This Time. fmri Data analysis
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1 This Time Reslice example Spatial Normalization Noise in fmri Methods for estimating and correcting for physiologic noise SPM Example Spatial Normalization: Remind ourselves what a typical functional image volume looks like: fmri Data analysis fmri Data analysis fmri time-series Reorient Slice order Unwarp Realignment Slice timing fmri time-series Realignment Normalisation fmri Data analysis Reorient Slice order Unwarp Slice timing Talairach Coordinate System to provide mechanism for comparing data across different labs/studies Talairach coordinate system (space) Atlas provided by Talairach and Tournoux Based on a French woman s brain Center (0,0,0 is on the anterior commissure) Line passes through the Anterior commissure Posterior commissure line (a.k.a., AC-PC line) Template 1
2 Talairach Coordinate System Individual brains are different shapes and sizes How can we compare or average brains? Rotate brain into ACPC plane Corpus Callosum Find anterior commisure (AC) Talairach & Tournoux, 1988 squish or stretch brain into shoe box extract 3D coordinate (x, y, z) for each activation focus Pineal Body bent asparagus Fornix Find posterior commisure (PC) ACPC line = horizontal axis Note: official Tal sez use top of AC and bottom of PC Source: Duvernoy, 1999 Note: That s TalAIRach, not TAILarach! Deform brain into Talairach space Mark 8 points in the brain: anterior commisure posterior commisure front back top bottom (of temporal lobe) left right Squish or stretch brain to fit in shoebox of Tal system y y<0 AC=0 y>0 z y>0 Extract 3 coordinates ACPC=0 y<0 x Talairach Atlas 2
3 Talairach Pros and Cons Left is what?!!! Advantages widespread system allows averaging of fmri data between subjects allows researchers to compare activation foci easy to use Disadvantages based on the squished brain of an elderly alcoholic woman (how representative is that?!) not appropriate for all brains (e.g., Japanese brains don t fit well) activation foci can vary considerably other landmarks like sulci may be more reliable Ignores left/right differences Neurologic (i.e. sensible) convention left is left, right is right L R - + x = 0 Radiologic (i.e. stupid) convention left is right, right is left R L Note: Make sure you know what your magnet and software are doing before publishing left/right info! Note: If you re really unsure which side is which, tape a vitamin E capsule to the one side of the subject s head. It will show up on the anatomical image. + x = 0 - Talairach vs MNI Talairach space Revised in spm96 using MNI brain Montreal Neurological Institute Based on 304 normal subjects More representative of population 10-15% larger than Talairach space Much confusion in the literature Must convert to Talairach to MNI space Meta-analyses Planning studies, regions of interest Different groups/software use different methods Manuscripts should specify the actual space used 3
4 Spatial Transformations: Warping Strategies Label-based (identifiable features) Identification of homologous structures (features, landmarks) between 2 images Find transformation that best superposes labeled points Non-label-based (no corresponding feature) Spatial transformation minimizing index of difference between images Spatial Normalization fmri time-series Mean functional EPI image Slice order Unwarp Realignment Slice timing Normalisation EPI template Structural image (T1) T1 template Template Spatial Normalization Spatial Normalization Mean functional image EPI template Coregister subject s mean EPI to structural T1 image Problems: - coreg is linear operation - EPI warping and - EPI distortion not accounted -Unless you have: field map correction -issues slow acquisition Structural image (T1) Normalize T1 to T1 Template T1 template Mean functional image EPI template Normalize mean EPI to EPI Template Structural image (T1) Good: - normalization has linear and nonlinear components - EPI warping and - EPI distortion accounted for -Even better if you have: field map correction -issues slow acquisition Bad If mean EPI is different from EPI template T1 template normalization can go wrong - tune normalization - customize basis functions - create site template Spatial Normalization Spatial Normalization Data must be resliced into new MNI (Talairach space) Data must be interpolated sinc most likely best Must choose voxel sizes to reslice into: Inputs were 3.44 x 3.44 x 5.00mm Default is 2.00 x 2.00 x 2.00 We use 3.00 x 3.00 x
