fMRI Analysis with emphasis on the general linear model

1
fMRI Analysiswith emphasis on the general linear
model
Jody Culham Department of Psychology University
of Western Ontario
http//www.fmri4newbies.com/
Last Update November 29, 2008 fMRI Course,
Louvain, Belgium
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What data do we start with

• 12 slices 64 voxels x 64 voxels 49,152 voxels
• Each voxel has 136 time points
• Therefore, for each run, we have 6.7 million data
  points
• We often have several runs for each experiment

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Why do we need stats?

• We could, in principle, analyze data by voxel
  surfing move the cursor over different areas and
  see if any of the time courses look interesting

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Why do we need stats?

• Clearly voxel surfing isnt a viable option.
  Wed have to do it 49,152 times in this data set
  and it would require a lot of subjective
  decisions about whether activation was real
• This is why we need statistics
• Statistics
• tell us where to look for activation that is
  related to our paradigm
• help us decide how likely it is that activation
  is real

The lies and damned lies come in when you write
the manuscript
5
Predicted Responses

• fMRI is based on the Blood Oxygenation Level
  Dependent (BOLD) response
• It takes about 5 sec for the blood to catch up
  with the brain
• We can model the predicted activation in one of
  two ways
• shift the boxcar by approximately 5 seconds (2
  images x 2 seconds/image 4 sec, close enough)
• convolve the boxcar with the hemodynamic response
  to model the shape of the true function as well
  as the delay

PREDICTED ACTIVATION IN OBJECT AREA
PREDICTED ACTIVATION IN VISUAL AREA
BOXCAR
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Types of Errors
p value probability of a Type I error e.g., p
lt.05 There is less than a 5 probability that a
voxel our stats have declared as active is in
reality NOT active
Slide modified from Duke course
7
Statistical Approaches in a Nutshell

• t-tests
• compare activation levels between two conditions
• use a time-shift to account for hemodynamic lag

• correlations
• model activation and see whether any areas show a
  similar pattern

• Fourier analysis
• Do a Fourier analysis to see if there is energy
  at your paradigm frequency

Fourier analysis images from Huettel, Song
McCarthy, 2004, Functional Magnetic Resonance
Imaging
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Effect of Thresholds
r 0 0 of variance p lt 1
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Complications

• Not only is it hard to determine whats real, but
  there are all sorts of statistical problems

• Potential problems
• data may be contaminated by artifacts (e.g., head
  motion, breathing artifacts)
• .05 49,152 2457 significant voxels by
  chance alone
• many assumptions of statistics (adjacent voxels
  uncorrelated with each other adjacent time
  points uncorrelated with one another) are false

Whats wrong with these data?
r .24 6 of variance p lt .05
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The General Linear Model

• T-tests, correlations and Fourier analysis work
  for simple designs and were common in the early
  days of imaging
• The General Linear Model (GLM) is now available
  in many software packages and tends to be the
  analysis of choice
• Why is the GLM so great?
• the GLM is an overarching tool that can do
  anything that the simpler tests do
• you can examine any combination of contrasts
  (e.g., intact - scrambled, scrambled - baseline)
  with one GLM rather than multiple correlations
• the GLM allows much greater flexibility for
  combining data within subjects and between
  subjects
• it also makes it much easier to counterbalance
  orders and discard bad sections of data
• the GLM allows you to model things that may
  account for variability in the data even though
  they arent interesting in and of themselves
  (e.g., head motion)
• as we will see later in the course, the GLM also
  allows you to use more complex designs (e.g.,
  factorial designs)

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A Simple Experiment

• Lateral Occipital Complex
• responds when subject views objects

Blank Screen
Intact Objects
Scrambled Objects
TIME
One volume (12 slices) every 2 seconds for 272
seconds (4 minutes, 32 seconds) Condition
changes every 16 seconds (8 volumes)
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Whats real?
A.
C.
B.
D.
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Whats real?

