afni robert w cox, phd national institute of mental health bethesda, md afni.nimh.nih.gov/afni...
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AFNIAFNIRobert W Cox, PhDRobert W Cox, PhD
National Institute of Mental Health
Bethesda, MD
http://afni.nimh.nih.gov/afniafni.nimh.nih.gov/afni
An FMRI Analysis An FMRI Analysis EnvironmentEnvironment 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
Steps in Processing with Steps in Processing with AFNIAFNI 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]
Interactive Analysis with Interactive Analysis with AFNIAFNI
Graphing voxeltime series data
Displaying EP imagesfrom time series
ControlPanel
SampleRendering:
Coronal sliceviewed from side;
function not cut out
Rendering is easy tosetup and carry outfrom control panel
Integration of ResultsIntegration 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
Extending Extending AFNIAFNI Package 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
Whole Brain Whole Brain Realtime FMRIRealtime 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
Realtime Realtime AFNIAFNI 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
The Goal: Interactive The Goal: Interactive Functional Brain MappingFunctional Brain Mapping
See functional map as scanning proceeds
1 minute 2 minutes 3 minutes
Registration Goals for Registration Goals for Online FMRIOnline 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?
Multiple ReferencesMultiple 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 0 h2 0 h1 0
Widely used for event-related FMRI
Linear DeconvolutionLinear Deconvolution v(t) a + bt + j h(tj) + noise
j = stimulation times [known]
h(t) = k kuk(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
Recursive Linear RegressionRecursive 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
v(t) a + bt + j h(tj) + noise
h(t) = h(t ; ) = nonlinearly dependent on
= vector of unknown parameters Example: h(t) = A (t)r exp((t)/c)
Nonlinear Regression orNonlinear Regression orDeconvolutionDeconvolution
0 1 2 3 4 5 6 7 8 9 10 11
r = 8.6 c = 0.54 = 0
Single Event FMRI:Single Event FMRI:Use Nonlinear Regression?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
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