fMRI and three fMRI tasks from the Adolescent Brain Cognitive Development Study ® (ABCD), we tested whether the observed improvement in behavioral prediction power of task-based FC can be attributed to changes in brain activity induced by the task design. We decomposed the task fMRI time course of each task into the task model fit (the fitted time course of the task condition regressors from the single-subject general linear model) and the task model residuals, calculated their respective FC, and compared the behavioral prediction performance of these FC estimates to resting-state FC and the original task-based FC. The FC of the task model fit was better than the FC of the task model residual and resting-state FC at predicting a measure of general cognitive ability or two measures of performance on the fMRI tasks. The superior behavioral prediction performance of the FC of the task model fit was content-specific insofar as it was only observed for fMRI tasks that probed similar cognitive constructs to the predicted behavior of interest. To our surprise, the task model parameters, the beta estimates of the task condition regressors, were equally if not more predictive of behavioral differences than all FC measures. These results showed that the observed improvement of behavioral prediction afforded by task-based FC was largely driven by the FC patterns associated with the task design. Together with previous studies, our findings highlighted the importance of task design in eliciting behaviorally meaningful brain activation and FC patterns.}
}

@article{vince-task,
title = {Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships},
journal = {NeuroImage},
volume = {207},
pages = {116370},
year = {2020},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2019.116370},
author = {Rongtao Jiang and Nianming Zuo and Judith M. Ford and Shile Qi and Dongmei Zhi and Chuanjun Zhuo and Yong Xu and Zening Fu and Juan Bustillo and Jessica A. Turner and Vince D. Calhoun and Jing Sui},
keywords = {Individualized prediction, Reading comprehension, Task state, Functional connectivity, Cognitive demand}
}

@article{gcn,
  author       = {Thomas N. Kipf and
                  Max Welling},
  title        = {Semi-Supervised Classification with Graph Convolutional Networks},
  journal      = {CoRR},
  volume       = {abs/1609.02907},
  year         = {2016},
  eprinttype    = {arXiv},
  eprint       = {1609.02907},
  timestamp    = {Mon, 13 Aug 2018 16:48:31 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/KipfW16.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{cheb,
  author       = {Micha{\"{e}}l Defferrard and
                  Xavier Bresson and
                  Pierre Vandergheynst},
  title        = {Convolutional Neural Networks on Graphs with Fast Localized Spectral
                  Filtering},
  journal      = {CoRR},
  volume       = {abs/1606.09375},
  year         = {2016},
  eprinttype    = {arXiv},
  eprint       = {1606.09375},
  timestamp    = {Mon, 13 Aug 2018 16:48:03 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/DefferrardBV16.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{sage,
  author       = {William L. Hamilton and
                  Rex Ying and
                  Jure Leskovec},
  title        = {Inductive Representation Learning on Large Graphs},
  journal      = {CoRR},
  volume       = {abs/1706.02216},
  year         = {2017},
  eprinttype    = {arXiv},
  eprint       = {1706.02216},
  timestamp    = {Mon, 13 Aug 2018 16:46:12 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/HamiltonYL17.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{gat,
    title={Graph Attention Networks},
    author={Petar Veličković and Guillem Cucurull and Arantxa Casanova and Adriana Romero and Pietro Liò and Yoshua Bengio},
    booktitle={International Conference on Learning Representations},
    year={2018}
}

@article{gin,
  author       = {Keyulu Xu and
                  Weihua Hu and
                  Jure Leskovec and
                  Stefanie Jegelka},
  title        = {How Powerful are Graph Neural Networks?},
  journal      = {CoRR},
  volume       = {abs/1810.00826},
  year         = {2018},
  eprinttype    = {arXiv},
  eprint       = {1810.00826},
  timestamp    = {Tue, 30 Oct 2018 10:49:09 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1810-00826.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{graph-transformer,
  author       = {Yunsheng Shi and
                  Zhengjie Huang and
                  Wenjin Wang and
                  Hui Zhong and
                  Shikun Feng and
                  Yu Sun},
  title        = {Masked Label Prediction: Unified Massage Passing Model for Semi-Supervised
                  Classification},
  journal      = {CoRR},
  volume       = {abs/2009.03509},
  year         = {2020},
  eprinttype    = {arXiv},
  eprint       = {2009.03509},
  timestamp    = {Fri, 18 Nov 2022 17:35:28 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2009-03509.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{gcgrulstm,
  title={Structured sequence modeling with graph convolutional recurrent networks},
  author={Seo, Youngjoo and Defferrard, Micha{\"e}l and Vandergheynst, Pierre and Bresson, Xavier},
  booktitle={Neural Information Processing: 25th International Conference, ICONIP 2018, Siem Reap, Cambodia, December 13-16, 2018, Proceedings, Part I 25},
  pages={362--373},
  year={2018},
  organization={Springer}
}

