Advanced Designs for fMRI

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Advanced Designsfor fMRI
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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Advanced designs and future directions

• parametric designs
• factorial designs
• adaptation designs (fMRA)
• multivoxel pattern analysis (MVPA)
• network and connectivity analyses

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Parametric Designs
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Why are parametric designs useful in fMRI?

• As weve seen, the assumption of pure insertion
  in subtraction logic is often false
• (A B) - (B) A
• In parametric designs, the task stays the same
  while the amount of processing varies thus,
  changes to the nature of the task are less of a
  problem
• (A A) - (A) A
• (A A A) - (A A) A

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Parametric Designs in Cognitive Psychology

• introduced to psychology by Saul Sternberg (1969)
• asked subjects to memorize lists of different
  lengths then asked subjects to tell him whether
  subsequent numbers belonged to the list
• Memorize these numbers 7, 3
• Memorize these numbers 7, 3, 1, 6
• Was this number on the list? 3

Saul Sternberg

• longer list lengths led to longer reaction times
• Sternberg concluded that subjects were searching
  serially through the list in memory to determine
  if target matched any of the memorized numbers

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An Example
Culham et al., 1998, J. Neuorphysiol.
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Analysis of Parametric Designs

• parametric variant
• passive viewing and tracking of 1, 2, 3, 4 or 5
  balls

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Potential problems

• Ceiling effects?
• If you see saturation of the activation, how do
  you know whether its due to saturation of
  neuronal activity or saturation of the BOLD
  response?

Perhaps the BOLD response cannot go any higher
than this?
BOLD Activity
Parametric variable

• Possible solution show that under other
  circumstances with lower overall activation, the
  BOLD signal still saturates

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Factorial Designs
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Factorial Designs

• Example Sugiura et al. (2005, JOCN) showed
  subjects pictures of objects and places. The
  objects and places were either familiar (e.g.,
  the subjects office or the subjects bag) or
  unfamiliar (e.g., a strangers office or a
  strangers bag)
• This is a 2 x 2 factorial design (2 stimuli x 2
  familiarity levels)

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Factorial Designs

• Main effects
• Difference between columns
• Difference between rows
• Interactions
• Difference between columns depending on status of
  row (or vice versa)

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Main Effect of Stimuli

• In LO, there is a greater activation to Objects
  than Places
• In the PPA, there is greater activation to Places
  than Objects

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Main Effect of Familiarity

• In the precuneus, familiar objects generated more
  activation than unfamiliar objects

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Interaction of Stimuli and Familiarity

• In the posterior cingulate, familiarity made a
  difference for places but not objects

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Why do People like Factorial Designs?

• If you see a main effect in a factorial design,
  it is reassuring that the variable has an effect
  across multiple conditions
• Interactions can be enlightening and form the
  basis for many theories

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Understanding Interactions

• Interactions are easiest to understand in line
  graphs -- When the lines are not parallel, that
  indicates an interaction is present

Places
Brain Activation
Objects
Unfamiliar
Familiar
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Combinations are Possible

• Hypothetical examples

Places
Places
Brain Activation
Objects
Objects
Unfamiliar
Familiar
Unfamiliar
Familiar
Main effect of Stimuli Main Effect of
Familiarity No interaction (parallel lines)
Main effect of Stimuli Main effect of
Familiarity Interaction
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Problems

• Interactions can occur for many reasons that may
  or may not have anything to do with your
  hypothesis
• A voxelwise contrast can reveal a significant for
  many reasons
• Consider the full pattern in choosing your
  contrasts and understanding the implications

Places
Brain Activation
Objects
Unfamiliar
Familiar
Unfamiliar
Familiar
Unfamiliar
Familiar
All these patterns indicate an interaction. Do
they all support the theory that this brain area
encodes familiar places?
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Problems

• Interactions become hard to interpret
• one recent psychology study suggests the human
  brain cannot understand interactions that involve
  more than three variables
• The more conditions you have, the fewer trials
  per condition you have
• ? Keep it simple!

