Preprocessing John VanMeter, Ph.D. Center for Functional and Molecular Imaging Georgetown University Medical Center

FixationThumb movement time Recall Analysis of fMRI Data is Based on Examining Changes in Voxel Across Time

Basic Preprocessing Chain

Slice Timing Correction fMRI data typically acquired using 2D acquisitions (i.e. one slice at a time) Order of slices can be acquired in different ways –Ascending or descending (slice order is sequential) –Interleave: even numbered slices acquired then odd numbered slices or vice versa Order of slice acquisition determines when in the hemodynamic response the signal for a slice is acquired

Slice Timing Correction Uses temporal interpolation to make it appear as though all of the slices were acquired at the same time Thus, HRF across slices are aligned Interpolation will be most effective if the sampling rate (time between slices) is higher than the changes in the data Generally more effective for short TR (1-2 secs) than larger TR (>3 secs)

Order of Slice Timing Correction and Motion Correction Slice timing first if data acquired interleaved –Small changes in head position between slices will cause timing differences to be off by 1/2 a TR Motion correct first if ascending or descending or short TR Slice timing correction generally unnecessary for block designs regardless of slice acquisition order and TR

Effect of Head Motion

Example of Ringing

Minimizing Head Motion Use of various types of restraints (padding, bite bar, vacuum cushion, face mask) Mock scanner training –Accustomization to environment –Reinforcement systems Impress importance of not moving on subjects

Motion Correction (aka Realignment) Coregister each volume in the run to a reference volume Possible reference volumes –First volume in the run –Middle volume in the run –Average of all the volumes in the run before motion correction No consensus on which choice of reference volume is best but typically first volume is used

Target Reslice

Surface Registration

Coregistration Algorithm Components Four components are needed for any coregistration algorithm –Model that defines the set of transformations (eg. translations, rotations, scaling, etc) used –Search algorithm to test different combinations of the model parameters to find the “best” set –Cost Function that provides estimate of how well registered the two volumes are –Interpolation algorithm to reslice/reformat and create a new volume based on the best model parameters

Transformation Described by number of ways that one volume can be changed to match another (degrees of freedom) Rigid-body transform allows for translation in all 3 directions and rotation about all 3 axes (6 degrees of freedom) Higher transforms could be used but for motion correction rigid-body transform is most common and appropriate

Search Algorithm Systematic method for searching for the best combination of transformation parameters that aligns the reslice volume to the target volume Cost function – provides measure of how closely aligned two images are

Standard Deviation of the Ratio = ? Standard Deviation of ratio of all brain voxels = 0 ResliceTargetRatio Image

Standard Deviation of the Ratio = ResliceTargetRatio Image Standard Deviation of ratio of all brain voxels >> 0 or

Reslice Target y x z 10 o

Image Interpolation Reslice

Interpolation Methods Nearest Neighbor –voxel intensity in new volume equal to the closest voxel in the original volume Trilinear –voxel intensity in new volume is based on weighted average of surrounding voxels from the original volume –weights based on distance from the new voxel

Reslice (Rz = -10 o ) Target Nearest Neighbor Trilinear

Original 90 o 180 o 360 o 10 o 20 o

SincWindowed Sinc

Original 10 o 20 o 90 o 180 o 360 o

Quality Assurance: Motion Correction Plots Examine the amount of motion in each run by looking at plots of motion Big spikes in motion plots problematic Run motion correction twice to see how much residual motion there was from first application of motion correction

Pre-motion correction Post-motion correction

How Much Motion is Acceptable? Examine residual motion correction plot for the maximal amount of motion in each dimension Ideal –Less than mm –Less than 0.5 o Patient population dependent –Children, rare diseases, hard to recruit subject, etc –Go with what you got

Distortion Correction Geometric distortion occurs in EPI sequences arising from inhomogeneity in static magnetic field Shimming adjusts for distortions due to head and body being in the field Automatic shimming generally available though not always sufficient to prevent distortion On our scanner geometric distortion is very minimal

Susceptibility Artifacts Ojemann, NeuroImage 1997

Examples of Geometric Distortion sinuses ear canals Poor ShimmingOur Scanner With Automatic Shimming

Geometric Distortion in Our Scanner Distortions

Field Mapping Collect two short scans at two different TE’s to determine differences in the phase component of the signal (difference is the field map) Used to correct distortion

Coregistration: Registering Functional Data to Structural Scan Registering functional and structural data useful for both looking at single subject results (e.g. overlaying statistical results onto that subjects structural scan) Also used as precursor to warping subject into standard or atlas space for multi-subject analyses

Spatial Normalization Variations between individual brains is large: –Overall size varies from 1100 cc to 1500 cc (30% variation) –Major landmarks such as central sulcus will vary considerable in position, branching, and length Spatial normalization warps individual brains into a common reference space Allows for examination of fMRI signal changes across individuals within a group or between groups of subjects Most commonly used reference space is based on the Talairach atlas