5 Spatial Normalisation - Non-linear Deformations consist of a linear combination of smooth basis functions Deformation Field These are the lowest frequencies of a 3D discrete cosine transform (DCT) Algorithm simultaneously minimises * Mean squared difference between template and source image * Squared distance between parameters and their known expectation Original Deformation field Warped Template Jacobians Spatial Normalisation - Procedure Begin with affine registration Non-linear registration (about 1000 parameters) Jacobian Matrix (or just Jacobian ) Jacobian Determinant (or just Jacobian ) - relative volumes Affine registration Non-linear registration Affine vs Non-Rigid A Look at the transformation Affine vs Non-Rigid Affine Non-Rigid Affine 12 parameters Non-Rigid ~ 2000 parameters Average Anatomical Images from 10 Subjects displayed at 1.5x1.5x1.5 mm 5
6 What are typical SNRs for fmri data? Signal amplitude MR units: 5-10 units (baseline: ~700) Percent signal change: 0.5-2% Noise amplitude MR units: Percent signal change: 0.5-5% SNR range Total range: 0.1 to 4.0 Typical: Measured Effects of Field Strength SNR usually increases by less than theoretical prediction Sub-linear increases in SNR; large vessel effects may be independent of field strength Where tested, clear advantages of higher field have been demonstrated But, physiological noise may counteract gains at high field ( > ~4.0T) Spatial extent increases with field strength Increased susceptibility artifacts 6
7 Types of Noise Thermal noise Responsible for variation in background Eddy currents, scanner heating Power fluctuations Typically caused by scanner problems Variation in subject cognition Timing of processes Head motion effects Physiological changes Differences across brain regions Functional differences Large vessel effects Artifact-induced problems Variability in Subject Behavior: Issues Cognitive processes are not static May take time to engage Often variable across trials Subjects attention/arousal wax and wane Subjects adopt different strategies Feedback- or sequence-based Problem-solving methods Subjects engage in non-task cognition Non-task periods do not have the absence of thinking Signal Size in fmri Contrast-to-Noise-Ratio (SNR) A 45 B 50 E Task-Related Variability C D (50-45)/45 Non-task-related Variability Filtering Approaches Identify unwanted frequency variation Drift (low-frequency) Physiology (high-frequency) Task overlap (high-frequency) Reduce power around those frequencies through application of filters Potential problem: removal of frequencies composing response of interest Standard Deviation Image 7
8 Drift Drift (low freq) CSF flow, spontaneous fluctations Field (gradient!) Receive Freq Linear/Cubic/Spline correction Drift Data Acquisition Need high temporal sampling rate to avoid aliasing cardiac and respiratory-rate effects BOLD fluctuations near middle cerebral arteries respiration Cardiac Pulse oximeter Data Acquisition need enough images to have good spectral resolution (i.e. enough d.o.f. for statistical power) Example: Acquire 512 images with TR=200ms Data Analysis: Functional Connectivity Physiological Noise Nyquist Frequency=2.5Hz Cardiac Respiratory Movement Spectral resolution=~0.01hz Only 10 spectral d.o.f after filtering > 0.1Hz Periodic Hz Periodic Hz Transitory / unknown 8
9 Physiological noise: Examples Cardiac noise Data Analysis: Data-Driven Functional Connectivity analysis Physiologic Signals Cardiac ( Hz) Gating Physiologic monitoring Bandpass filter Respiratory (.1-.5 Hz) Navigator echo Respiratory noise Chuang et al. (2001) MRM 46: RETROICOR Method RETROICOR method, (Glover et al. (2000)) models the physiological noise as a basis set of sines and cosines. 9
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13 Spatial Distribution of Noise A: Anatomical Image B: Noise image C: Physiological noise D: Motion-related noise E: Phantom (all noise) F: Phantom (Physiological) - Kruger & Glover (2001) 13
14 SPM Example Spatial Normalization 14
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