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fMR Adaptation
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Using fMR Adaptation to Study Coding

• Example We know that neurons in the brain can be
  tuned for individual faces

Jennifer Aniston neuron in human medial
temporal lobe Quiroga et al., 2005, Nature
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Using fMR Adaptation to Study Tuning

• fMRI resolution is typically around 3 x 3 x 6 mm
  so each sample comes from millions of neurons

Neuron 1 likes Jennifer Aniston
Neuron 2 likes Julia Roberts
Neuron 3 likes Brad Pitt
Even though there are neurons tuned to each
object, the population as a whole shows no
preference
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fMR Adaptation

• If you show a stimulus twice in a row, you get a
  reduced response the second time

Hypothetical Activity in Face-Selective Area
(e.g., FFA)
Unrepeated Face Trial
?
Activation
Repeated Face Trial
?
Time
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fMRI Adaptation
different trial
500-1000 msec
same trial
Slide modified from Russell Epstein
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And more

• We could use this technique to determine the
  selectivity of face-selective areas to many other
  dimensions

Repeated Individual, Different Expression
Repeated Expression, Different Individual
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Why is adaptation useful?

• Now we can ask what it takes for stimulus to be
  considered the same in an area
• For example, do face-selective areas care about
  viewpoint?

• Viewpoint selectivity
• area codes the face as different when viewpoint
  changes

Repeated Individual, Different Viewpoint
Activation
?

• Viewpoint invariance
• area codes the face as the same despite the
  viewpoint change

Time
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Are scene representations in FFA
viewpoint-invariant or viewpoint-specific?

viewpoint-invariant
viewpoint-specific
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Actual Results
LO
pFs (FFA)
Grill-Spector et al., 1999, Neuron
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Problems

• The basis for effect is not well-understood
• this is seen in the many terms used to describe
  it
• fMR adaptation (fMR-A)
• priming
• repetition suppression
• The effect could be due to many factors such as
• repeated stimuli are processed more efficiently
• more quickly?
• with fewer action potentials?
• with fewer neurons involved?
• repeated stimuli draw less attention
• repeated stimuli may not have to be encoded into
  memory
• repeated stimuli affect other levels of
  processing with input to area demonstrating
  adaptation (data from Vogels et al.)
• subjects may come to expect repetitions and their
  predictions may be violated by novel stimuli
  (Summerfield et al., 2008, Nat. Neurosci.)

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Problems

• Adaptation effects can be quite unreliable
• variability between labs and studies
• even effects that are well-established in
  neurophysiology and psychophysics dont always
  replicate in fMRA
• e.g., orientation selectivity in primary visual
  cortex
• David Heeger suggests that it may be critical to
  control attention
• The effect may also depend on other factors
• e.g., time elapsed from first and second
  presentation
• days, hours, minutes, seconds, milliseconds?
• number of intervening items

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Multivoxel Pattern Analyses
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Perhaps voxels contain useful information

• In traditional fMRI analyses, we average across
  the voxels within an area, but these voxels may
  contain valuable information
• In traditional fMRI analyses, we assume that an
  area encodes a stimulus if it responds more, but
  perhaps encoding depends on pattern of high and
  low activation instead
• But perhaps there is information in the pattern
  of activation across voxels

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Coding in Voxel Patterns

• Simple experiment Show subjects pictures of
  different objects (e.g., shoes vs. bottles) on
  different trials of different runs

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Simple Correlation Analysis

• Measure within-category correlations
• within bottles (B1B2)
• within shoes (S1S2)
• Measure between-category correlations
• between bottles shoes (B1 S2 S1 B2)
• If within-category correlations
  between-category correlations, conclude that area
  encodes different stimuli

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Decoding Algorithms

• Train algorithm to distinguish two object
  categories on a training set
• Test success of algorithm on distinguishing two
  object categories on a test set
• If algorithm succeeds better than chance,
  conclude that area encodes different stimuli

Norman et al., 2006, Trends Cogn. Sci.
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Network Analyses
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Networks and Connectivity

• In the analyses we have investigated so far, we
  have been considering brain areas in isolation
• More sophisticated statistical techniques have
  now become available to investigate networks of
  activation

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Anatomical Connectivity

• white matter tracts join two areas
• can be measured by using tracers in other species
• can be measured in living human brains with
  diffusion tensor imaging (DTI)

Catani et al., 2003, Brain
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Functional Connectivity

• Areas show correlations in activation
• Those areas may or may not be directly
  interconnected

Step 1 Extract time course from area of interest
MT motion complex resting state scan (10 mins)
Step 2 Look for other areas that are show
correlated activity in the same scan
V6 (another motion selective area correlation
with MT r .8
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Default Mode Network
Fox and Raichle, 2007, Nat. Rev. Neurosci.

