1.%20Preprocessing%20of%20FMRI%20Data

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1. Preprocessing of FMRI Data

• fMRI Graduate Course
• October 22, 2003

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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

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Signal, noise, and the General Linear Model
Amplitude (solve for)
Measured Data
Noise
Design Model
Cf. Boynton et al., 1996
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Signal-Noise-Ratio (SNR)
Task-Related Variability
Non-task-related Variability
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Preprocessing Steps

• Slice Timing Correction
• Motion Correction
• Coregistration
• Normalization
• Spatial Smoothing
• Segmentation
• Region of Interest Identification
• Bias field correction

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Tools for Preprocessing

• SPM
• Brain Voyager
• VoxBo
• AFNI
• Custom BIAC scripts

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Slice Timing Correction
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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

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Effects of uncorrected slice timing

• Base Hemodynamic Response
• Base HDR Noise
• Base HDR Slice Timing Errors
• Base HDR Noise Slice Timing Errors

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Base HDR 2s TR
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Base HDR Noise
r 0.77
r 0.81
r 0.80
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Base HDR Slice Timing Errors
r 0.92
r 0.85
r 0.62
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HDR Noise Slice Timing
r 0.65
r 0.67
r 0.19
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Interpolation Strategies

• Linear interpolation
• Spline interpolation
• Sinc interpolation

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Motion Correction
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Head Motion Good, Bad,
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and catastrophically bad
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Why does head motion introduce problems?
A
B
C
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Simulated Head Motion
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Severe Head Motion Simulation
Two 4s movements of 8mm in -Y direction (during
task epochs)
Motion
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Severe Head Motion Real Data
Two 4s movements of 8mm in Y direction (during
task epochs)
Motion
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Correcting Head Motion

• Rigid body transformation
• 6 parameters 3 translation, 3 rotation
• Minimization of some cost function
• E.g., sum of squared differences

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Effects of Head Motion Correction
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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

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Prevention is the best medicine
C
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Coregistration
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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

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Normalization
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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, National fMRI Database,
  International Consortium for Brain Mapping

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Normalization to Template
Normalization Template
Normalized Data
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Anterior and Posterior Commissures
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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

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

• Application of Gaussian kernel
• Usually expressed in mm FWHM
• Full Width Half Maximum
• Typically 2 times voxel size

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

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Segmentation

• Classifies voxels within an image into different
  anatomical divisions
• Gray Matter
• White Matter
• Cerebro-spinal Fluid (CSF)

Image courtesy J. Bizzell A. Belger
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Histogram of Voxel Intensities
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Region of Interest Drawing
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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

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Drawing ROIs

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

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ROI Examples
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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.
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Additional Resources

• SPM website
• http//www.fil.ion.ucl.ac.uk/spm/course/notes01.ht
  ml
• SPM Manual
• Brain viewers
• http//www.bic.mni.mcgill.ca/cgi/icbm_view/

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2. Issues in Experimental Design

• fMRI Graduate Course
• October 23, 2003

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What is Experimental Design?

• Controlling the timing and quality of presented
  stimuli to influence resulting brain processes
• What can we control?
• Experimental comparisons (what is to be
  measured?)
• Stimulus properties (what is presented?)
• Stimulus timing (when is it presented?)
• Subject instructions (what do subjects do with
  it?)

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Goals of Experimental Design

• To maximize the ability to test hypotheses
• To facilitate generation of new hypotheses

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What are hypotheses?

• Statements about the relations between
  independent and dependent variables.

A
B
C
D
Hemodynamic Hypotheses
Neuronal Hypotheses
Psychological Hypotheses
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Independent Variables

• Aspects of the experimental design that we want
  to manipulate
• Often have multiple levels (e.g., experimental
  and control conditions)
• Critical design choice lies in determining how to
  choose stimuli to match independent variable

A
B
C
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Dependent Variable BOLD signal
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Causal and non-causal relations between variables
A
B
Is the BOLD response epiphenomenal?
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Detection vs. Estimation

• Detection What is active?
• Estimation How does its activity change over
  time?

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Detection

• Detection power defined by SNR
• Depends greatly on hemodynamic response shape

SNR aM/?
M hemodynamic changes (unit) a measured
amplitude ? noise standard deviation
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Estimation

• Ability to determine the shape of fMRI response
• Accurate estimation relies on minimization of
  variance in estimate of HDR at each time point
• Efficiency of estimation is generally independent
  of HDR form

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Optimal Experimental Design

• Maximizing both Detection and Estimation
• Maximal variance in stimulus timing (increases
  estimation)
• Maximal variance in measured signal (increases
  detection)
• Limitations
• Refractory effects
• Signal saturation

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fMRI Design Types

• Blocked Designs
• Event-Related Designs
• Periodic Single Trial
• Jittered Single Trial
• Staggered Single Trial
• Mixed Designs
• Combination blocked/event-related
• Variable stimulus probability

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1. Blocked Designs
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What are Blocked Designs?

