Resting State fMRI: Going Through the Motions

@article{Maknojia2019RestingSF,
  title={Resting State fMRI: Going Through the Motions},
  author={Sanam Maknojia and Nathan William Churchill and Tom A. Schweizer and Simon J. Graham},
  journal={Frontiers in Neuroscience},
  year={2019},
  volume={13}
}
Resting state functional magnetic resonance imaging (rs-fMRI) has become an indispensable tool in neuroscience research. Despite this, rs-fMRI signals are easily contaminated by artifacts arising from movement of the head during data collection. The artifacts can be problematic even for motions on the millimeter scale, with complex spatiotemporal properties that can lead to substantial errors in functional connectivity estimates. Effective correction methods must be employed, therefore, to… 

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References

SHOWING 1-10 OF 198 REFERENCES

Integrated strategy for improving functional connectivity mapping using multiecho fMRI

TLDR
The proposed strategy results in fourfold improvements in signal-to-noise ratio, functional connectivity analysis with improved specificity, and valid statistical inference with nominal control of type 1 error in contrasts of connectivity between groups with different levels of subject motion.
...