• During resting state scans, there are two
  networks in which areas are correlated with each
  other and anticorrelated with areas in the other
  network

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Effective Connectivity

• Activation in one area may affect activation in
  another
• Some techniques require an a priori model of the
  anatomical connections between two areas
• can be dubious, especially given limited
  knowledge of human anatomical connectivity
• Other techniques are model-free (e.g., Granger
  causality modelling)

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Example of Effective Connecivity

• Subjects watched a moving pattern passively or
  paid attention to its speed
• With attention, activity in the primary visual
  cortex had a greater effect on the
  motion-selective area MT/V5

Friston et al., 1997, Neuroimage
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Summary of Connectivity
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EXTRA SLIDES
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Statistical Approaches

• In a 2 x 2 design, you can make up to six
  comparisons between pairs of conditions (A1 vs.
  A2, B1 vs. B2, A1 vs. B1, A2 vs. B2, A1 vs. B2,
  A2 vs. B1). This is a lot of comparisons (and if
  you do six comparisons with p p value is .05 x 6 .3 which is high). How do
  you decide which to perform?

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Statistical Approaches

• Without prior hypotheses
• Do an Analysis of Variance (ANOVA) to tease apart
  main effects and interactions
• If any of these are significant, do post hoc
  t-tests to determine where the differences arise
• These contrasts can sometimes turn out in
  unexpected ways
• Analysis of interactions involves looking at
  differences between differences
• With prior hypotheses
• Perform planned contrasts for comparisons of
  interest
• e.g., you might hypothesize that in area X
• FP UP but FO UO
• You could test this using just two contrasts

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Problems

• The basis for effect is not well-understood
• this is seen in the many terms used to describe
  it
• fMR adaptation (fMR-A)
• priming
• repetition suppression
• The effect could be due to many factors such as
• repeated stimuli are processed more efficiently
• more quickly?
• with fewer action potentials?
• with fewer neurons involved?
• repeated stimuli draw less attention
• repeated stimuli may not have to be encoded into
  memory

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Data-Driven Approaches
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Data Driven Analyses

• Hasson et al. (2004, Science) showed subjects
  clips from a movie and found voxels which showed
  significant time correlations between subjects

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Reverse correlation

• They went back to the movie clips to find the
  common feature that may have been driving the
  intersubject consistency

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Mental Chronometry
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Mental chronometry

• study of the timing of neural events
• long history in psychology

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Variability of HRF Between Areas

• Possible caveat HRF may also vary between areas,
  not just subjects
• Buckner et al., 1996
• noted a delay of .5-1 sec between visual and
  prefrontal regions
• vasculature difference?
• processing latency?
• Bug or feature?
• Menon Kim mental chronometry

Buckner et al., 1996
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Latency and Width
Menon Kim, 1999, TICS
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Mental Chronometry
Superior Parietal Cortex
Superior Parietal Cortex
Data Richter et al., 1997, Neuroreport Figures
Huettel, Song McCarthy, 2004
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Mental Chronometry
Vary ISI
Measure Latency Diff
Menon, Luknowsky Gati, 1998, PNAS
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Challenges

• Works best with stimuli that have strong
  differences in timing (on the order of seconds)
• It can be challenging to reliably quantify the
  latency in noisy signals

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Monkey fMRI
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Monkey fMRI

• compare physiology to neuroimaging (e.g.,
  Logothetis et al., 2001)
• enables interspecies comparisons
• missing link between monkey neurophysiology and
  human neuroimaging
• species differs but technique constant

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Monkey fMRI
Hand actions
Visuospatial tasks
Calculation
Language

• might provide clues as to how brain evolved
• compare locations of expected regions
• study locations of human functions like math,
  language, social processing
• e.g., ventral premotor cortex in macaque may be
  precursor to Brocas area in human
• could tell neurophysiologists where to stick
  electrodes

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Limitations of Monkey fMRI

• concerns about anesthesia
• awake monkeys move
• monkeys require extensive training
• concerns about interspecies contamination
• art of the barely possible squared?

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Social Cognitive Neuroscience
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Social Cognitive Neuroscience

• find neural substrates of social behaviors
• e.g., theory of mind, imitation/mirror responses,
  attributions, emotions, empathy, cheater
  detection, cooperation/competition
• biggest predictor of brainbody size ratio is
  social group size

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Example

• Phelps et al., 2000, Journal of Cognitive
  Neuroscience
• White American subjects viewed pictures of
  unfamiliar black faces
• amygdala activation was correlated with two
  implicit measures of racism but not with explicit
  racial attitudes
• difference went away when famous black faces were
  tested