AFNI & FMRI Introduction, Concepts, Principles

AFNI & FMRI Introduction, Concepts, Principles

AFNI & S UMA Concepts, Principles, Demos http://afni.nimh.nih.gov/afni Some Goals of FMRI Analyses Task-based experiments Per subject: estimate amplitude of BOLD response to each different type of stimulus Find+model inter-regional correlations between fluctuations in BOLD responses Resting-state experiments Measure spatial patterns in coherent

fluctuations in spontaneous BOLD Group level Combine and contrast per subject results pre-processing Conceptual Basis - 1 Time shifting = pretend get 3D snapshot Despiking = remove large blips Image Registration (AKA alignment) intra-EPI time series, and EPI-Structural Blurring in space = lower resolution :( & less noise :-) & more group overlap :-) Masking = ignore non-brain voxels Scaling = normalizing data amplitude Makes inter-subject comparisons more valid Conceptual Basis - 2

Time series regression model of the BOLD response in the data = Hemodynamic Response Function stimulus timing plus baseline (null hypothesis) model plus physiological noise plus allowing for serial correlation Talairach-ing = Spatial Normalization Talairach, MNI-152, affine and nonlinear spatial transformations Conceptual Basis - 3 Group Analyses = Putting it all together ANOVA, LME, Meta-Analyses, Blobs = Spatial models of activation

Assigning statistical significance to blobs Connectivity = Inter-regional analyses SEM, PPI, SVAR, DCM, Granger, Resting state FMRI (Connectome! ) Dimensional factorization Components, such as PCA, ICA, Conceptual Basis - 4 Data Formats = NIfTI-1.x is your friend Software for FMRI analyses: open-source * AFNI*, BrainVoyager, FSL*, SPM*, Whichever you use, don't blindly assume the software works perfectly all the time

Most important thing I will say today Understand and check the steps applied to your data! 2nd most important: Is no "best" way to analyze data, just "reasonable" ways AFNI = Analysis of Functional NeuroImages Developed to provide an environment for FMRI data analyses And a platform for development of new software tools AFNI refers to both the program of that name and the entire package of external programs and plugins (more than 200) The Prime Directive in the development of AFNI: Allow users to stay close to their data and view/analyze it in many different ways SSCC = Scientific Computing and Statistical Core

Our mission is help NIH (and beyond) investigators carry out the analyses of their (F)MRI data Development of data analysis methods and putting them into usable and (reasonably) reliable software Consulting and question answering and hand-holding Fundamental AFNI Concept Basic unit of data in AFNI is the dataset A collection of 1 or more 3D arrays of numbers Each entry in the array is in a particular spatial location in a 3D grid (a voxel = 3D pixel) o Image datasets: each array holds a collection of slices from the scanner

Each number is the signal intensity for that particular voxel o Derived datasets: each number is computed from other dataset(s) e.g., each voxel value is a t-statistic reporting activation significance from an FMRI time series dataset, for that voxel o Each 3D array in a dataset is called a sub-brick o There is one number in each voxel in each sub-brick 3x3x3 Dataset With 4 Sub-bricks Parts of AFNI

Interactive visualization and analysis AFNI and SUMA For looking at data and results AFNI is based on 3D volumes = data as gathered by MRI SUMA is based on 2D surfaces = models of cortical surfaces A few kinds of analysis can be done by pointing+clicking Batch mode programs and scripts Are run by typing commands directly to computer, or by putting commands into a text file (script) and later executing them Most AFNI complex analyses are done in batch programs Plugins and Plugouts Separate programs that attach themselves to AFNI and/or SUMA to provide extra capabilities 0

