1
Introduction
2
Methods
2.1
Data and data availability
2.1.1
Preprocessed Autism Brain Imaging Data Exchange
2.1.2
Twenty-one pain studies
2.1.3
Nathan-Kline Institute Rockland Sample
2.2
PBJ analysis methods
2.2.1
Model estimation
2.2.2
Statistical inference
2.3
Evaluation under the global null hypothesis
2.3.1
Null synthetic simulations
2.3.2
Null bootstrap simulations
2.4
Evaluation under an alternative hypothesis
2.5
Synthetic simulations
2.6
Bootstrap simulations
3
pbj
software overview
3.1
Installation
3.2
Analysis setup
3.3
Software structure and general workflow
3.3.1
Model estimation using the
lmPBJ
function
3.3.2
Statistical inference using the
pbjInference
function
4
pbj
tutorials
4.1
Preprocessed ABIDE analysis
4.1.1
Whole-brain exploratory analysis
4.1.2
Voxel-wise analysis with
lmPBJ
4.1.3
Topological inference (Maxima, CEI, CMI)
4.1.4
Interpreting and visualizing
pbjInference
results
4.2
Pain meta-analysis
4.2.1
Pain-related activation
4.2.2
Software related differences in pain meta-analysis
4.3
NKI-RS analysis
4.3.1
Loading data and basic image processing
4.3.2
Model specification
4.3.3
Visualizing the results
4.3.4
Writing the results and viewing interactively
5
Statistical inference
5.1
Running statistical inference for the robust test statistic images
5.2
pTFCE inference
6
Summarizing the model results
6.1
Cluster tables
6.2
Writing output and interactive visualization
6.3
Creating figures
7
Null simulation results
7.1
Synthetic null simulations
7.2
Bootstrap null simulations using a factor covariate
7.2.1
Marginal distribution
7.2.2
Global distributions
7.3
Simulations using a nonlinear continuous covariate
7.3.1
Marginal distribution
7.3.2
Global distributions
8
References
9
Appendix
10
Supplementary Material
10.1
NKI-RS Voxel-based morphometry processing and quality control
pbj User’s Guide
8
References
knitr
::
write_bib
(
.packages
(),
"packages.bib"
)