Talairach Atlas Atlas of brain anatomy Developed by two French neurosurgeons Jean Talairach and Pierre Tournoux Defines a coordinate system used for stereotaxic reference Stereotaxic space is a “precise” mapping of a brain into 3D coordinate system The Talairach atlas has become the de facto standard in Neuroimaging

Talairach Atlas Originally used to for surgical planning for deep subcortical structures in the brain (e.g. basal ganglion, thalamus, etc.) Based on the brain of 69 YO French female Numerous stories about the preparation of this woman’s brain for sectioning

Coronal Atlas Sections

Sagittal Atlas Sections

Disadvantages of Talairach Based on a single cadaver brain and not representative of younger living brains Left-Right hemispheric differences are ignored (assumed symmetric for spatial normalization) Cerebellum completely ignored Notorious problems (e.g. occipital lobe is much smaller in atlas than most brains) Non-European brains (e.g. Asian) do not fit well within the Talairach space

Talairach Coordinate System Origin located at the Anterior Commissure (AC) Three axes define coordinate system: –Axial plane parallel to the line passing through the AC – PC –Sagittal plane parallel to the AC – PC line –Coronal plane perpendicular to other two

Talairach Coordinate System X-axis goes from the Left Hemisphere to the Right Hemisphere. Positive X coordinate is Right Hemisphere. Range: -75 to +75mm Y-axis goes from the Anterior extent to the Posterior extent. Positive Y coordinate is anterior of the AC. Range: -105 to +70mm Z-axis goes from the Superior extent to the Inferior extent. Positive Z coordinate is above the plane passing through the AC-PC. Range: -40 to +65mm

Automated Talairach Registration Transform individual brains into a standard space – typically Talairach Space Colloquially referred to as Talairach’ing Formally referred to as Spatial Normalization Same components as motion correction – mathematical model, search algorithm, cost function, image interpolation

Spatial Normalization Register image of an individual brains to match a template image in a standard space, typically the Talairach atlas Linear models incorporate parameters for scale and skew (12 degrees of freedom) Nonlinear models use either polynomials or cosine functions to model warping Higher orders provide greater accuracy of fit

Templates As with the French woman’s brain, there are numerous stories about the various templates in use Several of the templates have been developed in an ad hoc and poorly documented fashion

T1-MRI Template (MNI 305)

EPI Template (FIL)

Methods of Template Creation Manually transform each individual brain into Talairach space using linear stretching then average - MNI 305 done this way with 305 young volunteer’s T1 MRI scans after Spatially normalize each subject to match an existing template then average Round-Robin registration of each subject to every other subject over multiple iterations, averaged then transformed into atlas space

Comparison of Template Creation Methods Round Robin Register to Existing

6-8 yrs yrs 32 yrs Pediatric Templates

Nonconformity of Templates with Talairach Atlas: MNI vs Talairach MNI 305 does not exactly match the atlas –MNI 305 is bigger in all three dimensions –Near center of the brain MNI 305 matches the atlas best –Towards the extents larger differences –Variations are not uniform, overall MNI 305 is ~5mm taller in the superior-inferior dimension however parts of the temporal lobes extend ~10mm lower

Accuracy of Talairach Registration Methods Classical Talairach method –Good alignment of subcortical structures –Cerebral structures substantially less well aligned –Subjective since landmarks especially PC and to lesser extent AC are difficult to identify Automated Spatial Normalization –Cerebral structures are aligned better –Overall alignment is better –But MNI and ICBM templates don’t conform to Talairach -> MNI vs Talairach space

Classical Talairach Method vs Nonlinear Spatial Normalization Classical Talairach Automated Nonlinear

Classical Talairach Method vs Nonlinear Spatial Normalization Classical Talairach T1 TemplateNonlinear Transformation

Converting MNI to Talairach: Piecewise Linear Above the AC (Z >= 0): –X'= X –Y'= Y Z –Z'= Y Z Below the AC (Z < 0): –X'= X –Y'= Y Z –Z'= Y Z

Converting MNI to Talairach: Nonlinear Atlas Piecewise-Linear correction Non-linear Correction

Comparison of Different Linear Models

SPM5 Spatial Normalization Method Co-register mean EPI image to subject’s MPRAGE (or other high resolution structural) Segment MPRAGE by tissue type (i.e. CSF, gray, and white matter) Spatially normalize subject’s gray matter map to template gray matter map Apply both transforms to each EPI volume in the run

SPM5 Spatial Normalization - Good Idea But… Structures of interest in fMRI are in gray matter Matching gray matter to gray matter bound to be an improvement But it relies on accuracy of co- register step

Coregistering EPI to MPRAGE Can be problematic Ratio Image EPI acquired with a FatSat pulse - thus scalp eliminated (good for fMRI; reduces ghosting) MPRAGE has bright fat signal from scalp Coregistration can get thrown off and try to fit edges of EPI to bright scalp instead of bran

Smoothing Form of a more general image processing technique called convolution Neighborhood operation New pixel value based on weighted sum of a pixel and its neighbors A convolution operator is referred to as a kernel

Average Kernel

* = ?

