bibtext: @InProceedings{ Zha_TARDRL_MICCAI2024,
                   author = { Zhao, Yunxi and Nie, Dong and Chen, Geng and Wu, Xia and Zhang, Daoqiang and Wen, Xuyun },
                   title = { { TARDRL: Task-Aware Reconstruction for Dynamic Representation Learning of fMRI } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15011 },
                   month = {October},
                   pages = { pending },
                }
abstract: Existing studies in fMRI analysis leverage mask autoencoder, a self-supervised framework, to build model to learn representations and conduct prediction for various fMRI-related tasks. It involves pretraining the model by reconstructing signals of brain regions that are randomly masked at different time segments and subsequently fine-tuning it for prediction tasks. Though it has shown improved performance in prediction tasks, we argue that directly applying this framework on fMRI data may result in sub-optimal results. Firstly, random masking is ineffective for highly redundant fMRI data. Secondly, the reconstruction process is not task-aware, ignoring a critical phenomenon: the varying contributions of different brain regions to different prediction tasks. In this work, we propose and demonstrate a hypothesis that learning representations by reconstructing signals from important ROIs at different time segments can enhance prediction performance. Specifically, we introduce a novel learning framework, Task-Aware Reconstruction Dynamic Representation Learning (TARDRL), to improve prediction performance through task-aware reconstruction. Our approach incorporates an attention-guided masking strategy, which leverages attention maps from the prediction process to guide signal masking during reconstruction, making the reconstruction task task-aware. Extensive experiments show that our model outperforms state-of-the-art methods on the ABIDE and ADNI datasets, with high interpretability.
title: TARDRL: Task-Aware Reconstruction for Dynamic Representation Learning of fMRI
authors: Zhao, Yunxi Nie, Dong Chen, Geng Wu, Xia Zhang, Daoqiang Wen, Xuyun