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Regarding DBM, in Jacobian group analysis #1794
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Have you seen this simple DBM example in our ANTsR/ANTsPy tutorial? |
Okay, this is the first time I've seen this. I will check out this example. thank you |
hi,
|
Do you know how to perform a two-sample t-test with covariates in R? The application in ANTsR is exactly the same. In particular, study these lines from the example: > # Create simulated outcomes and covariates
> neuroCondition <- rnorm( numberOfSubjects )
> age <- sample( 40:70, numberOfSubjects, replace = TRUE )
> sex <- sample( c( 0, 1 ), numberOfSubjects, replace = TRUE )
> covariates <- data.frame( neuroCondition = neuroCondition, age = age, sex = sex, brainVolume = brainVolume)
> rformula <- "neuroCondition ~ age + sex + brainVolume + logJacobian"
>
> # Run image-based regression
> dbm <- ilr( covariates, list( logJacobian = logJacobian ), rformula ) |
This summer school documentation on DBM methods in R may be of use |
thank for your reply I can use R to perform a two-sample t-test with covariates. I understand that you mean editing the data using a two-sample t-test. However, since this jacobian image has already been converted into matrix format, how should I interpret these results? Should I convert them into visual parameters, such as marking heatmaps in the brain? |
Thank you for your response. |
In the example, I added how to create an overlay image of the actual qvalues. > # Create overlay q-value image
> logJacobianQvaluesImage <- matrixToImages( matrix( data = logJacobianQvalues, nrow = 1 ), rtemplateGM ) |
Operating system and version
Ubuntu 20.04
CPU architecture
x86_64 (PC, Intel Mac, other Intel/AMD)
ANTs code version
ants-2.5.0
ANTs installation type
Compiled from source
Summary of the problem
I would like to know if using DBM methods for two-sample t-tests in group analysis has a significant impact when generating Jacobian data, specifically whether values like 1 0 or 1 1 have substantial effects. I have already generated Jacobian data for two groups. I want to know what software others typically use for analysis. I have tried using FSL's randomise and SPM for general linear model-based two-sample t-tests, but the results still show some differences. Do you have any suggestions for the DBM analysis process?
Commands to reproduce the problem.
CreateJacobianDeterminantImage 3 Warp.nii.gz LogJacobian.nii.gz 1 0
orCreateJacobianDeterminantImage 3 Warp.nii.gz LogJacobian.nii.gz 1 1
Output of the command with verbose output.
Data to reproduce the problem
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