Towards Zero-Shot Task-Generalizable Learning on fMRI
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arXiv
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| Format: | Preprint |
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2025
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| author | Wang, Jiyao Dvornek, Nicha C. Duan, Peiyu Staib, Lawrence H. Duncan, James S. |
| author_facet | Wang, Jiyao Dvornek, Nicha C. Duan, Peiyu Staib, Lawrence H. Duncan, James S. |
| contents | Functional MRI measuring BOLD signal is an increasingly important imaging modality in studying brain functions and neurological disorders. It can be acquired in either a resting-state or a task-based paradigm. Compared to resting-state fMRI, task-based fMRI is acquired while the subject is performing a specific task designed to enhance study-related brain activities. Consequently, it generally has more informative task-dependent signals. However, due to the variety of task designs, it is much more difficult than in resting state to aggregate task-based fMRI acquired in different tasks to train a generalizable model. To resolve this complication, we propose a supervised task-aware network TA-GAT that jointly learns a general-purpose encoder and task-specific contextual information. The encoder-generated embedding and the learned contextual information are then combined as input to multiple modules for performing downstream tasks. We believe that the proposed task-aware architecture can plug-and-play in any neural network architecture to incorporate the prior knowledge of fMRI tasks into capturing functional brain patterns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10662 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Zero-Shot Task-Generalizable Learning on fMRI Wang, Jiyao Dvornek, Nicha C. Duan, Peiyu Staib, Lawrence H. Duncan, James S. Image and Video Processing Machine Learning Functional MRI measuring BOLD signal is an increasingly important imaging modality in studying brain functions and neurological disorders. It can be acquired in either a resting-state or a task-based paradigm. Compared to resting-state fMRI, task-based fMRI is acquired while the subject is performing a specific task designed to enhance study-related brain activities. Consequently, it generally has more informative task-dependent signals. However, due to the variety of task designs, it is much more difficult than in resting state to aggregate task-based fMRI acquired in different tasks to train a generalizable model. To resolve this complication, we propose a supervised task-aware network TA-GAT that jointly learns a general-purpose encoder and task-specific contextual information. The encoder-generated embedding and the learned contextual information are then combined as input to multiple modules for performing downstream tasks. We believe that the proposed task-aware architecture can plug-and-play in any neural network architecture to incorporate the prior knowledge of fMRI tasks into capturing functional brain patterns. |
| title | Towards Zero-Shot Task-Generalizable Learning on fMRI |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2502.10662 |