1. National Institute of Mental Health
AFNI
Robert W Cox, PhD
National Institute of Mental Health
Bethesda, MD

2. An FMRI Analysis Environment
Philosophy:
Encompass all needed classes of data and computations
Extensibility + Openness + Scalability: Anticipating what will be needed to solve problems that have not yet been posed
Interactive vs. Batch operations: Stay close to data or view from a distance
Components:
Data Objects: Arrays of 3D arrays + auxiliary data
Data Viewers: Numbers, Graphs, Slices, Volumes
Data Processors: Plugins, Plugouts, Batch Programs

3. Steps in Processing with AFNI
Image assembly into datasets [to3d ]
Can be done at scanner with realtime plugin
Image registration [3dvolreg]
Functional activation [AFNI, 3dfim]
Linear and nonlinear time series regression [3dDeconvolve, 3dNLfim]
Transformation to Talairach [AFNI ]
Or: selection of anatomical ROIs [AFNI ]
Integration of results across subjects [many]
Visualization & thinking [AFNI & you]

4. AFNI Controller Window

5. Interactive Analysis with AFNI
Control
Panel
Displaying EP images
from time series
Graphing voxel
time series data

6. Looking at the Results Multislice layouts
FIM overlaid on SPGR, in Talairach coords

7. Volume Rendering Controls

8. Sample Rendering: Coronal slice viewed from side; function not cut out
Rendering is easy to
setup and carry out
from control panel

9. “ShowThru” Rendering of Function

10. Integration of Results
Done with batch programs (usually in scripts)
3dmerge: edit and combine 3D datasets
3dttest: voxel-by-voxel t-tests
3dANOVA:
Voxel-by-voxel: 1-, 2-, and 3-way layouts
Fixed and random effects
Other voxel-by-voxel statistics are available
3dpc: principal components (space  time)
ROI analyses are labor-intensive alternative

11. Extending AFNI Package
Batch programs
Output new 3D datasets for viewing with AFNI
Plugins — searched for & loaded at startup
Add interactive capabilities to AFNI program
“Fill in the blanks” menu for input from users
40 page manual and some samples included
Plugouts — attach themselves during run
External programs that communicate with AFNI with shared memory or TCP/IP sockets

12. Whole Brain Realtime FMRI
Assembly of images into AFNI datasets during acquisition
Can use AFNI tools to visualize during scanning
Realtime 3D registration
Graph of estimated motion parameters
Recursive signal processing to update activation map with each new data volume
Color overlay changes with each TR

13. Realtime AFNI
AFNI software package has a realtime plugin, distributed with every copy
Price: USD$0 [except for time & effort]
Runs on Unix/Linux
Requires input of reconstructed images and geometrical information about them
For more information see Web site

14. The Goal: Interactive Functional Brain Mapping
See functional map as scanning proceeds
1 minute minutes minutes

15. Registration Goals for Online FMRI
Estimate 3D (6 DOF) movement parameters as fast as volume acquisition happens
Realign each volume to a “target” volume during scanning
Display updating graphs of estimated motions to investigator
Feedback movements to slice selection?

16. Estimated
subject
movement
parameters

17. Multiple References v(t)  1 h1(t ) + 2 h2(t ) + 
1 , 2 are amplitudes (unknown)
h1 , h2 are known reference responses
Used for experiments with more than 1 stimulus condition:
rest task A rest task B rest task A 
h1  h2  h1  0
Widely used for event-related FMRI

18. Linear Deconvolution v(t)  a + bt + j h(tj) + noise
j = stimulation times [known]
h(t) = k kuk(t) = response function
uk(t) = basis functions [known]
k = amplitudes [unknown]
Goal is to find shape and amplitude of response function in each voxel
Unlike previous analyses, form of response is not completely bound to hemodynamic assumptions

19. Recursive Linear Regression
All methods above can be cast into form of linear regression:
Solution of linear equations to get estimated fit parameters
Estimation of significance from noise model [i.e., using what’s left after regression fit]
Recursive regression:
With each new time point, add one equation
Given previous solution, can re-compute new fit with relatively little work (much less than starting over)
Method used in AFNI for realtime analysis

20. Nonlinear Regression or Deconvolution
v(t)  a + bt + j h(tj) + noise
h(t) = h(t ;  ) = nonlinearly dependent on 
 = vector of unknown parameters
Example: h(t) = A (t)r exp((t)/c)
r = 8.6 c =  = 0

21. Single Event FMRI: Use Nonlinear Regression?
In this type of experiment, the stimulus and its consequences last a long time
Only have one stimulus/response event per imaging run
Administration of a drug
Presentation of an affect altering video
Know when stimulus started, but don’t know exactly what response should be
Nonlinear curve fitting seems appropriate