Attention: Confluence is not suitable for the storage of highly confidential data. Please ensure that any data classified as Highly Protected is stored using a more secure platform.
If you have any questions, please refer to the University's data classification guide or contact ict.askcyber@sydney.edu.au
Skip to end of banner
Go to start of banner

Longitudinal pre-processing

Skip to end of metadata
Go to start of metadata

You are viewing an old version of this content. View the current version.

Compare with Current View Version History

« Previous Version 2 Current »

Information on the importance of the longitudinal pre-processing stream, and steps on how to run it are listed here and here. In summary, the longitudinal stream extracts reliable volume and thickness estimations across individuals over time by using an unbiased within-subject template space and image. There are 3 main steps:

1. Recon-all

Cross sectionally process all time points with the default recon-all pipeline (manually or automatically)

recon-all -i FTD####.nii -subjid subject1_tp1 -all

2. Base

Create an unbiased template from all time points for each subject.

recon-all -base subject1_template -tp subject1_tp1 -tp subject1_tp2 -tp subject1_tp3 ... -all

3. Long

Longitudinally process all time points

recon-all -long subject1_tp1 subject1_template -all
recon-all -long subject1_tp2 subject1_template -all
recon-all -long subject1_tp3 subject1_template -all

  • No labels

0 Comments

You are not logged in. Any changes you make will be marked as anonymous. You may want to Log In if you already have an account.