I. Improving SNR (cont.) II. Preprocessing BIAC Graduate fMRI Course October 12, 2004

Increasing Field Strength

Theoretical Effects of Field Strength SNR = signal / noise SNR increases linearly with field strength –Signal increases with square of field strength –Noise increases linearly with field strength –A 4.0T scanner should have 2.7x SNR of 1.5T scanner T 1 and T 2 * both change with field strength –T 1 increases, reducing signal recovery –T 2 * decreases, increasing BOLD contrast

Adapted from Turner, et al. (1993)

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

Trial Averaging Static signal, variable noise –Assumes that the MR data recorded on each trial are composed of a signal + (random) noise Effects of averaging –Signal is present on every trial, so it remains constant through averaging –Noise randomly varies across trials, so it decreases with averaging –Thus, SNR increases with averaging

Fundamental Rule of SNR For Gaussian noise, experimental power increases with the square root of the number of observations

Example of Trial Averaging Average of 16 trials with SNR = 0.6

Increasing Power increases Spatial Extent Subject 1Subject 2 Trials Averaged ms s 500 ms …

AB

Number of Trials Averaged Number of Significant Voxels Subject 1 Subject 2 V N = V max [1 - e ( * N) ] Effects of Signal-Noise Ratio on extent of activation: Empirical Data

Active Voxel Simulation Signal + Noise (SNR = 1.0) Noise 1000 Voxels, 100 Active Signal waveform taken from observed data. Signal amplitude distribution: Gamma (observed). Assumed Gaussian white noise.

Effects of Signal-Noise Ratio on extent of activation: Simulation Data SNR = 0.10 SNR = 0.15 SNR = 0.25 SNR = 1.00 SNR = 0.52 (Young) SNR = 0.35 (Old) Number of Trials Averaged Number of Activated Voxels

Explicit and Implicit Signal Averaging r =.42; t(129) = 5.3; p <.0001 r =.82; t(10) = 4.3; p <.001 A B

Caveats Signal averaging is based on assumptions –Data = signal + temporally invariant noise –Noise is uncorrelated over time If assumptions are violated, then averaging ignores potentially valuable information –Amount of noise varies over time –Some noise is temporally correlated (physiology) Nevertheless, averaging provides robust, reliable method for determining brain activity

II. Preprocessing of FMRI Data

What is preprocessing? Correcting for non-task-related variability in experimental data –Usually done without consideration of experimental design; thus, pre-analysis –Occasionally called post-processing, in reference to being after acquisition Attempts to remove, rather than model, data variability

Quality Assurance

Tools for Preprocessing SPM Brain Voyager VoxBo AFNI Custom BIAC scripts

Slice Timing Correction

Why do we correct for slice timing? Corrects for differences in acquisition time within a TR –Especially important for long TRs (where expected HDR amplitude may vary significantly) –Accuracy of interpolation also decreases with increasing TR When should it be done? –Before motion correction: interpolates data from (potentially) different voxels Better for interleaved acquisition –After motion correction: changes in slice of voxels results in changes in time within TR Better for sequential acquisition

Effects of uncorrected slice timing Base Hemodynamic Response Base HDR + Noise Base HDR + Slice Timing Errors Base HDR + Noise + Slice Timing Errors

Base HDR: 2s TR

Base HDR + Noise r = 0.77 r = 0.80 r = 0.81

Base HDR + Slice Timing Errors r = 0.85 r = 0.92 r = 0.62

HDR + Noise + Slice Timing r = 0.65 r = 0.67 r = 0.19

Interpolation Strategies Linear interpolation Spline interpolation Sinc interpolation

Motion Correction

Head Motion: Good, Bad,…

… and catastrophically bad

Why does head motion introduce problems? ABC

Simulated Head Motion

Severe Head Motion: Simulation Two 4s movements of 8mm in -Y direction (during task epochs) Motion

Severe Head Motion: Real Data Two 4s movements of 8mm in -Y direction (during task epochs) Motion

Correcting Head Motion Rigid body transformation –6 parameters: 3 translation, 3 rotation Minimization of some cost function –E.g., sum of squared differences –Mutual information

Effects of Head Motion Correction

Limitations of Motion Correction Artifact-related limitations –Loss of data at edges of imaging volume –Ghosts in image do not change in same manner as real data Distortions in fMRI images –Distortions may be dependent on position in field, not position in head Intrinsic problems with correction of both slice timing and head motion

What is the best approach for minimizing the influence of head motion on your data?

Coregistration

Should you Coregister? Advantages –Aids in normalization –Allows display of activation on anatomical images –Allows comparison across modalities –Necessary if no coplanar anatomical images Disadvantages –May severely distort functional data –May reduce correspondence between functional and anatomical images

Normalization

Standardized Spaces Talairach space (proportional grid system) –From atlas of Talairach and Tournoux (1988) –Based on single subject (60y, Female, Cadaver) –Single hemisphere –Related to Brodmann coordinates Montreal Neurological Institute (MNI) space –Combination of many MRI scans on normal controls All right-handed subjects –Approximated to Talaraich space Slightly larger Taller from AC to top by 5mm; deeper from AC to bottom by 10mm –Used by SPM, fMRI Data Center, International Consortium for Brain Mapping

Normalization to Template Normalization TemplateNormalized Data

Anterior and Posterior Commissures Anterior Commissure Posterior Commissure

Should you normalize? Advantages –Allows generalization of results to larger population –Improves comparison with other studies –Provides coordinate space for reporting results –Enables averaging across subjects Disadvantages –Reduces spatial resolution –May reduce activation strength by subject averaging –Time consuming, potentially problematic Doing bad normalization is much worse than not normalizing (and using another approach)

Slice-Based Normalization Before Adjustment (15 Subjects) After Adjustment to Reference Image Registration courtesy Dr. Martin McKeown (BIAC)

Spatial Smoothing

Techniques for Smoothing Application of Gaussian kernel –Usually expressed in #mm FWHM –“Full Width – Half Maximum” –Typically ~2 times voxel size

Effects of Smoothing on Activity Unsmoothed Data Smoothed Data (kernel width 5 voxels)

Should you spatially smooth? Advantages –Increases Signal to Noise Ratio (SNR) Matched Filter Theorem: Maximum increase in SNR by filter with same shape/size as signal –Reduces number of comparisons Allows application of Gaussian Field Theory –May improve comparisons across subjects Signal may be spread widely across cortex, due to intersubject variability Disadvantages –Reduces spatial resolution –Challenging to smooth accurately if size/shape of signal is not known

Segmentation Classifies voxels within an image into different anatomical divisions –Gray Matter –White Matter –Cerebro-spinal Fluid (CSF) Image courtesy J. Bizzell & A. Belger

Histogram of Voxel Intensities

Bias Field Correction

Temporal Filtering

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

Power Spectra

Region of Interest Drawing

Why use an ROI-based approach? Allows direct, unbiased measurement of activity in an anatomical region –Assumes functional divisions tend to follow anatomical divisions Improves ability to identify topographic changes –Motor mapping (central sulcus) –Social perception mapping (superior temporal sulcus) Complements voxel-based analyses

Drawing ROIs Drawing Tools –BIAC software (e.g., Overlay2) –Analyze –IRIS/SNAP (G. Gerig from UNC) Reference Works –Print atlases –Online atlases Analysis Tools –roi_analysis_script.m

ROI Examples

BIAC is studying biological motion and social perception – here by determining how context modulates brain activity in elicited when a subject watches a character shift gaze toward or away from a target.

Additional Resources SPM website – –SPM Manual Brain viewers –