• I created each of those time courses based by
  taking the predictor function and adding a
  variable amount of random noise

signal


noise
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Whats real?
Which of the data sets below is more convincing?
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Formal Statistics

• Formal statistics are just doing what your
  eyeball test of significance did
• Estimate how likely it is that the signal is real
  given how noisy the data is
• confidence how likely is it that the results
  could occur purely due to chance?
• p value probability value
• If p .03, that means there is a .03/1 or 3
  chance that the results are bogus
• By convention, if the probability that a result
  could be due to chance is less than 5 (p lt .05),
  we say that result is statistically significant
• Significance depends on
• signal (differences between conditions)
• noise (other variability)
• sample size (more time points are more
  convincing)

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Lets create a time course for one LO voxel
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Well begin with activation
Response to Intact Objects is 4X greater than
Scrambled Objects
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Then well assume that our modelled activation is
off because a transient component
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Our modelled activation could be off for other
reasons

• All of the following could lead to inaccurate
  models
• different shape of function
• different width of function
• different latency of function

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Variability of HRF

• Aguirre, Zarahn DEsposito, 1998
• HRF shows considerable variability between
  subjects

different subjects

• Within subjects, responses are more consistent,
  although there is still some variability between
  sessions

same subject, same session
same subject, different session
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Now lets add some variability due to head motion
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though really motion is more complex

• Head motion can be quantified with 6 parameters
  given in any motion correction algorithm
• x translation
• y translation
• z translation
• xy rotation
• xz rotation
• yz rotation
• For simplicity, Ive only included parameter one
  in our model
• Head motion can lead to other problems not
  predictable by these parameters

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Now lets throw in a pinch of linear drift

• linear drift could arise from magnet noise (e.g.,
  parts warm up) or physiological noise (e.g.,
  subjects head sinks)

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and then well add a dash of low frequency noise

• low frequency noise can arise from magnet noise
  or physiological noise (e.g., subjects cycles of
  alertness/drowsiness)
• low frequency noise would occur over a range of
  frequencies but for simplicity, Ive only
  included one frequency (1 cycle per run) here
• Linear drift is really just very low frequency
  noise

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and our last ingredient some high frequency noise

• high frequency noise can arise from magnet noise
  or physiological noise (e.g., subjects breathing
  rate and heartrate)

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When we add these all together, we get a
realistic time course
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Now lets be the experimenter

• First, we take our time course and normalize it
  using z scores
• z (x - mean)/SD
• normalization leads to data where
• mean zero
• SD 1

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We create a GLM with 2 predictors
?1



?2

fMRI Signal
Residuals
Design Matrix

Betas
x
what we CAN explain
what we CANNOT explain
how much of it we CAN explain


x
our data
Statistical significance is basically a ratio of
explained to unexplained variance
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Implementation of GLM in SPM
Many thanks to Øystein Bech Gadmar for creating
this figure in SPM
? Time

• SPM represents time as going down
• SPM represents predictors within the design
  matrix as grayscale plots (where black low,
  white high) over time
• SPM includes a constant to take care of the
  average activation level throughout each run

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Effect of Beta Weights

• Adjustments to the beta weights have the effect
  of raising or lowering the height of the
  predictor while keeping the shape constant

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The beta weight is not a correlation

• correlations measure goodness of fit regardless
  of scale
• beta weights are a measure of scale

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We create a GLM with 2 predictors
when ?12


when ?20.5

Betas
x
Design Matrix
what we CAN explain
what we CANNOT explain
how much of it we CAN explain


x
our data
Statistical significance is basically a ratio of
explained to unexplained variance
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Correlated Predictors

• Where possible, avoid predictors that are highly
  correlated with one another
• This is why we NEVER include a baseline predictor
• baseline predictor is almost completely
  correlated with the sum of existing predictors

r -.53

r -.53
r -.95
Two stimulus predictors
Baseline predictor
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Which model accounts for this data?
x ß 1
x ß 0


OR
x ß 1
x ß 0


x ß 0
x ß -1

• Because the predictors are highly correlated, you
  cant tell which model is best

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Contrasts in the GLM

• We can examine whether a single predictor is
  significant (compared to the baseline)

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A Real Voxel

• Heres the time course from a voxel that was
  significant in the Intact -Scrambled comparison

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Maximizing Your Power
signal


noise

• As we saw earlier, the GLM is basically comparing
  the amount of signal to the amount of noise
• How can we improve our stats?
• increase signal
• decrease noise
• increase sample size (keep subject in longer)

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How to Reduce Noise

• If you cant get rid of an artifact, you can
  include it as a predictor of no interest to
  soak up variance

Example Some people include predictors from the
outcome of motion correction algorithms
Corollary Never leave out predictors for
conditions that will affect your data
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Reducing Residuals
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Whats this ing reviewer complaining about?!

