Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909234130583552 |
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| author | Qu, Youzhi Xia, Junfeng Jian, Xinyao Li, Wendu Peng, Kaining Liang, Zhichao Wu, Haiyan Liu, Quanying |
| author_facet | Qu, Youzhi Xia, Junfeng Jian, Xinyao Li, Wendu Peng, Kaining Liang, Zhichao Wu, Haiyan Liu, Quanying |
| contents | Data reconstruction is a widely used pre-training task to learn the generalized features for many downstream tasks. Although reconstruction tasks have been applied to neural signal completion and denoising, neural signal reconstruction is less studied. Here, we employ the masked autoencoder (MAE) model to reconstruct functional magnetic resonance imaging (fMRI) data, and utilize a transfer learning framework to obtain the cognitive taskonomy, a matrix to quantify the similarity between cognitive tasks. Our experimental results demonstrate that the MAE model effectively captures the temporal dynamics patterns and interactions within the brain regions, enabling robust cross-subject fMRI signal reconstruction. The cognitive taskonomy derived from the transfer learning framework reveals the relationships among cognitive tasks, highlighting subtask correlations within motor tasks and similarities between emotion, social, and gambling tasks. Our study suggests that the fMRI reconstruction with MAE model can uncover the latent representation and the obtained taskonomy offers guidance for selecting source tasks in neural decoding tasks for improving the decoding performance on target tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00033 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction Qu, Youzhi Xia, Junfeng Jian, Xinyao Li, Wendu Peng, Kaining Liang, Zhichao Wu, Haiyan Liu, Quanying Neurons and Cognition Artificial Intelligence Data reconstruction is a widely used pre-training task to learn the generalized features for many downstream tasks. Although reconstruction tasks have been applied to neural signal completion and denoising, neural signal reconstruction is less studied. Here, we employ the masked autoencoder (MAE) model to reconstruct functional magnetic resonance imaging (fMRI) data, and utilize a transfer learning framework to obtain the cognitive taskonomy, a matrix to quantify the similarity between cognitive tasks. Our experimental results demonstrate that the MAE model effectively captures the temporal dynamics patterns and interactions within the brain regions, enabling robust cross-subject fMRI signal reconstruction. The cognitive taskonomy derived from the transfer learning framework reveals the relationships among cognitive tasks, highlighting subtask correlations within motor tasks and similarities between emotion, social, and gambling tasks. Our study suggests that the fMRI reconstruction with MAE model can uncover the latent representation and the obtained taskonomy offers guidance for selecting source tasks in neural decoding tasks for improving the decoding performance on target tasks. |
| title | Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction |
| topic | Neurons and Cognition Artificial Intelligence |
| url | https://arxiv.org/abs/2407.00033 |