This tutorial presents afni_proc.py's quality control HTML for single subject FMRI.
The APQC HTML has systematic views of data and useful derived quantities. Users can instantly rate, comment and query the fully processed subject data.
www.youtube.com/watch?v=hD9z...
This tutorial presents afni_proc.py's quality control HTML for single subject FMRI.
The APQC HTML has systematic views of data and useful derived quantities. Users can instantly rate, comment and query the fully processed subject data.
www.youtube.com/watch?v=hD9z...
Quick reminder @ the next AFNI Bootcamp: May 28-30, 2025. Learn through interactive data analysis!
Day 1-2: data viz, single subject analysis and QC.
Day 3: statistics, results reporting and group analysis.
Details, registration and schedule:
afni.nimh.nih.gov/bootcamp
Quick reminder @ the next AFNI Bootcamp: May 28-30, 2025. Learn through interactive data analysis!
Day 1-2: data viz, single subject analysis and QC.
Day 3: statistics, results reporting and group analysis.
Details, registration and schedule:
afni.nimh.nih.gov/bootcamp
"Go Figure: Transparency in neuroscience images preserves context and clarifies interpretation"
arxiv.org/abs/2504.07824
TL;DR: The FMRI world can (and should) improve results interpretation and reproducibility *today*, via transparent thresholding.
"Go Figure: Transparency in neuroscience images preserves context and clarifies interpretation"
arxiv.org/abs/2504.07824
TL;DR: The FMRI world can (and should) improve results interpretation and reproducibility *today*, via transparent thresholding.
Conventional estimation methods ignore measurement error, leading to a bias. Don't worry: hierarchical modeling to the rescue!
www.frontiersin.org/journals/gen...
Conventional estimation methods ignore measurement error, leading to a bias. Don't worry: hierarchical modeling to the rescue!
www.frontiersin.org/journals/gen...