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AFNI. Robert W Cox, PhD National Institute of Mental Health Bethesda, MD http:// afni.nimh.nih.gov/afni. An FMRI Analysis Environment. Philosophy: Encompass all needed classes of data and computations

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## AFNI

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**AFNI**Robert W Cox, PhD National Institute of Mental Health Bethesda, MD http://afni.nimh.nih.gov/afni**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**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]**Interactive Analysis with AFNI**Control Panel Displaying EP images from time series Graphing voxel time series data**Looking at the Results**Multislice layouts FIM overlaid on SPGR, in Talairach coords**Sample**Rendering: Coronal slice viewed from side; function not cut out Rendering is easy to setup and carry out from control panel**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**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**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**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**The Goal: Interactive Functional Brain Mapping**• See functional map as scanning proceeds 1 minute 2 minutes 3 minutes**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?**Estimated**subject movement parameters**Multiple References**• v(t) 1h1(t) + 2h2(t) + • 1 , 2 are amplitudes (unknown) • h1 , h2are known reference responses • Used for experiments with more than 1 stimulus condition: rest task A rest task B rest task A h1 0h2 0 h1 0 • Widely used for event-related FMRI**Linear Deconvolution**• v(t) a + bt +jh(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 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**Nonlinear Regression orDeconvolution**• v(t) a + bt +jh(tj) + 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=0.54 =0**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

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