• Particularly if you do voxelwise stats, you have
  to be careful to follow the accepted standards of
  the field. In the past few years the following
  approaches have been recommended by the stats
  mavens
• Correction for multiple comparisons
• Random effects analyses
• Correction for serial correlations

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Correction for Multiple Comparisons
With conventional probability levels (e.g., p lt
.05) and a huge number of comparisons (e.g., 64 x
64 x 12 49,152), a lot of voxels will be
significant purely by chance e.g., .05 49,152
2458 voxels significant due to chance How can
we avoid this?

• Bonferroni correction
• divide desired p value by number of comparisons
• Example
• desired p value p lt .05
• number of voxels 50,000
• required p value p lt .05 / 50,000 ? p lt
  .000001
• quite conservative
• can use less stringent values
• e.g., Brain Voyager can use the number of voxels
  in the cortical surface
• small volume correction use more liberal
  thresholds in areas of the brain which you
  expected to be active

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Correction for Multiple Comparisons

• Gaussian field theory
• Fundamental to SPM
• If data are very smooth, then the chance of noise
  points passing threshold is reduced
• Can correct for the number of resolvable
  elements (resels) rather than number of voxels

Slide modified from Duke course
43

• 3) Cluster correction
• falsely activated voxels should be randomly
  dispersed
• set minimum cluster size to be large enough to
  make it unlikely that a cluster of that size
  would occur by chance
• assumes that data from adjacent voxels are
  uncorrelated (not true)

• Test-retest reliability
• Perform statistical tests on each half of the
  data
• The probability of a given voxel appearing in
  both purely by chance is the square of the p
  value used in each half
• e.g., .001 x .001 .000001
• Alternatively, use the first half to select an
  ROI and evaluate your hypothesis in the second
  half.

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• 5) Poor mans Bonferroni
• Jack up the threshold till you get rid of the
  schmutz (especially in air, ventricles, white
  matter)
• If you have a comparison where one condition is
  expected to produce much more activity than the
  other, turn on both tails of the comparison
• Jodys rule of thumb If ya cant trust the
  negatives, can ya trust the positives?

Example MT localizer data Moving rings gt
stationary rings (orange) Stationary rings gt
moving rings (blue)
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• 6) False discovery rate
• Genovese et al, 2002, NeuroImage
• also controls the proportion of rejected
  hypotheses that are falsely rejected
• standard p value (e.g., p lt .01) means that a
  certain proportion of all voxels will be
  significant by chance (1)
• FDR uses q value (e.g., q lt .01), meaning that a
  certain proportion of the activated (colored)
  voxels will be significant by chance (1)
• works in theory, though in practice, my lab
  hasnt been that satisfied

Is the region truly active?
Yes
No
Type I Error
HIT
Yes
Does our stat test indicate that the region is
active?
Type II Error
Correct Rejection
No
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Correction for Temporal Correlations
Statistical methods assume that each of our time
points is independent. In the case of fMRI, this
assumption is false. Even in a screen saver
scan, activation in a voxel at one time is
correlated with its activation within 6
sec This fact can artificially inflate your
statistical significance.
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Collapsed Fixed Effects Models

• assume that the experimental manipulation has
  same effect in each subject
• treats all data as one concatenated set with one
  beta per predictor (collapsed across all
  subjects)
• e.g., Intact 2
• Scrambled .5
• strong effect in one subject can lead to
  significance even when others show weak or no
  effects
• you can say that effect was significant in your
  group of subjects but cannot generalize to other
  subjects that you didnt test

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Separate Subjects Models

• one beta per predictor per subject
• e.g., JC Intact 2.1
• JC Scrambled 0.2
• DQ Intact 1.5
• DQ Scrambled 1.0
• KV Intact 1.2
• KV Scrambled 1.3
• weights each subject equally
• makes data less susceptible to effects of one
  rogue subject

