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Article summary:
1. A deep learning framework is proposed for automatic rs-fMRI denoising that simultaneously learns spatio-temporal features of noise in a data-driven manner.
2. The proposed framework achieves high performance on various datasets including infant cohorts and can be integrated into any pipelines by accelerating speed (<1s per component).
3. Data-driven approaches such as band-pass filtering, spatial/temporal smoothing, and nuisance signal regression have been widely used for rs-fMRI denoising.
Article analysis:
The article “Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising” provides an overview of the current state of research on automatic rs-fMRI denoising and proposes a novel deep learning framework to address this issue. The article is well written and provides a comprehensive overview of the existing methods for rs-fMRI denoising, as well as the advantages of the proposed deep learning approach.
The article is generally reliable and trustworthy, with no obvious biases or unsupported claims. It presents both sides of the argument fairly, noting both the advantages and disadvantages of existing methods, as well as potential risks associated with using deep learning for rs-fMRI denoising. The authors also provide evidence to support their claims, citing relevant studies throughout the article.
However, there are some points that could be explored further in future research. For example, while the authors note that manual selection of ICs can be time consuming and laborious, they do not discuss potential solutions to this problem or explore alternative methods for IC selection. Additionally, while the authors note that reference based approaches may not work well for more diverse and complex noise sources, they do not discuss potential solutions to this issue or explore alternative methods for dealing with these types of noise sources.
In conclusion, overall this article is reliable and trustworthy in its presentation of information regarding automatic rs-fMRI denoising techniques and provides a comprehensive overview of existing methods as well as a novel deep learning approach to address this issue. However, there are some points that could be explored further in future research in order to improve upon existing methods and develop more effective solutions for dealing with diverse noise sources in rs-fMRI data.