14.2. FMRI Codex¶
- 14.2.1. Taylor et al. (2025). Go Figure: Transparency in neuroscience images preserves context …
- 14.2.2. Yue et al. (2025). Ultrafast fMRI reveals serial queuing of information processing …
- 14.2.3. Wardle et al. (2025). Brief encounters with real objects modulate medial parietal …
- 14.2.4. Reynolds et al. (2024). Processing, evaluating and understanding FMRI data with afni_proc.py
- 14.2.5. Beynel et al. (2024). Lessons learned from an fMRI-guided rTMS study …
- 14.2.6. Taylor et al. (2024). A Set of FMRI Quality Control Tools in AFNI: Systematic, in-depth …
- 14.2.7. Reynolds et al. (2023). Quality control practices in FMRI analysis: Philosophy, methods …
- 14.2.8. Teves et al. (2023). The art and science of using quality control to understand and …
- 14.2.9. Lepping et al. (2023). Quality control in resting-state fMRI: the benefits of visual inspection
- 14.2.10. Birn (2023). Quality control procedures and metrics for resting-state functional MRI
- 14.2.11. Chen et al. (2023). BOLD response is more than just magnitude: improving detection …
- 14.2.12. Taylor et al. (2023). Highlight Results, Don’t Hide Them: Enhance interpretation, …
- 14.2.13. Steinhauser et al. (2023). Reduced vmPFC-insula functional connectivity in generalized …
- 14.2.14. Chen et al. (2022). Hyperbolic trade-off: The importance of balancing trial …
- 14.2.15. Atlas et al. (2022). Instructions and experiential learning have similar …
- 14.2.16. Yang et al. (2021). Different activation signatures in the primary sensorimotor …
- 14.2.17. Chen et al. (2018b). Handling Multiplicity in Neuroimaging Through Bayesian …
- 14.2.18. Chen et al. (2018a). A tail of two sides: Artificially doubled false positive …
- 14.2.19. Taylor et al. (2018). FMRI processing with AFNI: Some comments and corrections …
- 14.2.20. Chen et al. (2016). Untangling the Relatedness among Correlations, Part II: …