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Random Effects Analysis

• Typical fMRI stats test whether the differences
  between conditions are significant in the sample
  of subjects we have tested
• Often, we want to be able to generalize to the
  population as a whole including all potential
  subjects, not just the ones we tested
• Random effects analyses allow you to generalize
  to the population you tested
• Brain Voyager recommends you dont even toy with
  random effects unless youve got 10 or more
  subjects (and 50 is best)
• Random effects analyses can really squash your
  data, especially if you dont have many subjects.
  Sometimes we refer to the random effects button
  as the make my activation go away button.
• Reviewers are now requesting random effects
  analyses more frequently
• You dont have to worry about it if youre using
  the ROI approach because (1) presumably the ROI
  has already been well-established across multiple
  labs and (2) posthoc analyses of results in an
  ROI approach allow you to generalize to the
  population (assuming you include individual
  variance)

underpaid graduate students in need of a few
bucks!
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Fixed vs. Random Effects GLM
Sample Data 1
Sample Data 2
Subject Intact beta Scram beta Diff
1 4 3 1
2 2 3 -1
3 4 1 3
SUM 10 7 3
Subject Intact beta Scram beta Diff
1 4 3 1
2 2 1 1
3 4 3 1
SUM 10 7 3

• Fixed Effects GLM cannot tell the difference
  between these data sets because (Intact sum -
  Scram sum) is the same in both cases
• In Random Effects GLM, Data set 1 would be more
  likely to be significant because all 3 subjects
  show a trend in the same direction (intact gt
  scrambled), whereas in data set 2, only 2 of 3
  subjects show a difference in that direction

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Autocorrelation function
To calculate the magnitude of the problem, we can
compute the autocorrelation function For a voxel
or ROI, correlate its time course with itself
shifted in time Plot these correlations by the
degree of shift
original
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BV can correct for the autocorrelation to yield
revised (usually lower) p values
BEFORE
AFTER
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BV Preprocessing Options
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Temporal Smoothing of Data

• We have the option in our software to temporally
  smooth our data (i.e., remove high temporal
  frequencies)
• However, I recommended that you not use this
  option
• Now do you understand why?

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Clarification

• correction for temporal correlations is NOT
  necessary with random effects analyses, only for
  fixed effects and individual subjects analysis

WARNING UNDER CONSTRUCTION need to clarify
explanation
56
Approach 1 Voxelwise Statistics

1. You dont necessarily need a priori hypotheses
   (though sometimes you can use less conservative
   stats if you have them)
2. Average all of your data together in Talairach
   space
3. Compare two (or more) conditions using precise
   statistical procedures within every voxel of the
   brain. Any area that passes a carefully
   determined threshold is considered real.
4. Make a list of these areas and publish it.

This is the tricky part!
57
Voxelwise Approach Example

• Malach et al., 1995, PNAS
• Question Are there areas of the human brain that
  are more responsive to objects than scrambled
  objects
• You will recognize this as what we now call an LO
  localizer, but Malach was the first to identify LO

LO (red) responds more to objects, abstract
sculptures and faces than to textures, unlike
visual cortex (blue) which responds well to all
stimuli
LO activation is shown in red, behind MT
activation in green
58
The Danger of Voxelwise Approaches

• This is one of two tables from a paper
• Some papers publish tables of activation two
  pages long
• How can anyone make sense of so many areas?

Source Decety et al., 1994, Nature
59
To Localize or Not to Localize?
60
Methodological Fundamentalism
The latest review I received
61
Approach 2 Region of interest (ROI) analysis

• If you are looking at a well-established area
  (such as visual cortex, motor cortex, or the
  lateral occipital complex), its fairly easy to
  activate and identify the area
• Do the stats and play with the threshold till you
  get something believable in the right vicinity
  based on anatomical location (e.g., sulcal
  landmarks) or functional location (e.g.,
  Talairach coordinates from prior studies)
• Once you have found the ROI, do independent
  experiments, extract the time course information
  and determine whether activation differences
  between conditions are significant
• Because the runs that are used to generate the
  area are independent from those used to test the
  hypothesis, liberal statistics (p lt .05) can be
  used

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Example of ROI Approach
Culham et al., 2003, Experimental Brain
Research Does the Lateral Occipital Complex
compute object shape for grasping?
Step 1 Localize LOC
Intact Objects
Scrambled Objects
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Example of ROI Approach
Culham et al., 2003, Experimental Brain
Research Does the Lateral Occipital Complex
compute object shape for grasping?
Step 2 Extract LOC data from experimental runs
Grasping
Reaching
NS p .35
NS p .31
64
Example of ROI Approach
Very Simple Stats
BOLD Signal Change Left Hem. LOC BOLD Signal Change Left Hem. LOC
Subject Grasping Reaching
1 0.02 0.03
2 0.19 0.08
3 0.04 0.01
4 0.10 0.32
5 1.01 -0.27
6 0.16 0.09
7 0.19 0.12
Then simply do a paired t-test to see whether the
peaks are significantly different between
conditions
Extract average peak from each subject for each
condition
NS p .35
NS p .31

