BrainCodec: Neural fMRI codec for the decoding of cognitive brain states
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arXiv
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| Format: | Preprint |
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2024
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| author | Nishimura, Yuto Sawayama, Masataka Yamashita, Ayumu Nakayama, Hideki Amano, Kaoru |
| author_facet | Nishimura, Yuto Sawayama, Masataka Yamashita, Ayumu Nakayama, Hideki Amano, Kaoru |
| contents | Recently, leveraging big data in deep learning has led to significant performance improvements, as confirmed in applications like mental state decoding using fMRI data. However, fMRI datasets remain relatively small in scale, and the inherent issue of low signal-to-noise ratios (SNR) in fMRI data further exacerbates these challenges. To address this, we apply compression techniques as a preprocessing step for fMRI data. We propose BrainCodec, a novel fMRI codec inspired by the neural audio codec. We evaluated BrainCodec's compression capability in mental state decoding, demonstrating further improvements over previous methods. Furthermore, we analyzed the latent representations obtained through BrainCodec, elucidating the similarities and differences between task and resting state fMRI, highlighting the interpretability of BrainCodec. Additionally, we demonstrated that fMRI reconstructions using BrainCodec can enhance the visibility of brain activity by achieving higher SNR, suggesting its potential as a novel denoising method. Our study shows that BrainCodec not only enhances performance over previous methods but also offers new analytical possibilities for neuroscience. Our codes, dataset, and model weights are available at https://github.com/amano-k-lab/BrainCodec. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04383 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BrainCodec: Neural fMRI codec for the decoding of cognitive brain states Nishimura, Yuto Sawayama, Masataka Yamashita, Ayumu Nakayama, Hideki Amano, Kaoru Neurons and Cognition Computation and Language Recently, leveraging big data in deep learning has led to significant performance improvements, as confirmed in applications like mental state decoding using fMRI data. However, fMRI datasets remain relatively small in scale, and the inherent issue of low signal-to-noise ratios (SNR) in fMRI data further exacerbates these challenges. To address this, we apply compression techniques as a preprocessing step for fMRI data. We propose BrainCodec, a novel fMRI codec inspired by the neural audio codec. We evaluated BrainCodec's compression capability in mental state decoding, demonstrating further improvements over previous methods. Furthermore, we analyzed the latent representations obtained through BrainCodec, elucidating the similarities and differences between task and resting state fMRI, highlighting the interpretability of BrainCodec. Additionally, we demonstrated that fMRI reconstructions using BrainCodec can enhance the visibility of brain activity by achieving higher SNR, suggesting its potential as a novel denoising method. Our study shows that BrainCodec not only enhances performance over previous methods but also offers new analytical possibilities for neuroscience. Our codes, dataset, and model weights are available at https://github.com/amano-k-lab/BrainCodec. |
| title | BrainCodec: Neural fMRI codec for the decoding of cognitive brain states |
| topic | Neurons and Cognition Computation and Language |
| url | https://arxiv.org/abs/2410.04383 |