While the OT-GAN approach offers a promising solution for enhancing fMRI data quality, it may have limitations in capturing and preserving the intricate nuances of neural representations across different fMRI resolutions. One potential limitation is the reliance on unpaired training data, which may not fully capture the variability in neural responses across individuals or experimental conditions. This could lead to a lack of generalizability in the enhanced fMRI data generated by the GAN model, especially when applied to new subjects or tasks not present in the training data.
Another limitation is the complexity of aligning fMRI data from different resolutions, which may introduce distortions or artifacts in the enhanced data. The OT-GAN approach relies on optimizing the transportation cost between low-resolution and high-resolution fMRI data, which could result in information loss or misalignment of neural representations. Additionally, the GAN model's capacity to learn and preserve fine-grained details in neural activity patterns may be limited by the architecture and training process, potentially leading to oversimplified reconstructions that lack the richness of true neural representations.