1 * = ?

A kernel is defined by its weights and size x3 5x5

Gaussian Kernel Weights in Gaussian kernel defined by normal distribution Gaussian kernel specified by Full-Width at Half Maximum (FWHM)

2D Gaussian Kernel

FWHM ~ 12mm 2

Example Reduced Noise Using Smoothing

Original PET Image 12mm 3 6mm 3 24mm 3

Gaussian Smoothing Advantage – Removes isolated points of noise in an image – Compensates for imperfect spatial normalization – Helps to prepare data to meet statistical analysis requirements – Reduces false positives in statistical analysis Disadvantage – Reduces the spatial resolution of the image; looks blurrier – Peaks are squashed Ideal filter size is one that matches the size of the areas of activation (Matched Filter Theorem) - generally unknown and varies across brain General rule of thumb for fMRI data is to use a filter width that is twice the voxel size

Smoothing & Statistical False Positives Random dataset left is unsmoothed and right is smoothed

Smoothing and SNR Smoothing actually increases the ability to detect task-related signal changes (i.e. functional SNR increases) Cost is of course reduced spatial resolution Relies on spatial correlation of fMRI data (i.e. neighboring voxels have similar intensity and activation characteristics)

Temporal Filtering Similar to spatial filtering expect the data are processed across time Some sources of temporal noise –Transient jumps and drops in the global signal from timepoint to timepoint (ie. overall brightness fluxuates) –Drift in MRI signal over time –Head movement

Global Normalization Global signal is defined as the average intensity of all the voxels in the brain Variations in the global signal can swamp the regional changes in the MRI signal that we are trying to measure Ratio correction divides the intensity at each voxel by the global signal in that scan and multiples by a constant ANCOVA is an alternative that allows for differences in variations based on trial type or condition

Ratio Normalization

Signal Drift Across Time Gradually, over time the MRI signal in a voxel drifts This drift can vary from one voxel to the next A simple model of this drift is as a linear rise or fall in the signal

Linear Detrending

High-Pass Filtering Similar to linear detrending but higher order variations are also removed Types of signal variations that can be corrected are more complex All of the signal below a certain frequency is removed.

Non-linear Drift

Effect of Slow Non-linear Drifts

High-Pass Filtering Nyquist Sampling Theorem – removing signal fluxuations that are greater than half the frequency of the task potentially removes signal of interest –Thus, cut-off frequency should be half the task frequency For block designs the cut frequency can be determined from the periodicity of your paradigm The cut-off is specified in time in SPM –Thus, combined time of the on- and off-blocks times 2 is the cut-off for a block design For event related designs there is no simple formula; using a cut-off that is a ~2 minutes long is a safe bet

High-Pass Filtering

Band-Pass Filter Remove signal changes that are both higher and lower than task frequency in a block design Cut-offs would be set to 2x and 1/2x the task frequency Advantage is removal of both signal drift (high- pass) and cardiac, respiration signal changes (low-pass)

Low-Pass Filtering Typical Task Frequency

Quality Control in fMRI Very important to examine your data after each stage of preprocessing and statistics –Look at raw data for artifacts –Examine realignment plots –Examine mean & variance images before and after realignment (ana4dto3d toolbox for SPM) –Look at movies of time series before and after realignment (MEDx or AQuA toolbox for SPM) –Examine plots of image-to-image signal variance (tsdiffana toolbox for SPM) –Examine how well spatial normalization worked (use Check Reg in SPM) –Look at single subject statistics for ringing (best with task vs. lowest control condition) –Do statistics make sense (eg. if motor response used is the appropriate motor cortex activated?)

fMRI Raw Data Artifacts Spikes - caused by transient electrical discharges during scanning; something is leaking RF noise into the scanner room Ghosting - Nyquist reconstruction artifacts can sometimes be limited by choice of certain acquisition parameters (eg. echo spacing)

Ghosting on Our Scanner On our scanner adjusting echo spacing to be outside of ms minimizes ghosting Ghosting Artifact

BIG Technique Experimental Design Behavior Interleaved Gradient (BIG) technique (aka Sparse Sampling) puts a fixed delay between each EPI acquisition During this break auditory stimuli can be presented without noise from gradients Subjects can verbally respond with minimal effect on motion in the EPI image Relies on delay in hemodynamic response Also called Sparse Sampling

BIG Acquisition Requires accurate timing of the task relative to the the scan acquisition to capture peak BOLD response in the volume Greatly increases scan time