• Blocked designs segregate different cognitive
  processes into distinct time periods

Task A
Task B
Task A
Task B
Task A
Task B
Task A
Task B
Task A
Task B
REST
REST
Task A
Task B
REST
REST
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PET Designs

• Measurements done following injection of
  radioactive bolus
• Uses total activity throughout task interval
  (30s)
• Blocked designs necessary
• Task 1 Injection 1
• Task 2 Injection 2

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Choosing Length of Blocks

• Longer block lengths allow for stability of
  extended responses
• Hemodynamic response saturates following extended
  stimulation
• After about 10s, activation reaches max
• Many tasks require extended intervals
• Processing may differ throughout the task period
• Shorter block lengths allow for more transitions
• Task-related variability increases (relative to
  non-task) with increasing numbers of transitions
• Periodic blocks may result in aliasing of other
  variance in the data
• Example if the person breathes at a regular rate
  of 1 breath/5sec, and the blocks occur every 10s

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Effects of Block Interval upon HDR
40s
20s
15s
10s
8s
6s
4s
2s
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What baseline should you choose?

• Task A vs. Task B
• Example Squeezing Right Hand vs. Left Hand
• Allows you to distinguish differential activation
  between conditions
• Does not allow identification of activity common
  to both tasks
• Can control for uninteresting activity
• Task A vs. No-task
• Example Squeezing Right Hand vs. Rest
• Shows you activity associated with task
• May introduce unwanted results

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Interpreting Baseline Activity
From Gusnard Raichle, 2001
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Non-Task Processing

• In many experiments, activation is greater in
  baseline conditions than in task conditions!
• Requires interpretations of significant
  activation
• Suggests the idea of baseline/resting mental
  processes
• Emotional processes
• Gathering/evaluation about the world around you
• Awareness (of self)
• Online monitoring of sensory information
• Daydreaming

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From Shulman et al., 1997 (PET data)
From Binder et al., 1999
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From Huettel et al., 2002 (Baseline gt Target
Detection)
From Huettel et al., 2001 (Change Detection)
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Power in Blocked Designs

1. Summation of responses results in large variance

Single, unit amplitude HDR, convolved by 1, 2, 4
,8, 12, or 16 events (1s apart).
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HDR Estimation Blocked Designs
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Power in Blocked Designs

• 2. Transitions between blocks

Simulation of single run with either 2 or 10
blocks.
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Power in Blocked Designs

• 2. Transitions between blocks

Addition of linear drift within run.
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Power in Blocked Designs

• 2. Transitions between blocks

Addition of noise (SNR 0.67)
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Limitations of Blocked Designs

• Very sensitive to signal drift
• Sensitive to head motion, especially when only a
  few blocks are used.
• Poor choice of baseline may preclude meaningful
  conclusions
• Many tasks cannot be conducted repeatedly
• Difficult to estimate the HDR

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2. Event-Related Designs
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What are Event-Related Designs?

• Event-related designs associate brain processes
  with discrete events, which may occur at any
  point in the scanning session.

time
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Why use event-related designs?

• Some experimental tasks are naturally
  event-related
• Allows studying of trial effects
• Simple analyses
• Selective averaging
• No assumptions of linearity required

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Event-Related and Blocked Designs give Similar
Results
A
B
C
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2a. Periodic Single Trial Designs

• Stimulus events presented infrequently with long
  interstimulus intervals

500 ms
500 ms
500 ms
500 ms
18 s
18 s
18 s
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Trial Spacing Effects Periodic Designs
20sec
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ISI Interstimulus Interval SD Stimulus
Duration
From Bandettini and Cox, 2000
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2b. Jittered Single Trial Designs

• Varying the timing of trials within a run

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Randomization Jittering
Dale Buckner, 1997
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Extracting different task components
A
B
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Effects of Jittering on Stimulus Variance
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Effects of ISI on Power
Birn et al, 2002
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2c. Staggered Single Trial

• By presenting stimuli at different timings,
  relative to a TR, you can achieve sub-TR
  resolution
• Significant cost in number of trials presented
• Resulting loss in experimental power
• Very sensitive to scanner drift and other sources
  of variability

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0s
Two HDR epochs sampled at a 3s TR.
1s
Each row is sampled at a different phase.
2s
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0s
Two of the phases are normal.
1s
But, one has a change in one trial (e.g., head
motion)
2s
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Post-Hoc Sorting of Trials
Data from old/new episodic memory test.
From Konishi, et al., 2000
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Limitations of Event-Related Designs

• Differential effects of interstimulus interval
• Long intervals do not optimally increase stimulus
  variance
• Short intervals may result in refractory effects
• Detection ability dependent on form of HDR
• Length of event may not be known

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3. Mixed Designs
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3a. Combination Blocked/Event

• Both blocked and event-related design aspects are
  used (for different purposes)
• Blocked design is used to evaluate
  state-dependent effects
• Event-related design is used to evaluate
  item-related effects
• Analyses are conducted largely independently
  between the two measures
• Cognitive processes are assumed to be independent

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Mixed Blocked/Event-related Design
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Mixed designs
Donaldson et al., 2001
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3b. Variable Stimulus Probability

• Stimulus probability is varied in a blocked
  fashion
• Appears similar to the combination design
• Mixed design used to maximize experimental power
  for single design
• Assumes that processes of interest do not vary as
  a function of stimulus timing
• Cognitive processing
• Refractory effects

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Random and Semi-Random Designs
From Liu et al., 2001
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Summary of Experiment Design

• Main Issues to Consider
• What design constraints are induced by my task?
• What am I trying to measure?
• What sorts of non-task-related variability do I
  want to avoid?
• Rules of thumb
• Blocked Designs
• Powerful for detecting activation
• Useful for examining state changes
• Event-Related Designs
• Powerful for estimating time course of activity
• Allows determination of baseline activity
• Best for post hoc trial sorting
• Mixed Designs
• Best combination of detection and estimation
• Much more complicated analyses