@article{evolve,
  author       = {Aldo Pareja and
                  Giacomo Domeniconi and
                  Jie Chen and
                  Tengfei Ma and
                  Toyotaro Suzumura and
                  Hiroki Kanezashi and
                  Tim Kaler and
                  Charles E. Leiserson},
  title        = {EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs},
  journal      = {CoRR},
  volume       = {abs/1902.10191},
  year         = {2019},
  eprinttype    = {arXiv},
  eprint       = {1902.10191},
  timestamp    = {Tue, 15 Jun 2021 14:10:30 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1902-10191.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{lrgcn,
  author       = {Jia Li and
                  Zhichao Han and
                  Hong Cheng and
                  Jiao Su and
                  Pengyun Wang and
                  Jianfeng Zhang and
                  Lujia Pan},
  title        = {Predicting Path Failure In Time-Evolving Graphs},
  journal      = {CoRR},
  volume       = {abs/1905.03994},
  year         = {2019},
  eprinttype    = {arXiv},
  eprint       = {1905.03994},
  timestamp    = {Thu, 03 Dec 2020 16:48:26 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1905-03994.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{gclstm,
  author       = {Jinyin Chen and
                  Xuanheng Xu and
                  Yangyang Wu and
                  Haibin Zheng},
  title        = {{GC-LSTM:} Graph Convolution Embedded {LSTM} for Dynamic Link Prediction},
  journal      = {CoRR},
  volume       = {abs/1812.04206},
  year         = {2018},
  eprinttype    = {arXiv},
  eprint       = {1812.04206},
  timestamp    = {Tue, 01 Jan 2019 15:01:25 +0100},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1812-04206.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{brainetcnn,
title = {BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment},
journal = {NeuroImage},
volume = {146},
pages = {1038-1049},
year = {2017},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2016.09.046},
author = {Jeremy Kawahara and Colin J. Brown and Steven P. Miller and Brian G. Booth and Vann Chau and Ruth E. Grunau and Jill G. Zwicker and Ghassan Hamarneh},
keywords = {Convolutional neural networks, Brain networks, Preterm infants, Diffusion MRI, Prediction, Connectome, Deep learning, Neurodevelopment}
}

@article{zhao-stgnn,
  title={Spatio-Temporal Graph Convolution for Resting-State fMRI Analysis},
  author={Soham Gadgil and Qingyu Zhao and Adolf Pfefferbaum and Edith V. Sullivan and Ehsan Adeli and Kilian M. Pohl},
  journal={Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2020},
  volume={12267},
  pages={
          528-538
        }
}

@article{tgnn,
  author       = {Emanuele Rossi and
                  Ben Chamberlain and
                  Fabrizio Frasca and
                  Davide Eynard and
                  Federico Monti and
                  Michael M. Bronstein},
  title        = {Temporal Graph Networks for Deep Learning on Dynamic Graphs},
  journal      = {CoRR},
  volume       = {abs/2006.10637},
  year         = {2020},
  eprinttype    = {arXiv},
  eprint       = {2006.10637},
  timestamp    = {Tue, 23 Jun 2020 17:57:22 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2006-10637.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{gnnexplainer,
  author       = {Rex Ying and
                  Dylan Bourgeois and
                  Jiaxuan You and
                  Marinka Zitnik and
                  Jure Leskovec},
  title        = {{GNN} Explainer: {A} Tool for Post-hoc Explanation of Graph Neural
                  Networks},
  journal      = {CoRR},
  volume       = {abs/1903.03894},
  year         = {2019},
  eprinttype    = {arXiv},
  eprint       = {1903.03894},
  timestamp    = {Sun, 31 Mar 2019 19:01:24 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-1903-03894.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{asd-thalamus-2016,
  title={Morphological Alterations in the Thalamus, Striatum, and Pallidum in Autism Spectrum Disorder},
  author={Manuela Schuetze and Min Tae M. Park and Ivy YK Cho and Frank P MacMaster and Mallar Chakravarty and Signe L Bray},
  journal={Neuropsychopharmacology},
  year={2016},
  volume={41},
  pages={2627 - 2637}
}

@article{asd-thalamus-2017,
    author = {Tomasi, Dardo and Volkow, Nora D},
    title = "{Reduced Local and Increased Long-Range Functional Connectivity of the Thalamus in Autism Spectrum Disorder}",
    journal = {Cerebral Cortex},
    volume = {29},
    number = {2},
    pages = {573-585},
    year = {2017},
    month = {12},
    issn = {1047-3211},
    doi = {10.1093/cercor/bhx340},
    eprint = {https://academic.oup.com/cercor/article-pdf/29/2/573/27378846/bhx340.pdf},
}