AFNI & SUMA Interlude 1 AFNI Batch Programs Many many important capabilities in AFNI are only available in batch programs A few examples (of more than 100) ber-scripts: afni_proc.py and align_epi_anat.py FMRI time series pre-processing and analysis Driver for 3D image registration tools 3dDeconvolve + 3dREMLfit = multiple linear regression on 3D+time datasets; fits each voxels time series to activation

model, tests these fits for significance (3dNLfim = nonlinear fitting) 3dvolreg = 3D+time dataset registration, to correct for small subject head movements, and for inter-day head positioning 3dANOVA + 3dLME + 3dMEMA = ANOVA/t-test group analyses: combining & contrasting datasets in Talairach space 3dsvm = SVM multi-voxel pattern analysis program 3dDWItoDT = compute diffusion tensor from DWI (nonlinearly) 2 Analysis by Super-Script by hand Script to analyze one imaging run (5 min) of data from one subject [ cd AFNI_data6/afni ; tcsh quick.s1.afni_proc ] afni_proc.py -dsets epi_r1+orig -copy_anat anat+orig \ -tcat_remove_first_trs 2 \ -do_block align \

-regress_stim_times quick.r1_times.txt \ -regress_basis 'BLOCK(20,1)' \ -execute Stimulus timing in file quick.r1_times.txt 0 30 60 90 120 150 180 210 240 270 20 s of stimulus per block, starting at the given times FMRI data in file epi_r1+orig Anatomical volume in file anat+orig Actions: Align slices in time; align Anat to EPI; motion correct EPI; blur in space; activation analysis (thru time) in each voxel 3 Analysis by Super-Script by GUI 4

FMRI Experiment Design and Analysis All on one FMRI experiment design unreadable slide! Event-related, block, hybrid event-block? How many types of stimuli? How many of each type? Timing (intra- & inter-stim)? Will experiment show what you are looking for? (Hint: bench tests) How many subjects do you need? (Hint: the answer does not have 1 digit) Time series data analysis (individual subjects)

afni_proc.py & uber_subject.py Assembly of images into AFNI datasets; Visual & automated checks for bad data Registration of time series images (AKA motion correction and EPI-anat alignment) Smoothing & masking of images; Baseline normalization; Censoring bad data Catenation of multiple imaging runs into one big dataset Fit statistical model of stimulus timing+hemodynamic response to time series data o Fixed-shape or variable-shape response models [pattern matching in time] Segregation into differentially activated blobs (i.e., what got turned on or off?) o Threshold on statistic + clustering and/or Anatomically-defined ROI analysis Visual examination of maps and fitted time series for validity and meaning

Group analysis (inter-subject) Spatial normalization to Talairach-Tournoux atlas (or something like it; e.g., MNI) Smoothing of fitted parameters o Automatic global smoothing + voxel-wise analysis or ROI averaging ANOVA+ to combine and contrast activation magnitudes from the various subjects Visual examination of results (usually followed by confusion) Write paper, argue w/ mentor, submit paper, argue w/ referees, publish paper, 5 Getting and Installing AFNI AFNI runs on Unix systems: Linux, Sun, Mac OS X

Can run under Windows with Cygwin Unix emulator o This option is really just for trying it out not for regular use! If you are at the NIH: SSCC can install AFNI and update it on your system(s) You must give us an account with ssh access You can download precompiled binaries from our Website http://afni.nimh.nih.gov/afni Also: documentation, message board, humor, data, You can download source code and compile it AFNI is updated fairly frequently, so it is important to update occasionally We cant help you with old versions! 6

AFNI at the NIH Scanners AFNI can take 2D (or 3D) images in realtime from an external program and assemble them into 3D+time datasets slice-by-slice at each TR then update the images+graphs Jerzy Bodurka (ex-FMRIF) and Vinai Roopchansingh have set up the GE FMRI scanners (3 Ts, 1.5 T, and 7 T) to start AFNI automagically when scanning, and send reconstructed images over to the AFNI box as soon as they are available: For immediate display (images and graphs of time series) Plus: Plus graphs of estimated subject head movement Also possible: feedback to subject in realtime Goal is to let you see image data as they are acquired, so that if there are any big problems, you can fix them right away Sample problem: someone typed in the imaging field-of-view (FOV) size wrong (240 cm instead of 24 cm), and so got garbage data, but only realized this too late (after scanning 8 subjects this way) Doh!

7 Thats All, Folks

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