with the rate of tau accumulation in alzheimer's disease. Nature Communications, 11(1):347, 2020.
[5] Mary-Ellen Lynall, Danielle S Bassett, Robert Kerwin, Peter J McKenna, Manfred Kitzbichler, Ulrich Muller, and Ed Bullmore. Functional connectivity and brain networks in schizophrenia. Journal of Neuroscience, 30(28):9477-9487, 2010.
[6] Andrew T Drysdale, Logan Grosenick, Jonathan Downar, Katharine Dunlop, Farrokh Mansouri, Yue Meng, Robert N Fetcho, Benjamin Zebley, Desmond J Oathes, Amit Etkin, Alan F Schatzberg, Keith Sudheimer, Jennifer Keller, Helen S Mayberg, Faith M Gunning, George S Alexopoulos, Michael D Fox, Alvaro Pascual-Leone, Henning U Voss, B J Casey, Marc J Dubin, and Conor Liston. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nature Medicine, 23(1):28-38, January 2017.
[7] Jeanette C Mostert, Elena Shumskaya, Maarten Mennes, A Marten H Onnink, Martine Hoogman, Cornelis C Kan, Alejandro Arias Vasquez, Jan Buitelaar, Barbara Franke, and David G Norris. Characterising resting-state functional connectivity in a large sample of adults with ADHD. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 67:82-91, 2016.
[8] Byung-Hoon Kim, Jong Chul Ye, and Jae-Jin Kim. Learning dynamic graph representation of brain connectome with spatio-temporal attention. Advances in Neural Information Processing Systems, 34:4314-4327, 2021.
[9] Nima Asadi, Ingrid R Olson, and Zoran Obradovic. A transformer model for learning spatiotemporal contextual representation in fmri data. Network Neuroscience, pages 1-41, 2022.
[10] Sandra Vieira, Walter H L Pinaya, and Andrea Mechelli. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: Methods and applications. Neuroscience Biobehavioral Reviews, 74(Pt A):58-75, March 2017.
[11] Soham Gadgil, Qingyu Zhao, Adolf Pfefferbaum, Edith V Sullivan, Ehsan Adeli, and Kilian M Pohl. Spatio-temporal graph convolution for resting-state fmri analysis. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2020: 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part VII 23, pages 528-538. Springer, 2020.
[12] Xuan Kan, Wei Dai, Hejie Cui, Zilong Zhang, Ying Guo, and Carl Yang. Brain network transformer. In Advances in Neural Information Processing Systems, 2022.
[13] Jonathan D Power, Alexander L Cohen, Steven M Nelson, Gagan S Wig, Kelly Anne Barnes, Jessica A Church, Alecia C Vogel, Timothy O Laumann, Fran M Miezin, Bradley L Schlaggar, et al. Functional network organization of the human brain. Neuron, 72(4):665-678, 2011.
[14] Lisa D Nickerson, Stephen M Smith, Dost Öngür, and Christian F Beckmann. Using dual regression to investigate network shape and amplitude in functional connectivity analyses. Frontiers in Neuroscience, 11:115, 2017.
[15] Md Mahfuzur Rahman, Usman Mahmood, Noah Lewis, Harshvardhan Gazula, Alex Fedorov, Zening Fu, Vince D Calhoun, and Sergey M Plis. Interpreting models interpreting brain dynamics. Scientific Reports, 12(1):12023, July 2022.
[16] Wei Li, Xuefeng Lin, and Xi Chen. Detecting alzheimer's disease based on 4d fmri: An exploration under deep learning framework. Neurocomputing, 388:280-287, 2020.
[17] Itzik Malkiel, Gony Rosenman, Lior Wolf, and Talma Hendler. Self-supervised transformers for fmri representation. In International Conference on Medical Imaging with Deep Learning, pages 895-913. PMLR, 2022.
[18] Sam Nguyen, Brenda Ng, Alan D Kaplan, and Priyadip Ray. Attend and decode: 4d fmri task state decoding using attention models. In Machine Learning for Health, pages 267-279. PMLR, 2020.
[19] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10012-10022, 2021.