@article{parietal,
author = {Zoccante, Leonardo and Viviani, Anna and Ferro, Adele and Cerini, Roberto and Cerruti, Stefania and Rambaldelli, Gianluca and Bellani, Marcella and Dusi, Nicola and Perlini, Cinzia and Boscaini, Flavio and Pozzi Mucelli, Roberto and Tansella, Michele and Dalla Bernardina, Bernardo and Brambilla, Paolo},
year = {2010},
month = {10},
pages = {217-21},
title = {Increased left parietal volumes relate to delayed language development in autism: a structural mri study},
volume = {25},
journal = {Functional neurology}
}

@article{silu,
  author       = {Stefan Elfwing and
                  Eiji Uchibe and
                  Kenji Doya},
  title        = {Sigmoid-Weighted Linear Units for Neural Network Function Approximation
                  in Reinforcement Learning},
  journal      = {CoRR},
  volume       = {abs/1702.03118},
  year         = {2017},
  eprinttype    = {arXiv},
  eprint       = {1702.03118},
  timestamp    = {Mon, 13 Aug 2018 16:47:11 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/ElfwingUD17.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{dvornek2018,
  title={Learning generalizable recurrent neural networks from small task-fmri datasets},
  author={Dvornek, Nicha C and Yang, Daniel and Ventola, Pamela and Duncan, James S},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={329--337},
  year={2018},
  organization={Springer}
}

@article{2d_pe, title={2D Positional Embedding-based Transformer for Scene Text Recognition}, volume={6}, DOI={10.15353/jcvis.v6i1.3533}, abstractNote={&amp;lt;p&amp;gt;Recent state-of-the-art scene text recognition methods are primarily based on Recurrent Neural Networks (RNNs), however, these methods require one-dimensional (1D) features and are not designed for recognizing irregular-text instances due to the loss of spatial information present in the original two-dimensional (2D) images.&amp;amp;nbsp; In this paper, we leverage a Transformer-based architecture for recognizing both regular and irregular text-in-the-wild images. The proposed method takes advantage of using a 2D positional encoder with the Transformer architecture to better preserve the spatial information of 2D image features than previous methods. The experiments on popular benchmarks, including the challenging COCO-Text dataset, demonstrate that the proposed scene text recognition method outperformed the state-of-the-art in most cases, especially on irregular-text recognition.&amp;lt;/p&amp;gt;}, number={1}, journal={Journal of Computational Vision and Imaging Systems}, author={Raisi, Zobeir and Naiel, Mohamed A. and Fieguth, Paul and Wardell, Steven and Zelek, John}, year={2021}, month={Jan.}, pages={1–4}}

@inproceedings{learnable_pe,
 author = {Li, Yang and Si, Si and Li, Gang and Hsieh, Cho-Jui and Bengio, Samy},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan},
 pages = {15816--15829},
 publisher = {Curran Associates, Inc.},
 title = {Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding},
 volume = {34},
 year = {2021}
}

@misc{gnn_pe,
      title={Equivariant and Stable Positional Encoding for More Powerful Graph Neural Networks}, 
      author={Haorui Wang and Haoteng Yin and Muhan Zhang and Pan Li},
      year={2022},
      eprint={2203.00199},
      archivePrefix={arXiv},
      primaryClass={cs.LG} 
}

@article{hcp,
title = {The Human Connectome Project: A data acquisition perspective},
journal = {NeuroImage},
volume = {62},
number = {4},
pages = {2222-2231},
year = {2012},
note = {Connectivity},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2012.02.018},
author = {D.C. {Van Essen} and K. Ugurbil and E. Auerbach and D. Barch and T.E.J. Behrens and R. Bucholz and A. Chang and L. Chen and M. Corbetta and S.W. Curtiss and S. {Della Penna} and D. Feinberg and M.F. Glasser and N. Harel and A.C. Heath and L. Larson-Prior and D. Marcus and G. Michalareas and S. Moeller and R. Oostenveld and S.E. Petersen and F. Prior and B.L. Schlaggar and S.M. Smith and A.Z. Snyder and J. Xu and E. Yacoub},
keywords = {Connectivity, fMRI, Diffusion imaging, MEG/EEG, Twins, Behavior},
abstract = {The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.}
}

@article{shen268,
title = {Groupwise whole-brain parcellation from resting-state fMRI data for network node identification},
journal = {NeuroImage},
volume = {82},
pages = {403-415},
year = {2013},
issn = {1053-8119},
doi = {https://doi.org/10.1016/j.neuroimage.2013.05.081},
author = {X. Shen and F. Tokoglu and X. Papademetris and R.T. Constable},
keywords = {Functional MRI, Resting-state connectivity, Network analysis, Graph-theory-based parcellation, Whole-brain atlas},
abstract = {In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages.}
}

