3.1.1. NIH Bootcamp (Oct, 2017)¶
****IN PROGRESS!*****
Overview¶
The current set of Bootcamp recordings, unless otherwise noted, were
recorded during the AFNI Bootcamp held at the NIH in October, 2017.
The videos provided here are screen captures with audio and toggleable
captioning. They are organized by the day and title of the lecture
according to the schedule of that week, shown here.
For each lecture, links to accompanying PDF/TXT files are provided, which are either directly used or contain records of the information presented (particularly for visualization-based presentations using the AFNI and SUMA GUIs). Additional files that might are relevant, though perhaps not directly used in the presentations, are also provided in parentheses.
The Bootcamp demo data directory can be downloaded by following these “Boot up” instructions. We also strongly recommend that people unfamiliar with scripting take a quick tour through the handy Linux tutorial.
Bootcamp Videos¶
Monday¶
Video |
Associated handouts |
Contents, notes |
|---|---|---|
Overview; AFNI principles and concepts; AFNI datasets; installing; batch processing |
||
AFNI Introduction, pt 2 (Cox) |
AFNI GUI; image viewer; side controls (brightness/contrast, zoom); help; display panel; montage; multiple controllers |
|
graphing across time, overlay control (opacity and thresholding); clusterize plugin; other plugins; “driving” AFNI viewer from command line |
||
graphing across time, overlay control (opacity and thresholding); clusterize plugin; other plugins; “driving” AFNI viewer from command line |
||
Single Subject Analysis (Cox) |
||
hands-on: preprocessing overview and simple linear regression example |
||
hands-on: preprocessing overview and simple linear regression example |
Tuesday¶
Video |
Associated handouts |
Contents, notes |
|---|---|---|
Definitions and overview (rigid; linear affine; motion correction and regressors (3dvolreg); cross-modality (anat to epi); cost functions (esp. lpc and lpa); cross-modality (anat to epi; align_epi_anat.py); programs for visualization of alignment |
||
Visualization in GUI; checking alignment quality; left-right flipping; alignment to standard space; non-human alignment; afni_proc.py alignment; nonlinear warping (3dQwarp); blip-up/down (EPI distortion) correction; atlas definitions; templates; @auto_tlrc |
||
Hands-on: afni_proc.py; detailed analysis (preprocessing through linear regression) of FT_analysis data; looking at data |
||
Continuation of Start-to-Middle; @ss_review_driver |
||
Exercises and Consultations (class) |
afni19_jazz.pdf afni19_jazz_hints.pdf, afni19_jazz_answers.pdf |
This session does not contain any lecture; students can ask questions, and/or work on the AFNI Jazzercise questions, which are provided here. These PDFs are meant to help new users gain familiarity with some AFNI functions; feel free to use the “hints” and “answers” for assistance! |
Wednesday¶
Video |
Associated handouts |
Contents, notes |
|---|---|---|
Group Analysis in FMRI (Chen) |
Basic concepts and terminologies in group analysis; group analysis approaches: t-tests, GLM, ANOVA, ANCOVA, LME; miscellaneous issues: centering, intraclass correlation, inter-subject correlation |
|
Standard space/template definitions; atlas definitions; nonlinear alignment to template; choosing a template/examples; coordinate order/systems; visualizing atlases; “whereami” function (GUI+command line); atlas GUI features (“Go to atlas” location; atlas colors); “other” atlases (pediatric, infant, cerebellum, macaque, marmoset, rat) |
||
afni10_volreg_talairach.pdf, afni11_roi.pdf, afni11_roi_cmds.txt |
Creating templates+atlases using AFNI; ROI definitions; methods to create ROIs (draw; clusterize; from atlas); AFNI draw plugin; resampling ROIs; extracting quantities from ROIs (averages, masks); clustering ROIs; ROIs from atlas regions; transforming between native subject and template spaces |
|
Group Analysis Hands-On (Chen) |
Bayesian Multilevel Modeling; available group analysis programs in AFNI: 3dttest++, 3dMEMA, 3dANOVAx, 3dMVM, 3dLME; a few hands-on group analysis examples |
|
Regression features in AFNI and afni_proc.py |
||
regression features in AFNI and afni_proc.py… and more |
Thursday¶
Video |
Associated handouts |
Contents, notes |
|---|---|---|
SUMA & Surface Analysis (Taylor) |
Surface mapping in AFNI; what are surfaces/meshes; how to create surfaces for SUMA; how surface analysis complements volumetric analysis; SUMA data visualization (interactive). |
|
Hands-on: SUMA continuation; surface ROI drawing and mapping to volume; complete single subject surface-based analysis with afni_proc.py; viewing effects of processing on surface data |
||
Hands-on: SUMA continuation; surface ROI drawing and mapping to volume; complete single subject surface-based analysis with afni_proc.py; viewing effects of processing on surface data |
||
Group Analysis Hands-On++ (Chen) |
Available group analysis programs in AFNI: 3dttest++, 3dMEMA, 3dANOVAx, 3dMVM, 3dLME; a few hands-on group analysis examples |
|
Resting State & InstaCorr, pt1 (Cox) |
[tbd] |
regression features in AFNI |
Using AFNI GUI interactively to investigate single subject and group level correlation/statistic maps |
Friday¶
Video |
Associated handouts |
Contents, notes |
|---|---|---|
[tbd] |
[tbd] |
|
[tbd] |
[tbd] |
|
DTI, FATCAT & more SUMA (Taylor) |
Please see the “DTI & FATCAT videos” section, and in particular the “FATCAT Hands-On Demo” lecture, below. |
Supplementary lectures¶
The following lectures were recorded after the Bootcamp itself, but mirror those lectures given during the “DTI Breakout” session on the Monday of the Bootcamp. At some point in the near(ish) future, we hope to record the presentations given by the other DTI session presenters, as well, and add them here; at present, some brief notes by the TORTOISE group on EPI distortions in DTI and motivation for using TORTOISE to correct them are provided here: TORTOISE_Okan.pdf.
For the “Hands-On” demo viewing, it helps to have gone through the first SUMA session from Thursday, above.
DTI & FATCAT videos¶
Video |
Associated handouts |
Contents, notes |
|---|---|---|
what is diffusion; how it is used in MRI to describe structure; geometry of DTI parameters; what are DW images; what noise and uncertainty features are in DWI/DTI data |
||
assumptions in DTI; important scales to understand; AFNI’s tracking algorithm; different types (“modes”) of tracking; basic terminology; making targets for tracking; what useful quantities to we get out; caveats+philosophical musings |
||
motivating network-oriented paradigm; combining FMRI and DTI; FATCAT overview; comparing 3dTrackID modes, esp. probabilistic; options for tracking and target-making (with 3dROIMaker); @GradFlipTest to check grads; 3dDWUncert for estimating parameter uncertainty; extensions to HARDI and connectomes |
||
taking tracking outputs for quantitative analysis; tracked results into the multivariate modeling (MVM) framework; fat_mvm_prep.py and fat_mvm_scripter.py to combine MRI and non-MRI data for modeling; example analysis from real study |
||
FATCAT Hands-On Demo (Taylor) [first half only, at the moment] |
Overview of FATCAT Demo; visualization of DTI (+FMRI) data using AFNI and SUMA; viewing tractography output (solo, with surfaces, with ROIs, with FMRI data); making ROIs from FMRI; whole brain tracking and mask controller (with InstaTract); matrices and graphs in SUMA; FMRI+tractography visualizations |
