9. Tutorials, Examples

• 9.1. Self Guided Demo Scripts
  • 9.1.1. @DriveSuma
  • 9.1.2. @DriveAfni
  • 9.1.3. @DO.examples
• 9.2. APQC: afni_proc.py quality control
  • 9.2.1. Overview for APQC
  • 9.2.2. APQC-HTML navigation: example
  • 9.2.3. Browsing APQC outputs
• 9.3. FATCAT: DWI {Pre-pre-, Pre-, Post-pre}processing
  • 9.3.1. Background and additional software
  • 9.3.2. General fat_proc script properties
  • 9.3.3. Pre-preproc, I: convert and niceify
  • 9.3.4. Pre-preproc, II: examine and filter
  • 9.3.5. Preproc: FreeSurfer (+ @SUMA_Make_Spec_FS)
  • 9.3.6. Preproc: TORTOISE
  • 9.3.7. Post-preproc, I: tensor estimation and checking
  • 9.3.8. Post-preproc, II: mapping ROIs to DTI space
  • 9.3.9. Post-preproc, III: ROI map preparation
  • 9.3.10. Post-preproc, IV: Tracking
• 9.4. FATCAT Visualization
  • 9.4.1. Do_06_VISdti_SUMA_visual_ex1.tcsh
  • 9.4.2. Do_09_VISdti_SUMA_visual_ex2.tcsh
  • 9.4.3. Do_09_VISdti_SUMA_visual_ex3.tcsh
  • 9.4.4. Connectoming
• 9.5. Walnut Brain
  • 9.5.1. Prying Brains Apart
• 9.6. Drawing ROIs
  • 9.6.1. Demo:
• 9.7. Cluster Explorer
  • 9.7.1. Introducing Cluster Explorer
  • 9.7.2. Scripts
• 9.8. FATCAT Matrix Plot
  • 9.8.1. FATCAT_matplot
• 9.9. 3dMVM Validator
  • 9.9.1. Overview.
  • 9.9.2. Walking in your footsteps.
  • 9.9.3. Positive Role Model.
  • 9.9.4. Tab’s on me if you think I’m lyin’.
  • 9.9.5. General Linear Tests.
  • 9.9.6. More coming soon
• 9.10. Automatic Images
  • 9.10.1. Overview
  • 9.10.2. Using @chauffeur_afni
  • 9.10.3. Using @snapshot_volreg
  • 9.10.4. Using @djunct_4d_imager
  • 9.10.5. Using imcat
• 9.11. Sound features in AFNI
  • 9.11.1. Overview
  • 9.11.2. Keypresses, formats and functionality
  • 9.11.3. Example
• 9.12. Working with ROIs
  • 9.12.1. Combine ROI masks, visualize and make corr matrices
  • 9.12.2. Manifold ways to make+view spheres in SUMA
  • 9.12.3. Examples of things with ROIs: resampling and clusterizing