@article{multihop,
  author       = {Guangtao Wang and
                  Rex Ying and
                  Jing Huang and
                  Jure Leskovec},
  title        = {Direct Multi-hop Attention based Graph Neural Network},
  journal      = {CoRR},
  volume       = {abs/2009.14332},
  year         = {2020},
  eprinttype    = {arXiv},
  eprint       = {2009.14332},
  timestamp    = {Wed, 07 Oct 2020 15:59:26 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2009-14332.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}


@article{multiatlas,
title = {Multiview hyperedge-aware hypergraph embedding learning for multisite, multiatlas fMRI based functional connectivity network analysis},
journal = {Medical Image Analysis},
volume = {94},
pages = {103144},
year = {2024},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2024.103144},
author = {Wei Wang and Li Xiao and Gang Qu and Vince D. Calhoun and Yu-Ping Wang and Xiaoyan Sun},
keywords = {Autism, Functional connectivity, Graph convolution networks, Graph embedding, Hypergraph},
abstract = {Recently, functional magnetic resonance imaging (fMRI) based functional connectivity network (FCN) analysis via graph convolutional networks (GCNs) has shown promise for automated diagnosis of brain diseases by regarding the FCNs as irregular graph-structured data. However, multiview information and site influences of the FCNs in a multisite, multiatlas fMRI scenario have been understudied. In this paper, we propose a Class-consistency and Site-independence Multiview Hyperedge-Aware HyperGraph Embedding Learning (CcSi-MHAHGEL) framework to integrate FCNs constructed on multiple brain atlases in a multisite fMRI study. Specifically, for each subject, we first model brain network as a hypergraph for every brain atlas to characterize high-order relations among multiple vertexes, and then introduce a multiview hyperedge-aware hypergraph convolutional network (HGCN) to extract a multiatlas-based FCN embedding where hyperedge weights are adaptively learned rather than employing the fixed weights precalculated in traditional HGCNs. In addition, we formulate two modules to jointly learn the multiatlas-based FCN embeddings by considering the between-subject associations across classes and sites, respectively, i.e., a class-consistency module to encourage both compactness within every class and separation between classes for promoting discrimination in the embedding space, and a site-independence module to minimize the site dependence of the embeddings for mitigating undesired site influences due to differences in scanning platforms and/or protocols at multiple sites. Finally, the multiatlas-based FCN embeddings are fed into a few fully connected layers followed by the soft-max classifier for diagnosis decision. Extensive experiments on the ABIDE demonstrate the effectiveness of our method for autism spectrum disorder (ASD) identification. Furthermore, our method is interpretable by revealing ASD-relevant brain regions that are biologically significant.}
}

@InProceedings{festgnn,
author="Chen, Dongdong
and Zhang, Lichi",
editor="Greenspan, Hayit
and Madabhushi, Anant
and Mousavi, Parvin
and Salcudean, Septimiu
and Duncan, James
and Syeda-Mahmood, Tanveer
and Taylor, Russell",
title="FE-STGNN: Spatio-Temporal Graph Neural Network with Functional and Effective Connectivity Fusion for MCI Diagnosis",
booktitle="Medical Image Computing and Computer Assisted Intervention -- MICCAI 2023",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="67--76",
abstract="Brain connectivity patterns such as functional connectivity (FC) and effective connectivity (EC), describing complex spatio-temporal dynamic interactions in the brain network, are highly desirable for mild cognitive impairment (MCI) diagnosis. Major FC methods are based on statistical dependence, usually evaluated in terms of correlations, while EC generally focuses on directional causal influences between brain regions. Therefore, comprehensive integration of FC and EC with complementary information can further extract essential biomarkers for characterizing brain abnormality. This paper proposes Spatio-Temporal Graph Neural Network with Dynamic Functional and Effective Connectivity Fusion (FE-STGNN) for MCI diagnosis using resting-state fMRI (rs-fMRI). First, dynamic FC and EC networks are constructed to encode the functional brain networks into multiple graphs. Then, spatial graph convolution is employed to process spatial structural features and temporal dynamic characteristics. Finally, we design the position encoding-based cross-attention mechanism, which utilizes the causal linkage of EC during time evolution to guide the fusion of FC networks for MCI classification. Qualitative and quantitative experimental results demonstrate the significance of the proposed FE-STGNN method and the benefit of fusing FC and EC, which achieves {\$}{\$}82{\backslash}{\%}{\$}{\$}82{\%}of MCI classification accuracy and outperforms state-of-the-art methods. Our code is available at https://github.com/haijunkenan/FE-STGNN.",
isbn="978-3-031-43993-3"
}

@misc{geometric-edge,
      title={Lower Ricci Curvature for Efficient Community Detection}, 
      author={Yun Jin Park and Didong Li},
      year={2024},
      eprint={2401.10124},
      archivePrefix={arXiv},
      primaryClass={stat.ME},
      url={https://arxiv.org/abs/2401.10124}, 
}

  
  
  