National Institute of Mental Health 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]

AFNI Controller Window

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

Volume Rendering Controls

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

“ShowThru” Rendering of Function

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)  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 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

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 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 = 0.54  = 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