| Classifying autism spectrum disorder using the temporal statistics of resting-state functional MRI data with 3D convolutional neural networks RM Thomas, S Gallo, L Cerliani, P Zhutovsky, A El-Gazzar, G Van Wingen Frontiers in psychiatry 11, 440, 2020 | 123 | 2020 |
| A hybrid 3DCNN and 3DC-LSTM based model for 4D spatio-temporal fMRI data: an ABIDE autism classification study A El-Gazzar, M Quaak, L Cerliani, P Bloem, G van Wingen, ... International Workshop on OR 2.0 Context-Aware Operating Theaters, 95-102, 2019 | 58 | 2019 |
| Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies S Gallo, A El-Gazzar, P Zhutovsky, RM Thomas, N Javaheripour, M Li, ... Molecular Psychiatry 28 (7), 3013-3022, 2023 | 51* | 2023 |
| Simple 1-D convolutional networks for resting-state fMRI based classification in autism A El Gazzar, L Cerliani, G van Wingen, RM Thomas 2019 International Joint Conference on Neural Networks (IJCNN), 1-6, 2019 | 44 | 2019 |
| Fusing structural and functional MRIs using graph convolutional networks for autism classification D Arya, R Olij, DK Gupta, A El Gazzar, G Wingen, M Worring, RM Thomas Medical imaging with deep learning, 44-61, 2020 | 39 | 2020 |
| Non-Contact manipulation of microbeads via pushing and pulling using magnetically controlled clusters of paramagnetic microparticles AG El-Gazzar, LE Al-Khouly, A Klingner, S Misra, ISM Khalil 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015 | 21 | 2015 |
| Dynamic adaptive spatio-temporal graph convolution for fMRI modelling A El-Gazzar, RM Thomas, G van Wingen Machine Learning in Clinical Neuroimaging: 4th International Workshop, MLCN …, 2021 | 20 | 2021 |
| Benchmarking graph neural networks for fMRI analysis A ElGazzar, R Thomas, G Van Wingen arXiv preprint arXiv:2211.08927, 2022 | 11 | 2022 |
| Improving the diagnosis of psychiatric disorders with self-supervised graph state space models AE Gazzar, RM Thomas, G Van Wingen arXiv preprint arXiv:2206.03331, 2022 | 9 | 2022 |
| Medical imaging with deep learning D Arya, R Olij, DK Gupta, A El Gazzar, G Wingen, M Worring, RM Thomas PMLR,, 2020 | 5 | 2020 |
| fMRI-S4: learning short-and long-range dynamic fMRI dependencies using 1D Convolutions and State Space Models A El-Gazzar, RM Thomas, G Van Wingen International Workshop on Machine Learning in Clinical Neuroimaging, 158-168, 2022 | 4 | 2022 |
| Universal Differential Equations as a Common Modeling Language for Neuroscience A ElGazzar, M van Gerven arXiv preprint arXiv:2403.14510, 2024 | 2 | 2024 |
| Harmonization techniques for machine learning studies using multi-site functional MRI data A El-Gazzar, RM Thomas, G van Wingen bioRxiv, 2023.06. 14.544758, 2023 | 2 | 2023 |
| A dynamical systems approach to optimal foraging S Chaturvedi, A El-Gazzar, M van Gerven PLOS Complex Systems 1 (3), e0000018, 2024 | 1 | 2024 |
| Foragax: An Agent-Based Modelling Framework Based on JAX S Chaturvedi, A El-Gazzar, M van Gerven arXiv preprint arXiv:2409.06345, 2024 | 1 | 2024 |
| Analytical Characterization of Epileptic Dynamics in a Bistable System Y Qin, A El-Gazzar, DS Bassett, F Pasqualetti, M van Gerven arXiv preprint arXiv:2404.03409, 2024 | 1 | 2024 |
| Phenotyping functional brain dynamics: A deep learning prespective on psychiatry A Elgazzar | | 2024 |
Guido van WingenProfessor of Neuroimaging in Psychiatry, Amsterdam UMCVerified email at amc.uva.nl