• Instead of using BOLD Signal Change, you can
  use beta weights
• You can also do a planned contrast in Brain
  Voyager using a module called the ROI GLM

65
Utility of Doing Both Approaches

• We also verified the result with a voxelwise
  approach

Verification of no LOC activation for grasping gt
reaching even at moderate threshold (p lt .001,
uncorrected)
66
Example The Danger of ROI Approaches

• Example 1 LOC may be a heterogeneous area with
  subdivisions ROI analyses gloss over this
• Example 2 Some experiments miss important areas
  (e.g., Kanwisher et al., 1997 identified one
  important face processing area -- the fusiform
  face area, FFA -- but did not report a second
  area that is a very important part of the face
  processing network -- the occipital face area,
  OFA -- because it was less robust and consistent
  than the FFA.

67
Comparing the two approaches

• Voxelwise Analyses
• Require no prior hypotheses about areas involved
• Include entire brain
• Often neglect individual differences
• Can lose spatial resolution with intersubject
  averaging
• Can produce meaningless laundry lists of areas
  that are difficult to interpret
• You have to be fairly stats-savvy and include all
  the appropriate statistical corrections to be
  certain your activation is really significant
• Popular in Europe

68
Comparing the two approaches

• Region of Interest (ROI) Analyses
• Extraction of ROI data can be subjected to simple
  stats (no need for multiple comparisons,
  autocorrelation or random effects corrections)
• Gives you more statistical power (e.g., p lt .05)
• Hypothesis-driven
• Useful when hypotheses are motivated by other
  techniques (e.g., electrophysiology) in specific
  brain regions
• ROI is not smeared due to intersubject averaging
• Important for discriminating abutting areas
  (e.g., V1/V2)
• Easy to analyze and interpret
• Neglects other areas which may play a fundamental
  role
• If multiple ROIs need to be considered, you can
  spend a lot of scan time collecting localizer
  data (thus limiting the time available for
  experimental runs)
• Works best for reliable and robust areas with
  unambiguous definitions
• Popular in North America

69
A Proposed Resolution

• There is no reason not to do BOTH ROI analyses
  and voxelwise analyses
• ROI analyses for well-defined key regions
• Voxelwise analyses to see if other regions are
  also involved
• Ideally, the conclusions will not differ
• If the conclusions do differ, there may be
  sensible reasons
• Effect in ROI but not voxelwise
• perhaps region is highly variable in stereotaxic
  location between subjects
• perhaps voxelwise approach is not powerful enough
• Effect in voxelwise but not ROI
• perhaps ROI is not homogenous or is
  context-specific

70
Finding the Obvious
UNDER CONSTRUCTION Need to create animation of
cards

• Non-independence error
• occurs when statistical tests performed are not
  independent from the means used to select the
  brain region

Arguments from Vul Kanwisher, book chapter in
press
71
Non-independence Error

• Egregious example
• Identify Area X with contrast of A gt B
• Do post hoc stats showing that A is statistically
  higher than B
• Act surprised
• More subtle example of selection bias
• Identify Area X with contrast of A gt B
• Do post hoc stats showing that A is statistically
  higher than C and C is statistically greater than
  B

UNDER CONSTRUCTION Improve examples and make
graphs to illustrate
Arguments from Vul Kanwisher, book chapter in
press
72
Hypothesis- vs. Data-Driven Approaches

• Hypothesis-driven
• Examples t-tests, correlations, general linear
  model (GLM)
• a priori model of activation is suggested
• data is checked to see how closely it matches
  components of the model
• most commonly used approach
• Data-driven
• Example Independent Component Analysis (ICA)
• no prior hypotheses are necessary
• multivariate techniques determine the patterns
  in the data that account for the most variance
  across all voxels
• can be used to validate a model (see if the math
  comes up with the components you wouldve
  predicted)
• can be inspected to see if there are things
  happening in your data that you didnt predict
• can be used to identify confounds (e.g., head
  motion)

73
Example of ICA

• hardest part is sorting the many components into
  meaningful ones vs. artifacts
• fingerprints may help